articles

Initial

sequencing

and

comparative

analysis

of

the

mouse

genome

Mouse

Genome

Sequencing

Consortium

A

list

of

authors

and

their

affiliations

appears

at

the

end

of

the

paper

The

sequence

of

the

mouse

genome

is

a

key

informational

tool

for

understanding

the

contents

of

the

human

genome

and

a

key

experimental

tool

for

biomedical

research

Here

we

report

the

results

of

an

international

collaboration

to

produce

a

high-quality

draft

sequence

of

the

mouse

genome

We

also

present

an

initial

comparative

analysis

of

the

mouse

and

human

genomes

describing

some

of

the

insights

that

can

be

gleaned

from

the

two

sequences

We

discuss

topics

including

the

analysis

of

the

evolutionary

forces

shaping

the

size

structure

and

sequence

of

the

genomes

the

conservation

of

large-scale

synteny

across

most

of

the

genomes

the

much

lower

extent

of

sequence

orthology

covering

less

than

half

of

the

genomes

the

proportions

of

the

genomes

under

selection

the

number

of

protein-coding

genes

the

expansion

of

gene

families

related

to

reproduction

and

immunity

the

evolution

of

proteins

and

the

identification

of

intraspecies

polymorphism

With

the

complete

sequence

of

the

human

genome

nearly

in

the

next

challenge

is

to

extract

the

extraordinary

trove

of

information

encoded

within

its

roughly

billion

nucleotides

This

information

includes

the

blueprints

for

all

RNAs

and

proteins

the

regulatory

elements

that

ensure

proper

expression

of

all

genes

the

structural

elements

that

govern

chromosome

function

and

the

records

of

our

evolutionary

history

Some

of

these

features

can

be

recognized

easily

in

the

human

sequence

but

many

are

subtle

and

difficult

to

discern

One

of

the

most

powerful

general

approaches

for

unlocking

the

secrets

of

the

human

genome

is

comparative

genomics

and

one

of

the

most

powerful

starting

points

for

comparison

is

the

laboratory

mouse

Mus

musculus

Metaphorically

comparative

genomics

allows

one

to

read

evolution

's

laboratory

notebook

In

the

roughly

million

years

since

the

divergence

of

the

human

and

mouse

lineages

the

process

of

evolution

has

altered

their

genome

sequences

and

caused

them

to

diverge

by

nearly

one

substitution

for

every

two

nucleotides

-LRB-

see

below

-RRB-

as

well

as

by

deletion

and

insertion

The

divergence

rate

is

low

enough

that

one

can

still

align

orthologous

sequences

but

high

enough

so

that

one

can

recognize

many

functionally

important

elements

by

their

greater

degree

of

conservation

Studies

of

small

genomic

regions

have

demonstrated

the

power

of

such

cross-species

conservation

to

identify

putative

genes

or

regulatory

Genome-wide

analysis

of

sequence

conservation

holds

the

prospect

of

systematically

revealing

such

information

for

all

genes

Genomewide

comparisons

among

organisms

can

also

highlight

key

differences

in

the

forces

shaping

their

genomes

including

differences

in

mutational

and

selective

Literally

comparative

genomics

allows

one

to

link

laboratory

notebooks

of

clinical

and

basic

researchers

With

knowledge

of

both

genomes

biomedical

studies

of

human

genes

can

be

complemented

by

experimental

manipulations

of

corresponding

mouse

genes

to

accelerate

functional

understanding

In

this

respect

the

mouse

is

unsurpassed

as

a

model

system

for

probing

mammalian

biology

and

human

Its

unique

advantages

include

a

century

of

genetic

studies

scores

of

inbred

strains

hundreds

of

spontaneous

mutations

practical

techniques

for

random

mutagenesis

and

importantly

directed

engineering

of

the

genome

through

transgenic

knockout

and

knockin

For

these

and

other

reasons

the

Human

Genome

Project

-LRB-

HGP

-RRB-

recognized

from

its

outset

that

the

sequencing

of

the

human

genome

needed

to

be

followed

as

rapidly

as

possible

by

the

sequencing

of

the

mouse

genome

In

early

the

International

Human

Genome

Sequencing

Consortium

reported

a

draft

sequence

covering

about

of

the

euchromatic

human

genome

with

about

in

finished

Since

then

progress

towards

a

complete

human

sequence

has

proceeded

swiftly

with

approximately

of

the

genome

now

available

in

draft

form

and

about

in

finished

form

Here

we

report

the

results

of

an

international

collaboration

involving

centres

in

the

United

States

and

the

United

Kingdom

to

produce

a

high-quality

draft

sequence

of

the

mouse

genome

and

a

broad

scientific

network

to

analyse

the

data

The

draft

sequence

was

generated

by

assembling

about

sevenfold

sequence

coverage

from

female

mice

of

the

strain

-LRB-

referred

to

below

as

-RRB-

The

assembly

contains

about

of

the

sequence

of

the

euchromatic

genome

-LRB-

excluding

chromosome

Y

-RRB-

in

sequence

contigs

linked

together

into

large

units

usually

larger

than

megabases

-LRB-

Mb

-RRB-

With

the

availability

of

a

draft

sequence

of

the

mouse

genome

we

have

undertaken

an

initial

comparative

analysis

to

examine

the

similarities

and

differences

between

the

human

and

mouse

genomes

Some

of

the

important

points

are

listed

below

The

mouse

genome

is

about

smaller

than

the

human

genome

-LRB-

Gb

compared

with

Gb

-RRB-

The

difference

probably

reflects

a

higher

rate

of

deletion

in

the

mouse

lineage

Over

of

the

mouse

and

human

genomes

can

be

partitioned

into

corresponding

regions

of

conserved

synteny

reflecting

segments

in

which

the

gene

order

in

the

most

recent

common

ancestor

has

been

conserved

in

both

species

At

the

nucleotide

level

approximately

of

the

human

genome

can

be

aligned

to

the

mouse

genome

These

sequences

seem

to

represent

most

of

the

orthologous

sequences

that

remain

in

both

lineages

from

the

common

ancestor

with

the

rest

likely

to

have

been

deleted

in

one

or

both

genomes

The

neutral

substitution

rate

has

been

roughly

half

a

nucleotide

substitution

per

site

since

the

divergence

of

the

species

with

about

twice

as

many

of

these

substitutions

having

occurred

in

the

mouse

compared

with

the

human

lineage

By

comparing

the

extent

of

genome-wide

sequence

conservation

to

the

neutral

rate

the

proportion

of

small

-LRB-

bp

-RRB-

segments

in

the

mammalian

genome

that

is

under

-LRB-

purifying

-RRB-

selection

can

be

estimated

to

be

about

This

proportion

is

much

higher

than

can

be

explained

by

protein-coding

sequences

alone

implying

that

the

genome

contains

many

additional

features

-LRB-

such

as

untranslated

regions

regulatory

elements

non-protein-coding

genes

and

chromosomal

structural

elements

-RRB-

under

selection

for

biological

function

The

mammalian

genome

is

evolving

in

a

non-uniform

manner

NATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

with

various

measures

of

divergence

showing

substantial

variation

across

the

genome

The

mouse

and

human

genomes

each

seem

to

contain

about

protein-coding

genes

These

refined

estimates

have

been

derived

from

both

new

evidence-based

analyses

that

produce

larger

and

more

complete

sets

of

gene

predictions

and

new

de

novo

gene

predictions

that

do

not

rely

on

previous

evidence

of

transcription

or

homology

The

proportion

of

mouse

genes

with

a

single

identifiable

orthologue

in

the

human

genome

seems

to

be

approximately

The

proportion

of

mouse

genes

without

any

homologue

currently

detectable

in

the

human

genome

-LRB-

and

vice

versa

-RRB-

seems

to

be

less

than

Dozens

of

local

gene

family

expansions

have

occurred

in

the

mouse

lineage

Most

of

these

seem

to

involve

genes

related

to

reproduction

immunity

and

olfaction

suggesting

that

these

physiological

systems

have

been

the

focus

of

extensive

lineagespecific

innovation

in

rodents

Mouse

human

sequence

comparisons

allow

an

estimate

of

the

rate

of

protein

evolution

in

mammals

Certain

classes

of

secreted

proteins

implicated

in

reproduction

host

defence

and

immune

response

seem

to

be

under

positive

selection

which

drives

rapid

evolution

Despite

marked

differences

in

the

activity

of

transposable

elements

between

mouse

and

human

similar

types

of

repeat

sequences

have

accumulated

in

the

corresponding

genomic

regions

in

both

species

The

correlation

is

stronger

than

can

be

explained

simply

by

local

-LRB-

G

C

-RRB-

content

and

points

to

additional

factors

influencing

how

the

genome

is

moulded

by

transposons

By

additional

sequencing

in

other

mouse

strains

we

have

identified

about

single

nucleotide

polymorphisms

-LRB-

SNPs

-RRB-

The

distribution

of

SNPs

reveals

that

genetic

variation

among

mouse

strains

occurs

in

large

blocks

mostly

reflecting

contributions

of

the

two

subspecies

Mus

musculus

domesticus

and

Mus

musculus

musculus

to

current

laboratory

strains

The

mouse

genome

sequence

is

freely

available

in

public

databases

-LRB-

GenBank

accession

number

-RRB-

and

is

accessible

through

various

genome

browsers

-LRB-

and

nlm.nih.gov

genome/guide/mouse

-RRB-

In

this

paper

we

begin

with

information

about

the

generation

assembly

and

evaluation

of

the

draft

genome

sequence

the

conservation

of

synteny

between

the

mouse

and

human

genomes

and

the

landscape

of

the

mouse

genome

We

then

explore

the

repeat

sequences

genes

and

proteome

of

the

mouse

emphasizing

comparisons

with

the

human

This

is

followed

by

evolutionary

analysis

of

selection

and

mutation

in

the

mouse

and

human

lineages

as

well

as

polymorphism

among

current

mouse

strains

A

full

and

detailed

description

of

the

methods

underlying

these

studies

is

provided

as

Supplementary

Information

In

many

respects

the

current

paper

is

a

companion

to

the

recent

paper

on

the

human

genome

Extensive

background

information

about

many

of

the

topics

discussed

below

is

provided

there

Recent

molecular

studies

that

are

less

sensitive

to

the

differences

in

evolutionary

rates

have

suggested

that

the

eutherian

mammalian

radiation

took

place

throughout

the

Late

Cretaceous

period

-LRB-

Myr

-RRB-

but

that

rodents

and

primates

actually

represent

relatively

late-branching

In

the

analyses

below

we

use

a

divergence

time

for

the

human

and

mouse

lineages

of

Myr

for

the

purpose

of

calculating

evolutionary

rates

although

it

is

possible

that

the

actual

time

may

be

as

recent

as

Myr

Origins

of

mouse

genetics

The

origin

of

the

mouse

as

the

leading

model

system

for

biomedical

research

traces

back

to

the

start

of

human

civilization

when

mice

became

commensal

with

human

settlements

Humans

noticed

spontaneously

arising

coat-colour

mutants

and

recorded

their

observations

for

millennia

-LRB-

including

ancient

Chinese

references

to

dominant-spotting

waltzing

albino

and

yellow

mice

-RRB-

By

the

mouse

fanciers

in

Japan

and

China

had

domesticated

many

varieties

as

pets

and

Europeans

subsequently

imported

favourites

and

bred

them

to

local

mice

-LRB-

thereby

creating

progenitors

of

modern

laboratory

mice

as

hybrids

among

M.

m.

domesticus

M.

m.

musculus

and

other

subspecies

-RRB-

In

Victorian

England

fancy

mice

were

prized

and

traded

and

a

National

Mouse

Club

was

founded

in

-LRB-

refs

-RRB-

With

the

rediscovery

of

Mendel

's

laws

of

inheritance

in

pioneers

of

the

new

science

of

genetics

-LRB-

such

as

Cuenot

Castle

and

Little

-RRB-

were

quick

to

recognize

that

the

discontinuous

variation

of

fancy

mice

was

analogous

to

that

of

Mendel

's

peas

and

they

set

out

to

test

the

new

theories

of

inheritance

in

mice

Mating

programmes

were

soon

established

to

create

inbred

strains

resulting

in

many

of

the

modern

well-known

strains

-LRB-

including

-RRB-

Genetic

mapping

in

the

mouse

began

with

Haldane

's

in

of

linkage

between

the

pink-eye

dilution

and

albino

loci

on

the

linkage

group

that

was

eventually

assigned

to

mouse

chromosome

just

years

after

the

first

report

of

genetic

linkage

in

Drosophila

The

genetic

map

grew

slowly

over

the

next

years

as

new

loci

and

linkage

groups

were

added

chromosome

grew

to

three

loci

by

and

eight

by

The

accumulation

of

serological

and

enzyme

polymorphisms

from

the

to

the

early

began

to

fill

out

the

genome

with

the

map

of

chromosome

harbouring

loci

by

-LRB-

refs

-RRB-

The

real

explosion

however

came

with

the

development

of

recombinant

DNA

technology

and

the

advent

of

DNA-sequencebased

polymorphisms

Initially

this

involved

the

detection

of

restriction-fragment

length

polymorphisms

-LRB-

RFLPs

-RRB-

later

the

emphasis

shifted

to

the

use

of

simple

sequence

length

polymorphisms

-LRB-

SSLPs

also

called

microsatellites

-RRB-

which

could

be

assayed

easily

by

polymerase

chain

reaction

-LRB-

PCR

-RRB-

and

readily

revealed

polymorphisms

between

inbred

laboratory

strains

Origins

of

mouse

genomics

Background

to

the

mouse

genome

sequencing

project

Origins

of

the

mouse

The

precise

origin

of

the

mouse

and

human

lineages

has

been

the

subject

of

recent

debate

Palaeontological

evidence

has

long

indicated

a

great

radiation

of

placental

-LRB-

eutherian

-RRB-

mammals

about

million

years

ago

-LRB-

Myr

-RRB-

that

filled

the

ecological

space

left

by

the

extinction

of

the

dinosaurs

and

that

gave

rise

to

most

of

the

eutherian

Molecular

phylogenetic

analyses

indicate

earlier

divergence

times

of

many

of

the

mammalian

clades

Some

of

these

studies

have

suggested

a

very

early

date

for

the

divergence

of

mouse

from

other

mammals

-LRB-

-RRB-

but

these

estimates

partially

originate

from

the

fast

molecular

clock

in

rodents

-LRB-

see

below

-RRB-

NATURE

VOL

DECEMBER

When

the

Human

Genome

Project

-LRB-

HGP

-RRB-

was

launched

in

it

included

the

mouse

as

one

of

its

five

central

model

organisms

and

targeted

the

creation

of

genetic

physical

and

eventually

sequence

maps

of

the

mouse

genome

By

a

dense

genetic

map

with

nearly

highly

polymorphic

SSLP

markers

ordered

in

a

common

cross

had

been

providing

the

standard

tool

for

mouse

genetics

Subsequent

efforts

filled

out

the

map

to

over

polymorphic

markers

although

not

all

of

these

loci

have

been

positioned

precisely

relative

to

one

another

With

these

and

other

loci

Haldane

's

original

two-marker

linkage

group

on

chromosome

had

now

swelled

to

about

loci

Physical

maps

of

the

mouse

genome

also

proceeded

apace

using

sequence-tagged

sites

-LRB-

STS

-RRB-

together

with

radiation-hybrid

and

yeast

artificial

chromosome

-LRB-

YAC

-RRB-

libraries

to

construct

dense

landmark

Together

the

genetic

and

physical

maps

provide

thousands

of

anchor

points

that

can

be

used

to

tie

Nature

Publishing

Group

articles

clones

or

DNA

sequences

to

specific

locations

in

the

mouse

genome

Other

resources

included

large

collections

of

expressed-sequence

tags

-LRB-

EST

-RRB-

a

growing

number

of

full-length

complementary

and

excellent

bacterial

artificial

chromosome

-LRB-

BAC

-RRB-

The

latter

have

been

used

for

deriving

large

sets

of

BAC-end

and

as

part

of

this

collaboration

to

generate

a

fingerprint-based

physical

Furthermore

key

mouse

genome

databases

were

developed

at

the

Jackson

-LRB-

jax.org

-RRB-

Harwell

-LRB-

-RRB-

and

RIKEN

-LRB-

-RRB-

laboratories

to

provide

the

community

with

access

to

this

information

With

these

resources

it

became

straightforward

-LRB-

but

not

always

easy

-RRB-

to

perform

positional

cloning

of

classic

single-gene

mutations

for

visible

behavioural

immunological

and

other

phenotypes

Many

of

these

mutations

provide

important

models

of

human

disease

sometimes

recapitulating

human

phenotypes

with

uncanny

accuracy

It

also

became

possible

for

the

first

time

to

begin

dissecting

polygenic

traits

by

genetic

mapping

of

quantitative

trait

loci

-LRB-

QTL

-RRB-

for

such

traits

Continuing

advances

fuelled

a

growing

desire

for

a

complete

sequence

of

the

mouse

genome

The

development

of

improved

random

mutagenesis

protocols

led

to

the

establishment

of

largescale

screens

to

identify

interesting

new

mutants

increasing

the

need

for

more

rapid

positional

cloning

strategies

QTL

mapping

experiments

succeeded

in

localizing

more

than

loci

affecting

physiological

traits

creating

demand

for

efficient

techniques

capable

of

trawling

through

large

genomic

regions

to

find

the

underlying

genes

Furthermore

the

ability

to

perform

directed

mutagenesis

of

the

mouse

germ

line

through

homologous

recombination

made

it

possible

to

manipulate

any

gene

given

its

DNA

sequence

placing

an

increasing

premium

on

sequence

information

In

all

of

these

cases

it

was

clear

that

genome

sequence

information

could

markedly

accelerate

progress

Origin

of

the

Mouse

Genome

Sequencing

Consortium

With

the

sequencing

of

the

human

genome

well

underway

by

a

concerted

effort

to

sequence

the

entire

mouse

genome

was

organized

by

a

Mouse

Genome

Sequencing

Consortium

-LRB-

MGSC

-RRB-

The

MGSC

originally

consisted

of

three

large

sequencing

centres

the

Whitehead/Massachusetts

Institute

of

Technology

-LRB-

MIT

-RRB-

Center

for

Genome

Research

the

Washington

University

Genome

Sequencing

Center

and

the

Wellcome

Trust

Sanger

Institute

together

with

an

international

database

Ensembl

a

joint

project

between

the

European

Bioinformatics

Institute

and

the

Sanger

Institute

In

addition

to

the

genome-wide

efforts

of

the

MGSC

other

publicly

funded

groups

have

been

contributing

to

the

sequencing

of

the

mouse

genome

in

specific

regions

of

biological

interest

Together

the

MGSC

and

these

programmes

have

so

far

yielded

clone-based

draft

sequence

consisting

of

Mb

-LRB-

although

there

is

redundancy

-RRB-

and

finished

sequence

of

Mb

-LRB-

-RRB-

of

the

mouse

genome

Furthermore

Mural

and

recently

reported

a

draft

sequence

of

mouse

chromosome

containing

Mb

-LRB-

-RRB-

To

analyse

the

data

reported

here

the

MGSC

was

expanded

to

include

the

other

publicly

funded

sequencing

groups

and

a

Mouse

Genome

Analysis

Group

consisting

of

scientists

from

institutions

in

countries

Generating

the

draft

genome

sequence

Sequencing

strategy

ing

directed

sequencing

to

obtain

a

finished

sequence

with

gaps

closed

and

ambiguities

Ansorge

and

extended

the

technique

by

the

use

of

paired-end

sequencing

in

which

sequencing

is

performed

from

both

ends

of

a

cloned

insert

to

obtain

linking

information

which

is

then

used

in

sequence

assembly

More

recently

Myers

and

and

others

have

developed

efficient

algorithms

for

exploiting

such

linking

information

A

principal

issue

in

the

sequencing

of

large

complex

genomes

has

been

whether

to

perform

shotgun

sequencing

on

the

entire

genome

at

once

-LRB-

whole-genome

shotgun

WGS

-RRB-

or

to

first

break

the

genome

into

overlapping

large-insert

clones

and

to

perform

shotgun

sequencing

on

these

intermediates

-LRB-

hierarchical

shotgun

-RRB-

The

WGS

technique

has

the

advantage

of

simplicity

and

rapid

early

coverage

it

readily

works

for

simple

genomes

with

few

repeats

but

there

can

be

difficulties

encountered

with

genomes

that

contain

highly

repetitive

sequences

-LRB-

such

as

the

human

genome

which

has

near-perfect

repeats

spanning

hundreds

of

kilobases

-RRB-

Hierarchical

shotgun

sequencing

overcomes

such

difficulties

by

using

local

assembly

thus

decreasing

the

number

of

repeat

copies

in

each

assembly

and

allowing

comparison

of

large

regions

of

overlaps

between

clones

Consequently

efforts

to

produce

finished

sequences

of

complex

genomes

have

relied

on

either

pure

hierarchical

shotgun

sequencing

-LRB-

including

those

of

Caenorhabditis

Arabidopsis

and

-RRB-

or

a

combination

of

WGS

and

hierarchical

shotgun

sequencing

-LRB-

including

those

of

Drosophila

and

-RRB-

The

ultimate

aim

of

the

MGSC

is

to

produce

a

finished

richly

annotated

sequence

of

the

mouse

genome

to

serve

as

a

permanent

reference

for

mammalian

biology

In

addition

we

wished

to

produce

a

draft

sequence

as

rapidly

as

possible

to

aid

in

the

interpretation

of

the

human

genome

sequence

and

to

provide

a

useful

intermediate

resource

to

the

research

community

Accordingly

we

adopted

a

hybrid

strategy

for

sequencing

the

mouse

genome

The

strategy

has

four

components

-LRB-

-RRB-

production

of

a

BAC-based

physical

map

of

the

mouse

genome

by

fingerprinting

and

sequencing

the

ends

of

clones

of

a

BAC

-LRB-

-RRB-

WGS

sequencing

to

approximately

sevenfold

coverage

and

assembly

to

generate

an

initial

draft

genome

sequence

-LRB-

-RRB-

hierarchical

shotgun

sequencing

of

BAC

clones

covering

the

mouse

genome

combined

with

the

WGS

data

to

create

a

hybrid

WGS-BAC

assembly

and

-LRB-

-RRB-

production

of

a

finished

sequence

by

using

the

BAC

clones

as

a

template

for

directed

finishing

This

mixed

strategy

was

designed

to

exploit

the

simpler

organizational

aspects

of

WGS

assemblies

in

the

initial

phase

while

still

culminating

in

the

complete

high-quality

sequence

afforded

by

clone-based

maps

We

chose

to

sequence

DNA

from

a

single

mouse

strain

rather

than

from

a

mixture

of

to

generate

a

solid

reference

foundation

reasoning

that

polymorphic

variation

in

other

strains

could

be

added

subsequently

-LRB-

see

below

-RRB-

After

extensive

consultation

with

the

scientific

the

strain

was

selected

because

of

its

principal

role

in

mouse

genetics

including

its

wellcharacterized

phenotype

and

role

as

the

background

strain

on

which

many

important

mutations

arose

We

elected

to

sequence

a

female

mouse

to

obtain

equal

coverage

of

chromosome

X

and

autosomes

Chromosome

Y

was

thus

omitted

but

this

chromosome

is

highly

repetitive

-LRB-

the

human

chromosome

Y

has

multiple

duplicated

regions

exceeding

kb

in

size

with

sequence

-RRB-

and

seemed

an

unwise

target

for

the

WGS

approach

Instead

mouse

chromosome

Y

is

being

sequenced

by

a

purely

clonebased

-LRB-

hierarchical

shotgun

-RRB-

approach

Sequencing

and

assembly

Sanger

and

co-workers

developed

the

strategy

of

random

shotgun

sequencing

in

the

early

and

it

has

remained

the

mainstay

of

genome

sequencing

over

the

ensuing

two

decades

The

approach

involves

producing

random

sequence

reads

generating

a

preliminary

assembly

on

the

basis

of

sequence

overlaps

and

then

The

genome

assembly

was

based

on

a

total

of

million

sequence

reads

derived

from

both

ends

of

inserts

-LRB-

paired-end

reads

-RRB-

of

various

clone

types

prepared

from

female

DNA

The

inserts

ranged

in

size

from

to

kb

-LRB-

Table

-RRB-

The

three

large

MGSC

NATURE

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Nature

Publishing

Group

articles

Table

Distribution

of

sequence

reads

Insert

size

-LRB-

kb

-RRB-

Vector

All

Otherk

Total

Centre

All

Whitehead

Institute

Washington

University

Sanger

Institute

University

of

Utah

The

Institute

for

Genomic

Research

Total

Plasmid

Plasmid

Plasmid

Plasmid

Fosmid

BAC

Plasmid

Reads

-LRB-

millions

-RRB-

Used

Paired

Assembled

Bases

-LRB-

billions

-RRB-

Total

Sequence

coverage

Total

Physical

coverage

Physical

coverage

Reads

-LRB-

millions

-RRB-

Used

Paired

Assembled

Bases

-LRB-

billions

-RRB-

Total

Sequence

coverage

Total

The

approximate

mean

size

of

inserts

of

various

libraries

Each

library

was

individually

tracked

and

evaluated

Insert

sizes

were

intended

to

cover

a

narrow

range

as

determined

empirically

against

assembled

sequence

Bases

refers

to

the

bases

present

in

the

used

reads

after

trimming

for

quality

Sequence

coverage

estimated

on

the

basis

of

all

used

reads

after

trimming

for

quality

and

a

euchromatic

genome

This

excludes

the

heterochromatic

portion

which

contains

extensive

arrays

of

tandemly

repeated

sequence

such

as

that

found

in

the

centromeres

rDNA

satellites

and

the

array

Physical

coverage

refers

to

the

total

cloned

DNA

in

the

paired

reads

k

Consists

of

a

small

number

of

unpaired

reads

and

BAC-based

reads

used

for

methods

development

and

consistency

checks

sequencing

centres

generated

million

reads

and

million

reads

were

generated

at

the

University

of

Utah

In

addition

we

used

million

reads

from

both

ends

of

BAC

inserts

reported

by

The

Institute

for

Genome

A

total

of

million

reads

passed

extensive

checks

for

quality

and

source

of

which

million

were

paired

that

is

derived

from

opposite

ends

of

the

same

clone

-LRB-

Table

-RRB-

The

assembled

reads

represent

approximately

sequence

coverage

of

the

euchromatic

mouse

genome

-LRB-

coverage

in

bases

with

a

Phred

quality

score

of

-RRB-

Together

the

clone

inserts

provide

roughly

physical

coverage

of

the

genome

The

sequence

reads

together

with

the

pairing

information

were

used

as

input

for

two

recently

developed

sequence-assembly

programs

and

No

mapping

information

and

no

clone-based

sequences

were

used

in

the

WGS

assembly

with

the

exception

of

a

few

reads

-LRB-

of

the

total

-RRB-

derived

from

a

handful

of

BACs

which

were

used

as

internal

controls

The

assembly

programs

were

tested

and

compared

on

intermediate

data

sets

over

the

course

of

the

project

and

were

thereby

refined

The

programs

produced

comparable

outputs

in

the

final

assembly

The

assembly

generated

by

Arachne

was

chosen

as

the

draft

sequence

described

here

because

it

yielded

greater

short-range

and

long-range

continuity

with

comparable

accuracy

The

assembly

contains

sequence

contigs

which

are

connected

by

at

least

two

read-pair

links

into

supercontigs

-LRB-

or

scaffolds

-RRB-

There

are

a

total

of

supercontigs

at

least

kb

in

length

plus

a

further

smaller

supercontigs

representing

of

the

assembly

The

contigs

have

an

length

of

kb

whereas

the

supercontigs

have

an

length

that

is

approximately

larger

at

Mb

-LRB-

length

is

the

size

x

such

that

of

the

assembly

is

in

units

of

length

at

least

x

-RRB-

In

fact

most

of

the

genome

lies

in

supercontigs

that

are

extremely

large

the

largest

supercontigs

span

more

than

of

the

assembled

sequence

of

which

is

within

sequence

gaps

-LRB-

Table

-RRB-

Anchoring

to

chromosomes

We

assigned

as

many

supercontigs

as

possible

to

chromosomal

locations

in

the

proper

order

and

orientation

Supercontigs

were

localized

largely

by

sequence

alignments

with

the

extensively

validated

mouse

genetic

with

some

additional

localization

provided

by

the

mouse

radiation-hybrid

and

the

BAC

We

found

no

evidence

of

incorrect

global

joins

within

the

supercontigs

-LRB-

that

is

multiple

markers

supporting

two

discordant

locations

within

the

genome

-RRB-

and

thus

were

able

to

place

them

directly

Altogether

we

placed

supercontigs

including

all

supercontigs

kb

in

length

Once

much

of

the

sequence

was

anchored

it

was

possible

to

exploit

additional

read-pair

and

physical

mapping

information

to

obtain

greater

continuity

-LRB-

Table

-RRB-

For

example

some

adjacent

supercontigs

were

connected

by

BAC-end

-LRB-

or

other

-RRB-

links

satisfying

appropriate

length

and

orientation

constraints

including

single

links

Furthermore

some

adjacent

extended

supercontigs

were

connected

by

means

of

fingerprint

contigs

in

the

BAC-based

physical

map

These

additional

links

were

used

to

join

sequences

Table

Basic

statistics

of

the

assembly

Features

All

anchored

contigs

All

anchored

supercontigs

All

ultracontigs

Unanchored

contigs

Largest

supercontigs

Largest

supercontigs

Number

length

-LRB-

kb

-RRB-

Bases

-LRB-

Gb

-RRB-

Bases

plus

gaps

-LRB-

Gb

-RRB-

Percentage

of

genome

Not

including

gaps

Calculated

on

the

basis

of

a

euchromatic

genome

Includes

spanned

gaps

The

unanchored

contigs

grouped

into

unanchored

supercontigs

with

an

value

of

kb

The

value

for

all

contigs

is

kb

and

for

all

supercontigs

is

kb

-LRB-

excluding

gaps

-RRB-

Inspection

suggests

that

most

of

these

unanchored

contigs

fall

into

gaps

in

the

ultracontigs

and

are

thus

accounted

for

in

the

bases

plus

gaps

estimate

NATURE

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Publishing

Group

articles

into

ultracontigs

In

the

end

a

total

of

ultracontigs

with

an

length

of

Mb

-LRB-

exclusive

of

gaps

-RRB-

contained

of

the

assembled

sequence

-LRB-

Fig.

-RRB-

Continuity

near

telomeres

tends

to

be

lower

and

two

chromosomes

-LRB-

and

X

-RRB-

have

unusually

large

numbers

of

ultracontigs

Proportion

of

genome

contained

in

the

assembly

This

was

assessed

by

comparison

with

publicly

available

finished

genome

sequence

and

mouse

cDNA

sequences

Of

the

Mb

of

finished

mouse

sequence

was

contained

in

the

anchored

assembly

This

finished

sequence

however

is

not

a

completely

random

cross-section

of

the

genome

-LRB-

it

has

been

cloned

as

BACs

finished

and

in

some

cases

selected

on

the

basis

of

its

gene

content

-RRB-

Of

cDNA

sequences

from

the

curated

RefSeq

collection

of

the

cDNAs

could

be

aligned

to

the

genome

sequence

-LRB-

see

Supplementary

Information

-RRB-

These

alignments

contained

of

the

cDNA

bases

Together

this

indicates

that

the

draft

genome

sequence

includes

approximately

of

the

euchromatic

portion

of

the

mouse

genome

with

about

anchored

-LRB-

Table

-RRB-

Genome

size

was

estimated

to

be

about

Gb

-LRB-

see

Supplementary

Information

-RRB-

or

about

smaller

than

that

of

the

euchromatic

human

genome

-LRB-

about

Gb

-RRB-

-LRB-

Table

-RRB-

The

ultracontigs

include

spanned

gaps

whose

lengths

are

estimated

on

the

basis

of

paired-end

reads

and

alignment

against

the

human

sequence

-LRB-

see

below

-RRB-

To

test

the

accuracy

of

the

ultracontig

lengths

we

compared

the

actual

length

of

finished

mouse

BAC

sequences

-LRB-

from

the

strain

-RRB-

with

the

corresponding

estimated

length

from

the

draft

genome

sequence

The

ratio

of

estimated

length

to

actual

length

had

a

median

value

of

with

of

cases

falling

within

and

of

cases

within

Quality

assessment

at

intermediate

scale

On

the

basis

of

the

estimated

sizes

of

the

ultracontigs

and

gaps

between

them

the

total

length

of

the

euchromatic

mouse

genome

Although

no

evidence

of

large-scale

misassembly

was

found

when

anchoring

the

assembly

onto

the

mouse

chromosomes

we

examined

the

assembly

for

smaller

errors

To

assess

the

accuracy

at

an

intermediate

scale

we

compared

the

positions

of

well-studied

markers

on

the

mouse

genetic

map

and

in

the

genome

assembly

-LRB-

see

Supplementary

Information

-RRB-

Out

of

genetic

markers

that

were

unambiguously

mapped

to

the

sequence

assembly

-LRB-

BLAST

match

using

or

better

as

an

E-value

to

a

single

location

-RRB-

we

found

in

which

the

chromosomal

assignment

in

the

genetic

map

conflicted

with

that

in

the

sequence

This

is

well

within

the

known

range

of

erroneous

assignments

within

the

genetic

We

tested

such

discrepant

markers

by

re-mapping

them

in

a

mouse

cross

In

ten

cases

the

data

showed

that

the

previous

genetic

map

assignment

was

erroneous

and

supported

the

position

in

the

draft

sequence

In

one

case

the

data

supported

the

previous

genetic

map

assignment

and

contradicted

the

assembly

By

studying

the

one

erroneous

case

we

recognized

that

a

single

segment

had

been

erroneously

merged

into

a

sequence

contig

by

means

of

a

single

overlap

of

two

reads

We

screened

the

entire

assembly

for

similar

instances

affecting

regions

of

at

least

kb

Only

additional

cases

were

found

with

a

median

size

of

the

incorrectly

merged

segment

of

kb

These

are

being

corrected

in

the

next

release

of

the

MGSC

sequence

We

are

continuing

to

investigate

instances

involving

smaller

incorrectly

merged

segments

We

also

found

instances

-LRB-

-RRB-

of

conflicts

in

local

marker

order

between

the

genetic

map

and

sequence

assembly

A

conflict

was

defined

as

any

instance

that

would

require

changing

more

than

a

single

genotype

in

the

data

underlying

the

genetic

map

to

resolve

We

studied

ten

cases

by

re-mapping

the

genetic

markers

and

eight

were

found

to

be

due

to

errors

in

the

genetic

map

On

the

basis

of

this

analysis

we

estimate

that

chromosomal

misassignment

and

local

misordering

affects

of

the

assembled

sequence

Quality

assessment

at

fine

scale

Figure

The

mouse

genome

in

sequence-based

ultracontigs

The

position

and

extent

of

the

ultracontigs

of

the

assembly

are

shown

adjacent

to

ideograms

of

the

mouse

chromosomes

All

mouse

chromosomes

are

acrocentric

with

the

centromeric

end

at

the

top

of

each

chromosome

The

supercontigs

of

the

sequence

assembly

were

anchored

to

the

mouse

chromosomes

using

the

MIT

genetic

map

Neighbouring

supercontigs

were

linked

together

into

ultracontigs

using

information

from

single

BAC

links

and

the

fingerprint

and

radiation-hybrid

maps

resulting

in

ultracontigs

containing

of

the

bases

in

the

euchromatic

genome

We

also

assessed

fine-scale

accuracy

of

the

assembly

by

carefully

aligning

it

to

about

Mb

of

finished

BAC-derived

sequence

from

the

strain

This

revealed

a

total

of

discrepancies

of

bp

in

length

-LRB-

median

size

of

bp

-RRB-

reflecting

small

misassemblies

either

in

the

draft

sequence

or

the

finished

BAC

sequences

These

discrepancies

typically

occurred

at

the

ends

of

contigs

in

the

WGS

assembly

indicating

that

they

may

represent

the

incorrect

incorporation

of

a

single

terminal

read

At

the

single

nucleotide

level

in

the

assembly

the

observed

discrepancy

rates

varied

in

a

manner

consistent

with

the

quality

scores

assigned

to

the

bases

in

the

WGS

assembly

-LRB-

see

Supplementary

Information

-RRB-

Overall

of

nucleotides

in

the

assembly

have

Arachne

quality

scores

corresponding

to

a

predicted

error

rate

of

per

bases

Such

bases

had

an

observed

discrepancy

rate

against

finished

sequence

of

or

errors

per

bases

Comparison

with

the

draft

sequence

of

chromosome

We

also

compared

the

sequence

reported

here

to

a

draft

sequence

of

mouse

chromosome

recently

published

by

Mural

and

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Because

the

latter

was

produced

from

strain

and

other

mouse

strains

it

is

expected

to

differ

slightly

at

the

nucleotide

level

but

should

otherwise

show

good

agreement

The

sequences

align

well

at

large

scales

-LRB-

hundreds

of

kilobases

-RRB-

although

the

assembly

by

Mural

and

co-workers

contains

less

total

sequence

-LRB-

compared

with

Mb

-RRB-

and

includes

a

region

of

approximately

kb

that

we

place

on

chromosome

X

There

were

differences

at

intermediate

scales

with

our

draft

sequence

showing

better

agreement

with

finished

BAC-derived

sequences

-LRB-

approximately

fourfold

fewer

discrepancies

of

length

bp

compared

with

in

about

Mb

of

finished

sequence

-RRB-

These

could

not

be

explained

by

strain

differences

as

similar

results

were

seen

with

finished

sequence

from

the

and

strains

Collapse

of

duplicated

regions

The

human

genome

contains

many

large

duplicated

regions

estimated

to

comprise

roughly

of

the

with

nearly

identical

sequence

If

such

regions

are

also

common

in

the

mouse

genome

they

might

collapse

into

a

single

copy

in

the

WGS

assembly

Such

artefactual

collapse

could

be

detected

as

regions

with

unusually

high

read

coverage

compared

with

the

average

depth

of

in

long

assembled

contigs

We

searched

for

contigs

that

were

kb

in

size

and

contained

kb

of

sequence

in

which

the

read

coverage

was

at

least

twofold

higher

than

the

average

Such

regions

comprised

only

a

tiny

fraction

-LRB-

-RRB-

of

the

total

assembly

of

which

only

half

had

been

anchored

to

a

chromosome

of

these

windows

had

coverage

exceeding

the

average

by

more

than

threefold

This

may

indicate

that

the

mouse

genome

contains

fewer

large

regions

of

near-exact

duplication

than

the

human

Alternatively

regions

of

near-exact

duplication

may

have

been

systematically

excluded

by

the

WGS

assembly

programme

This

issue

is

better

addressed

through

hierarchical

shotgun

than

WGS

sequencing

and

will

be

examined

more

carefully

in

the

course

of

producing

a

finished

mouse

genome

sequence

Unplaced

reads

and

large

tandem

repeats

Indeed

million

of

the

million

passing

reads

were

not

part

of

anchored

sequence

with

of

these

not

assembled

into

sequence

contigs

and

assembled

into

small

contigs

but

not

chromosomally

localized

A

striking

example

of

unassembled

sequence

is

a

large

region

on

mouse

chromosome

that

contains

a

tandem

expansion

of

sequence

containing

the

gene

fusion

This

region

is

highly

variable

among

mouse

species

and

even

laboratory

strains

with

estimated

lengths

ranging

from

to

The

bulk

of

this

region

was

not

reliably

assembled

in

the

draft

genome

sequence

The

individual

sequence

reads

together

were

found

to

contain

coverage

of

the

gene

suggesting

that

there

are

roughly

copies

in

the

genome

-LRB-

corresponding

to

a

region

of

about

Mb

-RRB-

This

is

consistent

with

an

estimate

of

copies

in

obtained

by

Southern

We

also

examined

centromeric

sequences

including

the

euchromatin-proximal

major

satellite

repeat

-LRB-

bases

-RRB-

and

the

telomereproximal

minor

repeat

-LRB-

bases

-RRB-

found

on

some

-LRB-

Note

that

mouse

chromosomes

are

all

acrocentric

meaning

that

the

centromere

is

adjacent

to

one

telomere

-RRB-

The

minor

satellite

was

poorly

represented

among

the

sequence

reads

-LRB-

present

in

about

reads

or

of

the

total

-RRB-

suggesting

that

this

satellite

sequence

is

difficult

to

isolate

in

the

cloning

systems

used

The

major

satellite

was

found

in

about

of

the

reads

this

is

also

lower

than

previous

estimates

based

on

density

gradient

experiments

which

found

that

major

satellites

comprise

about

of

the

mouse

genome

or

approximately

Mb

per

Evaluation

of

WGS

assembly

strategy

We

expected

that

highly

repetitive

regions

of

the

genome

would

not

be

assembled

or

would

not

be

anchored

on

the

chromosomes

The

WGS

assembly

described

here

involved

only

random

reads

without

any

additional

map-based

information

By

many

criteria

the

assembly

is

of

very

high

quality

The

supercontig

size

of

Mb

far

exceeds

that

achieved

by

any

previous

WGS

assembly

and

the

agreement

with

genome-wide

maps

is

excellent

The

assembly

quality

may

be

due

to

several

factors

including

the

use

of

high-quality

libraries

the

variety

of

insert

lengths

in

multiple

Table

Mouse

chromosome

size

estimates

Chromosome

Actual

bases

in

sequence

-LRB-

Mb

-RRB-

Ultracontigs

-LRB-

Mb

-RRB-

Number

size

Gaps

within

supercontigs

Number

Mb

Captured

by

additional

read

pairs

Number

All

X

183 169 149 140

137 138 122 119

116 121 115 105

Mb

Gaps

between

supercontigs

Captured

by

fingerprint

contigs

Number

Mb

Uncaptured

Total

estimated

size

-LRB-

Mb

-RRB-

Number

192 176 157 147

144 146 131 125

121 127 119 110

113 112 100

These

gaps

had

fingerprint

contigs

spanning

them

The

size

for

out

of

were

estimated

using

conserved

synteny

to

determine

the

size

of

the

region

in

the

human

genome

The

remaining

gaps

were

arbitrarily

given

the

average

size

of

the

assessed

gaps

-LRB-

kb

-RRB-

adjusted

to

reflect

the

difference

in

genome

size

Uncaptured

gaps

were

estimated

by

mouse

human

synteny

to

have

a

total

size

of

Mb

However

because

some

of

these

gaps

are

due

to

repetitive

expansions

in

mouse

-LRB-

absent

in

human

-RRB-

the

actual

total

for

the

uncaptured

gaps

is

probably

substantially

higher

For

example

one

large

uncaptured

gap

on

chromosome

-LRB-

the

region

-RRB-

is

roughly

Mb

-LRB-

see

text

-RRB-

Omitting

centromeres

and

telomeres

These

would

add

on

average

approximately

Mb

per

chromosome

or

about

Mb

to

the

genome

Also

omitting

uncaptured

gaps

between

supercontigs

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libraries

the

improved

assembly

algorithms

and

the

inbred

nature

of

the

mouse

strain

-LRB-

in

contrast

to

the

polymorphisms

in

the

human

genome

sequences

-RRB-

Another

contributing

factor

may

be

that

the

mouse

differs

from

the

human

in

having

less

recent

segmental

duplication

to

confound

assembly

Notwithstanding

the

high

quality

of

the

draft

genome

sequence

we

are

mindful

that

it

contains

many

gaps

small

misassemblies

and

nucleotide

errors

It

is

likely

that

these

could

not

all

be

resolved

by

further

WGS

sequencing

therefore

directed

sequencing

will

be

needed

to

produce

a

finished

sequence

The

results

also

suggest

that

WGS

sequencing

may

suffice

for

large

genomes

for

which

only

draft

sequence

is

required

provided

that

they

contain

minimal

amounts

of

sequence

associated

with

recent

segmental

duplications

or

large

recent

interspersed

repeat

elements

Adding

finished

sequence

As

a

final

step

we

enhanced

the

WGS

sequence

assembly

by

substituting

available

finished

BAC-derived

sequence

from

the

strain

In

total

we

replaced

draft

sequence

contigs

with

Mb

of

finished

sequence

from

finished

BACs

available

at

the

time

of

the

assembly

The

resulting

draft

genome

sequence

was

submitted

to

the

public

databases

and

is

freely

available

in

electronic

form

through

various

sources

-LRB-

see

below

-RRB-

The

sequence

data

and

assemblies

have

been

freely

available

throughout

the

course

of

the

project

The

next

step

of

the

project

which

is

already

underway

is

to

convert

the

draft

sequence

into

a

finished

sequence

As

the

MGSC

produces

additional

BAC

assemblies

and

finished

sequence

we

plan

to

continue

to

revise

and

release

enhanced

versions

of

the

genome

sequence

en

route

to

a

completely

finished

thereby

providing

a

permanent

foundation

for

biomedical

research

in

the

twenty-first

century

Conservation

of

synteny

between

mouse

and

human

genomes

With

the

draft

sequence

in

hand

we

began

our

analysis

by

investigating

the

strong

conservation

of

synteny

between

the

mouse

and

human

genomes

Beyond

providing

insight

into

evolutionary

events

that

have

moulded

the

chromosomes

this

analysis

facilitates

further

comparisons

between

the

genomes

Starting

from

a

common

ancestral

genome

approximately

Myr

the

mouse

and

human

genomes

have

each

been

shuffled

by

chromosomal

rearrangements

The

rate

of

these

changes

however

is

low

enough

that

local

gene

order

remains

largely

intact

It

is

thus

possible

to

recognize

syntenic

-LRB-

literally

same

thread

-RRB-

regions

in

the

two

species

that

have

descended

relatively

intact

from

the

common

ancestor

The

earliest

indication

that

genes

reside

in

similar

relative

positions

in

different

mammalian

species

traces

to

the

observation

that

the

albino

and

pink-eye

dilution

mutants

are

genetically

closely

linked

in

both

mouse

and

Significant

experimental

evidence

came

from

genetic

studies

of

somatic

In

Nadeau

and

used

mouse

linkage

data

and

human

cytogenetic

data

to

compare

the

chromosomal

locations

of

orthologous

genes

On

the

basis

of

a

small

data

set

-LRB-

loci

-RRB-

they

extrapolated

that

the

mouse

and

human

genomes

could

be

parsed

into

roughly

syntenic

regions

During

two

decades

of

subsequent

work

the

density

of

the

synteny

map

has

been

increased

but

the

estimated

number

of

syntenic

regions

has

remained

close

to

the

original

projection

A

recent

gene-based

synteny

used

more

than

orthologous

loci

to

define

about

regions

of

conserved

synteny

However

it

is

recognized

that

such

maps

might

still

miss

regions

owing

to

insufficient

marker

density

With

a

robust

draft

sequence

of

the

mouse

genome

and

finished

sequence

of

the

human

genome

in

hand

it

is

possible

to

undertake

a

more

comprehensive

analysis

of

conserved

synteny

Rather

than

simply

relying

on

known

human

mouse

gene

pairs

we

identified

a

much

larger

set

of

orthologous

landmarks

as

follows

We

performed

sequence

comparisons

of

the

entire

mouse

and

human

genome

sequences

using

the

PatternHunter

to

identify

regions

having

a

similarity

score

exceeding

a

high

threshold

-LRB-

corresponding

to

a

minimum

of

a

perfect

match

with

penalties

for

mismatches

and

gaps

-RRB-

with

the

additional

property

that

each

sequence

is

the

other

's

unique

match

above

this

threshold

Such

regions

probably

reflect

orthologous

sequence

pairs

derived

from

the

same

ancestral

sequence

About

orthologous

landmarks

were

identified

in

the

mouse

assembly

these

sequences

have

a

mean

spacing

of

about

kb

and

an

length

of

about

bp

The

landmarks

had

a

total

length

of

roughly

Mb

comprising

about

of

the

mouse

genome

It

should

be

emphasized

that

the

landmarks

represent

only

a

small

subset

of

the

sequences

consisting

of

those

that

can

be

aligned

with

the

highest

similarity

between

the

mouse

and

human

genomes

-LRB-

Indeed

below

we

show

that

about

of

the

human

genome

can

be

aligned

confidently

with

the

mouse

genome

-RRB-

The

locations

of

the

landmarks

in

the

two

genomes

were

then

compared

to

identify

regions

of

conserved

synteny

We

define

a

syntenic

segment

to

be

a

maximal

region

in

which

a

series

of

landmarks

occur

in

the

same

order

on

a

single

chromosome

in

both

species

A

syntenic

block

in

turn

is

one

or

more

syntenic

segments

that

are

all

adjacent

on

the

same

chromosome

in

human

and

on

the

same

chromosome

in

mouse

but

which

may

otherwise

be

shuffled

with

respect

to

order

and

orientation

To

avoid

small

artefactual

syntenic

segments

owing

to

imperfections

in

the

two

draft

genome

sequences

we

only

considered

regions

above

kb

and

ignored

occasional

isolated

interruptions

in

conserved

order

-LRB-

see

Supplementary

Information

-RRB-

Thus

some

small

syntenic

segments

have

probably

been

omitted

this

issue

will

be

addressed

best

when

finished

sequences

of

the

two

genomes

are

completed

Marked

conservation

of

landmark

order

was

found

across

most

of

the

two

genomes

-LRB-

Fig.

-RRB-

Each

genome

could

be

parsed

into

a

total

of

conserved

syntenic

segments

On

average

each

landmark

resides

in

a

segment

containing

other

landmarks

The

segments

vary

greatly

in

length

from

kb

to

Mb

with

a

mean

of

Mb

and

an

length

of

Mb

In

total

about

of

the

human

genome

and

of

the

mouse

genome

Figure

Conservation

of

synteny

between

human

and

mouse

We

detected

highly

conserved

reciprocally

unique

landmarks

within

the

mouse

and

human

genomes

which

can

be

joined

into

conserved

syntenic

segments

and

blocks

-LRB-

defined

in

text

-RRB-

A

typical

segment

of

mouse

chromosome

that

shares

common

ancestry

with

a

section

of

human

chromosome

is

shown

Blue

lines

connect

the

reciprocal

unique

matches

in

the

two

genomes

The

cyan

bars

represent

sequence

coverage

in

each

of

the

two

genomes

for

the

regions

In

general

the

landmarks

in

the

mouse

genome

are

more

closely

spaced

reflecting

the

smaller

overall

genome

size

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articles

pared

with

-RRB-

but

only

slightly

more

conserved

syntenic

blocks

-LRB-

compared

with

-RRB-

Most

of

the

conserved

syntenic

blocks

had

previously

been

recognized

and

are

consistent

with

the

new

map

but

many

rearrangements

of

segments

within

blocks

had

been

missed

-LRB-

notably

on

the

X

chromosome

-RRB-

The

occurrence

of

many

local

rearrangements

is

not

surprising

Compared

with

interchromosomal

rearrangements

-LRB-

for

example

translocations

-RRB-

paracentric

inversions

-LRB-

that

is

those

within

a

single

chromosome

and

not

including

the

centromere

-RRB-

carry

a

lower

selective

disadvantage

in

terms

of

the

frequency

of

aneuploidy

among

offspring

These

are

also

seen

at

a

higher

frequency

in

genera

such

as

Drosophila

in

which

extensive

cytogenetic

comparisons

have

been

carried

The

block

and

segment

sizes

are

broadly

consistent

with

the

random

breakage

model

of

genome

-LRB-

Fig.

-RRB-

At

this

gross

level

there

is

no

evidence

of

extensive

selection

for

gene

order

across

the

genome

Selection

in

specific

regions

however

is

by

no

means

excluded

and

indeed

seems

probable

-LRB-

for

example

for

the

major

histocompatibility

complex

-RRB-

Moreover

the

analysis

does

not

exclude

the

possibility

that

chromosomal

breaks

may

tend

to

occur

with

higher

frequency

in

some

locations

With

a

map

of

conserved

syntenic

segments

between

the

human

and

mouse

genomes

it

is

possible

to

calculate

the

minimal

number

of

rearrangements

needed

to

transform

one

genome

into

the

When

applied

to

the

syntenic

segments

above

the

most

parsimonious

path

has

rearrangements

The

analysis

suggests

that

chromosomal

breaks

may

have

a

tendency

to

reoccur

in

certain

regions

With

only

two

species

however

it

is

not

yet

possible

to

recover

the

ancestral

chromosomal

order

or

reconstruct

the

precise

pathway

of

rearrangements

As

more

mammalian

species

are

sequenced

it

should

be

possible

to

draw

such

inferences

and

study

the

nature

of

chromosome

rearrangement

Figure

Segments

and

blocks

kb

in

size

with

conserved

synteny

in

human

are

superimposed

on

the

mouse

genome

Each

colour

corresponds

to

a

particular

human

chromosome

The

segments

are

separated

from

each

other

by

thin

white

lines

within

the

blocks

of

consistent

colour

unambiguously

reside

within

conserved

syntenic

segments

The

segments

can

be

aggregated

into

a

total

of

conserved

syntenic

blocks

with

an

length

of

Mb

The

nature

and

extent

of

conservation

of

synteny

differs

substantially

among

chromosomes

-LRB-

Fig.

and

Table

-RRB-

In

accordance

with

expectation

the

X

chromosomes

are

represented

as

single

reciprocal

syntenic

Human

chromosome

corresponds

entirely

to

a

portion

of

mouse

chromosome

with

nearly

perfect

conservation

of

order

along

almost

the

entire

length

disrupted

only

by

a

small

central

segment

-LRB-

Fig.

d

-RRB-

Human

chromosome

corresponds

entirely

to

a

portion

of

mouse

chromosome

but

extensive

rearrangements

have

divided

it

into

at

least

segments

-LRB-

Fig.

e

-RRB-

Other

chromosomes

however

show

evidence

of

much

more

extensive

interchromosomal

rearrangement

than

these

cases

-LRB-

Fig.

f

-RRB-

We

compared

the

new

sequence-based

map

of

conserved

synteny

with

the

most

recent

previous

map

based

on

The

new

map

reveals

many

more

conserved

syntenic

segments

-LRB-

com

Genome

landscape

We

next

sought

to

analyse

the

contents

of

the

mouse

genome

both

in

its

own

right

and

in

comparison

with

corresponding

regions

of

the

human

genome

The

poster

included

with

this

issue

provides

a

high-level

view

of

the

mouse

genome

showing

such

features

as

genes

and

gene

predictions

repetitive

sequence

content

-LRB-

G

C

-RRB-

content

synteny

with

the

human

genome

and

mouse

QTLs

Table

Syntenic

properties

of

human

and

mouse

chromosomes

Chromosome

Blocks

Segments

Human

Fraction

of

chromosome

in

segments

Blocks

Segments

Mouse

Fraction

of

chromosome

in

segments

Size

-LRB-

mouse/human

-RRB-

X

Total

not

applicable

as

mouse

has

only

autosomes

Mean

size

ratio

-LRB-

mouse/human

-RRB-

on

the

basis

of

orthologous

mouse

windows

NATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

Figure

Dot

plots

of

conserved

syntenic

segments

in

three

human

and

three

mouse

chromosomes

For

each

of

three

human

-LRB-

a

c

-RRB-

and

mouse

-LRB-

d

f

-RRB-

chromosomes

the

positions

of

orthologous

landmarks

are

plotted

along

the

x

axis

and

the

corresponding

position

of

the

landmark

on

chromosomes

in

the

other

genome

is

plotted

on

the

y

axis

Different

chromosomes

in

the

corresponding

genome

are

differentiated

with

distinct

colours

In

a

remarkable

example

of

conserved

synteny

human

chromosome

-LRB-

a

-RRB-

consists

of

just

three

segments

from

mouse

chromosome

-LRB-

d

-RRB-

with

only

one

small

segment

altered

in

order

Human

chromosome

-LRB-

b

-RRB-

also

shares

segments

with

only

one

mouse

chromosome

-LRB-

-RRB-

-LRB-

e

-RRB-

but

the

segments

are

extensively

rearranged

However

most

of

the

mouse

and

human

chromosomes

consist

of

multiple

segments

from

multiple

chromosomes

as

shown

for

human

chromosome

-LRB-

c

-RRB-

and

mouse

chromosome

-LRB-

f

-RRB-

Circled

areas

and

arrows

denote

matching

segments

in

mouse

and

human

All

of

the

mouse

genome

information

is

accessible

in

electronic

form

through

various

browsers

Ensembl

-LRB-

-RRB-

the

University

of

California

at

Santa

Cruz

-LRB-

-RRB-

and

the

National

Center

for

Biotechnology

Information

-LRB-

http

-RRB-

These

browsers

allow

users

to

scroll

along

the

chromosomes

and

zoom

in

or

out

to

any

scale

as

well

as

to

display

information

at

any

desired

level

of

detail

The

mouse

genome

information

has

also

been

integrated

into

existing

human

genome

browsers

at

these

same

organizations

In

this

section

we

compare

general

properties

of

the

mouse

and

human

genomes

Genome

expansion

and

contraction

mouse

and

human

but

the

availability

of

nearly

complete

genome

sequences

provides

the

first

detailed

picture

of

the

phenomenon

The

mouse

has

a

slightly

higher

overall

-LRB-

G

C

-RRB-

content

than

the

human

-LRB-

compared

with

-RRB-

but

the

distribution

is

tighter

When

local

-LRB-

G

C

-RRB-

content

is

measured

in

windows

across

the

genome

the

human

genome

has

about

of

the

windows

with

-LRB-

G

C

-RRB-

content

and

with

-LRB-

G

C

-RRB-

content

Such

extreme

deviations

are

virtually

absent

in

the

mouse

genome

The

contrast

is

even

seen

at

the

level

of

entire

chromosomes

The

The

projected

total

length

of

the

euchromatic

portion

of

the

mouse

genome

-LRB-

Gb

-RRB-

is

about

smaller

than

that

of

the

human

genome

-LRB-

Gb

-RRB-

To

investigate

the

source

of

this

difference

we

examined

the

relative

size

of

intervals

between

consecutive

orthologous

landmarks

in

the

human

and

mouse

genomes

The

mouse

human

ratio

has

a

mean

at

for

autosomes

but

varies

widely

with

the

mouse

interval

being

larger

than

the

human

in

of

cases

-LRB-

Fig.

-RRB-

Chromosome

X

by

contrast

shows

no

net

relative

expansion

or

contraction

with

a

mouse/human

ratio

of

-LRB-

Fig.

and

Table

-RRB-

What

accounts

for

the

smaller

size

of

the

mouse

genome

We

address

this

question

below

in

the

sections

on

repeat

sequences

and

on

genome

evolution

-LRB-

-RRB-

content

Figure

Size

distribution

of

segments

and

blocks

with

synteny

conserved

between

mouse

and

human

a

b

The

number

of

segments

-LRB-

a

-RRB-

and

blocks

-LRB-

b

-RRB-

with

synteny

conserved

between

mouse

and

human

in

bins

-LRB-

starting

with

Mb

-RRB-

is

plotted

on

a

logarithmic

scale

The

dots

indicate

the

expected

values

for

the

exponential

curve

of

random

breakage

given

the

number

of

blocks

and

segments

respectively

NATURE

VOL

DECEMBER

The

overall

distribution

of

local

-LRB-

G

C

-RRB-

content

is

significantly

different

between

the

mouse

and

human

genomes

-LRB-

Fig.

-RRB-

Such

differences

have

been

noted

in

biochemical

and

in

comparative

analyses

of

fourfold

degenerate

sites

in

codons

of

Nature

Publishing

Group

articles

identical

when

expressed

in

terms

of

percentiles

of

-LRB-

G

C

-RRB-

content

-LRB-

Fig.

-RRB-

For

example

both

species

have

of

genes

residing

in

the

-LRB-

G

C

-RRB-

richest

half

of

their

genome

Mouse

and

human

thus

show

similar

degrees

of

homogeneity

in

the

distribution

of

genes

despite

the

overall

differences

in

-LRB-

G

C

-RRB-

content

Notably

the

mouse

shows

similar

extremes

of

gene

density

despite

being

less

extreme

in

-LRB-

G

C

-RRB-

content

What

accounts

for

the

differences

in

-LRB-

G

C

-RRB-

content

between

mouse

and

human

Does

it

reflect

altered

selection

for

-LRB-

G

C

-RRB-

altered

mutational

or

repair

or

possibly

both

Data

from

additional

species

will

probably

be

needed

to

address

these

issues

Any

explanation

will

need

to

account

for

various

mysterious

phenomena

For

example

although

overall

-LRB-

G

C

-RRB-

content

in

mouse

is

slightly

higher

than

in

human

-LRB-

compared

with

-RRB-

the

-LRB-

G

C

-RRB-

content

of

chromosome

X

is

slightly

lower

-LRB-

compared

with

-RRB-

The

effect

is

even

more

pronounced

if

one

excludes

lineage-specific

repeats

-LRB-

see

below

-RRB-

thereby

focusing

primarily

on

shared

DNA

In

that

case

mouse

autosomes

have

an

overall

-LRB-

G

C

-RRB-

content

that

is

higher

than

human

autosomes

-LRB-

compared

with

-RRB-

whereas

mouse

chromosome

X

has

a

-LRB-

G

C

-RRB-

content

that

is

lower

than

human

chromosome

X

-LRB-

compared

with

-RRB-

CpG

islands

Figure

Size

ratio

of

mouse

to

human

for

orthologous

windows

For

each

region

of

the

mouse

genome

the

size

ratio

to

the

related

segment

of

the

human

genome

was

determined

The

frequency

of

the

various

ratios

is

plotted

on

a

logarithmic

scale

for

both

the

autosomes

-LRB-

blue

line

-RRB-

and

the

X

chromosome

-LRB-

red

line

-RRB-

The

ratio

for

autosomes

shows

a

mean

of

but

the

ratio

varies

widely

with

the

mouse

genome

larger

for

of

the

intervals

The

X

chromosome

by

contrast

has

a

mean

ratio

of

just

over

Indeed

chromosome

X

is

slightly

smaller

in

human

human

has

extreme

outliers

with

respect

to

-LRB-

G

C

-RRB-

content

-LRB-

the

most

extreme

being

chromosome

-RRB-

whereas

the

mouse

chromosomes

tend

to

be

far

more

uniform

-LRB-

Fig.

-RRB-

There

is

a

strong

positive

correlation

in

local

-LRB-

G

C

-RRB-

content

between

orthologous

regions

in

the

mouse

and

human

genomes

-LRB-

Fig.

-RRB-

but

with

the

mouse

regions

showing

a

clear

tendency

to

be

less

extreme

in

-LRB-

G

C

-RRB-

content

than

the

human

regions

This

tendency

is

not

uniform

with

the

most

extreme

differences

seen

at

the

tails

of

the

distribution

In

mammalian

genomes

there

is

a

positive

correlation

between

gene

density

and

-LRB-

G

C

-RRB-

Given

the

differences

in

-LRB-

G

C

-RRB-

content

between

human

and

mouse

we

compared

the

distribution

of

genes

using

the

sets

of

orthologous

mouse

and

human

genes

described

below

with

respect

to

-LRB-

G

C

-RRB-

content

for

both

genomes

-LRB-

Fig.

-RRB-

The

density

of

genes

differed

markedly

when

expressed

in

terms

of

absolute

-LRB-

G

C

-RRB-

content

but

was

nearly

In

mammalian

genomes

the

palindromic

dinucleotide

CpG

is

usually

methylated

on

the

cytosine

residue

Methyl-CpG

is

mutated

by

deamination

to

TpG

leading

to

approximately

fivefold

underrepresentation

of

CpG

across

the

and

mouse

genomes

In

some

regions

of

the

genome

that

have

been

implicated

in

gene

regulation

CpG

dinucleotides

are

not

methylated

and

thus

are

not

subject

to

deamination

and

mutation

Such

regions

termed

CpG

islands

are

usually

a

few

hundred

nucleotides

in

length

have

high

-LRB-

G

C

-RRB-

content

and

above

average

representation

of

CpG

dinucleotides

We

applied

a

computer

program

that

attempts

to

recognize

CpG

islands

on

the

basis

of

-LRB-

G

C

-RRB-

and

CpG

content

of

arbitrary

lengths

of

to

the

non-repetitive

portions

of

human

and

mouse

genome

sequences

-LRB-

see

Supplementary

Information

-RRB-

The

mouse

genome

contains

fewer

CpG

islands

than

the

human

genome

-LRB-

about

compared

with

-RRB-

which

is

qualitatively

consistent

with

previous

The

absolute

number

of

islands

identified

Figure

Distribution

of

-LRB-

G

C

-RRB-

content

in

the

mouse

-LRB-

blue

-RRB-

and

human

-LRB-

red

-RRB-

genomes

Mouse

has

a

higher

mean

-LRB-

G

C

-RRB-

content

than

human

-LRB-

compared

with

-RRB-

but

human

has

a

larger

fraction

of

windows

with

either

high

or

low

-LRB-

G

C

-RRB-

content

The

distribution

was

determined

using

the

unmasked

genomes

in

non-overlapping

windows

with

the

fraction

of

windows

-LRB-

y

axis

-RRB-

in

each

percentage

bin

-LRB-

x

axis

-RRB-

plotted

for

both

human

and

mouse

NATURE

VOL

DECEMBER

Figure

-LRB-

G

C

-RRB-

content

and

density

of

CpG

islands

shows

more

variability

in

human

-LRB-

red

-RRB-

than

mouse

-LRB-

blue

-RRB-

chromosomes

a

The

-LRB-

G

C

-RRB-

content

for

each

of

the

mouse

chromosomes

is

relatively

similar

whereas

human

chromosomes

show

more

variation

chromosomes

and

have

higher

-LRB-

G

C

-RRB-

content

and

chromosome

lower

-LRB-

G

C

-RRB-

content

b

Similarly

the

density

of

CpG

islands

is

relatively

homogenous

for

all

mouse

chromosomes

and

more

variable

in

human

with

the

same

exceptions

Note

that

the

mouse

and

human

chromosomes

are

matched

by

chromosome

number

not

by

regions

of

conserved

synteny

Nature

Publishing

Group

articles

depends

on

the

precise

definition

of

a

CpG

island

used

but

the

ratio

between

the

two

species

remains

fairly

constant

The

reason

for

the

smaller

number

of

predicted

CpG

islands

in

mouse

may

relate

simply

to

the

smaller

fraction

of

the

genome

with

extremely

high

-LRB-

G

C

-RRB-

and

its

effect

on

the

computer

algorithm

Approximately

of

the

predicted

CpG

islands

in

each

species

show

significant

sequence

conservation

with

CpG

islands

in

the

orthologous

intervals

in

the

other

species

falling

within

the

orthologous

landmarks

described

above

Perhaps

these

represent

functional

CpG

islands

a

proposition

that

can

now

be

tested

Mouse

has

accumulated

more

new

repetitive

sequence

than

human

Repeats

The

single

most

prevalent

feature

of

mammalian

genomes

is

their

repetitive

sequences

most

of

which

are

interspersed

repeats

representing

fossils

of

transposable

elements

Transposable

elements

are

a

principal

force

in

reshaping

the

genome

and

their

fossils

thus

provide

powerful

reporters

for

measuring

evolutionary

forces

acting

on

the

genome

A

recent

paper

on

the

human

genome

provided

extensive

background

on

mammalian

transposons

describing

their

biology

and

illustrating

many

applications

to

evolutionary

studies

Here

we

will

focus

primarily

on

comparisons

between

the

repeat

content

of

the

mouse

and

human

genomes

Approximately

of

the

human

genome

can

be

recognized

currently

as

interspersed

repeats

resulting

from

insertions

of

transposable

elements

that

were

active

in

the

last

million

years

The

total

fraction

of

the

human

genome

derived

from

transposons

may

be

considerably

larger

but

it

is

not

possible

to

recognize

fossils

older

than

a

certain

age

because

of

the

high

degree

of

sequence

divergence

Because

only

of

the

mouse

genome

is

recognized

as

transposon-derived

-LRB-

Table

-RRB-

it

is

tempting

to

conclude

that

the

smaller

size

of

the

mouse

genome

is

due

to

lower

transposon

activity

since

the

divergence

of

the

human

and

mouse

lineages

Closer

analysis

however

shows

that

this

is

not

the

case

As

we

discuss

below

transposition

has

been

more

active

in

the

mouse

lineage

The

apparent

deficit

of

transposon-derived

sequence

in

the

mouse

genome

is

mostly

due

to

a

higher

nucleotide

substitution

rate

which

makes

it

difficult

to

recognize

ancient

repeat

sequences

Lineage-specific

versus

ancestral

repeats

Interspersed

repeats

can

be

divided

into

lineage-specific

repeats

-LRB-

defined

as

those

introduced

by

transposition

after

the

divergence

of

mouse

and

human

-RRB-

and

ancestral

repeats

-LRB-

defined

as

those

already

present

in

a

common

ancestor

-RRB-

Such

a

division

highlights

the

fact

that

transposable

elements

have

been

more

active

in

the

mouse

lineage

than

in

the

human

lineage

Approximately

of

the

mouse

genome

-LRB-

about

Mb

-RRB-

but

only

of

the

human

genome

-LRB-

about

Mb

-RRB-

consists

of

lineage-specific

repeats

-LRB-

Table

-RRB-

Contrary

to

initial

appearances

transposon

insertions

have

added

at

least

Mb

more

transposon-derived

sequence

to

the

mouse

genome

than

to

the

human

genome

since

their

divergence

This

observation

is

consistent

with

the

previous

report

that

the

rate

of

transposition

in

the

human

genome

has

fallen

markedly

over

the

past

million

The

overall

lower

interspersed

repeat

density

in

mouse

is

the

result

of

an

apparent

lack

of

ancestral

repeats

they

comprise

only

of

the

mouse

genome

compared

with

of

the

human

genome

The

ancestral

repeats

recognizable

in

mouse

tend

to

be

those

of

more

recent

origin

that

is

those

that

originated

closest

to

the

mouse

human

divergence

This

difference

may

be

due

partly

to

a

higher

deletion

rate

of

non-functional

DNA

in

the

mouse

lineage

so

that

more

of

the

older

interspersed

repeats

have

been

lost

However

the

deficit

largely

reflects

a

much

higher

neutral

substitution

rate

in

the

mouse

lineage

than

in

the

human

lineage

rendering

many

older

ancestral

repeats

undetectable

with

available

computer

programs

Higher

substitution

rate

in

mouse

lineage

Figure

Comparison

of

-LRB-

G

C

-RRB-

and

gene

content

in

mouse

and

human

a

Scatter

plot

of

mouse

-LRB-

y

axis

-RRB-

compared

with

human

-LRB-

x

axis

-RRB-

-LRB-

G

C

-RRB-

content

for

all

non-overlapping

orthologous

windows

In

general

-LRB-

G

C

-RRB-

content

is

correlated

between

the

two

species

but

very

few

mouse

windows

have

a

-LRB-

G

C

-RRB-

content

over

even

where

the

related

human

window

has

over

-LRB-

G

C

-RRB-

content

b

Average

mouse

-LRB-

G

C

-RRB-

content

of

syntenic

windows

binned

by

human

-LRB-

G

C

-RRB-

content

-LRB-

intervals

-RRB-

The

red

line

indicates

median

values

with

standard

deviation

and

-LRB-

green

-RRB-

and

-LRB-

blue

-RRB-

confidence

intervals

The

black

line

indicates

identical

-LRB-

G

C

-RRB-

content

in

orthologous

segments

c

e

Gene

content

increases

with

-LRB-

G

C

-RRB-

content

when

comparing

-LRB-

G

C

-RRB-

and

gene

content

in

non-overlapping

unmasked

windows

for

mouse

-LRB-

blue

lines

-RRB-

and

human

-LRB-

red

lines

-RRB-

c

Cumulative

proportions

of

genes

-LRB-

solid

lines

-RRB-

and

genome

-LRB-

dashed

lines

-RRB-

having

-LRB-

G

C

-RRB-

content

below

a

given

level

The

tighter

distribution

of

-LRB-

G

C

-RRB-

content

in

mouse

results

in

the

curve

for

mouse

crossing

that

for

human

at

for

both

genes

and

total

sequence

The

tendency

for

both

genomes

to

be

gene-poor

at

low

-LRB-

G

C

-RRB-

content

and

gene-rich

at

high

-LRB-

G

C

-RRB-

content

is

shown

directly

in

d

which

shows

the

fraction

of

genes

residing

within

the

portion

of

the

genome

having

-LRB-

G

C

-RRB-

content

below

a

given

level

-LRB-

for

example

the

half

of

the

genome

with

the

lowest

-LRB-

G

C

-RRB-

content

contains

of

the

genes

-RRB-

e

The

average

number

of

genes

per

window

is

plotted

against

the

-LRB-

G

C

-RRB-

content

of

the

window

for

both

genomes

showing

that

the

gene

density

in

mouse

reaches

the

same

level

as

in

human

but

at

a

lower

level

of

-LRB-

G

C

-RRB-

content

The

hypothesis

that

the

neutral

substitution

rate

is

higher

in

mouse

than

in

human

was

suggested

as

early

as

-LRB-

refs

-RRB-

The

idea

has

continued

to

be

challenged

on

the

basis

that

the

apparent

differences

may

be

due

to

inaccuracies

in

mammalian

The

explanation

however

remains

unclear

with

some

attributing

it

to

generation

and

others

pointing

to

a

closer

correlation

with

body

Ancestral

repeats

provide

a

powerful

measure

of

neutral

substitution

rates

on

the

basis

of

comparing

thousands

of

current

copies

to

the

inferred

consensus

sequence

of

the

ancestral

element

The

large

copy

number

and

ubiquitous

distribution

of

ancestral

repeats

overcome

issues

of

local

variation

in

substitution

rates

-LRB-

see

below

-RRB-

Most

notably

differences

in

divergence

levels

are

not

affected

by

phylogenetic

assumptions

as

the

time

spent

by

an

ancestral

repeat

family

in

either

lineage

is

necessarily

identical

The

median

divergence

levels

of

subfamilies

of

interspersed

repeats

that

were

active

shortly

before

the

human

rodent

speciation

-LRB-

Table

-RRB-

indicates

an

approximately

twofold

higher

average

substitution

rate

in

the

mouse

lineage

than

in

the

human

lineage

corresponding

closely

to

an

early

estimate

by

Wu

and

In

human

the

least-diverged

ancestral

repeats

have

about

misNATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

Table

Composition

of

interspersed

repeats

in

the

mouse

genome

Mouse

Thousands

of

copies

LINEs

SINEs

-LRB-

Alu

-RRB-

ID

LTR

elements

MaLRs

-LRB-

III

-RRB-

DNA

elements

Charlie

Other

hATs

Tigger

Mariner

Unclassified

Total

Small

RNAs

Satellites

Simple

repeats

564 348 391

79 115 631

127 37 388 112

Length

occupied

-LRB-

Mb

-RRB-

Fraction

of

genome

-LRB-

-RRB-

Lineage

specific

-LRB-

-RRB-

Human

Fraction

of

genome

-LRB-

-RRB-

Lineage

specific

-LRB-

-RRB-

The

two

right

columns

show

the

fractions

in

the

human

genome

-LRB-

excluding

chromosome

Y

-RRB-

for

comparison

These

and

all

other

interspersed

repeat-related

data

are

based

on

RepeatMasker

analysis

-LRB-

version

July

sensitive

settings

RepBase

release

-RRB-

of

the

February

mouse

and

June

human

draft

assemblies

Each

repeat

family

in

the

RepeatMasker

library

was

determined

to

be

either

order-specific

or

ancestral

repeats

present

at

orthologous

sites

usually

on

the

basis

of

the

average

divergence

level

of

the

interspersed

repeat

family

copies

For

elements

with

an

average

divergence

of

in

human

copies

were

checked

to

be

present

or

absent

at

mouse

orthologous

sites

to

have

inserted

in

known

primate-specific

repeats

or

to

have

inserts

of

known

mammalian-wide

elements

No

mammalian-wide

repeats

were

found

in

the

mouse

genome

that

were

not

already

known

from

the

human

genome

not

applicable

match

to

their

consensus

sequences

which

corresponds

to

approximately

substitutions

per

site

In

contrast

mouse

repeats

have

diverged

by

at

least

or

about

substitutions

per

site

which

is

about

twofold

higher

than

in

the

human

lineage

The

total

number

of

substitutions

in

the

two

lineages

can

be

estimated

at

Below

we

obtain

an

estimate

of

a

combined

rate

of

substitutions

per

site

on

the

basis

of

an

analysis

that

counts

only

substitutions

since

the

divergence

of

the

species

-LRB-

see

Supplementary

Information

concerning

the

methods

used

-RRB-

Assuming

a

speciation

time

of

Myr

the

average

substitution

rates

would

have

been

and

in

the

human

and

mouse

lineages

respectively

This

is

in

accord

with

previous

estimates

of

neutral

substitution

rates

in

these

organisms

-LRB-

Reports

of

highly

similar

substitution

rates

in

human

and

mouse

lineages

relied

on

a

much

earlier

divergence

time

of

rodents

from

other

-RRB-

Comparison

of

ancestral

repeats

to

their

consensus

sequence

also

allows

an

estimate

of

the

rate

of

occurrence

of

small

-LRB-

bp

-RRB-

insertions

and

deletions

-LRB-

indels

-RRB-

Both

species

show

a

net

loss

of

nucleotides

-LRB-

with

deleted

bases

outnumbering

inserted

bases

by

at

least

-RRB-

but

the

overall

loss

owing

to

small

indels

in

ancestral

repeats

is

at

least

twofold

higher

in

mouse

than

in

human

This

may

contribute

a

small

amount

-LRB-

-RRB-

to

the

difference

in

genome

size

noted

above

Table

Divergence

levels

of

interspersed

repeats

predating

the

human

mouse

speciation

Interspersed

repeat

Family

Looper

Class

MaLR

MaLR

MaLR

MaLR

MaLR

DNA

DNA

DNA

DNA

DNA

kb

160 627 725

33 435 232

Mouse

Divergence

Range

JC

kb

Human

Divergence

Range

JC

Substitution

ratio

Adjusted

ratio

Shown

are

the

number

of

kilobases

matched

by

each

subfamily

-LRB-

kb

-RRB-

the

median

divergence

-LRB-

mismatch

-RRB-

level

of

all

copies

from

the

consensus

sequence

the

interquartile

range

of

these

mismatch

levels

-LRB-

range

-RRB-

and

a

Jukes

Cantor

estimate

of

the

substitution

level

to

which

the

median

divergence

level

corresponds

-LRB-

JC

-RRB-

Notice

that

RepeatMasker

found

on

average

four

to

fivefold

more

copies

in

the

human

than

in

the

mouse

genome

as

a

result

of

the

higher

DNA

loss

in

the

rodent

lineage

as

well

a

failure

to

identify

many

highly

diverged

copies

The

two

right

columns

contain

the

ratio

of

the

JC

substitution

level

in

mouse

over

human

and

an

adjusted

ratio

-LRB-

AR

-RRB-

of

the

mouse

and

human

substitution

level

after

subtraction

from

both

of

the

approximate

fraction

accumulated

in

the

common

human

mouse

ancestor

For

this

fraction

we

have

taken

the

difference

between

the

ancestral

repeat

average

substitution

level

and

least

diverged

ancestral

repeat

family

-LRB-

-RRB-

See

the

Supplementary

Information

for

a

discussion

of

the

origin

of

the

variance

in

the

human

and

mouse

ratios

NATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

It

should

be

noted

that

the

roughly

twofold

higher

substitution

rate

in

mouse

represents

an

average

rate

since

the

time

of

divergence

including

an

initial

period

when

the

two

lineages

had

comparable

rates

Comparison

with

more

recent

relatives

-LRB-

mouse

rat

and

human

gibbon

each

about

Myr

-RRB-

indicate

that

the

current

substitution

rate

per

year

in

mouse

is

probably

much

higher

perhaps

about

fivefold

higher

-LRB-

see

Supplementary

Information

-RRB-

Also

note

that

these

estimates

refer

to

substitution

rate

per

year

rather

than

per

generation

Because

the

human

generation

time

is

much

longer

than

that

of

the

mouse

-LRB-

by

at

least

-RRB-

the

substitution

rate

is

greater

in

human

than

mouse

when

measured

per

generation

Higher

substitution

rate

obscures

old

repeats

A

higher

rate

of

interspersed

repeat

insertion

does

not

explain

the

larger

size

of

the

human

genome

Below

we

suggest

that

the

explanation

lies

in

a

higher

rate

of

large

deletions

in

the

mouse

lineage

Comparison

of

mouse

and

human

repeats

We

measured

the

impact

of

the

higher

substitution

rate

in

mouse

on

the

ability

to

detect

ancestral

repeats

in

the

mouse

genome

By

computer

simulation

the

ability

of

the

program

to

detect

repeats

was

found

to

fall

off

rapidly

for

divergence

levels

above

about

If

we

simulate

the

events

in

the

mouse

lineage

by

adjusting

the

ancestral

repeats

in

the

human

genome

for

the

higher

substitution

levels

that

would

have

occurred

in

the

mouse

genome

the

proportion

of

the

genome

that

would

still

be

recognizable

as

ancestral

repeats

falls

to

only

This

is

in

close

agreement

with

the

proportion

actually

observed

for

the

mouse

Thus

the

current

analysis

of

repeated

sequences

allows

us

to

see

further

back

into

human

history

-LRB-

roughly

Myr

-RRB-

than

into

mouse

history

-LRB-

roughly

Myr

-RRB-

All

mammals

have

essentially

the

same

four

classes

of

transposable

elements

-LRB-

-RRB-

the

autonomous

long

interspersed

nucleotide

element

-LRB-

LINE

-RRB-

like

elements

-LRB-

-RRB-

the

LINE-dependent

short

RNA-derived

short

interspersed

nucleotide

elements

-LRB-

SINEs

-RRB-

-LRB-

-RRB-

retrovirus-like

elements

with

long

terminal

repeats

-LRB-

LTRs

-RRB-

and

-LRB-

-RRB-

DNA

transposons

The

first

three

classes

procreate

by

reverse

transcription

of

an

RNA

intermediate

-LRB-

retroposition

-RRB-

whereas

DNA

transposons

move

by

a

cut-and-paste

mechanism

of

DNA

sequence

-LRB-

see

refs

for

further

information

about

these

classes

-RRB-

A

comparison

of

these

repeat

classes

in

the

mouse

and

human

genomes

can

be

enlightening

On

the

one

hand

differences

between

the

two

species

reveal

the

dynamic

nature

of

transposable

elements

on

the

other

hand

similarities

in

the

location

of

lineage-specific

elements

point

to

common

biological

factors

that

govern

insertion

and

retention

of

interspersed

repeats

Differences

between

mouse

and

human

The

most

notable

difference

is

in

the

changing

rate

of

transposition

over

time

the

rate

has

remained

fairly

constant

in

mouse

but

markedly

increased

to

a

peak

at

about

Myr

in

human

and

then

plummeted

This

phenomenon

was

noted

in

our

initial

analysis

of

Figure

Age

distribution

of

interspersed

repeats

in

the

mouse

and

human

genomes

This

is

an

update

of

Fig.

in

the

IHGC

human

genome

a

b

Distribution

for

mouse

and

human

of

copies

of

each

repeat

class

in

bins

corresponding

to

increments

in

substitution

level

calculated

using

Jukes

Cantor

formula

K

ln

D

rest

-LRB-

see

Supplementary

Information

for

definition

-RRB-

The

first

bin

for

mouse

is

artificially

low

because

the

WGS

assembly

used

for

mouse

excludes

a

larger

percentage

of

very

recent

repeats

c

d

Interspersed

repeats

grouped

into

bins

of

approximately

equal

time

periods

after

adjusting

for

the

different

rates

of

substitution

in

the

two

genomes

On

average

the

substitution

level

has

been

twofold

higher

in

the

mouse

than

in

the

human

lineage

-LRB-

Table

-RRB-

but

the

difference

was

initially

less

and

has

increased

over

time

The

present

rates

may

differ

over

fourfold

The

activity

of

transposable

elements

in

the

mouse

lineage

has

been

quite

uniform

compared

with

the

human

lineage

where

an

overall

decline

was

interrupted

temporarily

by

a

burst

of

Alu

activity

The

apparent

absence

of

diverged

interspersed

repeats

in

mouse

is

primarily

due

to

the

shotgun

sequencing

strategy

long

closely

similar

interspersed

repeats

very

often

were

not

assembled

This

is

supported

by

an

up

to

tenfold

higher

concentration

of

young

and

ERV

elements

at

the

edges

of

gaps

The

gradually

decreasing

density

of

repeats

beyond

a

substitution

level

reflects

in

part

the

limits

of

the

detection

method

NATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

the

human

genome

the

availability

of

the

mouse

genome

sequence

now

confirms

and

sharpens

the

observation

-LRB-

Fig.

-RRB-

Beyond

this

overall

tendency

there

are

specific

differences

in

each

of

the

four

repeat

classes

The

first

class

that

we

discuss

is

LINEs

Copies

of

-LRB-

-RRB-

form

the

single

largest

fraction

of

interspersed

repeat

sequence

in

both

human

and

mouse

No

other

LINE

seems

to

have

been

active

in

either

lineage

The

extant

elements

in

both

species

derive

from

a

common

ancestor

-LRB-

in

Table

-RRB-

by

means

of

a

series

of

subfamilies

defined

primarily

by

the

rapidly

evolving

non-coding

The

untranslated

regions

-LRB-

UTRs

-RRB-

in

both

lineages

have

been

even

more

variable

occasionally

through

acquisition

of

entirely

new

Indeed

the

three

active

subfamilies

in

mouse

which

are

otherwise

identical

have

unrelated

or

highly

diverged

seems

to

have

remained

highly

active

in

mouse

whereas

it

has

declined

in

the

human

lineage

Goodier

and

estimated

that

the

mouse

genome

contains

at

least

potentially

active

elements

-LRB-

full-length

with

two

intact

open

reading

frames

-LRB-

ORFs

-RRB-

-RRB-

The

current

draft

sequence

of

the

mouse

genome

contains

only

young

full-length

elements

of

these

only

have

two

intact

ORFs

This

is

probably

a

reflection

of

the

WGS

shotgun

approach

used

to

assemble

the

genome

Indeed

most

of

the

young

elements

in

the

draft

genome

sequence

are

incomplete

owing

to

internal

sequence

gaps

reflecting

the

difficulty

that

WGS

assembly

has

with

highly

similar

repeat

sequences

This

is

a

notable

limitation

of

the

draft

sequence

The

second

repeat

class

is

SINEs

Whereas

only

a

single

SINE

-LRB-

Alu

-RRB-

was

active

in

the

human

lineage

the

mouse

lineage

has

been

exposed

to

four

distinct

SINEs

-LRB-

ID

-RRB-

Each

is

thought

to

rely

on

for

retroposition

although

share

sequence

similarity

as

is

the

rule

for

other

LINE

SINE

The

mouse

and

human

Alu

SINEs

are

unique

among

known

SINEs

in

being

derived

from

RNA

they

probably

have

a

common

The

mouse

is

typical

among

SINEs

in

having

a

transfer

RNA-derived

promoter

region

Recent

ID

elements

seem

to

be

derived

from

a

neuronally

expressed

RNA

gene

called

which

may

itself

have

been

recruited

from

an

earlier

SINE

This

subfamily

is

minor

in

mouse

with

copies

but

has

expanded

rapidly

in

rat

where

it

has

produced

more

than

copies

since

the

mouse

rat

Both

and

ID

closely

resemble

Ala-tRNA

but

seem

to

have

independent

origins

The

family

resembles

a

fusion

between

and

We

found

that

of

the

identified

ID

elements

were

located

within

bp

of

a

element

of

similar

orientation

suggesting

that

perhaps

most

older

ID

elements

are

mislabelled

or

truncated

SINEs

More

rodent-specific

SINEs

are

present

in

the

mouse

genome

than

Alu

SINEs

in

human

-LRB-

and

million

respectively

-RRB-

but

they

occupy

a

smaller

portion

of

the

genome

-LRB-

and

respectively

-RRB-

because

of

their

smaller

sizes

The

existence

of

four

families

in

mouse

provides

independent

opportunities

to

investigate

the

properties

of

SINEs

-LRB-

see

below

-RRB-

The

third

repeat

class

is

LTR

elements

All

interspersed

LTRcontaining

elements

in

mammals

are

derivatives

of

the

vertebratespecific

retrovirus

clade

of

retrotransposons

The

earliest

infectious

retroviruses

probably

originated

from

endogenous

retroviral-like

-LRB-

ERV

-RRB-

elements

that

acquired

mechanisms

for

horizontal

whereas

many

current

endogenous

retroviral

elements

have

probably

arisen

from

infection

by

retroviruses

Endogenous

retroviruses

fall

into

three

classes

-LRB-

I

III

-RRB-

which

show

a

markedly

dissimilar

evolutionary

history

in

human

and

mouse

-LRB-

see

Fig.

-RRB-

Notably

ERVs

are

nearly

extinct

in

human

whereas

all

three

classes

have

active

members

in

mouse

Class

III

accounts

for

of

recognized

LTR

element

copies

predating

the

human

mouse

speciation

This

class

includes

the

non-autonomous

MaLRs

with

recognizable

copies

in

mouse

it

is

the

single

most

successful

LTR

element

It

is

still

active

NATURE

VOL

DECEMBER

in

mouse

-LRB-

represented

by

MERVL

and

the

MT

and

MaLRs

-RRB-

but

died

out

some

Myr

in

Copies

of

class

II

elements

are

tenfold

denser

in

mouse

than

in

human

Among

the

active

class

II

elements

in

mouse

are

two

abundant

and

active

groups

the

intracisternal-A

particles

-LRB-

IAP

-RRB-

and

the

early-transposons

-LRB-

ETn

-RRB-

About

of

all

spontaneous

mouse

mutants

have

an

allele

associated

with

IAP

or

ETn

insertion

demonstrating

the

functional

consequences

of

class

I

element

activity

in

mice

A

third

active

class

the

mouse

mammary

tumour

virus

is

present

in

only

a

few

-LRB-

see

Supplementary

Information

-RRB-

In

human

there

is

evidence

for

at

most

a

few

active

elements

-LRB-

and

-LRB-

ref

-RRB-

-RRB-

No

class

II

ERVs

are

known

to

predate

the

human

mouse

speciation

In

contrast

class

I

element

copies

are

fourfold

more

common

in

the

human

than

the

mouse

genome

-LRB-

although

it

is

possible

that

some

have

not

yet

been

recognized

in

mouse

-RRB-

In

mouse

this

class

includes

active

ERVs

such

as

the

murine

leukaemia

virus

MuRRS

MuRVY

and

-LRB-

several

of

which

have

caused

insertional

mutations

in

mouse

-RRB-

no

similar

activity

is

known

to

exist

in

human

It

is

unclear

why

the

class

I

ERVs

have

been

more

successful

in

the

human

lineage

whereas

the

class

II

ERVs

have

flourished

in

the

mouse

lineage

The

fourth

repeat

class

is

the

DNA

transposons

Although

most

transposable

elements

have

been

more

active

in

mouse

than

human

DNA

transposons

show

the

reverse

pattern

Only

four

lineagespecific

DNA

transposon

families

could

be

identified

in

mouse

-LRB-

the

mariner

element

and

the

hAT

elements

and

-RRB-

compared

with

in

the

primate

lineage

For

evolutionary

survival

DNA

transposons

are

thought

to

depend

on

frequent

horizontal

transfer

to

new

host

genomes

by

means

of

vectors

such

as

viruses

and

other

intracellular

The

mammalian

immune

system

probably

forms

a

large

obstacle

to

the

successful

invasion

of

DNA

transposons

Perhaps

the

rodent

germ

line

has

been

harder

to

infiltrate

by

horizontal

transfer

than

the

primate

genome

Alternatively

it

is

possible

that

highly

diverged

families

active

in

early

rodent

evolution

have

not

been

detected

yet

Notably

most

copies

in

the

human

genome

were

deposited

early

in

primate

evolution

An

interesting

case

is

the

mariner

element

which

seems

to

have

infiltrated

independently

both

the

rodent

and

human

genomes

The

mariner

element

is

represented

by

elements

-LRB-

in

mouse

and

in

human

-RRB-

that

are

identical

The

average

substitution

level

outside

CpG

sites

of

is

and

of

is

both

well

below

the

divergence

of

elements

predating

the

human

mouse

speciation

-LRB-

Table

-RRB-

Some

of

the

above

differences

in

the

nature

of

interspersed

repeats

in

human

and

mouse

could

reflect

systematic

factors

in

mouse

and

human

biology

whereas

others

may

represent

random

fluctuations

Deeper

understanding

of

the

biology

of

transposable

elements

and

detailed

knowledge

of

interspersed

repeat

populations

in

other

mammals

should

clarify

these

issues

Similar

repeats

accumulate

in

orthologous

locations

One

of

the

most

notable

features

about

repeat

elements

is

the

contrast

in

the

genomic

distribution

of

LINEs

and

SINEs

Whereas

LINEs

are

strongly

biased

towards

-LRB-

A

T

-RRB-

rich

regions

SINEs

are

strongly

biased

towards

-LRB-

G

C

-RRB-

rich

regions

The

contrast

is

all

the

more

notable

because

both

elements

are

inserted

into

the

genome

through

the

action

of

the

same

Such

preferences

were

studied

in

detail

in

the

initial

analysis

of

the

human

and

essentially

equivalent

preferences

are

seen

in

the

mouse

genome

-LRB-

Fig.

-RRB-

With

the

availability

of

two

mammalian

genomes

however

it

is

possible

to

extend

this

analysis

to

explore

whether

-LRB-

A

T

-RRB-

and

-LRB-

G

C

-RRB-

content

are

truly

causative

factors

or

merely

reflections

of

an

underlying

biological

process

Towards

that

end

we

studied

the

insertion

of

lineage-specific

repeat

elements

in

orthologous

segments

in

the

human

and

mouse

Nature

Publishing

Group

articles

genomes

-LRB-

Fig.

-RRB-

Each

insertion

represents

a

new

independent

event

occurring

in

one

lineage

and

thus

any

correlation

between

the

two

species

reflects

underlying

proclivity

to

insert

or

retain

repeats

in

particular

regions

Visual

inspection

reveals

a

strong

correlation

in

the

sites

of

lineage-specific

repeats

of

the

various

classes

-LRB-

Fig.

-RRB-

Lineage-specific

repeats

also

correlate

with

other

genomic

features

as

discussed

in

the

section

on

genome

evolution

The

correlation

of

local

lineage-specific

SINE

density

is

extremely

strong

-LRB-

Fig.

-RRB-

Moreover

local

SINE

density

in

one

species

is

better

predicted

by

SINE

density

in

the

other

species

than

it

is

by

local

-LRB-

G

C

-RRB-

content

-LRB-

Table

-RRB-

The

local

density

of

each

distinct

rodent-specific

type

of

SINE

is

a

strong

predictor

of

Alu

density

at

the

orthologous

locus

in

human

although

the

Alu

equivalent

SINEs

show

the

strongest

correlation

-LRB-

r

-RRB-

-LRB-

Table

-RRB-

We

interpret

these

results

to

mean

that

SINE

density

is

influenced

by

genomic

features

that

are

correlated

with

-LRB-

G

C

-RRB-

content

but

that

are

distinct

from

-LRB-

G

C

-RRB-

content

per

se

The

fact

that

-LRB-

G

C

-RRB-

content

alone

does

not

determine

SINE

density

is

consistent

with

the

observation

that

some

-LRB-

G

C

-RRB-

rich

regions

of

the

human

genome

are

not

Alu

Lineage-specific

LINE

density

is

also

clearly

correlated

between

mouse

and

human

-LRB-

Fig.

-RRB-

although

the

relationship

does

not

seem

to

be

linear

and

it

is

not

as

strong

-LRB-

Spearman

rank

analysis

r

-RRB-

-LRB-

G

C

-RRB-

content

seems

to

contribute

as

an

independent

variable

-LRB-

increasing

r

to

-RRB-

suggesting

that

-LRB-

G

C

-RRB-

content

itself

directly

affects

LINE

integration

Genomic

outliers

of

These

same

four

regions

are

exceptions

in

the

mouse

genome

as

well

The

strong

selective

constraints

against

insertion

in

these

regions

probably

reflect

dense

long-range

regulatory

information

across

this

developmentally

important

gene

cluster

Other

repeatpoor

loci

in

the

human

-LRB-

about

regions

on

human

chromosomes

and

-RRB-

have

independently

remained

repeat-poor

in

mouse

-LRB-

and

respectively

-RRB-

over

roughly

million

years

of

evolution

we

speculate

that

this

similarly

reflects

dense

regulatory

information

in

the

region

Conversely

we

searched

the

mouse

genome

for

repeat-poor

regions

of

at

least

kb

Again

the

outliers

show

a

clear

tendency

to

be

repeat-poor

in

human

-LRB-

see

Supplementary

Information

-RRB-

A

notable

feature

is

that

in

half

of

the

selected

loci

the

repeat-poor

region

is

confined

almost

exactly

to

the

extent

of

a

single

gene

Figure

shows

this

for

the

locus

and

also

shows

coincidence

of

exclusion

of

interspersed

repeats

with

high

conservation

between

human

and

mouse

In

addition

to

examining

the

general

correlation

in

repeat

density

between

mouse

and

human

we

also

considered

some

of

the

extreme

examples

In

the

human

genome

the

four

homeobox

clusters

-LRB-

HOXA

HOXB

HOXC

and

HOXD

-RRB-

are

by

far

the

most

repeatpoor

regions

of

the

human

genome

with

repeat

content

in

the

range

Figure

Density

of

interspersed

repeat

classes

at

different

-LRB-

G

C

-RRB-

content

in

the

mouse

-LRB-

a

-RRB-

and

human

-LRB-

b

-RRB-

genomes

In

both

species

there

is

a

strong

increase

in

SINE

density

and

a

decrease

in

density

with

increasing

-LRB-

G

C

-RRB-

content

with

the

latter

particularly

marked

in

the

mouse

Another

notable

contrast

is

that

in

mouse

overall

interspersed

repeat

density

gradually

decreases

with

increasing

-LRB-

G

C

-RRB-

content

whereas

in

human

the

overall

repeat

density

remains

quite

uniform

This

reflects

both

the

abundance

of

elements

in

the

mouse

-LRB-

G

C

-RRB-

poor

regions

and

the

unusually

high

density

of

Alu

in

human

-LRB-

G

C

-RRB-

rich

regions

Figure

Conservation

of

-LRB-

G

C

-RRB-

content

and

convergence

of

interspersed

repeat

distribution

between

the

human

and

mouse

genomes

For

each

mouse

chromosome

its

-LRB-

G

C

-RRB-

content

is

depicted

as

a

greyscale

-LRB-

centre

right

-RRB-

with

darker

shades

indicating

-LRB-

G

C

-RRB-

richer

regions

Rodent-specific

repeats

are

shown

as

cumulative

histograms

-LRB-

far

right

-RRB-

with

red

green

and

blue

indicating

SINEs

LINEs

and

other

repeats

respectively

The

-LRB-

G

C

-RRB-

content

of

the

orthologous

human

sequence

is

similarly

shown

-LRB-

centre

left

-RRB-

as

well

as

the

primate-specific

repeats

-LRB-

far

left

-RRB-

Gaps

in

the

human

sequence

appear

opposite

those

regions

of

the

mouse

genome

lacking

assigned

conserved

syntenic

segments

Note

the

correlation

in

-LRB-

G

C

-RRB-

and

repeat

content

between

orthologous

regions

of

the

two

genomes

Many

abrupt

shifts

in

-LRB-

G

C

-RRB-

content

and

repeat

density

are

clearly

associated

with

syntenic

breaks

which

are

therefore

more

likely

to

be

breaks

associated

with

the

rodent

NATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

Figure

Correlation

of

order-specific

SINEs

and

LINEs

in

human

and

mouse

orthologous

regions

SINE

and

LINE

densities

were

calculated

for

orthologous

pairs

with

a

constant

size

of

kb

in

mouse

a

b

Strong

linear

correlation

of

Alu

density

in

human

and

both

the

Alu-like

SINEs

-LRB-

a

-RRB-

and

the

unrelated

SINEs

-LRB-

b

-RRB-

densities

in

mouse

These

correlations

are

stronger

than

the

correlation

of

SINE

density

with

-LRB-

G

C

-RRB-

level

-LRB-

c

-RRB-

d

The

relationship

of

density

in

human

and

mouse

orthologous

regions

is

not

linear

reflecting

the

more

extreme

bias

of

for

-LRB-

A

T

-RRB-

rich

DNA

in

mouse

LINE

elements

prefer

sex

chromosomes

A

conspicuous

feature

of

the

repeat

distribution

is

that

LINE

elements

in

both

human

and

mouse

show

a

preference

for

accumulating

on

sex

chromosomes

-LRB-

Figs

and

-RRB-

Mouse

chromosome

X

contains

almost

twice

the

density

of

lineage-specific

copies

as

the

mouse

autosomes

-LRB-

compared

with

-RRB-

Human

sex

chromosomes

show

an

even

stronger

bias

-LRB-

on

X

and

on

Y

compared

with

for

the

autosomes

-RRB-

The

enrichment

is

still

highly

significant

even

after

accounting

for

the

generally

higher

-LRB-

A

T

-RRB-

content

of

the

sex

chromosomes

-LRB-

Fig.

-RRB-

The

higher

density

of

on

sex

chromosomes

had

been

noted

in

early

hybridization

and

has

led

to

the

suggestion

that

copies

may

help

facilitate

X

For

chromosome

Y

the

accumulation

probably

reflects

a

greater

tolerance

for

insertion

-LRB-

owing

to

the

paucity

of

genes

-RRB-

and

the

inability

to

purge

deleterious

mutations

by

recombination

Consistent

with

the

latter

explanation

chromosome

Yalso

shows

a

threefold

higher

density

of

full-length

copies

-LRB-

which

are

rapidly

eliminated

elsewhere

in

the

-RRB-

and

an

overall

excess

of

LTR

element

insertions

Chromosome

X

shows

an

excess

of

copies

but

not

a

marked

excess

of

either

full-length

or

LTR

copies

The

explanation

for

this

preferential

accumulation

of

elements

on

chromosome

X

in

both

the

mouse

and

human

lineages

remains

unclear

Simple

sequence

repeats

Mammalian

genomes

are

scattered

with

simple

sequence

repeats

-LRB-

SSRs

-RRB-

consisting

of

short

perfect

or

near-perfect

tandem

repeats

that

presumably

arise

through

slippage

during

DNA

replication

SSRs

have

had

a

particularly

important

role

as

genetic

markers

in

linkage

studies

in

both

mouse

and

human

because

their

lengths

tend

to

be

polymorphic

in

populations

and

can

be

readily

assayed

by

PCR

It

is

possible

that

such

SSRs

arising

as

they

do

through

replication

errors

would

be

largely

equivalent

between

mouse

and

human

however

there

are

impressive

differences

between

the

two

Table

Predicted

repeat

density

in

human

based

on

mouse

Predicted

density

of

repeat

in

human

Alu

density

based

on

Density

in

orthologous

sites

for

all

SINEs

Density

in

orthologous

sites

for

Density

in

orthologous

sites

for

Density

in

orthologous

sites

for

ID

SINE

density

in

mouse

DNA

of

equivalent

-LRB-

G

C

-RRB-

content

Local

-LRB-

G

C

-RRB-

content

Density

in

orthologous

sites

for

all

SINEs

and

local

-LRB-

G

C

-RRB-

content

Linear

regression

Spearman

rank

density

based

on

density

at

orthologous

mouse

regions

density

in

mouse

DNA

of

equivalent

-LRB-

G

C

-RRB-

content

Local

-LRB-

G

C

-RRB-

content

density

at

orthologous

mouse

regions

and

local

-LRB-

G

C

-RRB-

content

Figure

The

zinc-finger

homeobox

-LRB-

-RRB-

loci

in

human

and

mouse

are

both

repeat

poor

The

repeat

content

for

mouse

-LRB-

blue

-RRB-

and

human

-LRB-

red

-RRB-

in

windows

is

shown

for

a

region

surrounding

the

gene

-LRB-

green

-RRB-

Dotted

lines

indicate

genome

average

for

repeat

content

in

mouse

-LRB-

blue

-RRB-

and

human

-LRB-

red

-RRB-

The

repeat-poor

regions

-LRB-

repeat

content

in

mouse

and

human

-RRB-

coincide

with

the

location

of

the

gene

and

regions

of

high

conservation

between

human

and

mouse

NATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

Overall

mouse

has

more

short

SSRs

-LRB-

bp

unit

-RRB-

than

human

-LRB-

Table

-RRB-

the

precise

ratio

depends

on

the

percentage

identity

required

in

defining

a

tandem

repeat

The

mouse

seems

to

represent

an

exception

among

mammals

on

the

basis

of

comparison

with

the

small

amount

of

genomic

sequence

available

from

dog

-LRB-

Mb

-RRB-

and

pig

-LRB-

Mb

-RRB-

both

of

which

show

proportions

closer

to

-LRB-

E.

Green

unpublished

data

Table

-RRB-

The

analysis

can

be

refined

however

by

excluding

transposable

elements

that

contain

SSRs

at

their

ends

For

example

of

A-rich

SSRs

in

human

are

provided

by

or

spawned

from

poly

-LRB-

A

-RRB-

tails

of

Alu

and

elements

and

of

-LRB-

CA

-RRB-

n-like

SSRs

in

mouse

are

contained

in

element

tails

When

these

sources

are

eliminated

the

contrast

between

mouse

and

human

grows

to

roughly

fourfold

The

reason

for

the

greater

density

of

SSRs

in

mouse

is

unknown

Table

shows

that

SSRs

of

bp

are

not

only

more

frequent

but

are

generally

also

longer

in

the

mouse

than

in

the

human

genome

suggesting

that

this

difference

is

due

to

extension

rather

than

to

initiation

The

equilibrium

distribution

of

SSR

length

has

been

to

be

determined

by

slippage

between

exact

copies

of

the

repeat

during

meiotic

The

shorter

lengths

of

SSRs

in

human

may

result

from

the

higher

rate

of

point

substitutions

per

generation

-LRB-

see

above

-RRB-

which

disrupts

the

exactness

of

the

repeats

Apart

from

the

absolute

number

of

SSRs

there

are

also

some

marked

differences

in

the

frequency

of

certain

SSR

classes

-LRB-

Table

-RRB-

The

most

extreme

is

the

tetramer

-LRB-

ACAG

-RRB-

n

which

is

more

common

in

mouse

than

human

-LRB-

even

after

eliminating

copies

associated

with

and

SINEs

-RRB-

the

sequence

does

not

occur

in

large

clusters

but

rather

is

distributed

throughout

the

genome

In

general

SSRs

in

which

one

strand

is

a

polypurine

tract

and

the

other

a

polypyrimidine

tract

are

much

more

common

and

extended

in

mouse

than

human

For

the

six

such

di

tri

and

tetramer

SSRs

-LRB-

AG

AAG

AGG

AAAG

AAGG

AGGG

-RRB-

copies

with

at

least

bp

and

identity

are

longer

and

tenfold

more

common

in

mouse

than

human

Analysis

of

the

distribution

of

SSRs

across

chromosomes

also

reveals

an

interesting

feature

common

to

both

organisms

-LRB-

see

Supplementary

Information

-RRB-

In

both

human

and

mouse

there

is

a

nearly

twofold

increase

in

density

of

SSRs

near

the

distal

ends

of

chromosome

arms

Because

mouse

chromosomes

are

acrocentric

they

show

the

effect

only

at

one

end

The

increased

density

of

SSRs

in

telomeric

regions

may

reflect

the

tendency

towards

higher

recombination

rates

in

subtelomeric

Mouse

genes

Genes

comprise

only

a

small

portion

of

the

mammalian

genome

but

they

are

understandably

the

focus

of

greatest

interest

One

of

the

most

notable

findings

of

the

initial

sequencing

and

analysis

of

the

human

was

that

the

number

of

protein-coding

genes

was

only

in

the

range

of

far

less

than

the

widely

cited

textbook

figure

of

but

in

accord

with

more

recent

rigorous

The

lower

gene

count

was

based

on

the

observed

and

predicted

gene

counts

statistically

adjusted

for

systematic

under

and

overcounting

Our

goal

here

is

to

produce

an

improved

catalogue

of

mammalian

protein-coding

genes

and

to

revisit

the

gene

count

Genome

analysis

has

been

enhanced

by

a

number

of

recent

developments

These

include

burgeoning

mammalian

EST

and

cDNA

collections

knowledge

of

the

genomes

and

proteomes

of

a

growing

number

of

organisms

increasingly

complete

coverage

of

the

mouse

and

human

genomes

in

high-quality

sequence

assemblies

and

the

ability

to

use

de

novo

gene

prediction

methodologies

that

exploit

information

from

two

mammalian

genomes

to

avoid

potential

biases

inherent

in

using

known

transcripts

or

homology

to

known

genes

We

focus

here

on

protein-coding

genes

because

the

ability

to

recognize

new

RNA

genes

remains

rudimentary

As

used

below

the

terms

gene

catalogue

and

gene

count

refer

to

protein-coding

genes

only

We

briefly

discuss

RNA

genes

at

the

end

of

the

section

Evidence-based

gene

prediction

We

constructed

catalogues

of

human

and

mouse

gene

predictions

on

the

basis

of

available

experimental

evidence

The

main

computational

tool

was

the

Ensembl

gene

prediction

augmented

with

the

Genie

gene

prediction

Briefly

the

Ensembl

Figure

Comparison

of

characteristics

of

autosomes

and

sex

chromosomes

as

a

function

of

-LRB-

G

C

-RRB-

content

in

mouse

-LRB-

blue

-RRB-

and

human

-LRB-

red

-RRB-

Error

bars

depict

standard

deviation

over

all

autosomes

-LRB-

circles

-RRB-

Diamonds

X

chromosomes

squares

human

Y

chromosome

The

mouse

Y

chromosome

is

not

represented

in

the

whole-genome

assembly

and

too

little

clone-based

information

is

available

to

be

included

a

The

number

of

lineage-specific

copies

per

megabase

declines

to

from

lowest

to

highest

-LRB-

G

C

-RRB-

content

This

relationship

is

stronger

in

mouse

and

on

the

sex

chromosomes

Note

that

for

the

same

-LRB-

G

C

-RRB-

content

density

is

to

twofold

higher

on

the

sex

chromosomes

b

The

average

length

of

lineage-specific

copies

peaks

at

around

the

-LRB-

G

C

-RRB-

level

where

it

is

three

-LRB-

human

-RRB-

to

fourfold

-LRB-

mouse

-RRB-

higher

than

in

the

-LRB-

G

C

-RRB-

richest

regions

The

average

length

in

mouse

is

underestimated

owing

to

the

bias

against

full-length

young

elements

in

the

shotgun

assembly

On

average

copies

are

longer

on

human

Y

than

on

either

X

chromosome

or

the

autosomes

Table

Density

of

short

SSRs

in

mouse

compared

with

other

mammals

Cutoff

-LRB-

-RRB-

Mouse

-LRB-

-RRB-

All

unit

SSRs

including

those

in

IRs

Human

-LRB-

-RRB-

Ratio

Dog

-LRB-

-RRB-

Pig

-LRB-

-RRB-

Excluding

SSRs

within

IRs

and

IR

tails

Mouse

-LRB-

-RRB-

Human

-LRB-

-RRB-

Ratio

The

cutoff

indicates

the

maximum

level

of

imperfect

copies

allowed

with

indicating

that

all

SSRs

are

recognized

by

RepeatMasker

To

determine

which

SSRs

were

spawned

from

within

IRs

or

IR

tails

the

locations

of

SSRs

were

overlapped

with

the

RepeatMasker

output

We

excluded

those

SSRs

overlapped

by

IRs

and

those

resembling

unmasked

tails

that

is

occurring

more

than

onethird

of

the

time

adjacent

to

the

same

type

of

IR

IR

interspersed

repeat

SSR

simple

sequence

repeat

Ratio

was

determined

by

dividing

mouse

percentage

by

human

percentage

Nature

Publishing

Group

NATURE

VOL

DECEMBER

articles

Table

Frequency

of

different

SSRs

in

mouse

and

human

Simple

sequence

repeat

-LRB-

SSR

-RRB-

Fraction

of

mouse

genome

-LRB-

bp

-RRB-

A

C

AC

AG

AT

CG

AAC

AAG

AAT

ACC

AGC

AGG

AAAC

AAAG

AAAT

AACC

AAGG

AATG

ACAG

ACAT

AGAT

AGGG

AAAAC

555 11 3070 1365

148 152 147

30 46 465 203

Average

no

units

per

SSR

-LRB-

mouse

-RRB-

Including

SSRs

in

IRs

Excluding

SSRs

from

SINE

and

LINE

tails

and

within

IRs

Average

no

units

per

SSR

-LRB-

human

-RRB-

Frequency

-LRB-

number

SSRs

per

Mb

-RRB-

Frequency

ratio

-LRB-

mouse/human

-RRB-

Frequency

-LRB-

number

per

Mb

-RRB-

Frequency

ratio

-LRB-

mouse/human

-RRB-

Frequency

and

density

of

monomeric

and

dimeric

SSRs

and

the

most

common

trimeric

and

tetrameric

SSRs

The

numbers

are

for

SSRs

containing

substitutions

and

indels

The

two

right

columns

show

the

density

of

each

repeat

excluding

those

SSRs

spawned

from

the

tails

of

SINEs

and

LINEs

or

inherently

part

of

other

IRs

For

this

SSRs

were

counted

in

a

default

-LRB-

m

-RRB-

RepeatMasker

run

system

uses

three

tiers

of

input

First

known

protein-coding

cDNAs

are

mapped

onto

the

genome

Second

additional

protein-coding

genes

are

predicted

on

the

basis

of

similarity

to

proteins

in

any

organism

using

the

GeneWise

Third

de

novo

gene

predictions

from

the

GENSCAN

that

are

supported

by

experimental

evidence

-LRB-

such

as

ESTs

-RRB-

are

considered

These

three

strands

of

evidence

are

reconciled

into

a

single

gene

catalogue

by

using

heuristics

to

merge

overlapping

predictions

detect

pseudogenes

and

discard

misassemblies

These

results

are

then

augmented

by

using

conservative

predictions

from

the

Genie

system

which

predicts

gene

structures

in

the

genomic

regions

delimited

by

paired

and

ESTs

on

the

basis

of

cDNA

and

EST

information

from

the

region

We

also

examined

predictions

from

a

variety

of

other

computational

systems

-LRB-

see

Supplementary

Information

-RRB-

These

methods

tended

to

have

significant

overlap

with

the

above-generated

gene

catalogues

but

each

tended

to

introduce

significant

numbers

of

predictions

that

were

unsupported

by

other

methods

and

that

appeared

to

be

false

positives

Accordingly

we

did

not

add

these

predictions

to

our

gene

catalogues

however

we

did

use

them

to

fill

in

missing

exons

in

existing

predictions

-LRB-

see

Supplementary

Information

-RRB-

The

computational

pipeline

produces

predicted

transcripts

which

may

represent

fragmentary

products

or

alternative

products

of

a

gene

They

may

also

represent

pseudogenes

which

can

be

difficult

in

some

cases

to

distinguish

from

real

genes

The

predicted

transcripts

are

then

aggregated

into

predicted

genes

on

the

basis

of

sequence

overlaps

-LRB-

see

Supplementary

Information

-RRB-

The

computational

pipeline

remains

imperfect

and

the

predictions

are

tentative

Initial

and

current

human

gene

catalogue

known

full-length

human

genes

-LRB-

mean

median

-RRB-

This

initial

gene

catalogue

was

used

to

estimate

the

number

of

human

protein-coding

genes

on

the

basis

of

estimates

of

the

fragmentation

rate

false

positive

rate

and

false

negative

rate

for

true

human

genes

Such

corrections

were

particularly

important

because

a

typical

human

gene

was

represented

in

the

predictions

by

about

half

of

its

coding

sequence

or

was

significantly

fragmented

The

analysis

suggested

that

the

roughly

predicted

genes

represented

about

actual

human

genes

-LRB-

on

the

basis

of

fragmentation

and

false

positive

rates

-RRB-

out

of

the

best-estimate

total

of

approximately

human

protein-coding

genes

on

the

basis

of

estimated

false

We

suggested

a

range

of

to

allow

for

additional

genes

Several

papers

have

re-analysed

the

initial

gene

catalogue

and

argued

for

a

substantially

larger

human

gene

Most

of

these

analyses

however

did

not

account

for

the

incomplete

nature

of

the

the

complexities

arising

from

alternative

splicing

and

the

difficulty

of

interpreting

evidence

from

fragmentary

messenger

RNAs

-LRB-

such

as

ESTs

and

serial

analysis

of

gene

expression

-LRB-

SAGE

-RRB-

tags

-RRB-

that

may

not

represent

protein-coding

Since

the

initial

the

human

gene

catalogue

has

been

refined

as

sequence

becomes

more

complete

and

methods

are

Table

Gene

count

in

human

and

mouse

genomes

Genome

feature

Human

Initial

-LRB-

Feb.

-RRB-

Predicted

transcripts

Predicted

genes

Known

cDNAs

New

predictions

Mean

exons/transcript

Total

predicted

exons

-LRB-

-RRB-

Current

-LRB-

Sept.

-RRB-

-LRB-

-RRB-

Mouse

Initial

-LRB-

this

paper

-RRB-

-LRB-

-RRB-

Extended

-LRB-

this

paper

-RRB-

-LRB-

-RRB-

The

initial

human

gene

contained

about

predicted

transcripts

which

were

aggregated

into

about

predicted

genes

containing

a

total

of

approximately

distinct

exons

-LRB-

Table

-RRB-

Many

of

the

predicted

transcripts

clearly

represented

only

gene

fragments

because

the

overall

set

contained

considerably

fewer

exons

per

gene

-LRB-

mean

median

-RRB-

than

NATURE

VOL

DECEMBER

Without

RIKEN

cDNA

set

With

RIKEN

cDNA

set

Median

values

are

in

parentheses

Nature

Publishing

Group

articles

revised

The

current

catalogue

-LRB-

Ensembl

build

-RRB-

contains

predicted

transcripts

aggregated

into

predicted

genes

containing

about

distinct

exons

-LRB-

Table

-RRB-

The

predicted

transcripts

are

larger

with

the

mean

number

of

exons

roughly

doubling

-LRB-

to

-RRB-

and

the

catalogue

has

increased

in

completeness

with

the

total

number

of

exons

increasing

by

nearly

We

return

below

to

the

issue

of

estimating

the

mammalian

gene

count

Mouse

gene

catalogue

Pseudogenes

We

sought

to

create

a

mouse

gene

catalogue

using

the

same

methodology

as

that

used

for

the

human

gene

catalogue

-LRB-

Table

-RRB-

An

initial

catalogue

was

created

by

using

the

same

evidence

set

as

for

the

human

analysis

including

cDNAs

and

proteins

from

various

organisms

This

set

included

a

previously

published

collection

of

mouse

cDNAs

produced

at

the

RIKEN

Genome

We

also

created

an

extended

mouse

gene

catalogue

by

including

a

much

larger

set

of

about

mouse

cDNAs

with

significant

ORFs

-LRB-

see

Supplementary

Information

-RRB-

that

were

sequenced

by

RIKEN

-LRB-

see

ref

-RRB-

These

additional

mouse

cDNAs

improved

the

catalogue

by

increasing

the

average

transcript

length

through

the

addition

of

exons

-LRB-

raising

the

total

from

about

to

about

including

many

from

untranslated

regions

-RRB-

and

by

joining

fragmented

transcripts

The

set

contributed

roughly

new

predicted

genes

The

total

number

of

predicted

genes

did

not

change

significantly

however

because

the

increase

was

offset

by

a

decrease

due

to

mergers

of

predicted

genes

These

mouse

cDNAs

have

not

yet

been

used

to

extend

the

human

gene

catalogue

Accordingly

comparisons

of

the

mouse

and

human

gene

catalogues

below

use

the

initial

mouse

gene

catalogue

The

extended

mouse

gene

catalogue

contains

predicted

transcripts

corresponding

to

predicted

genes

that

contain

about

distinct

exons

These

include

transcripts

corresponding

to

cDNAs

in

the

public

databases

with

of

these

in

the

well-curated

RefSeq

There

are

predicted

transcripts

that

do

not

correspond

to

known

cDNAs

but

these

are

built

on

the

basis

of

similarity

to

known

proteins

The

new

mouse

and

human

gene

catalogues

contain

many

new

genes

not

previously

identified

in

either

genome

These

include

new

paralogues

for

genes

responsible

for

at

least

five

diseases

responsible

for

a

type

of

severe

combined

immunodeficiency

resulting

from

lack

of

expression

of

human

leukocyte

antigen

-LRB-

HLA

-RRB-

antigens

on

certain

haematopoietic

bestrophin

responsible

for

a

form

of

muscular

otoferlin

responsible

for

a

non-syndromic

prelingual

mutated

in

two

inherited

eye

and

adiponectin

a

deficiency

of

which

leads

to

diet-induced

insulin

resistance

in

The

case

is

interesting

because

disruption

of

the

known

mouse

homologue

alone

does

not

reproduce

the

human

disease

but

may

do

so

in

conjunction

with

disruption

of

the

newly

identified

Recently

Mural

and

analysed

the

sequence

of

mouse

chromosome

and

reported

gene

predictions

-LRB-

compared

with

gene

predictions

in

our

set

for

chromosome

-RRB-

Our

gene

catalogue

contains

of

these

gene

predictions

indicating

extensive

agreement

between

these

two

independent

analyses

Most

of

the

remaining

genes

reported

by

ref

seem

to

be

systematic

errors

-LRB-

common

to

all

such

programs

-RRB-

such

as

relatively

short

gene

predictions

arising

from

protein

matches

to

low-complexity

regions

It

should

be

emphasized

that

the

human

and

mouse

gene

catalogues

although

increasingly

complete

remain

imperfect

Both

genome

sequences

are

still

incomplete

Some

authentic

genes

are

missing

fragmented

or

otherwise

incorrectly

described

and

some

predicted

genes

are

pseudogenes

or

are

otherwise

spurious

We

describe

below

further

analysis

of

these

challenges

An

important

issue

in

annotating

mammalian

genomes

is

distinguishing

real

genes

from

pseudogenes

that

is

inactive

gene

copies

Processed

pseudogenes

arise

through

retrotransposition

of

spliced

or

partially

spliced

mRNA

into

the

genome

they

are

often

recognized

by

the

loss

of

some

or

all

introns

relative

to

other

copies

of

the

gene

Unprocessed

pseudogenes

arise

from

duplication

of

genomic

regions

or

from

the

degeneration

of

an

extant

gene

that

has

been

released

from

selection

They

sometimes

contain

all

exons

but

often

have

suffered

deletions

and

rearrangements

that

may

make

it

difficult

to

recognize

their

precise

parentage

Over

time

pseudogenes

of

either

class

tend

to

accumulate

mutations

that

clearly

reveal

them

to

be

inactive

such

as

multiple

frameshifts

or

stop

codons

More

generally

they

acquire

a

larger

ratio

of

non-synonymous

to

synonymous

substitutions

-LRB-

K

A/K

S

ratio

see

section

on

proteins

below

-RRB-

than

functional

genes

These

features

can

sometimes

be

used

to

recognize

pseudogenes

although

relatively

recent

pseudogenes

may

escape

such

filters

The

well-studied

Gapdh

gene

and

its

pseudogenes

illustrate

the

The

mouse

genome

contains

only

a

single

functional

Gapdh

gene

-LRB-

on

chromosome

-RRB-

but

we

find

evidence

for

at

least

pseudogenes

distributed

across

of

the

mouse

chromosomes

Some

of

these

are

readily

identified

as

pseudogenes

but

have

retained

enough

genic

structure

that

they

appear

as

predicted

genes

in

our

gene

catalogue

They

were

identified

as

pseudogenes

only

after

manual

inspection

The

Gapdh

pseudogenes

typically

have

no

orthologous

human

gene

in

the

corresponding

region

of

conserved

synteny

To

assess

the

impact

of

pseudogenes

on

gene

prediction

we

focused

on

two

classes

of

gene

predictions

-LRB-

-RRB-

those

that

lack

a

corresponding

gene

prediction

in

the

region

of

conserved

synteny

in

the

human

genome

-LRB-

-RRB-

and

-LRB-

-RRB-

those

that

are

members

of

apparent

local

gene

clusters

and

that

lack

a

reciprocal

best

match

in

the

human

genome

-LRB-

-RRB-

A

random

sample

of

such

predicted

genes

was

selected

and

the

predictions

were

manually

reviewed

We

estimate

that

about

of

the

first

class

and

about

of

the

second

class

correspond

to

pseudogenes

Overall

this

would

correspond

to

roughly

of

the

predicted

genes

in

mouse

-LRB-

A

similar

proportion

of

gene

predictions

on

chromosome

by

Mural

and

seem

by

the

same

criteria

to

be

pseudogenes

-RRB-

These

two

classes

contain

relatively

few

exons

-LRB-

average

-RRB-

and

thus

comprise

only

about

exons

of

the

in

the

mouse

gene

catalogue

Pseudogenes

similarly

arise

among

human

gene

predictions

and

are

greatly

enriched

in

the

two

classes

above

This

analysis

shows

the

benefit

of

comparative

genome

analysis

and

suggests

ways

to

improve

gene

prediction

We

also

sought

to

identify

the

many

additional

pseudogenes

that

had

been

correctly

excluded

during

the

gene

prediction

process

To

do

so

we

searched

the

genomic

regions

lying

outside

the

predicted

genes

in

the

current

catalogue

for

sequence

with

significant

similarity

to

known

proteins

We

identified

about

intergenic

regions

containing

such

putative

pseudogenes

Most

-LRB-

-RRB-

appear

to

be

clear

pseudogenes

-LRB-

on

the

basis

of

such

tests

as

ratio

of

non-synonymous

to

synonymous

substitutions

see

Supplementary

Information

and

the

section

on

proteins

below

-RRB-

with

more

than

half

being

processed

pseudogenes

This

is

surely

an

underestimate

of

the

total

number

of

pseudogenes

owing

to

the

limited

sensitivity

of

the

search

Further

refinement

We

analysed

the

mouse

gene

predictions

further

focusing

on

those

whose

best

human

match

fell

outside

the

region

of

conserved

synteny

and

those

without

clear

orthologues

in

the

human

genome

Two

suspicious

classes

were

identified

The

first

-LRB-

-RRB-

consists

of

predicted

genes

that

seem

to

encode

Gag/Pol

proteins

from

mouse-specific

retrovirus

elements

The

second

-LRB-

about

-RRB-

consists

of

predicted

genes

for

which

the

only

supporting

evidence

NATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

comes

from

a

single

collection

of

mouse

cDNAs

-LRB-

the

initial

RIKEN

-RRB-

These

cDNAs

are

very

short

on

average

with

few

exons

-LRB-

median

-RRB-

and

small

ORFs

-LRB-

average

length

of

amino

acids

-RRB-

whereas

some

of

these

may

be

true

genes

most

seem

unlikely

to

reflect

true

protein-coding

genes

although

they

may

correspond

to

RNA

genes

or

other

kinds

of

transcripts

Both

groups

were

omitted

in

the

comparative

analysis

below

Comparison

of

mouse

and

human

gene

sets

We

then

sought

to

assess

the

extent

of

correspondence

between

the

mouse

and

human

gene

sets

Approximately

of

mouse

genes

have

a

homologue

in

the

human

genome

For

the

homologue

lies

within

a

similar

conserved

syntenic

interval

in

the

human

genome

For

of

mouse

genes

the

best

match

in

the

human

genome

in

turn

has

its

best

match

against

that

same

mouse

gene

in

the

conserved

syntenic

interval

These

latter

cases

probably

represent

genes

that

have

descended

from

the

same

common

ancestral

gene

termed

here

orthologues

Comprehensive

identification

of

all

orthologous

gene

relationships

however

is

challenging

If

a

single

ancestral

gene

gives

rise

to

a

gene

family

subsequent

to

the

divergence

of

the

species

the

family

members

in

each

species

are

all

orthologous

to

the

corresponding

gene

or

genes

in

the

other

species

Accordingly

orthology

need

not

be

a

relationship

and

can

sometimes

be

difficult

to

discern

from

paralogy

-LRB-

see

protein

section

below

concerning

lineage-specific

gene

family

expansion

-RRB-

There

was

no

homologous

predicted

gene

in

human

for

less

than

-LRB-

-RRB-

of

the

predicted

genes

in

mouse

In

all

these

cases

the

mouse

gene

prediction

was

supported

by

clear

protein

similarity

in

other

organisms

but

a

corresponding

homologue

was

not

found

in

the

human

genome

The

homologous

genes

may

have

been

deleted

in

the

human

genome

for

these

few

cases

or

they

could

represent

the

creation

of

new

lineage-specific

genes

in

the

rodent

lineage

this

seems

unlikely

because

they

show

protein

similarity

to

genes

in

other

organisms

There

are

however

several

other

possible

reasons

why

this

small

set

of

mouse

genes

lack

a

human

homologue

The

gene

predictions

themselves

or

the

evidence

on

which

they

are

based

may

be

incorrect

Genes

that

seem

to

be

mouse-specific

may

correspond

to

human

genes

that

are

still

missing

owing

to

the

incompleteness

of

the

available

human

genome

sequence

Alternatively

there

may

be

true

human

homologues

present

in

the

available

sequence

but

the

genes

could

be

evolving

rapidly

in

one

or

both

lineages

and

thus

be

difficult

to

recognize

The

answers

should

become

clear

as

the

human

genome

sequence

is

completed

and

other

mammalian

genomes

are

sequenced

In

any

case

the

small

number

of

possible

mouse-specific

genes

demonstrates

that

de

novo

gene

addition

in

the

mouse

lineage

and

gene

deletion

in

the

human

lineage

have

not

significantly

altered

the

gene

repertoire

Mammalian

gene

count

percentage

of

true

exons

among

all

predicted

exons

or

the

specificity

of

the

initial

mouse

gene

catalogue

is

estimated

to

be

Together

these

estimates

suggest

a

count

of

about

exons

in

proteincoding

genes

in

mouse

-LRB-

-RRB-

To

estimate

the

number

of

genes

in

the

genome

we

used

an

exonlevel

analysis

because

it

is

less

sensitive

to

artefacts

such

as

fragmentation

and

pseudogenes

among

the

gene

predictions

One

can

estimate

the

number

of

genes

by

dividing

the

estimated

number

of

exons

by

a

good

estimate

of

the

average

number

of

exons

per

gene

A

typical

mouse

RefSeq

transcript

contains

coding

exons

per

gene

and

alternative

splicing

adds

a

small

number

of

exons

per

gene

The

estimated

gene

count

would

then

be

about

with

exons

per

gene

or

about

with

exons

per

gene

If

the

sensitivity

is

only

-LRB-

rather

than

-RRB-

the

exon

count

rises

to

yielding

a

range

of

In

the

next

section

we

show

that

gene

predictions

that

avoid

many

of

the

biases

of

evidence-based

gene

prediction

result

in

only

a

modest

increase

in

the

predicted

gene

count

-LRB-

in

the

range

of

about

genes

-RRB-

Together

these

estimates

suggest

that

the

mammalian

gene

count

may

fall

at

the

lower

end

of

-LRB-

or

perhaps

below

-RRB-

our

previous

prediction

of

based

on

the

human

draft

Although

small

single-exon

genes

may

add

further

to

the

count

the

total

seems

unlikely

to

greatly

exceed

This

lower

estimate

for

the

mammalian

gene

number

is

consistent

with

other

recent

However

there

are

important

caveats

It

is

possible

that

the

genome

contains

many

additional

small

singleexon

genes

expressed

at

relatively

low

levels

Such

genes

would

be

hard

to

detect

by

our

various

techniques

and

would

also

decrease

the

average

number

of

exons

per

gene

used

in

the

analysis

above

De

novo

gene

prediction

To

re-estimate

the

number

of

mammalian

protein-coding

genes

we

studied

the

extent

to

which

exons

in

the

new

set

of

mouse

cDNAs

sequenced

by

were

already

represented

in

the

set

of

exons

contained

in

our

initial

mouse

gene

catalogue

which

did

not

use

this

set

as

evidence

in

gene

prediction

This

cDNA

collection

is

a

much

broader

and

deeper

survey

of

mammalian

cDNAs

than

previously

available

on

the

basis

of

sampling

of

diverse

embryonic

and

adult

If

the

RIKEN

cDNAs

are

assumed

to

represent

a

random

sampling

of

mouse

genes

the

completeness

of

our

exon

catalogue

can

be

estimated

from

the

overlap

with

the

RIKEN

cDNAs

We

recognize

this

assumption

is

not

strictly

valid

but

nonetheless

is

a

reasonable

starting

point

The

initial

mouse

gene

catalogue

of

predicted

exons

included

of

the

exons

revealed

by

the

RIKEN

set

This

is

an

upper

bound

of

sensitivity

as

some

RIKEN

cDNAs

are

probably

less

than

full

length

and

many

tissues

remain

to

be

sampled

On

the

basis

of

the

fraction

of

mouse

exons

with

human

counterparts

the

NATURE

VOL

DECEMBER

The

gene

predictions

above

have

the

strength

of

being

based

on

experimental

evidence

but

the

weakness

of

being

unable

to

detect

new

exons

without

support

from

known

transcripts

or

homology

to

known

cDNAs

or

ESTs

in

some

organism

In

particular

genes

that

are

expressed

at

very

low

levels

or

that

are

evolving

very

rapidly

are

less

likely

to

be

present

in

the

catalogue

-LRB-

R.

Guigo

unpublished

data

-RRB-

Ideally

one

would

like

to

perform

de

novo

gene

prediction

directly

from

genomic

sequence

by

recognizing

statistical

properties

of

coding

regions

splice

sites

introns

and

other

gene

features

Although

this

approach

works

relatively

well

for

small

genomes

with

a

high

proportion

of

coding

sequence

it

has

much

lower

specificity

when

applied

to

mammalian

genomes

in

which

coding

sequences

are

sparser

Even

the

best

de

novo

gene

prediction

programs

-LRB-

such

as

-RRB-

predict

many

apparently

false-positive

exons

In

principle

de

novo

gene

prediction

can

be

improved

by

analysing

aligned

sequences

from

two

related

genomes

to

increase

the

signal-to-noise

Gene

features

-LRB-

such

as

splice

sites

-RRB-

that

are

conserved

in

both

species

can

be

given

special

credence

and

partial

gene

models

-LRB-

such

as

pairs

of

adjacent

exons

-RRB-

that

fail

to

have

counterparts

in

both

species

can

be

filtered

out

Together

these

techniques

can

increase

sensitivity

and

specificity

We

developed

three

new

computer

programs

for

dual-genome

de

novo

gene

prediction

-LRB-

refs

-RRB-

and

We

describe

here

results

from

the

first

two

programs

The

results

of

the

SLAM

analysis

can

be

viewed

at

To

predict

genes

in

the

mouse

genome

these

two

programs

first

find

the

highest-scoring

local

mouse

human

alignment

-LRB-

if

any

-RRB-

in

the

human

genome

They

then

search

for

potential

exonic

features

modifying

the

probability

scores

for

the

features

according

to

the

presence

and

quality

of

these

human

alignments

We

filtered

the

initial

predictions

of

these

programs

retaining

only

multi-exon

gene

predictions

for

which

there

were

corresponding

consecutive

exons

with

an

intron

in

an

aligned

position

in

both

After

enrichment

based

on

the

presence

of

introns

in

aligned

Nature

Publishing

Group

articles

locations

TWINSCAN

identified

exons

as

being

part

of

multi-exon

genes

Most

of

the

gene

predictions

-LRB-

about

-RRB-

were

present

in

the

above

evidence-based

gene

catalogue

Conversely

about

of

the

predicted

genes

and

about

of

the

exons

in

this

catalogue

were

at

least

partially

represented

by

TWINSCAN

predictions

TWINSCAN

predicted

an

extra

-LRB-

-RRB-

new

exons

not

predicted

by

the

evidence-based

methods

produced

qualitatively

similar

results

The

total

number

of

predicted

exons

was

contained

in

multi-exon

genes

with

of

the

predicted

genes

in

the

evidence-based

gene

catalogue

at

least

partially

represented

Approximately

of

the

exons

in

the

catalogue

were

detected

by

which

predicted

an

additional

-LRB-

-RRB-

new

exons

There

is

considerable

overlap

between

the

two

sets

of

new

predicted

exons

with

the

TWINSCAN

predictions

largely

being

a

subset

of

the

predictions

the

union

of

the

two

sets

contains

new

exons

We

attempted

to

validate

a

sample

of

of

the

new

predictions

by

performing

PCR

with

reverse

transcription

-LRB-

RT

-RRB-

between

consecutive

exons

using

RNA

from

adult

mouse

and

verifying

resulting

PCR

products

by

direct

DNA

sequencing

Our

sampling

involved

selecting

gene

predictions

without

nearby

evidence-based

predictions

on

the

same

strand

and

with

an

intron

of

at

least

kb

The

validation

rate

was

approximately

for

TWINSCAN

and

about

for

-LRB-

which

had

about

twice

as

many

new

exons

see

above

-RRB-

Extrapolating

from

these

success

rates

we

estimate

that

the

entire

collection

would

yield

about

validated

gene

predictions

that

do

not

overlap

with

the

evidencebased

catalogue

The

second

step

of

filtering

de

novo

gene

predictions

-LRB-

by

requiring

the

presence

of

adjacent

exons

in

both

species

-RRB-

turns

out

to

greatly

increase

prediction

specificity

Predicted

genes

that

were

removed

by

this

criterion

had

a

very

low

validation

rate

In

a

sample

of

predictions

that

failed

to

meet

the

criteria

the

validation

rate

was

for

genes

with

strong

homology

to

human

sequence

and

for

those

without

The

filtering

process

thus

removed

more

apparent

false

positives

than

true

positives

Extrapolating

from

these

results

testing

the

entire

set

of

such

predicted

genes

-LRB-

that

is

those

that

fail

the

test

of

having

adjacent

homologous

exons

in

the

two

species

-RRB-

would

be

expected

to

yield

only

about

additional

validated

predictions

Overall

we

expect

that

about

-LRB-

-RRB-

of

the

new

gene

predictions

would

be

validated

by

RT

PCR

This

probably

corresponds

to

a

smaller

number

of

actual

new

genes

because

some

of

these

may

belong

to

the

same

transcription

unit

as

an

adjacent

de

novo

or

evidence-based

prediction

Conversely

some

true

genes

may

fail

to

have

been

detected

by

RT

PCR

owing

to

lack

of

sensitivity

or

tissue

or

developmental

stage

An

example

of

a

new

gene

prediction

validated

by

RT

PCR

is

a

homologue

of

dystrophin

-LRB-

Fig.

-RRB-

Dystrophin

is

encoded

by

the

DMD

gene

which

is

mutated

in

individuals

with

Duchenne

muscular

A

gene

prediction

was

found

on

mouse

chromosome

and

human

chromosome

showing

amino

acid

identity

over

of

the

dystrophin

protein

-LRB-

the

carboxy

terminal

portion

which

interacts

with

the

transmembrane

protein

b-dystroglycan

-RRB-

Other

new

gene

predictions

include

homologues

of

aquaporin

These

gene

predictions

were

missed

by

the

evidencebased

methods

because

they

were

below

various

thresholds

These

and

other

examples

are

described

in

a

companion

The

overall

results

of

the

de

novo

gene

prediction

are

encouraging

in

two

respects

First

the

results

show

that

de

novo

gene

prediction

on

the

basis

of

two

genome

sequences

can

identify

-LRB-

at

least

partly

-RRB-

most

predicted

genes

in

the

current

mammalian

gene

catalogues

with

remarkably

high

specificity

and

without

any

information

about

cDNAs

ESTs

or

protein

homologies

from

other

organisms

It

can

also

identify

some

additional

genes

not

detected

in

the

evidence-based

analysis

Second

the

results

suggest

that

methods

that

avoid

some

of

the

inherent

biases

of

evidence-based

gene

prediction

do

not

identify

more

than

a

few

thousand

additional

predicted

exons

or

genes

These

results

are

thus

consistent

with

an

estimate

in

the

vicinity

of

genes

subject

to

the

uncertainties

noted

above

RNA

genes

Figure

Structure

of

a

new

homologue

of

dystrophin

as

predicted

on

mouse

chromosome

and

human

chromosome

Mouse

and

human

gene

structures

are

shown

in

blue

on

the

chromosomes

-LRB-

pink

-RRB-

The

mouse

intron

marked

with

an

asterisk

was

verified

by

RT

PCR

from

primers

complementary

to

the

flanking

exons

followed

by

direct

product

Regions

of

high-scoring

alignment

to

the

entire

other

genome

-LRB-

computed

before

gene

predictions

and

identification

of

predicted

orthologues

-RRB-

are

shown

in

yellow

Note

the

weak

correspondence

between

predicted

exons

and

blocks

of

high-scoring

whole-genome

alignment

Nonetheless

the

predicted

proteins

considered

in

isolation

show

good

alignment

across

several

splice

sites

The

genome

also

encodes

many

RNAs

that

do

not

encode

proteins

including

abundant

RNAs

involved

in

mRNA

processing

and

translation

-LRB-

such

as

ribosomal

RNAs

and

tRNAs

-RRB-

and

more

recently

discovered

RNAs

involved

in

the

regulation

of

gene

expression

and

other

functions

-LRB-

such

as

micro

RNAs

-RRB-

There

are

probably

many

new

RNAs

not

yet

discovered

but

their

computational

identification

has

been

difficult

because

they

contain

few

hallmarks

Genomic

comparisons

have

the

potential

to

significantly

increase

the

power

of

such

predictions

by

using

conservation

to

reveal

relatively

weak

signals

such

as

those

arising

from

RNA

secondary

We

illustrate

this

by

showing

how

comparative

genomics

can

improve

the

recognition

of

even

an

extremely

well

understood

gene

family

the

tRNA

genes

In

our

initial

analysis

of

the

human

the

program

predicted

tRNA

genes

and

pseudogenes

A

small

number

-LRB-

about

of

the

total

-RRB-

were

filtered

out

by

the

RepeatMasker

program

as

being

fossils

of

the

MIR

transposon

a

long-dead

SINE

element

that

was

derived

from

a

The

analysis

of

the

mouse

genome

is

much

more

challenging

because

the

mouse

contains

an

active

SINE

-LRB-

-RRB-

that

is

derived

from

a

tRNA

and

thus

vastly

complicates

the

task

of

identifying

true

tRNA

genes

The

tRNAscan-SE

program

predicted

tRNA

genes

and

pseudogenes

in

mouse

but

the

RepeatMasker

program

classified

of

the

genes

and

of

the

pseudogenes

as

SINEs

After

eliminating

these

the

remaining

set

contained

putative

tRNA

genes

Close

analysis

of

this

set

suggested

that

it

was

still

contaminated

with

a

substantial

number

of

pseudogenes

Specifically

of

the

putative

tRNA

genes

violated

the

wobble

rules

that

specify

that

only

distinct

anticodons

are

expected

to

decode

the

standard

sense

codons

plus

a

selenocysteine

tRNA

species

complementary

to

the

UGA

stop

In

contrast

the

initial

analysis

of

the

human

genome

identified

only

three

putative

tRNA

genes

that

violated

the

wobble

To

improve

discrimination

of

functional

tRNA

genes

we

exploited

comparative

genomic

analysis

of

mouse

and

human

True

functional

tRNA

genes

would

be

expected

to

be

highly

conserved

Indeed

the

putative

mouse

tRNA

genes

differ

on

average

by

less

than

-LRB-

four

differences

in

about

bp

-RRB-

from

their

nearest

human

match

and

nearly

half

are

identical

In

contrast

non-genic

tRNA-related

sequences

-LRB-

those

labelled

as

pseudogenes

by

tRNAscan-SE

or

as

SINEs

by

RepeatMasker

-RRB-

differ

by

an

average

of

and

is

within

divergence

Notably

the

suspect

predictions

that

violate

the

wobble

rules

show

an

average

of

NATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

divergence

from

their

nearest

human

homologue

and

is

within

divergence

On

the

basis

of

these

observations

we

identified

the

set

of

tRNA

genes

having

cross-species

homologues

with

sequence

divergence

The

set

contained

tRNA

genes

in

mouse

and

in

human

In

both

cases

the

set

represents

all

expected

anti-codons

and

exactly

satisfies

the

expected

wobble

rules

The

sets

probably

more

closely

represent

the

true

complement

of

functional

tRNA

genes

Although

the

excluded

putative

genes

-LRB-

in

mouse

and

in

human

-RRB-

may

include

some

true

genes

it

seems

likely

that

our

earlier

estimate

of

approximately

tRNA

genes

in

human

is

an

overestimate

The

actual

count

in

mouse

and

human

is

probably

closer

to

We

also

analysed

the

mouse

genome

for

other

known

classes

of

non-coding

RNAs

Because

many

of

these

classes

also

seem

to

have

given

rise

to

many

pseudogenes

we

conservatively

considered

only

those

loci

that

are

identical

or

that

are

highly

similar

to

RNAs

that

have

been

published

as

true

genes

We

identified

a

total

of

noncoding

RNA

genes

which

includes

small

nucleolar

RNAs

micro

RNAs

and

other

non-coding

RNA

genes

including

rRNAs

spliceosomal

RNAs

and

telomerase

RNA

We

also

classified

other

loci

with

significant

similarities

to

known

RNA

genes

as

probable

pseudogenes

by

intragenome

duplication

-RRB-

permitting

an

assessment

of

the

evolutionary

pressures

exerted

on

different

classes

of

proteins

The

differences

between

the

mouse

and

human

proteomes

primarily

in

gene

family

expansions

might

reveal

how

physiological

anatomical

and

behavioural

differences

are

reflected

at

the

genome

level

Overall

proteome

comparison

We

compared

the

largest

transcript

for

each

gene

in

the

mouse

gene

catalogue

to

the

National

Center

for

Biotechnology

Information

Mouse

proteome

Eukaryotic

protein

invention

appears

to

have

occurred

largely

through

two

important

mechanisms

The

first

is

the

combination

of

protein

domains

into

new

architectures

-LRB-

Domains

are

compact

structures

serving

as

evolutionarily

conserved

functional

building

blocks

that

are

often

assembled

in

various

arrangements

-LRB-

architectures

-RRB-

in

different

-RRB-

The

second

is

lineage-specific

expansions

of

gene

families

that

often

accompany

the

emergence

of

lineage-specific

functions

and

-LRB-

for

example

expansions

of

the

vertebrate

immunoglobulin

superfamily

reflecting

the

invention

of

the

immune

receptor-like

kinases

in

A.

thaliana

associated

with

plant-specific

self-incompatibility

and

disease-resistance

and

the

trypsin-like

serine

protease

homologues

in

D.

melanogaster

associated

with

dorsal

ventral

patterning

and

innate

immune

-RRB-

The

availability

of

the

human

and

mouse

genome

sequences

provides

an

opportunity

to

explore

issues

of

protein

evolution

that

are

best

addressed

through

the

study

of

more

closely

related

genomes

The

great

similarity

of

the

two

proteomes

allows

extensive

comparison

of

orthologous

proteins

-LRB-

those

that

descended

by

speciation

from

a

single

gene

in

the

common

ancestor

rather

than

Figure

Taxonomic

breakdown

of

homologues

of

mouse

proteins

according

to

taxonomic

range

Note

that

only

a

small

fraction

of

genes

are

possibly

rodent-specific

-LRB-

-RRB-

as

compared

with

those

shared

with

other

mammals

-LRB-

not

rodent-specific

-RRB-

shared

with

chordates

-LRB-

not

mammalian-specific

-RRB-

shared

with

metazoans

-LRB-

not

chordate-specific

-RRB-

shared

with

eukaryotes

-LRB-

not

metazoan-specific

-RRB-

and

shared

with

prokaryotes

and

other

organisms

-LRB-

not

eukaryotic-specific

-RRB-

NATURE

VOL

DECEMBER

Figure

Gene

ontology

-LRB-

GO

-RRB-

annotations

for

mouse

and

human

proteins

The

GO

terms

assigned

to

mouse

-LRB-

blue

-RRB-

and

human

-LRB-

red

-RRB-

proteins

based

on

sequence

matches

to

InterPro

domains

are

grouped

into

approximately

a

dozen

categories

These

categories

fell

within

each

of

the

larger

ontologies

of

cellular

component

-LRB-

a

-RRB-

molecular

function

-LRB-

b

-RRB-

and

biological

process

-LRB-

c

-RRB-

-LRB-

D.

Hill

personal

communication

-RRB-

In

general

mouse

has

a

similar

percentage

of

proteins

compared

with

human

in

most

categories

The

apparently

significant

difference

between

the

number

of

mouse

and

human

proteins

in

the

translational

apparatus

category

of

the

cellular

component

ontology

may

be

due

to

ribosomal

protein

pseudogenes

incorrectly

assigned

as

genes

in

mouse

Nature

Publishing

Group

articles

-LRB-

NCBI

-RRB-

database

-LRB-

nr

set

-RRB-

using

the

BLASTP

Mouse

proteins

predicted

to

be

homologues

-LRB-

E

-RRB-

of

other

proteins

were

classified

into

one

of

six

taxonomic

groupings

-LRB-

-RRB-

rodent-specific

-LRB-

-RRB-

mammalian-specific

-LRB-

-RRB-

chordate-specific

-LRB-

-RRB-

metazoan-specific

-LRB-

-RRB-

eukaryote-specific

and

-LRB-

-RRB-

other

-LRB-

Fig.

-RRB-

The

results

were

similar

to

those

from

an

analysis

of

human

We

annotated

the

current

sets

of

mouse

and

human

proteins

with

respect

to

the

InterPro

classification

of

domains

motifs

and

proteins

using

the

InterProScan

computer

In

this

way

the

proteins

were

assigned

Gene

Ontology

-LRB-

GO

-RRB-

which

describe

biological

process

cellular

compartment

and

molecular

function

Comparisons

of

GO

annotations

between

the

two

mammals

showed

no

large-scale

differences

in

molecular

and

cellular

functions

between

the

two

protein

sets

-LRB-

Fig.

-RRB-

that

were

not

accountable

by

imperfections

in

gene

prediction

and

annotation

Overall

about

of

proteins

contained

at

least

one

InterPro

domain

As

expected

most

of

the

protein

or

domain

families

have

similar

sizes

in

human

and

mouse

-LRB-

Table

-RRB-

However

of

the

most

populous

InterPro

families

in

mouse

show

significant

differences

in

numbers

between

the

two

proteomes

most

notably

high

mobility

group

box

and

ubiquitin

domains

On

close

analysis

the

differences

for

six

of

these

families

can

be

accounted

for

by

differential

expansion

of

endogenous

retroviral

sequences

in

the

genomes

We

return

below

to

the

issue

of

expansion

of

gene

families

Evolution

of

orthologues

To

study

the

evolutionary

forces

that

conserve

proteins

we

examined

the

set

of

orthologues

between

human

and

mouse

described

above

expanding

by

nearly

an

order

of

magnitude

the

set

of

orthologues

used

for

evolutionary

These

are

genes

for

which

lineage-specific

duplications

seem

not

to

have

occurred

in

either

lineage

For

each

orthologous

gene

pair

we

aligned

the

cDNA

sequences

in

accordance

with

their

pairwise

amino

acid

alignments

and

Table

Domain-based

and

family-based

InterPro

analysis

InterPro

Name

M.

musculus

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

H.

sapiens

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

T.

rubripes

-LRB-

fish

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB----RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB----RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB----RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

C.

elegans

-LRB-

nematode

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB----RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB----RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB----RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB----RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

D.

melanogaster

-LRB-

insect

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB----RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB----RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB----RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB----RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

The

top

protein

families/domains

in

mouse

are

listed

and

for

each

genome

the

comparative

values

are

shown

as

the

percentage

of

total

genes

in

the

genome

and

in

parentheses

the

relative

rank

in

the

genome

This

is

based

on

analysis

of

gene

products

with

a

significant

match

to

the

InterPro

collection

of

protein

family

and

domain

signatures

A

conservative

domain

prediction

scheme

was

used

that

excluded

uncertain

matches

PROSITE

patterns

PRINTS

predictions

and

two

PROSITE

profiles

Only

signatures

of

the

family

domain

and

repeat

types

were

considered

In

the

case

of

multiple

annotated

transcripts

per

gene

only

the

longest

one

was

considered

Briefly

all

InterPro

hierarchical

relationships

among

signatures

with

different

specificity

were

collapsed

to

the

broadest

categories

InterPro

entries

in

italic

font

represent

families

that

have

numerous

members

among

transposons

endogenous

retroviruses

and

pseudogenes

Significant

expansions

in

mouse

compared

with

human

are

marked

in

bold

-LRB-

P

for

chi-squared

test

on

number

of

family/domain

members

with

respect

to

total

genes

examined

using

Dunn

Sidak

corrections

for

multiple

-RRB-

-LRB----RRB-

indicates

entries

with

a

rank

of

absolute

Rhodopsin-like

GPCR

superfamily

Zn-finger

type

Immunoglobulin/major

histocompatibility

complex

Eukaryotic

protein

kinase

ATPase

RNA-binding

region

-LRB-

RNA

recognition

motif

-RRB-

transposable

element

G-protein

beta

repeat

Zn-finger

RING

RNA-directed

DNA

polymerase

-LRB-

reverse

transcriptase

-RRB-

Pleckstrin-like

domain

Leucine-rich

repeat

Homeobox

KRAB

box

Calcium-binding

EF-hand

Ankyrin

domain

EGF-like

domain

Integrase

catalytic

domain

Fibronectin

type

III

Small

GTP-binding

protein

domain

BTB/POZ

domain

TPR

repeat

PDZ/DHR/GLGF

domain

domain

Cation

channel

non-ligand

gated

Helicase

C-terminal

domain

Basic

helix-loop-helix

dimerization

domain

bHLH

Serine

protease

trypsin

family

Integrase

N-terminal

zinc

binding

Esterase/lipase/thioesterase

active

site

Ubiquitin

domain

Histone-fold/TFIID-TAF/NF-Y

domain

Tyrosine-specific

protein

phosphatase

and

dual-specificity

protein

phosphatase

RNase

H

Eukaryotic/viral

aspartic

protease

active

site

Zn-finger-like

PHD

finger

Zn-finger

CCHC

type

-LRB-

high

mobility

group

-RRB-

box

Sterile

alpha

motif

-LRB-

SAM

-RRB-

Cysteine-rich

flanking

region

C-terminal

Cadherin

domain

Collagen

triple

helix

repeat

Cysteine-rich

flanking

region

N-terminal

Cytochrome

Retroviral

nucleocapsid

protein

Gag

IQ

calmodulin-binding

region

Endonuclease/exonuclease/phosphatase

family

C-type

lectin

domain

Nature

Publishing

Group

NATURE

VOL

DECEMBER

articles

calculated

two

measures

of

sequence

evolution

the

percentage

of

amino

acid

identities

and

the

K

A/K

S

The

latter

quantity

reflects

the

ratio

between

the

rates

of

non-synonymous

-LRB-

amino-acid

replacing

-RRB-

mutations

per

non-synonymous

site

and

synonymous

-LRB-

silent

-RRB-

mutations

per

synonymous

site

-LRB-

see

ref

-RRB-

Non-synonymous

mutations

are

typically

subject

to

strong

selective

pressure

whereas

synonymous

changes

are

thought

typically

to

be

neutral

Orthologue

pairs

generally

have

low

values

of

K

A/K

S

-LRB-

for

example

-RRB-

which

implies

that

the

proteins

are

subject

to

relatively

strong

purifying

Proteins

with

K

A/K

S

are

formally

defined

as

being

subject

to

positive

selection

that

is

amino

acid

changes

are

accumulating

faster

than

would

be

expected

given

the

underlying

silent

substitution

rate

However

proteins

with

KA

KS

may

still

contain

sites

under

positive

selection

but

the

contribution

of

those

sites

to

the

KA

KS

for

the

whole

protein

is

offset

by

purifying

selection

at

other

Some

care

is

needed

however

to

exclude

pseudogenes

in

such

analyses

Because

pseudogenes

do

not

encode

functional

proteins

the

distinction

between

synonymous

and

non-synonymous

mutations

is

irrelevant

and

the

apparent

KA

KS

ratio

will

converge

towards

For

the

pairs

of

mouse

human

orthologues

of

the

residues

were

identical

The

median

amino

acid

identity

was

and

the

median

KA

KS

ratio

was

-LRB-

Fig.

and

Table

-RRB-

Most

mouse

and

human

orthologue

pairs

thus

have

a

high

degree

of

sequence

identity

and

are

under

strong-to-moderate

purifying

selection

One

consequence

of

the

strong

sequence

similarity

is

that

computer

programs

such

as

that

use

iterative

alignment

to

detect

distant

homologues

gain

little

by

using

both

mouse

and

human

sequence

compared

with

using

either

genome

singly

For

human

proteins

for

which

no

non-primate

homologue

could

be

recognized

on

the

basis

of

the

human

sequence

the

addition

of

a

mouse

orthologue

added

nothing

new

We

sought

to

quantify

the

relative

selective

pressures

on

protein

regions

containing

known

domains

About

of

gene

pairs

encode

transcripts

that

contain

at

least

one

InterPro

domain

prediction

-LRB-

we

considered

only

predicted

domains

present

in

corresponding

positions

in

both

orthologues

-RRB-

Regions

containing

predicted

domains

had

higher

average

percentage

identities

and

lower

KA

KS

values

than

regions

without

predicted

domains

or

than

full-length

proteins

-LRB-

Fig.

and

Table

-RRB-

Thus

domains

are

under

greater

purifying

selection

than

are

regions

not

containing

domains

This

is

consistent

with

the

hypothesis

that

domains

are

under

greater

structural

and

functional

constraints

than

unstructured

domain-free

regions

In

this

analysis

-LRB-

as

in

those

below

-RRB-

the

differences

in

KA

KS

were

largely

due

to

variations

in

KA

-LRB-

Table

-RRB-

Median

KS

values

clustered

around

synonymous

substitutions

per

synonymous

site

-LRB-

Table

-RRB-

indicating

that

each

of

the

sets

of

proteins

has

a

similar

neutral

substitution

rate

-LRB-

These

results

are

broadly

consistent

with

measures

of

neutral

substitution

rate

provided

in

the

repeat

and

evolution

sections

although

the

precise

methodologies

used

and

categories

of

sites

examined

affect

the

magnitude

of

estimates

-LRB-

see

Supplementary

Information

-RRB-

-RRB-

Figure

Protein

evolution

The

figure

shows

percentage

residue

identity

and

cumulative

non-synonymous

to

synonymous

codon

rate

ratios

for

total

proteins

and

for

regions

with

and

without

predicted

InterPro

domains

predicted

SMART

domains

with

or

without

known

enzymatic

activity

and

SMART

domains

specific

to

three

different

subcellular

compartments

The

orthologous

gene

pairs

referred

to

in

Table

were

used

for

analysis

a

Proteins

were

divided

into

regions

with

and

without

InterPro

domains

and

per

cent

identity

was

calculated

for

total

proteins

-LRB-

black

-RRB-

and

for

domain-containing

-LRB-

red

line

-RRB-

and

domain-free

-LRB-

grey

line

-RRB-

regions

The

higher

conservation

of

domain-containing

regions

relative

to

domain-free

regions

is

consistent

with

their

greater

functional

conservation

The

protein

sequences

are

plotted

in

bins

of

identity

In

calculating

the

per

cent

amino

acid

identity

between

two

sequences

the

number

of

identical

residues

was

divided

by

the

total

number

of

alignment

positions

including

positions

where

one

sequence

was

aligned

with

a

gap

b

Cumulative

KA

KS

ratios

for

total

proteins

-LRB-

black

line

-RRB-

and

for

regions

with

-LRB-

red

line

-RRB-

and

without

-LRB-

grey

line

-RRB-

predicted

Interpro

domains

ProteinNATURE

VOL

DECEMBER

domain-containing

regions

have

low

KA

KS

ratios

-LRB-

-RRB-

suggesting

that

they

may

be

subject

to

greater

degrees

of

purifying

selection

than

are

the

domain-free

regions

The

differences

in

functional

constraints

between

predicted

domain

regions

and

the

rest

of

the

protein

may

be

found

to

be

even

more

pronounced

as

a

significant

proportion

of

sequences

may

contain

as

yet

unpredicted

protein

domains

c

Cumulative

KA

KS

ratios

for

SMART

domain

predictions

with

-LRB-

red

line

-RRB-

or

without

-LRB-

black

line

-RRB-

known

enzymatic

activity

The

higher

proportion

of

catalytic

domains

with

low

KA

KS

ratios

is

an

indication

of

the

greater

purifying

selection

acting

on

these

sequences

d

Cumulative

KA

KS

ratios

for

predicted

SMART

domains

that

are

specific

to

one

of

three

different

subcellular

compartments

Compared

with

intracellular

-LRB-

cytoplasmic

-LRB-

red

-RRB-

and

nuclear

-LRB-

black

-RRB-

-RRB-

domains

a

greater

proportion

of

secreted

domains

-LRB-

grey

-RRB-

possess

higher

KA

KS

values

This

indicates

that

secreted

often

extracellular

domains

are

subject

on

average

to

greater

positive

diversifying

selection

Nature

Publishing

Group

articles

Table

K

A

K

S

K

A/K

S

and

pairwise

percentage

amino

acid

identities

for

mouse-human

orthologues

Orthologue

regions

Aligned

positions

in

the

full-length

protein

Domain-containing

protein

regions

Domain-free

protein

regions

All

predicted

domains

Catalytic

domains

Non-catalytic

domains

Secreted

domains

Cytoplasmic

domains

Nuclear

domains

Amino

acid

identity

-LRB-

-RRB-

median

-LRB-

n

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

K

A

median

-LRB-

n

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

K

S

median

-LRB-

n

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

K

A/K

S

median

-LRB-

n

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Domains

predicted

by

Pfam

and

SMART

Domains

predicted

by

SMART

We

next

considered

how

the

molecular

functions

of

domains

affect

their

evolution

Domain

families

with

enzymatic

activity

were

found

to

have

a

lower

KA

KS

ratio

than

non-enzymatic

domains

-LRB-

Fig.

and

Table

-RRB-

Fewer

substitutions

are

thus

tolerated

in

catalytic

regions

suggesting

that

a

larger

proportion

of

amino

acids

contribute

to

substrate

binding

specificity

and

catalysis

in

enzymes

Although

enzymatic

domains

are

significantly

larger

than

nonenzymatic

domains

-LRB-

compared

with

amino

acids

on

average

-RRB-

analysis

indicates

that

there

is

no

significant

correlation

between

domain

length

and

KA

KS

-LRB-

r

-RRB-

We

also

examined

how

rates

of

evolution

correlate

with

the

cellular

compartments

in

which

a

protein

functions

We

partitioned

of

the

domain

families

in

the

SMART

into

secreted

cytoplasmic

or

nuclear

classes

on

the

basis

of

published

The

KA

KS

values

for

the

three

classes

showed

that

domains

in

the

secreted

class

typically

are

under

less

purifying

selection

than

are

either

nuclear

or

cytoplasmic

domains

-LRB-

Fig.

and

Table

-RRB-

Of

eight

domain

families

with

the

highest

-LRB-

-RRB-

median

KA

KS

values

six

are

specific

to

the

secreted

portions

of

proteins

and

are

implicated

in

the

mammalian

defence

and

immune

response

system

-LRB-

Table

-RRB-

The

fact

that

these

proteins

have

the

highest

K

A/K

S

values

indicates

that

they

are

under

reduced

purifying

selection

increased

positive

selection

or

both

Increased

positive

selection

may

be

the

result

of

antagonistic

coevolution

between

a

mammalian

host

and

its

pathogens

in

a

genetic

arms

race

where

each

is

under

strong

pressure

to

respond

to

innovations

in

the

other

genome

Mouse

orthologues

of

human

disease

genes

are

of

particular

interest

to

biomedical

research

We

examined

human

disease

genes

having

clear

orthologues

in

A

total

of

amino

acid

variants

reported

to

be

were

mapped

to

corresponding

positions

in

the

mouse

sequence

The

mouse

sequence

was

identical

to

the

normal

human

sequence

for

of

these

positions

and

it

differed

from

both

the

normal

and

diseaseassociated

sequence

in

human

for

of

the

positions

To

our

surprise

the

mouse

sequence

was

identical

to

the

human

diseaseassociated

sequence

in

a

small

number

of

cases

-LRB-

-RRB-

Although

the

causal

connection

with

disease

has

not

yet

been

proven

in

every

one

of

these

cases

there

are

at

least

instances

where

the

link

between

disease

and

mutation

has

been

documented

-LRB-

Table

-RRB-

These

include

mutations

in

the

cystic

fibrosis

transmembrane

conductance

regulator

gene

and

the

a-synuclein

gene

which

is

associated

with

a

familial

form

of

Parkinson

's

In

such

cases

the

mouse

may

not

provide

the

most

appropriate

model

system

for

direct

study

of

the

mutation

although

understanding

the

basis

for

the

species

difference

may

prove

enlightening

We

performed

a

similar

analysis

with

SNPs

in

coding

regions

of

human

genes

We

found

the

location

of

high-quality

codingregion

SNPs

from

within

human

genes

using

the

tBLASTn

computer

and

in

turn

within

the

corresponding

positions

in

mouse

orthologues

The

mouse

sequence

encoded

the

identical

amino

acid

as

the

major

-LRB-

more

common

-RRB-

human

allele

in

of

cases

and

as

the

minor

human

allele

in

of

cases

The

former

proportion

is

similar

to

the

of

human

amino

acids

that

are

conserved

in

mouse

orthologues

indicating

that

most

of

such

coding-region

SNPs

are

not

under

strong

selective

constraint

Evolution

of

gene

families

in

mouse

As

noted

above

of

mouse

proteins

seem

to

have

strict

orthologues

in

the

human

genome

Many

of

the

remainder

belong

to

gene

families

that

have

undergone

differential

expansion

in

at

least

one

of

the

two

genomes

resulting

in

the

lack

of

a

strict

relationship

Such

gene

family

changes

represent

an

insight

into

aspects

of

physiology

that

have

emerged

since

the

last

common

ancestor

Table

Protein

domains

with

high

K

A/K

S

values

SMART

or

Pfam

domain

family

CLECT

IG

SR

TNFR

Pfam

CCP

SCY

KRAB

Domain

family

function

Immunity

-LRB-

-RRB-

Immunity

-LRB-

immunoglobulins

-RRB-

Immunity

-LRB-

scavenger

receptors

-RRB-

Immunity

-LRB-

-RRB-

Metabolism

of

toxic

compounds

Immunity

-LRB-

-RRB-

Immunity

-LRB-

CXC

chemokines

-RRB-

Transcription

-LRB-

-RRB-

n

K

A/K

S

median

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

The

eight

highest

K

A/K

S

ratios

K

A

K

S

for

domain

families

in

Pfam

and

SMART

that

are

present

more

than

times

in

the

mouse

and

human

orthologue

pairs

are

shown

Examples

of

proteins

are

given

in

parentheses

When

equivalent

families

in

both

Pfam

and

SMART

had

median

values

of

K

A/K

S

only

the

SMART

version

is

shown

Nature

Publishing

Group

NATURE

VOL

DECEMBER

articles

Table

Human

disease-associated

sequence

variants

Disease

-LRB-

OMIM

code

-RRB-

Hirschsprung

disease

-LRB-

-RRB-

Leukencephaly

with

vanishing

white

matter

-LRB-

-RRB-

Mucopolysaccharidosis

type

IVA

-LRB-

-RRB-

Breast

cancer

-LRB-

-RRB-

Breast

cancer

-LRB-

-RRB-

Parkinson

's

disease

-LRB-

-RRB-

Tuberous

sclerosis

-LRB-

-RRB-

Bardet

Biedl

syndrome

type

-LRB-

-RRB-

Mesothelioma

-LRB-

-RRB-

Long

QT

syndrome

-LRB-

-RRB-

Cystic

fibrosis

-LRB-

-RRB-

Porphyria

variegata

-LRB-

-RRB-

Non-Hodgkin

's

lymphoma

-LRB-

-RRB-

Severe

combined

immunodeficiency

disease

-LRB-

-RRB-

Limb-girdle

muscular

dystrophy

type

-LRB-

-RRB-

LCAD

deficiency

-LRB-

-RRB-

Usher

syndrome

type

-LRB-

-RRB-

Chronic

non-spherocytic

haemolytic

anaemia

-LRB-

-RRB-

Mantle

cell

lymphoma

-LRB-

in

-RRB-

Becker

muscular

dystrophy

-LRB-

-RRB-

Complete

androgen

insensitivity

syndrome

-LRB-

-RRB-

Prostate

cancer

-LRB-

-RRB-

Crohn

's

disease

-LRB-

-RRB-

Mutation

The

variant

amino

acids

of

the

sequence

variants

listed

are

identical

in

wild-type

mouse

orthologues

A

well-documented

example

of

family

expansion

is

the

olfactory

receptor

gene

family

which

represents

a

branch

of

the

larger

Gprotein-coupled

receptor

superfamily

Duplication

of

olfactory

receptor

genes

seems

to

have

occurred

frequently

in

both

rodent

and

primate

lineages

and

differences

in

number

and

sequence

have

been

seen

as

distinguishing

the

degrees

and

repertoires

of

odorant

detection

between

mice

and

humans

Moreover

an

estimated

of

the

mouse

olfactory

receptor

and

a

higher

percentage

of

human

are

pseudogenes

indicating

that

there

is

a

dynamic

interplay

between

gene

birth

and

gene

death

in

the

recent

evolution

of

this

family

The

importance

of

these

genes

in

reproductive

behaviour

is

evident

from

defects

in

pheromone

responses

that

result

from

deletion

of

the

vomeronasal

olfactory

receptor

gene

Another

example

is

the

cytochrome

gene

family

which

is

of

considerable

pharmacological

and

clinical

interest

cytochromes

are

normally

terminal

oxidases

in

multicomponent

electron

transfer

chains

which

metabolize

large

numbers

of

xenobiotic

as

well

as

endogenous

compounds

Their

numbers

often

vary

among

different

This

gene

family

is

moderately

but

significantly

expanded

in

mouse

-LRB-

genes

-RRB-

relative

to

human

-LRB-

genes

-RRB-

By

comparing

the

cytochrome

gene

families

from

mouse

human

and

pufferfish

-LRB-

Takifugu

rubripes

-RRB-

we

found

clear

expansions

in

four

subfamilies

-LRB-

and

-RRB-

in

mouse

relative

to

human

-LRB-

Fig.

-RRB-

These

occur

in

local

gene

clusters

that

also

contain

unprocessed

pseudogenes

The

expansions

appear

to

be

associated

in

part

with

gender

differences

in

the

metabolism

of

androgens

and

xenobiotics

-LRB-

see

below

-RRB-

To

explore

systematically

recent

evolution

of

the

mouse

proteome

we

searched

for

mouse-specific

gene

clusters

We

identified

genomic

regions

containing

four

or

more

homologous

mouse

genes

that

descended

from

a

single

gene

in

the

human

mouse

common

ancestor

these

represent

local

expansions

in

the

mouse

lineage

To

detect

such

clusters

we

compared

all

transcripts

of

each

gene

with

those

of

five

genes

on

either

side

-LRB-

using

the

program

with

a

threshold

of

E

-RRB-

A

total

of

mouse

genes

were

found

to

have

at

least

one

such

homologue

within

this

window

A

total

of

such

clusters

containing

at

least

four

homologues

was

identified

of

which

contained

multiple

olfactory

receptor

genes

which

have

been

studied

and

are

not

discussed

further

here

For

the

remaining

clusters

we

then

constructed

dendrograms

to

examine

the

evolutionary

relationship

among

the

mouse

proteins

and

their

human

homologues

This

allowed

us

to

identify

those

clusters

containing

mouse

genes

that

are

descendants

of

a

single

ancestral

gene

or

for

which

multiple

gene

deletions

had

occurred

in

the

human

lineage

In

total

such

mouse-specific

clusters

were

identified

-LRB-

Table

Figure

Cytochrome

protein

families

in

mouse

human

and

pufferfish

a

Phylogenetic

tree

based

on

the

neighbour-joining

applied

to

the

alignment

of

the

whole

protein

family

Each

triangle

represents

a

cytochrome

family

cluster

Asterisks

next

to

a

triangle

represent

mouse

pseudogenes

defined

by

the

presence

of

either

an

in-frame

stop

codon

or

a

frameshift

The

colour

codes

are

indicated

in

the

lower-right

panel

When

the

family

presents

one

member

in

each

of

the

studied

organisms

the

triangle

is

labelled

in

orange

The

height

of

the

triangle

is

proportional

to

the

number

of

proteins

which

is

indicated

by

white-line

subdivisions

Chromosomal

location

in

mouse

is

shown

on

each

of

the

branches

for

each

subfamily

The

lengths

of

the

branches

are

not

drawn

to

scale

Branches

with

significant

nodes

-LRB-

bootstrapping

value

-RRB-

are

in

black

with

the

remainder

in

blue

In

out

of

the

family

cases

the

NATURE

VOL

DECEMBER

localization

of

the

genomic

region

from

which

they

are

derived

remains

unassigned

b

Detailed

phylogenetic

tree

of

the

family

based

on

the

neighbour-joining

method

The

root

of

the

tree

was

determined

using

a

sequence

as

out-group

Eight

out

of

the

mouse

sequences

are

excluded

in

this

tree

as

they

are

very

short

Sequence

identifiers

are

coloured

on

the

basis

of

their

source

red

mouse

green

human

Sequence

identifiers

followed

by

an

asterisk

indicate

that

the

sequences

contain

either

a

premature

in-frame

stop

codon

or

frameshift

Bootstrap

values

are

shown

at

the

branches

Colour

codes

of

branches

are

as

for

a.

Although

the

bootstrap

value

for

the

branch

containing

and

is

rather

low

-LRB-

-RRB-

it

might

seem

that

has

only

recently

lost

its

function

as

a

putative

orthologue

in

human

-LRB-

-RRB-

is

still

clustered

with

it

Nature

Publishing

Group

articles

Table

Mouse

homologous

gene

clusters

Cluster

Abbreviation

Chr

No

of

predicted

genes

Strand

-LRB-

major/minor

-RRB-

Function

and

expression

Reference

HOX

cluster

including

Pem

Gpbox

and

Hox

X

Odorant

binding

proteins

-LRB-

Obp

-RRB-

aphrodisin

homologues

-LRB-

lipocalins

-RRB-

Obp

X

Claudins

Claudins

X

Elafin

eppin

and

antileukoproteinase

homologues

Hydroxysteroid

dehydrogenase

Elafin

Hsd

Class

mu

glutathione

S-transferases

Butyrophilin

homologues

Class

cytochromes

Prolactin-inducible

protein

seminal

vesicle-antigen

-LRB-

Sva

-RRB-

GST

Butyr

Pip

Proline-rich

proteins

Submandibular

gland

secretory

proteins

Obox

family

of

homeobox

proteins

Salivary

androgen-binding

protein

alpha-subunit

Beta-defensin

proteins

-LRB-

-RRB-

SmGSP

Obox

Beta-defensin

proteins

Carboxylesterase

Carboxylesterase

Glioma

pathogenesis-related

protein

homologues

containing

SCP

domains

Prolactin-related

proteins

Cathepsin

J-like

enzymes

Eosinophil-associated

ribonuclease

Class

cytochromes

GPrP

Probable

functions

in

embryogenesis

placentation

and

rodent

oogenesis

Sequence

evidence

for

positive

selection

Expression

in

the

placenta/embryo

Pem

is

under

androgen

control

in

the

testis

and

epididymis

Lipocalin

family

members

probably

bind

volatile

odorants

Aphrodisin

is

an

aphrodisiac

hormone

of

hamster

vaginal

discharges

Obps

are

highly

expressed

in

the

nasal

area

Prostate

probasin

is

regulated

by

androgens

Claudins

form

an

intercellular

barrier

in

tight

junctions

Mutations

in

claudins

cause

reproductive

defects

Four

homologues

were

found

in

a

testis

cDNA

library

Reproduction-related

WAP

domain

proteins

antimicrobial

properties

Some

proteins

are

specific

to

the

epididymis

Biosynthesis

of

hormonal

steroids

Controls

binding

of

hormone

to

receptor

Gender-specific

expression

in

olfactory

bulb

and

olfactory

tubercle

Conjugation

of

glutathione

to

hormones

or

metabolites

Unknown

functions

Contain

immunoglobulinlike

domain

-LRB-

s

-RRB-

Oxidation

of

compounds

possibly

fatty

acids

Gender

differences

in

expression

-LRB-

and

-RRB-

Sva

role

in

suppression

of

spermatozoa

motility

kDa

submandibular

gland

protein

is

expressed

in

lacrimal

salivary

and

sweat

glands

Salivary

proteins

of

unknown

function

Salivary

proteins

of

unknown

function

related

to

proline-rich

proteins

-LRB-

above

-RRB-

Expression

is

androgen-dependent

Homeobox

proteins

preferentially

expressed

in

the

gonads

Mate

selection

Genes

may

be

under

positive

selection

due

to

role

in

subspecies

recognition

Antimicrobial

peptides

Beta-defensin

is

expressed

in

salivary

glands

epididymis

ovary

and

pancreas

Beta-defensin

is

expressed

in

trachea

oesophagus

and

tongue

Antimicrobial

peptides

Beta-defensins

and

are

expressed

in

kidney

Involved

in

detoxification

of

xenobiotics

Involved

in

detoxification

of

xenobiotics

Expression

of

egasyn

is

differentially

regulated

by

androgens

Unknown

315 316 316

Prolactin

Cath-J

RNAses

Cystatins/stefins

Proteins

of

unknown

function

Cy

-LRB-

-RRB-

MHC

class

Ib

MHCI

-LRB-

-RRB-

indicates

that

reliable

alignments

could

not

be

determined

and

thus

they

were

not

included

in

Fig.

Where

paralogues

were

aligned

into

two

sequence-similar

subfamilies

this

is

indicated

by

appending

a

slash

followed

by

the

ordinal

to

the

abbreviation

Mouse

chromosome

number

Numbers

of

predicted

genes

in

each

cluster

In

many

cases

this

number

is

probably

an

underestimate

of

the

true

number

of

genes

in

the

cluster

owing

to

mispredictions

or

incomplete

genomic

data

or

else

an

overestimate

owing

to

pseudogenes

included

in

these

totals

Placentation

probable

role

in

development

of

placental

blood

vessels

Placentation

expressed

in

murine

placenta

only

Probable

roles

in

pathogen

response

in

eosinophils

Oxidation

of

compounds

possibly

fatty

acids

is

a

testosterone

hydroxylase

regulated

by

androgens

is

expressed

in

mammary

epithelial

cells

Inhibitors

of

papain-like

cystein

proteinases

Gly

Cys

and

Tyr-rich

proteins

of

unknown

function

ESTs

-LRB-

and

-RRB-

are

derived

from

a

testis

cDNA

library

Some

genes

involved

in

antigen

presentation

but

most

are

not

Very

different

between

human

and

mouse

and

between

mouse

strains

Class

Ia

peptide-binding

region

shows

increased

non-synonymous-to-synonymous

substitution

319 202 224

Nature

Publishing

Group

NATURE

VOL

DECEMBER

articles

see

Supplementary

Information

-RRB-

In

most

cases

-LRB-

-RRB-

the

mousespecific

cluster

corresponds

to

only

a

single

gene

in

the

human

genome

Among

these

clusters

two

major

functional

themes

emerge

contain

genes

involved

in

rodent

reproduction

and

contain

genes

involved

in

host

defence

and

immunity

Each

of

the

reproduction

clusters

contains

at

least

one

gene

whose

expression

is

modulated

by

androgens

is

involved

in

the

biosynthesis

or

metabolism

of

hormones

has

an

established

role

in

the

placenta

gonads

or

spermatozoa

or

has

documented

roles

in

mate

selection

including

pheromone

olfaction

-LRB-

Table

-RRB-

The

fact

that

so

many

of

the

clusters

are

related

to

reproduction

is

unlikely

to

be

coincidental

Many

of

the

most

pronounced

physiological

differences

between

rodents

and

primates

relate

to

reproduction

including

substantial

variations

in

placental

structures

litter

sizes

oestrous

cycles

and

gestation

periods

It

seems

likely

that

reproductive

traits

have

been

responsible

for

some

of

the

most

powerful

evolutionary

pressures

on

the

mouse

genome

and

that

the

demand

for

innovation

has

been

met

through

gene

family

expansions

Examination

of

the

human

genome

in

this

way

may

similarly

reveal

gene

clusters

that

reflect

particular

aspects

of

human

reproduction

Some

of

the

clusters

may

be

related

to

the

principal

differences

between

mice

and

humans

in

placental

structure

Although

both

mouse

and

human

have

discoid

they

differ

in

the

number

and

types

of

cell

layers

between

the

maternal

and

fetal

blood

Of

the

expanded

gene

families

the

cathepsin

cluster

on

chromosome

and

cystatins

on

chromosome

are

expressed

in

the

and

may

affect

its

development

A

non-canonical

homeobox

cluster

on

chromosome

X

includes

Pem

and

Gpbox

-LRB-

-RRB-

which

are

all

expressed

in

the

Another

cluster

is

related

to

a

different

specialized

aspect

of

reproductive

physiology

This

cluster

on

chromosome

contains

seminal

vesicle

secretory

proteins

that

are

rapidly

evolving

androgen-regulated

proteins

involved

in

the

formation

of

the

copulatory

plug

and

influence

the

survival

and

efficacy

of

Other

clusters

are

closely

related

to

hormone

metabolism

and

response

These

include

clusters

of

prolactin-like

genes

on

chromosome

-LRB-

ref

-RRB-

prolactin-inducible

genes

on

chromosome

-LRB-

refs

-RRB-

dehydrogenases

on

chromosome

-LRB-

refs

-RRB-

and

cytochrome

Cypd

genes

on

chromosome

-LRB-

refs

see

Table

-RRB-

Several

of

the

clusters

are

related

to

olfactory

cues

which

have

crucial

roles

in

rodent

reproduction

For

example

the

lipocalin-like

gene

cluster

on

chromosome

X

encodes

proteins

that

are

proposed

to

bind

odorant

molecules

in

the

mucous

layer

overlying

the

receptors

of

the

vomeronasal

The

salivary

androgen-binding

protein

alpha

-LRB-

Abpa

-RRB-

pheromone

gene

lies

within

a

cluster

on

mouse

chromosome

that

contains

numerous

highly

related

genes

and

pseudogenes

Males

apply

Abpa

to

their

pelts

by

licking

and

then

deposit

it

on

their

surroundings

within

their

territory

In

laboratory

behavioural

experiments

female

mice

have

been

shown

to

have

a

mating

preference

for

males

with

a

similar

Abpa

genotype

possibly

to

avoid

intersubspecies

Consequently

Abpa

has

been

proposed

to

have

a

key

role

in

the

sexual

isolation

between

M.

musculus

subspecies

The

hitherto

unknown

Abpa

paralogues

on

chromosome

may

represent

evolutionary

vestiges

of

previously

functioning

Abpa-like

molecules

and/or

additional

functional

Abpa-like

pheromones

Another

notable

cluster

of

probable

pheromone

genes

was

found

on

chromosome

X.

Aphrodisin

is

an

aphrodisiac

pheromone

of

the

female

hamster

Cricetus

cricetus

that

elicits

copulatory

behaviour

from

The

mouse

chromosome

X

cluster

contains

predicted

genes

that

are

highly

sequence-similar

to

aphrodisin

and

might

possess

similar

behavioural

functions

The

five

mouse

clusters

that

encode

genes

involved

in

immunity

suggest

that

another

major

evolutionary

force

is

acting

on

host

defence

genes

The

five

clusters

include

the

major

histocompatNATURE

VOL

DECEMBER

ibility

complex

-LRB-

MHC

-RRB-

class

Ib

genes

two

clusters

of

antimicrobial

b-defensins

a

cluster

of

WAP

domain

antimicrobial

proteins

and

a

cluster

of

type

A

ribonucleases

Ribonuclease

A

genes

appear

to

have

been

under

strong

positive

selection

possibly

due

to

their

significant

role

in

host-defence

The

mouse

genome

also

contains

other

interesting

examples

of

recently

expanded

gene

clusters

involved

in

immunity

which

fall

short

of

our

strict

definition

of

mouse-specific

clusters

because

small

families

consisting

of

a

few

genes

appear

to

have

been

present

in

the

common

ancestor

Examples

include

the

and

gene

families

which

are

greatly

expanded

on

chromosomes

and

The

genes

are

of

particular

interest

because

equivalent

functional

niches

are

occupied

in

humans

and

primates

by

a

different

gene

family

-LRB-

the

non-homologous

KIR

family

of

natural

killer

cell

receptors

-RRB-

an

instance

of

convergent

functional

The

two

major

themes

reproduction

and

immunity

may

not

be

entirely

unrelated

that

is

the

MHC

class

Ib

genes

have

roles

in

both

pregnancy

and

immunity

MHC

genotype

is

also

known

from

ethological

studies

to

influence

mate

selection

although

the

molecular

mechanisms

underlying

this

effect

remain

unknown

Within

the

MHC

complex

the

class

I

genes

are

the

most

divergent

having

arisen

after

the

rodent

human

The

mouse-specific

clusters

have

been

generated

predominantly

by

local

gene

duplication

For

of

genes

in

these

clusters

the

most

similar

homologue

in

the

mouse

genome

can

be

found

either

in

the

same

cluster

or

within

five

genes

from

that

cluster

As

well

as

gene

birth

the

clusters

bear

witness

to

gene

death

the

Abpa

and

cytochrome

and

carboxylesterase

families

all

contain

one

or

more

predicted

pseudogene

Members

of

the

clusters

also

seem

to

be

undergoing

rapid

sequence

evolution

as

measured

by

the

KA

KS

ratio

-LRB-

Fig.

-RRB-

The

relatively

high

values

of

KA

KS

may

reflect

both

positive

selection

-LRB-

as

genes

diverge

to

take

up

new

function

-RRB-

and

the

accumulation

of

mutations

in

moribund

or

dead

genes

Previous

studies

have

documented

rapid

evolution

for

a

number

of

these

clusters

including

eosinophil-associated

MHC

class

class

cytochromes

-LRB-

ref

-RRB-

Abpa

the

Gpbox

homeobox

and

submandibular

gland

secretory

and

proline-rich

Genome

evolution

selection

Investigation

of

the

two

principal

forces

that

shape

the

evolution

of

the

mouse

and

human

genomes

mutation

and

selection

requires

looking

beyond

coarse-scale

identification

of

regions

of

conserved

synteny

and

purely

codon-based

analysis

of

orthologues

to

finescale

alignment

of

the

two

genomes

at

the

nucleotide

level

The

substantial

sequence

divergence

between

the

mouse

and

human

genomes

is

still

low

enough

that

orthologous

sequences

undergoing

neutral

drift

remain

conserved

enough

for

them

to

be

aligned

reliably

The

challenge

then

is

to

use

such

alignments

to

tease

apart

the

effects

of

neutral

drift

which

can

teach

us

about

underlying

mutational

processes

and

selection

which

can

inform

us

about

functionally

important

elements

It

should

be

emphasized

that

sequence

similarity

alone

does

not

imply

functional

constraint

In

this

section

we

use

whole-genome

alignments

to

explore

the

extent

of

sequence

conservation

in

neutral

sites

-LRB-

such

as

ancestral

repeat

sequences

-RRB-

known

functional

elements

-LRB-

such

as

coding

regions

-RRB-

and

the

genome

as

a

whole

By

comparing

these

we

are

able

to

estimate

the

proportion

of

regions

of

the

mammalian

genome

under

evolutionary

selection

-LRB-

about

-RRB-

which

far

exceeds

the

amount

attributable

to

protein-coding

sequences

In

the

next

section

we

then

use

the

neutral

sites

to

study

how

mutational

forces

vary

across

the

genome

Fine-scale

alignment

of

genomes

We

began

by

creating

a

catalogue

of

sequence

alignments

between

the

mouse

and

human

genomes

The

alignments

were

produced

by

Nature

Publishing

Group

articles

the

program

by

comparing

all

non-repeat

sequences

across

the

genome

to

identify

all

high-scoring

matches

-LRB-

see

Supplementary

Information

available

for

download

at

ucsc.edu/downloads.html

-RRB-

then

using

these

as

seeds

we

extended

the

alignments

into

the

surrounding

regions

including

into

repeat

sequences

To

make

the

catalogue

as

comprehensive

as

possible

a

given

region

in

one

genome

was

allowed

to

align

to

multiple

possibly

non-syntenically

conserved

regions

in

the

other

genome

In

fact

only

a

small

proportion

of

the

genome

aligned

to

multiple

regions

-LRB-

about

-RRB-

or

to

non-syntenic

regions

-LRB-

about

-RRB-

the

conclusions

below

are

not

significantly

altered

if

we

restrict

attention

to

sequences

that

match

uniquely

in

syntenic

regions

Within

the

regions

forming

alignments

about

of

individual

human

bases

were

aligned

to

bases

in

mouse

with

the

remainder

aligned

to

indels

-LRB-

insertions

or

deletions

-RRB-

The

alignments

included

approximately

of

known

coding

regions

indicating

that

they

correctly

captured

known

well-conserved

sequence

Regions

that

could

be

aligned

clearly

at

the

nucleotide

level

totalled

about

Gb

corresponding

to

roughly

of

the

human

genome

-LRB-

Fig.

-RRB-

This

proportion

may

seem

high

if

one

imagines

that

all

such

sequence

conservation

reflects

biological

function

but

it

does

not

Simulation

experiments

show

that

DNA

sequences

subjected

to

random

mutation

at

the

neutral

rate

that

has

occurred

between

the

human

and

mouse

genomes

-LRB-

see

below

-RRB-

can

still

be

readily

aligned

by

computer

In

other

words

most

of

the

non-functional

orthologous

sequences

should

still

be

alignable

Consistent

with

this

analysis

the

alignable

portion

of

the

genomes

contains

a

vast

number

of

ancestral

repeats

primarily

relics

of

transposons

that

were

present

in

the

genome

of

our

common

ancestor

with

mouse

and

most

of

which

are

non-functional

But

if

orthologous

sequences

should

be

readily

alignable

the

question

becomes

why

is

n't

the

alignable

portion

much

higher

than

In

fact

the

proportion

is

broadly

consistent

with

what

would

be

expected

given

the

probable

rate

of

turnover

of

sequence

in

the

mouse

and

human

genomes

As

a

starting

point

let

us

assume

that

the

genome

size

of

the

last

common

ancestor

was

about

Gb

-LRB-

similar

to

the

modern

genomes

of

human

and

most

other

mammals

-RRB-

and

let

us

focus

only

on

large-scale

insertions

and

deletions

ignoring

nucleotide-level

indels

within

aligned

regions

and

lineage-specific

duplications

These

assumptions

will

be

relaxed

below

This

would

imply

no

net

change

in

genome

size

in

the

human

lineage

despite

the

accumulation

of

about

Mb

of

lineagespecific

repeat

sequence

since

the

common

ancestor

-LRB-

see

section

on

repeats

-RRB-

This

would

require

approximately

Mb

of

deletions

implying

that

about

-LRB-

out

of

-RRB-

of

the

ancestral

genome

was

deleted

and

about

retained

in

the

human

lineage

It

would

also

imply

a

net

loss

of

about

Mb

in

the

mouse

lineage

despite

the

probable

addition

of

about

Mb

of

lineage-specific

repeat

sequences

an

estimate

about

higher

than

that

given

by

the

Figure

Protein

evolution

a

Cumulative

histogram

of

K

A/K

S

values

for

locally

duplicated

paralogous

mouse-specific

gene

clusters

-LRB-

black

boxes

-RRB-

in

comparison

with

mouse

human

orthologues

-LRB-

red

boxes

-RRB-

The

mouse-specific

paralogues

are

more

likely

to

be

under

positive

diversifying

selection

b

Box

plot

of

K

A/K

S

values

for

different

locally

duplicated

paralogous

mouse-specific

gene

clusters

Full

descriptions

are

found

in

Table

The

chromosome

on

which

the

clusters

are

found

is

indicated

in

brackets

after

the

abbreviated

cluster

name

The

K

A/K

S

values

for

each

sequence

pair

in

the

cluster

was

calculated

from

sequences

aligned

using

ClustalW

-LRB-

see

Supplementary

Information

-RRB-

The

red

horizontal

line

represents

the

median

and

the

box

indicates

the

middle

of

the

data

between

the

and

percentiles

All

of

the

paralogous

clusters

have

median

K

A/K

S

values

that

are

higher

than

the

mouse

human

orthologue

median

K

A/K

S

-LRB-

-RRB-

and

out

of

have

values

greater

than

the

percentile

orthologue

K

A/K

S

-LRB-

-RRB-

The

category

includes

K

A/K

S

values

calculated

separately

over

two

sequencesimilar

regions

in

the

alignment

Figure

Dot

plot

showing

genomic

alignment

between

mouse

and

human

Typically

of

the

human

genome

sequence

aligns

to

mouse

a

b

Approximately

of

a

region

on

human

chromosome

aligns

to

the

orthologous

region

on

mouse

chromosome

-LRB-

a

-RRB-

and

of

a

region

on

human

chromosome

aligns

to

the

orthologous

region

on

mouse

chromosome

-LRB-

b

-RRB-

In

both

cases

the

alignment

skips

over

young/lineage-specific

repeats

-LRB-

red

boxes

-RRB-

but

aligns

through

most

of

the

ancestral

repeats

-LRB-

blue

boxes

-RRB-

and

non-repetitive

sequence

-LRB-

no

colour

-RRB-

The

ancestral

repeats

that

do

align

are

not

unexpectedly

identified

as

the

same

repeat

category

Mouse

also

has

a

larger

number

of

simple-sequence

repeats

-LRB-

green

boxes

-RRB-

NATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

RepeatMasker

program

to

allow

for

incomplete

sensitivity

in

the

more

rapidly

changing

mouse

genome

This

would

imply

roughly

Mb

of

deletions

corresponding

to

the

deletion

of

about

-LRB-

out

of

-RRB-

and

retention

of

of

the

ancestral

genome

If

there

was

no

correlation

in

the

fixation

of

deletions

in

the

two

lineages

the

expected

proportion

of

the

ancestral

genome

retained

in

both

lineages

would

be

about

-LRB-

-RRB-

Complete

independence

is

unlikely

because

deletions

of

functional

sequences

would

have

been

selectively

disadvantageous

However

deletions

of

modest

size

may

largely

be

neutral

given

the

relatively

low

proportion

of

functional

sequence

in

the

genome

The

estimates

can

be

adjusted

-LRB-

see

Supplementary

Information

-RRB-

to

account

for

nucleotide-level

insertions

and

deletions

and

lineagespecific

duplications

-LRB-

the

expectation

remains

roughly

the

same

-RRB-

or

to

allow

for

different

assumptions

about

ancestral

genome

size

-LRB-

the

expectation

increases

by

for

an

intermediate

size

of

about

Gb

-RRB-

This

simple

analysis

suggests

that

the

observed

proportion

of

alignable

genome

-LRB-

about

-RRB-

is

not

surprising

but

rather

it

probably

reflects

the

actual

proportion

of

orthologous

genome

remaining

after

the

deletion

in

the

two

lineages

In

a

preliminary

test

of

this

hypothesis

we

identified

ancestral

repeats

in

the

mouse

that

lay

in

intervals

defined

by

orthologous

landmarks

Examination

of

the

corresponding

interval

in

the

human

genome

showed

a

rate

of

loss

of

these

elements

broadly

consistent

with

the

deletion

rate

in

the

human

lineage

assumed

above

-LRB-

see

Supplementary

Information

-RRB-

Such

a

deletion

rate

in

the

human

lineage

over

about

million

years

is

also

roughly

compatible

with

the

observation

that

roughly

has

been

deleted

over

about

million

years

since

the

divergence

from

baboon

an

estimate

derived

from

the

sequencing

of

specific

regions

in

human

and

baboon

-LRB-

E.

Green

unpublished

data

-RRB-

Although

we

do

not

have

a

corresponding

direct

estimate

of

large-scale

deletions

in

the

mouse

lineage

the

predicted

rate

of

about

is

roughly

twice

as

high

as

for

the

human

lineage

which

is

similar

to

the

ratio

seen

for

nucleotide

substitutions

Rate

of

neutral

substitution

ebrates

such

as

Drosophila

but

the

situation

is

unclear

for

mammals

We

examined

alignments

between

fourfold

degenerate

codons

in

orthologous

genes

The

fourfold

degenerate

codons

were

defined

as

GCX

-LRB-

Ala

-RRB-

CCX

-LRB-

Pro

-RRB-

TCX

-LRB-

Ser

-RRB-

ACX

-LRB-

Thr

-RRB-

CGX

-LRB-

Arg

-RRB-

GGX

-LRB-

Gly

-RRB-

CTX

-LRB-

Leu

-RRB-

and

GTX

-LRB-

Val

-RRB-

Thus

for

Leu

Ser

and

Arg

we

used

four

of

their

six

codons

Only

fourfold

degenerate

codons

in

which

the

first

two

positions

were

identical

in

both

species

were

considered

so

that

the

encoded

amino

acid

was

identical

Slightly

fewer

than

million

such

sites

were

studied

defined

in

the

human

genome

from

about

human

RefSeq

cDNAs

and

aligned

to

their

mouse

orthologues

The

observed

sequence

identity

in

fourfold

degenerate

sites

was

and

the

estimated

number

of

substitutions

per

site

between

and

was

similar

to

that

in

the

ancestral

repeat

sites

-LRB-

see

Supplementary

Information

-RRB-

Conservation

in

gene-related

features

We

used

the

genome-wide

alignments

to

examine

the

extent

of

conservation

in

gene-related

features

including

coding

regions

introns

untranslated

regions

upstream

regions

and

CpG

islands

For

each

type

of

feature

we

characterized

the

nature

of

sequence

conservation

-LRB-

including

typical

percentage

identity

inferred

substitution

rates

and

insertion/deletion

rate

-RRB-

We

also

defined

a

conservation

score

S

that

measures

the

extent

to

which

a

given

window

-LRB-

typically

or

bp

in

applications

below

-RRB-

shows

higher

conservation

than

expected

by

chance

The

conservation

score

S

for

an

aligned

region

R

is

the

normalized

fraction

of

aligned

bases

that

are

identical

-LRB-

obtained

by

subtracting

the

mean

and

dividing

by

the

standard

deviation

-RRB-

and

is

given

by

p

m

S

S

R

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

m

m

n

where

n

is

the

number

of

sites

within

the

window

that

are

aligned

p

is

the

fraction

of

aligned

sites

that

are

identical

in

the

two

genomes

and

m

is

the

average

fraction

of

sites

that

are

identical

in

aligned

ancestral

repeats

in

the

surrounding

region

-LRB-

m

as

a

genomewide

average

but

as

discussed

below

fluctuates

locally

-RRB-

When

the

conservation

score

S

is

calculated

for

the

set

of

all

ancestral

repeats

it

has

a

mean

of

-LRB-

by

definition

-RRB-

and

a

standard

deviation

of

and

for

windows

of

and

bp

respectively

-LRB-

Fig.

-RRB-

This

defines

the

typical

fluctuation

in

conservation

score

in

neutral

sequences

The

properties

of

the

alignments

are

shown

in

Table

and

the

distribution

of

conservation

scores

relative

to

neutral

substitution

is

shown

in

Fig.

Coding

regions

are

distinctive

in

many

ways

They

show

the

highest

degree

of

conservation

-LRB-

sequence

identity

or

substitutions

per

nucleotide

site

-RRB-

Alignment

gaps

are

tenfold

less

common

than

in

non-coding

regions

In

addition

of

coding

regions

have

highly

significant

alignments

to

more

than

one

genomic

region

-LRB-

typically

paralogues

and

pseudogenes

-RRB-

whereas

only

of

the

genome

shows

such

multiple

alignments

Introns

are

very

similar

in

most

respects

to

the

genome

as

a

whole

in

terms

of

percentage

identity

gaps

and

multiple

alignment

statistics

Conservation

levels

in

and

UTRs

are

similar

to

one

another

and

intermediate

between

levels

in

coding

regions

and

introns

The

sequence

identity

of

is

well

above

the

intronic

level

of

Note

that

our

estimate

of

sequence

identity

is

higher

than

the

reported

in

large

part

because

that

study

used

a

global

rather

than

a

local

alignment

programme

The

insertion

and

deletion

characteristics

of

the

UTRs

are

very

similar

to

those

of

introns

Overall

UTRs

are

slightly

better

conserved

than

UTRs

however

significantly

more

of

UTR

sequence

is

covered

by

multiple

alignments

than

UTR

sequence

-LRB-

compared

with

-RRB-

This

may

reflect

the

fact

that

pseudogene

insertion

tends

to

proceed

from

the

end

and

often

terminates

before

completion

Promoter

regions

are

of

considerable

interest

We

analysed

the

regions

located

bp

upstream

of

transcription

start

because

they

The

genome-wide

alignments

can

be

used

to

measure

divergence

rates

for

different

types

of

sequence

The

neutral

substitution

rate

for

example

can

be

estimated

from

the

alignment

of

non-functional

DNA

We

believe

that

the

best

representative

of

this

class

is

ancestral

repeat

sequence

representing

transposable

elements

inserted

and

fixed

before

the

mouse

human

divergence

Such

ancestral

repeats

are

more

likely

than

any

other

sequence

in

the

genome

to

have

been

under

no

functional

constraint

The

human

mouse

alignment

catalogue

contains

approximately

Mb

of

ancestral

repeat

sequences

with

most

being

clearly

orthologous

by

alignment

of

adjacent

non-repetitive

DNA

These

alignments

show

sequence

identity

The

observed

base

changes

can

be

used

to

infer

the

underlying

substitution

rate

which

includes

back

mutations

by

using

various

continuous-time

Markov

Applying

the

REV

to

the

ancestral

repeat

sites

we

estimate

that

neutral

divergence

has

led

to

between

and

substitutions

per

site

-LRB-

see

Supplementary

Information

-RRB-

Similar

results

are

obtained

for

any

of

the

other

published

continuous-time

Markov

models

that

distinguish

between

transitions

and

transversions

-LRB-

D.

Haussler

unpublished

data

-RRB-

Although

the

model

does

not

assign

substitutions

separately

to

the

mouse

and

human

lineages

as

discussed

above

in

the

repeat

section

the

roughly

twofold

higher

mutation

rate

in

mouse

-LRB-

see

above

-RRB-

implies

that

the

substitutions

distribute

as

per

site

-LRB-

about

per

year

-RRB-

in

the

mouse

lineage

and

-LRB-

about

per

year

-RRB-

in

the

human

lineage

Having

established

the

neutral

substitution

rate

by

examining

aligned

ancestral

repeats

we

then

investigated

a

second

class

of

potentially

neutral

sites

fourfold

degenerate

sites

in

codons

of

genes

Fourfold

degenerate

sites

are

subject

to

selection

in

invertNATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

Figure

Distribution

of

the

conservation

score

S

-LRB-

R

-RRB-

The

empirical

distribution

of

S

-LRB-

R

-RRB-

for

all

million

non-overlapping

windows

-LRB-

blue

-RRB-

containing

at

least

aligned

ancestral

repeat

sites

-LRB-

standard

deviation

-RRB-

and

million

non-overlapping

windows

-LRB-

green

-RRB-

containing

at

least

aligned

ancestral

repeat

sites

-LRB-

standard

deviation

-RRB-

Both

curves

are

bell-shaped

with

a

mean

of

zero

but

the

standard

deviations

are

higher

than

would

be

expected

if

the

sites

in

each

window

were

independent

and

conserved

with

-LRB-

locally

estimated

-RRB-

probability

m

In

that

case

the

distribution

of

S

would

be

approximately

normal

with

a

standard

deviation

of

Thus

these

data

show

that

there

is

some

dependency

between

the

substitutions

within

the

window

were

likely

to

contain

important

promoter

and

regulatory

signals

However

such

analysis

is

necessarily

limited

by

the

fact

that

transcriptional

start

sites

remain

poorly

defined

for

many

genes

With

this

caveat

the

upstream

regions

share

many

characteristics

of

UTRs

but

have

a

lower

percentage

identity

a

significantly

lower

proportion

covered

by

multiple

alignments

and

a

higher

-LRB-

G

C

-RRB-

content

CpG

islands

show

a

conservation

level

similar

to

those

of

promoter

and

UTR

regions

-LRB-

Fig.

-RRB-

We

also

observed

that

levels

of

conservation

were

not

uniform

across

these

features

-LRB-

coding

regions

introns

UTRs

upstream

regions

and

CpG

islands

-RRB-

Figure

shows

how

conservation

levels

vary

regionally

within

the

features

of

a

typical

gene

Sequence

identity

rises

gradually

from

a

background

level

to

near

the

approximate

transcription

start

site

where

the

level

reaches

a

plateau

It

is

possible

that

sharper

definitions

of

transcriptional

start

sites

would

allow

the

footprint

of

the

TATA

box

and

other

common

structures

near

the

transcription

start

site

to

emerge

Conversely

many

human

promoters

lack

a

TATA

box

and

transcription

start

at

such

promoters

is

not

typically

sharply

Sequence

identity

falls

slowly

across

the

UTR

and

then

starts

to

rise

again

near

the

Figure

Comparison

of

histograms

for

conservation

scores

for

windows

in

ancient

transposons

-LRB-

red

-RRB-

with

windows

in

other

kinds

of

regions

-LRB-

blue

and

green

-RRB-

We

required

that

at

least

bp

be

aligned

in

each

window

a

d

Comparisons

with

coding

exons

-LRB-

blue

-RRB-

and

introns

-LRB-

green

-RRB-

-LRB-

a

-RRB-

UTR

-LRB-

blue

-RRB-

and

UTR

-LRB-

green

-RRB-

-LRB-

b

-RRB-

upstream

of

transcription

start

-LRB-

blue

-RRB-

and

bp

downstream

of

transcription

end

-LRB-

green

-RRB-

-LRB-

c

-RRB-

and

CpG

islands

-LRB-

blue

-RRB-

and

known

regulatory

regions

-LRB-

green

-RRB-

-LRB-

d

-RRB-

are

shown

The

RefSeq

database

was

used

to

define

gene

features

CpG

islands

were

determined

as

discussed

in

the

text

and

known

regulatory

regions

were

collected

as

discussed

in

the

text

start

codon

As

expected

conservation

levels

rise

sharply

at

the

translation

start

remain

high

throughout

the

coding

regions

and

have

sharp

peaks

at

splice

sites

After

the

stop

codon

the

per

cent

identity

is

relatively

low

for

most

of

the

UTR

but

then

begins

to

increase

about

bases

before

the

polyadenylation

site

The

Table

Alignment

statistics

for

various

known

features

in

human

Feature

Coding

-LRB-

-RRB-

UTR

-LRB-

-RRB-

UTR

-LRB-

-RRB-

Upstream

bp

-LRB-

-RRB-

Downstream

bp

-LRB-

-RRB-

Intron

-LRB-

-RRB-

Known

regulatory

regions

-LRB-

-RRB-

Ancient

repeats

-LRB-

-RRB-

Genome

-LRB-

-RRB-

The

coding

intron

and

UTR

regions

are

defined

by

alignments

of

human

mRNA

from

the

RefSeq

database

against

the

genome

Upstream

bp

and

downstream

bp

indicate

the

regions

upstream

and

downstream

of

these

alignments

From

the

collection

of

the

known

regulatory

regions

described

in

the

text

The

ancient

repeats

are

a

collection

of

million

transposon

relics

that

predate

the

mouse

human

split

as

discussed

in

the

text

The

figures

for

the

genome

as

a

whole

The

percentage

of

aligned

bases

in

these

regions

that

are

identical

kThe

number

of

gap

initiations

in

the

human

and

mouse

sequences

respectively

as

a

percentage

of

the

human

bases

in

the

alignments

-LCB-

The

number

of

gap

extensions

as

a

percentage

of

the

human

bases

in

the

alignments

The

percentage

of

human

bases

covered

by

at

least

and

significant

alignments

respectively

These

numbers

are

taken

before

the

last

step

in

the

construction

of

the

alignment

when

all

but

the

best

alignments

for

each

human

region

are

discarded

qThe

percentage

of

-LRB-

G

C

-RRB-

in

the

human

sequence

Identity

-LRB-

-RRB-

Gap

initiationsk

Human

Mouse

Gap

extensions

-LCB-

Human

Mouse

Alignment

-LRB-

G

C

-RRB-

-LRB-

-RRB-

q

Nature

Publishing

Group

NATURE

VOL

DECEMBER

articles

main

polyadenylation

signal

is

AATAAA

or

ATTAAA

positioned

bases

upstream

of

The

region

of

increased

conservation

is

considerably

longer

than

can

be

explained

by

the

polyadenylation

signal

alone

suggesting

that

other

UTR

regulatory

signals

such

as

those

that

affect

mRNA

stability

and

localization

may

frequently

occur

near

the

end

of

the

mRNA

After

the

polyadenylation

site

there

is

a

plateau

of

moderate

conservation

corresponding

to

the

weaker

-LRB-

T

-RRB-

rich

or

-LRB-

G

T

-RRB-

rich

downstream

region

following

the

polyadenylation

signal

Conservation

of

gene

structure

We

also

examined

the

conservation

of

exon

structure

and

splice

signals

in

more

detail

using

pairs

of

human

mouse

RefSeq

genes

confidently

assigned

to

be

orthologous

-LRB-

http

-RRB-

As

previously

reported

using

smaller

data

overall

gene

structures

are

highly

conserved

between

orthologous

pairs

of

the

cases

-LRB-

out

of

-RRB-

have

the

identical

number

of

coding

exons

and

-LRB-

out

of

-RRB-

have

the

identical

coding

sequence

length

When

we

consider

all

exons

rather

than

just

coding

exons

we

find

that

pairs

-LRB-

-RRB-

have

the

same

number

of

exons

The

true

concordance

of

gene

structure

between

the

two

species

is

probably

higher

because

differences

will

be

exaggerated

by

differential

representation

of

alternative

splice

forms

between

the

two

data

sets

difficulties

in

mapping

the

cDNA

sequences

back

to

the

genome

and

the

absence

of

true

and

ends

The

set

of

genes

with

an

identical

number

of

coding

exons

contains

pairs

of

orthologous

exons

-LRB-

plus

intronless

genes

-RRB-

Exon

length

between

orthologous

exons

is

highly

conserved

-LRB-

-RRB-

of

these

human

mouse

exon

pairs

have

identical

exon

length

When

exon

pairs

do

have

different

lengths

the

differences

are

predominantly

multiples

of

three

-LRB-

out

of

the

with

different

lengths

-RRB-

as

expected

from

coding-frame

constraints

Nearly

all

orthologous

exons

conserve

phase

-LRB-

or

-RRB-

In

contrast

only

out

of

orthologous

introns

-LRB-

-RRB-

have

identical

length

although

there

is

strong

correlation

between

the

lengths

of

orthologous

introns

Consistent

with

the

smaller

size

of

the

mouse

genome

overall

orthologous

mouse

introns

tend

to

be

shorter

Excluding

outliers

the

average

human

intron

in

this

data

set

is

bp

whereas

the

average

mouse

intron

is

bp

Within

the

set

of

orthologous

human

mouse

gene

pairs

there

are

cases

in

which

the

overall

coding

length

is

identical

between

the

gene

pairs

but

they

differ

in

the

number

of

exons

Most

of

these

cases

can

be

explained

by

a

single

intron

insertion/deletion

-LRB-

Fig.

-RRB-

demonstrating

the

dynamic

-LRB-

but

slow

-RRB-

evolution

of

gene

structure

We

also

found

several

non-canonical

splice

sites

in

the

set

of

orthologous

introns

including

RTATCCTY

splice

signals

characteristic

of

introns

which

are

singularly

conserved

-LRB-

see

ref

for

review

-RRB-

We

found

this

splice

signal

in

human

and

mouse

introns

from

the

set

of

and

of

these

cases

correspond

to

orthologous

introns

indicating

high

levels

of

conservation

of

this

distinct

splicing

mechanism

Also

conserved

are

the

non-canonical

GC-AG

introns

-LRB-

mechanistically

identical

to

the

GT-AG

canonical

introns

-RRB-

in

the

set

there

are

non-canonical

GC-AG

introns

in

human

and

in

mouse

including

orthologous

pairs

Conservation

in

known

regulatory

regions

We

similarly

sought

to

study

the

extent

of

conservation

in

regulatory

control

regions

of

So

far

relatively

few

regulatory

elements

have

been

studied

extensively

We

compiled

a

list

of

well-characterized

regulatory

regions

including

some

and

general

regulatory

The

sequences

were

carefully

checked

against

the

primary

publications

and

trimmed

to

contain

the

smallest

reported

functional

unit

The

distribution

of

the

elements

was

in

introns

in

the

immediate

vicinity

-LRB-

kb

-RRB-

of

promoters

and

more

distal

from

promoters

About

overlapped

a

CpG

island

Figure

Variation

in

conservation

across

a

gene

a

Conservation

across

a

generic

gene

on

the

basis

of

human

RefSeq

mRNAs

with

known

position

in

the

genome

We

sampled

evenly

spaced

bases

across

each

of

the

variable-length

regions

labelled

resampling

completely

from

regions

shorter

than

bp

The

graph

shows

the

average

percentage

of

bases

aligning

and

the

average

base

identity

when

there

is

an

alignment

over

each

sample

There

are

peaks

of

conservation

at

the

transition

from

one

region

to

another

Here

in

contrast

to

Table

only

reviewed

RefSeq

mRNAs

were

used

and

only

those

having

at

least

bases

of

annotated

and

UTRs

The

resulting

picture

however

is

nearly

indistinguishable

from

that

obtained

by

using

all

RefSeq

genes

with

at

least

base

UTRs

b

Conservation

near

translation

start

site

using

the

same

data

set

as

in

a

The

bars

show

per

cent

identity

of

the

bases

to

either

side

of

translation

start

Note

NATURE

VOL

DECEMBER

the

extreme

conservation

of

the

first

codon

After

this

there

is

substantially

less

conservation

at

the

third

codon

position

The

peak

at

position

corresponds

to

a

purine

in

the

Kozak

consensus

sequence

c

Conservation

near

the

splice

site

The

peak

of

conservation

corresponds

to

the

AG/GT

consensus

at

this

location

with

the

first

G

in

the

intron

being

nearly

invariant

A

G

in

the

fifth

base

of

the

intron

is

also

found

in

a

large

majority

of

splice

sites

An

echo

of

the

variation

in

the

third

codon

position

occurs

here

because

it

is

common

for

exons

to

begin

and

end

at

codon

boundaries

d

Conservation

near

the

splice

site

Conservation

in

the

last

two

bases

of

the

intron

always

AG

for

introns

processed

by

the

major

spliceosome

is

very

apparent

The

polypyrimidine

tract

beginning

five

bases

into

the

intron

is

also

visibly

conserved

Once

again

an

echo

of

the

variation

in

the

third

codon

position

can

be

seen

Nature

Publishing

Group

articles

S

genome

of

conservation

scores

for

the

genome

to

the

neutral

distribution

S

neutral

of

conservation

scores

for

ancestral

repeats

-LRB-

Fig.

blue

curve

-RRB-

using

a

genome-wide

set

of

million

non-overlapping

-LRB-

human

-RRB-

windows

each

containing

at

least

bp

-LRB-

mean

bp

-RRB-

of

aligned

sequence

The

genomewide

score

distribution

for

these

windows

has

a

prominent

tail

extending

to

the

right

reflecting

a

substantial

excess

of

windows

with

high

conservation

scores

relative

to

the

neutral

rate

-LRB-

Fig.

-RRB-

The

excess

can

be

estimated

by

decomposing

the

genome-wide

distribution

S

genome

as

a

mixture

of

two

components

S

neutral

and

S

selected

-LRB-

reflecting

windows

under

selection

-RRB-

The

mixture

coefficients

indicate

that

at

least

of

the

windows

are

under

selection

with

the

remainder

consistent

with

neutral

substitution

Because

about

of

all

human

bases

are

contained

in

the

windows

this

suggests

that

at

least

-LRB-

of

-RRB-

of

the

windows

in

the

human

genome

is

under

selection

Repeating

the

analysis

on

more

stringently

filtered

alignments

-LRB-

with

non-syntenic

and

non-reciprocal

best

matches

removed

-RRB-

requiring

different

numbers

of

aligned

bases

per

window

and

with

windows

yields

similar

estimates

ranging

mostly

from

to

about

of

windows

under

selection

-LRB-

D.

Haussler

unpublished

data

-RRB-

as

does

using

an

alternative

score

function

that

considers

flanking

base

context

effects

and

uses

a

gap

Significantly

smaller

window

sizes

for

example

bp

do

not

provide

sufficient

statistical

separation

between

the

neutral

and

genome-wide

score

distributions

to

provide

useful

estimates

of

the

share

under

selection

The

analysis

thus

suggests

that

about

of

small

segments

-LRB-

bp

-RRB-

in

the

human

genome

are

under

evolutionary

selection

for

biological

functions

common

to

human

and

mouse

This

corresponds

to

regions

totalling

about

Mb

of

human

genomic

DNA

although

not

all

of

the

nucleotides

in

these

windows

are

under

selection

In

addition

some

bases

outside

these

windows

are

likely

to

be

under

selection

In

a

loose

sense

these

regions

might

be

regarded

as

containing

the

functional

conserved

subset

of

the

mammalian

genome

Of

course

it

should

be

noted

that

nonconserved

sequence

may

have

important

roles

for

example

as

a

passive

spacer

or

providing

a

function

specific

to

one

lineage

Notably

protein-coding

regions

of

genes

can

account

for

only

a

fraction

of

the

genome

under

selection

From

our

analysis

of

the

Figure

The

human

spermidine

synthase

gene

-LRB-

SRM

-RRB-

on

chromosome

involved

in

the

biosynthesis

of

polyamines

and

its

mouse

orthologue

-LRB-

Srm

-RRB-

on

chromosome

The

fifth

exon

in

the

mouse

gene

-LRB-

green

-RRB-

is

interrupted

by

an

intron

in

the

human

homologue

All

other

exons

are

purple

The

extent

of

conservation

-LRB-

Fig.

and

Table

-RRB-

was

considerably

lower

than

in

coding

regions

but

much

higher

than

the

neutral

rate

in

ancestral

repeats

or

than

the

average

rate

across

the

genome

Overall

the

known

regulatory

regions

showed

a

level

of

conservation

similar

to

that

of

UTRs

The

-LRB-

G

C

-RRB-

content

is

also

substantially

higher

for

the

regulatory

elements

than

for

the

genome

as

a

whole

a

property

shared

with

exons

and

UTRs

Although

the

extent

of

conservation

in

regulatory

regions

as

measured

by

the

score

S

-LRB-

R

-RRB-

overlaps

with

that

in

neutral

DNA

-LRB-

Fig.

-RRB-

this

does

not

preclude

the

use

of

this

measure

to

identify

candidate

regulatory

elements

An

example

is

given

by

the

insulinlike

growth

factor

binding

protein

acid-labile

subunit

gene

-LRB-

IGFALS

-RRB-

where

the

region

surrounding

a

well-known

transcription

factor

binding

stands

out

as

unusually

conserved

using

this

measure

-LRB-

Fig.

-RRB-

More

sophisticated

models

such

as

Markov

models

on

the

fine

texture

of

the

alignments

-LRB-

matches

transitions

transversions

and

gaps

-RRB-

may

discriminate

regulatory

regions

under

selection

from

neutrally

evolving

regions

with

better

Proportion

of

genome

under

selection

We

then

set

out

to

investigate

the

fraction

of

a

mammalian

genome

under

evolutionary

selection

for

biological

function

To

do

this

we

estimated

the

proportion

of

the

genome

that

is

better

conserved

than

would

be

expected

given

the

underlying

neutral

rate

of

substitution

We

compared

the

overall

distribution

Figure

Conservation

scores

for

windows

in

a

region

containing

the

human

insulin-like

growth

factor

binding

protein

acid

labile

subunit

-LRB-

IGFALS

-RRB-

gene

In

the

track

near

the

top

of

figure

the

two

coding

exons

of

the

gene

are

displayed

as

taller

blue

rectangles

UTRs

as

shorter

rectangles

and

the

intron

which

separates

the

coding

exons

is

shown

as

a

barbed

line

indicating

direction

of

transcription

-LRB-

the

gene

is

on

the

reverse

strand

-RRB-

Log

probability

scores

-LRB-

L-scores

-RRB-

for

all

windows

are

shown

below

the

gene

The

L-score

is

-LRB-

p

-RRB-

where

p

is

the

probability

under

the

neutral

density

S

neutral

of

getting

a

conservation

score

as

high

as

is

observed

in

the

window

Many

windows

in

the

coding

region

get

L-scores

greater

than

indicating

less

than

a

chance

of

occurring

under

neutral

evolution

-LRB-

P

selected

-LRB-

S

-RRB-

see

Fig.

-RRB-

and

some

in

a

local

peak

in

the

upstream

region

of

the

gene

on

the

right

show

L-scores

greater

than

indicating

less

than

a

chance

of

occurring

-LRB-

P

selected

-LRB-

S

-RRB-

-RRB-

The

red

bar

shows

the

location

of

the

interferon-g-activated

sequence-like

element

-LRB-

GLE

-RRB-

which

is

bound

by

transcription

factors

from

the

and

protein

family

to

control

expression

of

this

Additional

regulatory

elements

may

be

located

in

the

other

peaks

of

conservation

This

figure

is

taken

with

permission

from

the

UCSC

browser

-LRB-

-RRB-

Nature

Publishing

Group

NATURE

VOL

DECEMBER

articles

number

and

properties

of

genes

coding

regions

comprise

only

about

of

the

human

genome

and

account

for

less

than

half

of

the

segments

under

selection

What

accounts

for

the

remainder

of

the

genome

under

selection

About

of

the

genome

is

contained

in

untranslated

regions

of

protein-coding

genes

and

some

of

this

sequence

is

under

some

functional

constraint

Another

main

class

of

interest

are

those

sequences

that

control

gene

expression

such

as

the

control

element

for

the

IGFALS

gene

shown

in

Fig.

if

a

typical

gene

contains

a

few

such

regulatory

sequences

there

may

be

tens

to

hundreds

of

thousands

of

such

elements

In

addition

conserved

sequences

probably

encode

non-protein-coding

RNAs

-LRB-

which

remain

difficult

to

discern

-RRB-

and

chromosomal

structural

elements

Furthermore

some

of

the

conserved

fraction

may

correspond

to

sequences

that

were

under

selection

for

some

period

of

time

but

are

no

longer

functional

these

could

include

recent

pseudogenes

In

an

accompanying

paper

Dermitzakis

and

colleagues

show

that

a

large

number

of

conserved

sequences

on

human

chromosome

are

actively

conserved

but

are

unlikely

to

be

genes

suggesting

that

a

large

number

of

non-coding

sequence

are

under

Characterization

of

the

conserved

sequences

should

be

a

high

priority

for

genomics

in

the

years

ahead

The

analysis

above

allows

us

to

infer

the

proportion

of

the

genome

under

selection

by

decomposing

the

curve

S

genome

into

curves

S

neutral

and

S

selected

Importantly

it

does

not

definitively

assign

an

individual

conserved

sequence

as

being

neutral

or

selected

One

can

calculate

for

a

sequence

with

conservation

score

S

the

probability

P

selected

-LRB-

S

-RRB-

that

the

window

of

sequence

belongs

to

the

selected

subset

-LRB-

Fig.

-RRB-

The

probability

exceeds

for

sequences

with

S

and

for

S

but

is

only

for

S

In

other

words

some

functionally

important

sequence

can

not

be

separated

cleanly

from

the

tail

of

the

distribution

of

neutral

conservation

How

can

we

cleanly

separate

neutral

and

selected

sequences

One

solution

is

to

extend

the

analysis

from

two

species

to

multiple

species

from

different

branches

of

the

mammalian

radiation

Neutral

sequences

will

tend

to

drift

in

different

ways

along

each

lineage

whereas

selected

sequences

will

tend

to

preserve

specific

sites

Multiple

species

comparisons

should

thus

sharpen

and

separate

the

distributions

of

conservation

scores

S

neutral

and

S

selected

Genome

evolution

mutation

Genome-wide

alignments

also

allow

us

to

investigate

how

the

patterns

of

neutral

substitution

deletion

and

insertion

vary

across

the

genome

providing

an

insight

on

the

underlying

mutational

processes

Substitution

rate

varies

across

the

genome

Figure

Proportion

of

the

human

genome

under

selection

and

the

probability

of

a

genomic

window

to

be

under

selection

on

the

basis

of

conservation

score

a

The

genome-wide

density

of

conservation

scores

S

genome

-LRB-

dark

blue

-RRB-

was

decomposed

into

a

mixture

of

two

component

densities

S

neutral

-LRB-

red

-RRB-

and

S

selected

-LRB-

light

blue

and

grey

-RRB-

S

genome

is

derived

from

the

conservation

scores

S

-LRB-

R

-RRB-

for

all

windows

of

bp

in

the

human

genome

with

at

least

bases

aligning

to

mouse

S

neutral

is

a

scaled

version

of

the

S

neutral

density

from

the

blue

curve

in

Fig.

for

the

windows

in

ancestral

repeats

representing

neutrally

evolving

DNA

S

selected

is

the

difference

between

the

blue

density

and

the

red

component

and

thus

represents

a

scaled

version

of

S

selected

the

predicted

density

for

conservation

scores

of

windows

in

the

human

genome

that

are

evolving

under

selection

The

scaling

factors

are

the

estimated

mixture

coefficients

which

are

p

for

S

neutral

and

p

for

S

selected

The

coefficient

p

is

calculated

as

the

minimum

of

the

ratio

between

S

genome

-LRB-

S

-RRB-

and

S

neutral

-LRB-

S

-RRB-

for

all

values

of

S

giving

a

conservative

estimate

that

maximizes

the

share

of

the

mixture

attributed

to

S

neutral

b

The

probability

P

selected

-LRB-

S

-RRB-

that

a

window

is

under

selection

as

a

function

of

its

conservation

score

S

S

-LRB-

R

-RRB-

This

function

is

derived

from

the

mixture

decomposition

by

setting

P

selected

S

p

S

neutral

S

S

genome

S

NATURE

VOL

DECEMBER

Significant

variation

in

the

level

of

sequence

conservation

has

been

reported

in

several

small-scale

studies

of

human

and

mouse

genomic

and

in

several

larger-scale

studies

of

coding

It

has

not

been

clear

in

all

cases

whether

the

variation

reflects

differences

in

neutral

substitution

rates

or

in

selection

The

human

mouse

genome

alignments

allow

us

to

address

the

variation

more

comprehensively

and

to

test

for

covariation

with

the

rates

of

other

processes

such

as

insertions

of

transposable

and

meiotic

We

used

the

collection

of

aligned

ancestral

repeats

and

aligned

fourfold

degenerate

sites

to

calculate

the

apparent

neutral

substitution

rate

for

about

overlapping

windows

across

the

human

genome

To

accurately

follow

fluctuations

while

accounting

for

regional

changes

in

base

composition

the

regional

nucleotide

substitution

rate

in

ancestral

repeat

sites

t

AR

was

calculated

separately

for

each

window

by

maximum

likelihood

estimation

of

the

parameters

of

the

REV

model

using

only

the

ancestral

repeat

sites

in

the

window

-LRB-

average

of

about

sites

per

window

-RRB-

The

regional

nucleotide

substitution

rate

in

fourfold

degenerate

sites

t

was

calculated

similarly

from

an

average

of

about

fourfold

degenerate

sites

per

window

Windows

with

fewer

than

ancestral

repeats

or

fourfold

degenerate

sites

were

discarded

The

mean

and

standard

deviations

across

the

windows

were

t

AR

and

t

substitutions

per

site

The

standard

deviation

is

much

larger

-LRB-

over

tenfold

and

threefold

respectively

-RRB-

than

would

be

expected

from

sampling

variance

These

data

clearly

indicate

substantial

regional

fluctuation

Regional

variation

is

also

evident

in

comparing

the

average

rates

on

different

chromosomes

-LRB-

Fig.

-RRB-

Notably

the

neutral

substitution

rate

is

lowest

for

chromosome

X

This

observation

is

consistent

with

recent

reports

including

our

initial

analysis

of

the

human

that

the

mutation

rate

is

about

twofold

lower

in

female

meiosis

than

male

meiosis

Because

the

proportion

of

time

spent

in

the

female

germ

line

for

chromosome

X

is

and

for

autosomes

is

the

predicted

substitution

rate

for

chromosome

X

should

be

about

or

of

the

genome-wide

average

In

fact

the

observed

ratio

is

for

fourfold

degenerate

sites

and

for

ancestral

repeat

sites

This

would

be

consistent

with

-LRB-

but

does

not

prove

-RRB-

a

roughly

twofold

lower

mutation

rate

in

the

female

germ

line

during

the

history

of

both

the

human

and

mouse

lineages

and

it

explains

a

small

amount

of

the

variation

in

the

genome-wide

substitution

rate

Nonetheless

the

variability

among

autosomes

is

still

much

greater

than

could

occur

under

a

uniform

substitution

process

suggesting

the

existence

of

long-range

factors

that

affect

the

mutation

rate

Looking

at

a

finer

scale

the

two

measures

t

AR

and

t

are

strongly

Nature

Publishing

Group

articles

correlated

across

the

genome

-LRB-

Fig.

-RRB-

They

often

exhibit

similar

behaviour

across

a

human

chromosome

as

seen

for

human

chromosome

-LRB-

Fig.

-RRB-

What

properties

of

chromosomal

DNA

could

account

for

the

variation

in

substitution

rate

One

possible

explanation

is

local

-LRB-

G

C

-RRB-

content

but

previous

studies

disagree

on

whether

it

correlates

strongly

with

We

find

that

t

AR

and

t

vary

with

local

-LRB-

G

C

-RRB-

content

although

the

dependence

is

and

is

better

fitted

by

regression

with

a

quadratic

-LRB-

Fig.

-RRB-

In

other

words

the

substitution

rate

seems

to

be

higher

in

regions

of

extremely

high

or

low

-LRB-

G

C

-RRB-

content

with

the

sign

of

the

correlation

differing

in

regions

with

high

versus

low

-LRB-

G

C

-RRB-

content

This

pattern

persists

if

CpG

substitutions

are

removed

from

the

analysis

-LRB-

data

not

shown

-RRB-

Notably

t

AR

and

t

show

different

dependence

on

local

-LRB-

G

C

-RRB-

content

In

particular

t

increases

more

sharply

with

high

-LRB-

G

C

-RRB-

content

whereas

t

AR

does

not

show

as

much

divergence

In

this

and

some

other

properties

t

AR

and

t

show

differing

patterns

hence

they

are

not

equivalent

neutral

sites

Differences

in

the

nature

of

the

dependence

on

local

-LRB-

G

C

-RRB-

content

imply

that

the

-LRB-

G

C

-RRB-

content

is

a

confounding

variable

in

comparing

t

AR

and

t

Accordingly

we

normalized

the

rates

for

local

-LRB-

G

C

-RRB-

content

by

calculating

the

residuals

t

R

and

t

D

with

respect

to

the

quadratic

regressions

A

above

The

correspondence

along

chromosome

-LRB-

a

particularly

-LRB-

G

C

-RRB-

rich

chromosome

-RRB-

is

markedly

enhanced

-LRB-

r

increases

from

to

-RRB-

by

this

correction

-LRB-

Fig.

-RRB-

as

is

the

overall

genomewide

correlation

-LRB-

r

increases

from

to

-RRB-

Substitution

rate

co-varies

with

other

evolutionary

rates

In

addition

to

nucleotide

substitutions

genomes

evolve

by

insertion

-LRB-

primarily

of

transposable

elements

-RRB-

and

deletion

We

examined

the

rate

of

deletion

in

the

mouse

genome

as

measured

by

the

fraction

of

non-aligning

ancestral

human

DNA

-LRB-

anc

-RRB-

Although

some

of

the

non-alignable

sequence

may

represent

lineage-specific

insertions

not

detected

by

RepeatMasker

-LRB-

cgi-bin/RepeatMasker

-RRB-

or

failure

to

align

some

orthologous

sequences

the

great

bulk

probably

represents

deletions

in

the

mouse

genome

The

fraction

anc

varies

markedly

across

overlapping

windows

of

Mb

with

a

range

from

to

and

mean

and

standard

deviation

We

also

examined

the

rate

of

insertion

-LRB-

and

retention

-RRB-

in

the

human

genome

since

its

divergence

from

mouse

as

measured

by

the

proportion

of

lineage-specific

repeats

in

overlapping

windows

across

the

human

genome

The

overall

level

of

insertion

and

retention

showed

substantial

variation

across

the

genome

ranging

from

to

with

a

mean

of

To

avoid

complications

from

the

tendency

of

some

repeats

such

as

Alus

to

be

selectively

removed

from

some

regions

of

the

we

used

one

family

of

repeats

the

LTRs

to

monitor

the

relative

frequency

of

insertion

and

retention

Similar

to

repeats

as

a

whole

the

fraction

of

Figure

Estimated

average

number

of

substitutions

per

site

in

ancestral

repeat

sites

-LRB-

t

AR

-RRB-

-LRB-

red

-RRB-

and

in

fourfold

degenerate

-LRB-

-RRB-

sites

-LRB-

t

-RRB-

-LRB-

blue

-RRB-

for

each

human

chromosome

a

Estimates

are

made

from

the

REV

model

using

all

aligned

sites

of

the

given

type

in

the

chromosome

Dashed

lines

show

the

genome-wide

averages

Human

chromosome

is

a

conspicuous

outlier

for

its

very

large

number

of

substitutions

in

fourfold

degenerate

sites

-LRB-

also

noted

in

ref

-RRB-

notably

its

substitution

rate

in

ancestral

repeat

sites

is

normal

Chromosome

X

shows

lower

rates

of

substitution

in

both

types

of

sites

consistent

with

the

observation

that

the

male

mutation

rate

is

approximately

twice

the

female

-LRB-

see

text

-RRB-

Variability

in

neutral

rates

among

autosomes

is

significant

as

noted

in

ref

b

Scatter

plot

of

t

AR

against

t

for

windows

in

the

human

genome

with

at

least

aligning

sites

The

red

line

is

the

linear

regression

line

-LRB-

r

P

-RRB-

Figure

Variation

in

features

along

human

chromosome

a

Variation

in

t

AR

-LRB-

red

-RRB-

and

-LRB-

blue

-RRB-

in

windows

overlapping

by

along

human

chromosome

Only

windows

with

at

least

aligned

fourfold

degenerate

sites

and

aligned

ancestral

repeat

sites

are

shown

The

position

of

the

window

is

plotted

at

the

midpoint

Horizontal

dotted

lines

indicate

the

genome-wide

estimates

of

tAR

and

Confidence

intervals

were

computed

on

the

basis

of

the

number

of

ancestral

repeat

and

fourfold

degenerate

sites

aligning

in

each

window

points

where

the

confidence

interval

does

not

overlap

the

genome-wide

estimate

indicate

windows

with

significant

differences

in

evolutionary

rate

b

Similar

to

a

but

with

t

AR

and

t

the

normalized

rates

obtained

taking

residuals

of

tAR

and

from

the

quadratic

functions

of

-LRB-

G

C

-RRB-

content

shown

in

Fig.

c

Fraction

of

DNA

-LRB-

blue

-RRB-

that

is

not

in

lineage-specific

repeats

identified

by

RepeatMasker

and

does

not

align

to

mouse

NAanc

and

the

fraction

of

DNA

-LRB-

green

-RRB-

contained

in

human

lineage-specific

LTR

repeats

identified

by

RepeatMasker

along

with

t

AR

-LRB-

red

-RRB-

calculated

in

overlapping

windows

as

in

b.

d

SNP

density

-LRB-

blue

-RRB-

in

each

overlapping

window

-LRB-

average

number

of

SNPs

per

kb

-RRB-

calculated

using

SNPs

from

random

reads

-LRB-

The

SNP

Consortium

website

data

were

collected

in

July

-RRB-

The

average

recombination

rate

-LRB-

black

-RRB-

in

each

window

in

cM

per

Mb

estimated

from

the

deCode

genetic

is

shown

as

well

as

t

AR

-LRB-

red

-RRB-

calculated

in

overlapping

windows

as

in

b.

NATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

each

window

occupied

by

lineage-specific

LTRs

varies

substantially

across

the

human

genome

ranging

from

to

with

a

mean

of

All

three

forces

that

alter

the

genome

-LRB-

nucleotide

substitution

deletion

and

insertion

-RRB-

thus

vary

substantially

across

the

genome

Moreover

they

are

significantly

correlated

and

tend

to

co-vary

along

chromosomes

-LRB-

Fig.

and

Table

-RRB-

Notably

these

three

measures

of

interspecies

divergence

are

also

correlated

with

recent

substitutions

in

the

human

genome

as

measured

by

the

density

of

SNPs

identified

by

the

SNP

-LRB-

Fig.

-RRB-

Furthermore

recent

studies

report

that

divergence

at

fourfold

degenerate

sites

and

SNP

frequency

are

both

correlated

with

the

local

rate

of

meiotic

We

examined

the

relationship

between

our

measures

of

genome-wide

divergence

and

recombination

rate

using

recently

reported

high-resolution

measurements

of

recombination

rates

in

the

human

Both

measures

of

neutral

substitution

rate

and

SNP

rate

showed

a

significant

correlation

with

recombination

rate

-LRB-

Fig.

and

Table

-RRB-

The

correlations

above

are

not

explained

by

co-variation

with

local

-LRB-

G

C

-RRB-

content

All

except

the

correlation

between

SNP

frequency

and

LTR

insertion

rate

remain

significant

when

dependence

on

underlying

human

-LRB-

G

C

-RRB-

content

is

factored

out

by

taking

the

residuals

of

a

quadratic

regression

on

regional

human

-LRB-

G

C

-RRB-

content

indeed

the

correlations

are

for

the

most

part

enhanced

-LRB-

Table

-RRB-

Similarly

correlations

remain

significant

when

the

difference

between

the

-LRB-

G

C

-RRB-

content

of

orthologous

mouse

and

human

regions

is

also

factored

Thus

-LRB-

G

C

-RRB-

content

changes

between

mouse

and

human

as

explored

do

not

adequately

explain

the

correlations

Finally

to

obtain

more

rigorous

estimates

of

significance

the

correlations

were

re-evaluated

on

non-overlapping

sets

of

windows

and

on

non-overlapping

windows

as

well

with

similar

Possible

explanations

for

variation

What

explains

the

correlation

among

these

many

measures

of

genome

divergence

It

seems

unlikely

that

direct

selection

would

account

for

variation

and

co-variation

at

such

large

scales

-LRB-

about

Mb

-RRB-

and

involving

abundant

neutral

sites

taken

from

ancestral

transposon

relics

Selection

against

deleterious

mutations

can

remove

linked

but

it

is

not

clear

that

such

effects

or

related

could

extend

to

such

large

scales

or

to

interspecies

divergence

over

such

large

time

It

seems

more

probable

that

these

features

reflect

local

variation

in

underlying

mutation

rate

caused

by

differences

in

DNA

metabolism

or

chromosome

physiology

The

causative

factors

may

include

recombination-associated

transcription-associated

transposon-associated

deletion

and

genomic

and

replication

Nuclear

location

may

also

be

involved

including

proximity

to

matrix

attachment

sites

heterochromatin

nuclear

membrane

and

origins

of

replication

It

is

clear

that

the

mammalian

genome

is

evolving

under

the

influence

of

non-uniform

local

forces

It

remains

an

important

challenge

to

unravel

the

mechanistic

basis

and

evolutionary

consequences

of

such

variation

Genetic

variation

among

strains

To

facilitate

genetic

mapping

studies

it

would

be

valuable

to

create

a

mouse

genetic

map

based

on

SNPs

The

use

of

SNPs

would

allow

the

generation

of

an

even

denser

map

which

would

allow

mouse

geneticists

to

fully

exploit

the

recombinational

resolution

that

can

be

achieved

in

large

crosses

A

cross

with

meioses

divides

the

genome

-LRB-

with

a

genetic

length

of

about

morgans

-RRB-

into

approximately

distinct

recombinational

bins

and

it

would

be

convenient

to

have

an

even

higher

density

of

genetic

markers

available

for

fine-scale

mapping

In

addition

SNPs

offer

potential

advantages

in

terms

of

automation

and

Given

a

reference

sequence

of

the

strain

it

is

straightforward

to

find

SNPs

relative

to

any

other

strain

One

simply

needs

to

generate

random

shotgun

reads

from

the

strain

align

them

to

the

reference

sequence

and

search

for

high-quality

sequence

differences

As

a

pilot

project

we

created

initial

SNP

collections

from

three

strains

-LRB-

-RRB-

-LRB-

-RRB-

and

BALB/cByJ

-LRB-

BALB

-RRB-

-LRB-

Table

-RRB-

So

far

we

have

identified

high-quality

candidate

SNPs

between

the

and

strains

SNPs

between

and

and

between

BALB

and

The

initial

SNP

collection

thus

contains

more

than

SNPs

This

total

is

expected

to

grow

with

deeper

coverage

and

the

inclusion

of

Table

Pairwise

correlations

for

six

divergence

features

Neutral

sub

-LRB-

AR

-RRB-

Original

variables

Neutral

sub

-LRB-

-RRB-

Deletion

SNP

Recombination

Insertion

Neutral

sub

-LRB-

-RRB-

Deletion

SNP

Recombination

Figure

Expected

number

of

substitutions

in

fourfold

degenerate

-LRB-

-RRB-

sites

-LRB-

a

-RRB-

and

ancestral

repeat

sites

-LRB-

b

-RRB-

plotted

against

human

-LRB-

G

C

-RRB-

content

The

second-order

-LRB-

quadratic

-RRB-

polynomial

regression

curve

is

shown

in

red

NATURE

VOL

DECEMBER

Residuals

from

quadratic

regression

on

-LRB-

G

C

-RRB-

Neutral

sub

-LRB-

-RRB-

Deletion

SNP

Recombination

Insertion

AR

ancestral

repeat

fourfold

degenerate

site

Nature

Publishing

Group

articles

Table

SNPs

generated

by

WGS

sequencing

Strain

BALB/cByJ

Total

Reads

SNPs

Reads

passing

all

filters

sequence

quality

SSAHA-SNP

and

unique

placement

on

the

genome

additional

strains

We

tested

a

random

sample

of

candidate

SNPs

by

resequencing

and

found

that

all

were

authentic

indicating

that

most

of

the

candidate

SNPs

are

true

variants

The

average

density

of

SNPs

between

and

each

of

the

three

strains

was

in

the

range

per

bp

The

distribution

of

SNPs

is

highly

non-uniform

-LRB-

consistent

with

earlier

-RRB-

Some

regions

of

the

genome

appear

to

be

unusually

rich

in

SNPs

whereas

others

are

devoid

of

SNPs

In

an

accompanying

paper

Wade

and

analyse

this

non-uniform

distribution

of

SNPs

and

demonstrate

that

genetic

variation

between

strains

occurs

in

a

harlequin

pattern

of

alternating

blocks

of

either

high

or

low

SNP

rate

typically

extending

more

than

Mb

Genotyping

of

additional

strains

reveals

that

the

SNPs

largely

represent

alternative

alleles

from

M.

m.

domesticus

and

M.

m.

musculus

and

that

the

blocks

probably

represent

the

distinct

segmental

contributions

of

the

two

subspecies

to

existing

laboratory

mouse

strains

Detailed

knowledge

of

these

blocks

can

thus

allow

reconstruction

of

the

history

and

relationship

among

mouse

strains

Furthermore

it

can

be

used

to

perform

association

studies

on

mouse

strains

by

correlating

differences

in

phenotype

across

multiple

strains

with

the

underlying

block

structure

of

genetic

variation

Implications

for

the

laboratory

mouse

recessive

screens

for

a

wide

variety

of

viable

clinically

relevant

Hundreds

of

new

mutants

with

biochemical

development

and

behavioural

phenotypes

are

being

generated

each

year

For

each

mutant

identification

of

the

molecular

cause

will

require

positional

cloning

Another

means

of

generating

mutants

the

so-called

gene

trap

approach

uses

a

promoterless

reporter

construct

for

random

insertion

into

the

genome

of

embryonic

stem

cells

Expression

of

the

reporter

correlates

with

integration

into

a

transcriptional

unit

which

is

disrupted

by

the

event

and

confers

its

tissue

and

developmental

specificity

to

the

reporter

Several

large-scale

gene-trap

programmes

are

underway

Availability

of

the

genome

sequence

now

makes

the

determination

of

the

precise

integration

site

in

an

interesting

mutant

an

almost

trivial

exercise

Identification

of

quantitative

trait

loci

The

promise

of

genomics

is

the

ability

to

connect

phenotypes

with

genotypes

for

a

wide

variety

of

traits

and

to

use

the

resulting

molecular

insights

to

develop

new

approaches

for

the

cure

and

prevention

of

disease

The

laboratory

mouse

occupies

a

central

place

in

this

vision

both

as

a

prototype

for

all

mammalian

biology

and

as

a

well-characterized

organism

for

modelling

human

disease

In

this

section

we

briefly

discuss

ways

in

which

the

mouse

genome

sequence

will

accelerate

biomedical

progress

in

the

future

Because

the

sequence

has

been

made

available

in

public

databases

in

advance

of

publication

examples

for

many

of

the

predictions

can

already

be

cited

Positional

cloning

of

genes

for

mendelian

phenotypes

More

than

spontaneously

arising

and

radiation-induced

mouse

mutants

causing

heritable

mendelian

phenotypes

are

catalogued

in

the

Mouse

Genome

Informatics

-LRB-

MGI

-RRB-

database

-LRB-

http

-RRB-

Largely

through

positional

cloning

the

molecular

defect

is

now

known

for

about

of

these

mutants

The

availability

of

an

annotated

mouse

genome

sequence

now

provides

the

most

efficient

tool

yet

in

the

gene

hunter

's

toolkit

One

can

move

directly

from

genetic

mapping

to

identification

of

candidate

genes

and

the

experimental

process

is

reduced

to

PCR

amplification

and

sequencing

of

exons

and

other

conserved

elements

in

the

candidate

interval

With

this

streamlined

protocol

it

is

anticipated

that

many

decades-old

mouse

mutants

will

be

understood

precisely

at

the

DNA

level

in

the

near

future

An

example

of

how

the

draft

genome

sequence

has

already

been

successfully

used

is

the

recent

identification

of

the

mouse

mutation

chocolate

in

the

melanosome

protein

-LRB-

ref

-RRB-

The

mouse

genome

sequence

will

be

even

more

crucial

in

efforts

to

exploit

the

growing

repertoire

of

mutant

mice

being

generated

by

chemical

mutagenesis

with

N-ethyl-N-nitrosurea

-LRB-

ENU

-RRB-

and

other

agents

At

least

ten

large-scale

ENU

mutagenesis

centres

have

recently

been

established

worldwide

focusing

on

dominant

or

The

availability

of

more

than

commonly

used

laboratory

inbred

strains

of

mice

each

with

its

own

phenotype

for

multiple

continuously

variable

traits

has

provided

an

important

opportunity

to

map

QTLs

that

underlie

heritable

phenotypic

variation

A

systematic

initiative

is

currently

to

define

parameters

such

as

body

weight

behavioural

patterns

and

disease

susceptibility

among

a

standard

set

of

inbred

lines

and

to

make

these

data

freely

available

to

the

scientific

community

in

the

Mouse

Phenome

Database

-LRB-

-RRB-

Appropriate

crosses

between

such

lines

followed

by

genotyping

will

enable

the

mapping

of

QTLs

which

can

then

be

subjected

to

positional

cloning

The

degree

of

difficulty

is

substantially

greater

for

a

QTL

cloning

project

than

for

a

mendelian

disorder

however

as

the

responsible

intervals

are

usually

much

larger

the

boundaries

more

difficult

to

delineate

precisely

and

the

causative

variant

often

much

more

For

these

reasons

only

a

handful

of

the

approximately

mapped

QTLs

have

been

identified

at

the

molecular

level

The

availability

of

the

mouse

sequence

should

greatly

improve

the

chances

for

future

success

Success

in

QTL

identification

will

be

enhanced

if

genetic

mapping

can

be

combined

with

genomic

sequence

expression

array

data

and

proteomic

data

Furthermore

the

use

of

high-density

SNP

maps

to

identify

blocks

of

ancestral

identity

among

mouse

strains

and

to

correlate

them

with

phenotypes

may

assist

in

the

design

of

QTL

experiments

The

availability

of

BAC

libraries

from

several

strains

will

facilitate

testing

candidate

genes

for

QTLs

through

the

construction

of

transgenic

The

combination

of

multiple

perspectives

on

genome

sequence

variation

and

function

should

thus

provide

a

powerful

platform

for

revealing

molecular

mechanisms

of

phenotypic

variation

Creation

of

knockout

and

knockin

mice

The

wide

application

of

homologous

recombination

in

embryonic

stem

cells

has

provided

a

remarkable

abundance

of

custom

mice

with

specifically

engineered

loss

or

gain-of-function

mutations

in

specific

genes

of

biological

or

medical

interest

Yet

this

remains

a

time-consuming

process

The

design

of

recombinant

DNA

constructs

for

injection

has

often

been

delayed

by

incomplete

knowledge

of

gene

structure

requiring

tedious

restriction

mapping

or

sequencing

and

occasionally

giving

rise

to

unsatisfying

outcomes

due

to

incorrect

information

The

availability

of

the

mouse

genome

sequence

will

both

speed

the

design

of

such

constructs

and

reduce

the

likelihood

of

unfortunate

choices

Furthermore

the

long-range

continuity

of

the

sequence

should

facilitate

the

generation

of

models

of

contiguous

gene-deletion

syndromes

Creation

of

transgenic

animals

For

many

transgenic

experiments

it

is

important

to

maintain

copydependent

tissue-specific

expression

of

the

transgene

This

is

most

readily

accomplished

through

BAC

transgenesis

The

availability

of

a

deep

end-sequenced

BAC

library

from

the

strain

mapped

to

NATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

the

genome

sequence

now

makes

it

straightforward

to

obtain

a

desired

gene

in

a

BAC

for

such

experiments

end-sequenced

BAC

libraries

from

other

strains

should

be

available

in

the

future

BACs

also

provide

the

ability

to

make

mutant

alleles

with

relative

ease

by

taking

advantage

of

powerful

genetic

engineering

techniques

for

custom

mutagenesis

in

the

Escherichia

coli

host

Applications

to

cancer

The

mouse

genome

sequence

also

has

powerful

applications

to

the

molecular

characterization

of

the

somatic

mutations

that

result

in

neoplasia

High-density

SNP

mapping

to

identify

loss

of

combined

with

comparative

genomic

hybridization

using

cDNA

or

BAC

can

be

used

to

identify

chromosomal

segments

showing

loss

or

gain

of

copy

number

in

particular

tumour

types

The

combination

of

such

approaches

with

expression

arrays

that

include

all

mouse

genes

should

further

enhance

the

ability

to

pinpoint

the

molecular

lesions

that

result

in

carcinogenesis

Full

sequencing

of

all

the

exons

and

regulatory

regions

of

known

tumour

suppressors

oncogenes

and

other

candidate

genes

can

now

be

contemplated

as

has

been

initiated

in

a

few

centres

for

human

As

a

specific

example

of

the

use

of

the

draft

sequence

for

oncogene

discovery

several

groups

recently

used

retroviral

infection

in

mice

to

recover

new

cancer

susceptibility

loci

The

ability

to

compare

rapidly

retrieved

sequence

tags

to

the

draft

genome

sequence

greatly

accelerated

the

process

of

cancer

gene

Making

better

mouse

models

that

diminishes

a

mammal

's

fitness

by

one

part

in

is

undetectable

at

the

laboratory

bench

but

is

lethal

from

the

standpoint

of

evolution

Comparative

analysis

of

genomes

should

thus

make

it

possible

to

discern

by

virtue

of

evolutionary

conservation

biological

features

that

would

otherwise

escape

our

notice

In

this

way

it

will

play

a

crucial

role

in

our

understanding

of

the

human

genome

and

thereby

help

lay

the

foundation

for

biomedicine

in

the

twenty-first

century

The

initial

sequence

of

the

mouse

genome

reported

here

is

merely

a

first

step

in

this

intellectual

programme

The

sequencing

of

many

additional

mammalian

and

other

vertebrate

genomes

will

be

needed

to

extract

the

full

information

hidden

within

our

chromosomes

Moreover

as

we

begin

to

understand

the

common

elements

shared

among

species

it

may

also

become

possible

to

approach

the

even

harder

challenge

of

identifying

and

understanding

the

functional

differences

that

make

each

species

unique

A

Methods

Production

of

sequence

reads

Paired-end

reads

from

libraries

with

different

insert

sizes

were

produced

as

previously

using

trays

to

ensure

linkages

Availability

of

sequence

and

assembly

data

Unprocessed

sequence

reads

are

available

from

the

NCBI

trace

archive

-LRB-

-RRB-

Raw

assembly

data

-LRB-

before

removal

of

contaminants

anchoring

to

chromosomes

and

addition

of

finished

sequence

-RRB-

are

available

from

the

Whitehead

Institute

for

Biomedical

Research

-LRB-

WIBR

-RRB-

-LRB-

-RRB-

The

released

assembly

is

available

from

Ensembl

-LRB-

-RRB-

NCBI

-LRB-

-RRB-

UCSC

-LRB-

-RRB-

and

WIBR

-LRB-

-RRB-

-LRB-

See

Supplementary

Information

for

detailed

Methods

-RRB-

Received

September

accepted

October

doi

International

Human

Genome

Sequencing

Consortium

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sequencing

and

analysis

of

the

human

genome

Nature

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-RRB-

Venter

J.

C.

et

al.

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sequence

of

the

human

genome

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O'Brien

S.

J.

et

al.

The

promise

of

comparative

genomics

in

mammals

Science

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Loots

G.

G.

et

al.

Identification

of

a

coordinate

regulator

of

interleukins

and

by

crossspecies

sequence

comparisons

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Pennacchio

L.

A.

Rubin

E.

M.

Genomic

strategies

to

identify

mammalian

regulatory

sequences

Nature

Rev.

Genet

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Oeltjen

J.

C.

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al.

Large-scale

comparative

sequence

analysis

of

the

human

and

murine

Bruton

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regulatory

domains

Genome

Res

-LRB-

-RRB-

Ellsworth

R.

E.

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al.

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genomic

sequence

analysis

of

the

human

and

mouse

cystic

fibrosis

transmembrane

conductance

regulator

genes

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Natl

Acad

Sci

USA

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-RRB-

Mallon

A.

M.

et

al.

Comparative

genome

sequence

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the

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region

in

mouse

and

man

Genome

Res

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Dehal

P.

et

al.

Human

chromosome

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related

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in

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conservative

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lineagespecific

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al.

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and

comparative

analysis

of

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of

mouse

genomic

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orthologous

to

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of

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chromosome

implicated

in

Williams

syndrome

Genome

Res

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Toyoda

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genomic

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critical

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Genome

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Ansari-Lari

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its

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Genome

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J.

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E.

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Hurst

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Biol

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Boguski

M.

S.

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of

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transcribed

mammalian

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Natl

Acad

Sci

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J.

McKerlie

C.

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phenogenomics

for

modelling

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Mol

Med

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miracle

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power

of

mice

Nature

Med

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Hogan

B.

Beddington

R.

Costantini

F.

Lacy

E.

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the

Mouse

Embryo

A

Laboratory

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Cold

Spring

Harbor

Laboratory

Press

Woodbury

New

York

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Joyner

A.

L.

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A

Practical

Approach

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Oxford

Univ.

Press

New

York

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Copeland

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G.

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N.

A.

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D.

L.

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functional

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rearrangements

in

mice

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Rev.

Genet

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Bucan

M.

Abel

T

The

mouse

genetics

meets

behaviour

Nature

Rev.

Genet

-LRB-

-RRB-

Not

all

mouse

models

replicate

the

human

phenotype

in

the

expected

way

The

availability

of

the

full

human

and

mouse

sequences

provides

an

opportunity

to

anticipate

these

differences

and

perhaps

to

compensate

for

them

In

some

instances

it

may

turn

out

that

the

murine

mutation

did

not

reside

in

the

true

orthologue

of

the

human

disease

gene

Alternatively

in

a

circumstance

where

the

human

genome

contains

only

a

single

gene

family

member

but

the

mouse

genome

contains

a

paralogue

as

well

as

the

orthologue

one

can

anticipate

that

knockout

of

the

orthologue

alone

may

give

a

much

milder

phenotype

-LRB-

or

at

all

-RRB-

Such

was

the

case

for

instance

with

the

occulocerebrorenal

syndrome

described

by

Lowe

and

Creating

double

knockout

mice

may

then

provide

a

closer

match

to

the

human

disease

phenotype

Understanding

gene

regulation

Of

the

approximately

of

windows

of

the

mammalian

genome

that

are

under

selection

most

do

not

appear

to

code

for

protein

Much

of

this

sequence

is

probably

involved

in

the

regulation

of

gene

expression

It

should

be

possible

to

pinpoint

these

regulatory

elements

more

precisely

with

the

availability

of

additional

related

genomes

However

mouse

is

likely

to

provide

the

most

powerful

experimental

platform

for

generating

and

testing

hypotheses

about

their

function

An

example

is

the

recent

demonstration

based

on

mouse

human

sequence

alignment

followed

by

knockout

manipulation

of

several

long-range

locus

control

regions

that

affect

expression

of

the

Conclusion

The

mouse

provides

a

unique

lens

through

which

we

can

view

ourselves

As

the

leading

mammalian

system

for

genetic

research

over

the

past

century

it

has

provided

a

model

for

human

physiology

and

disease

leading

to

major

discoveries

in

such

fields

as

immunology

and

metabolism

With

the

availability

of

the

mouse

genome

sequence

it

now

provides

a

model

and

informs

the

study

of

our

genome

as

well

Comparative

genome

analysis

is

perhaps

the

most

powerful

tool

for

understanding

biological

function

Its

power

lies

in

the

fact

that

evolution

's

crucible

is

a

far

more

sensitive

instrument

than

any

other

available

to

modern

experimental

science

a

functional

alteration

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VOL

DECEMBER

Nature

Publishing

Group

articles

Silver

L.

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Mouse

Genetics

Concepts

and

Practice

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New

York

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Bromham

L.

Phillips

M.

J.

Penny

D.

Growing

up

with

dinosaurs

molecular

dates

and

the

mammalian

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Trends

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Evol

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Nei

M.

Xu

P.

Glazko

G.

Estimation

of

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times

from

multiprotein

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for

a

few

mammalian

species

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organisms

Proc

Natl

Acad

Sci

USA

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Kumar

S.

Hedges

S.

B

A

molecular

timescale

for

vertebrate

evolution

Nature

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Madsen

O.

et

al.

Parallel

adaptive

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in

two

major

clades

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placental

mammals

Nature

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Murphy

W.

J.

et

al.

Molecular

phylogenetics

and

the

origins

of

placental

mammals

Nature

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E

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Mouse

Its

Origin

Heredity

and

Culture

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Harvard

Univ.

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Massachusetts

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Morse

H

The

Mouse

in

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eds

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L.

Small

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D.

Fox

J.

G.

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Academic

New

York

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H.

C.

Origins

of

Inbred

Mice

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C.

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York

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J.

B.

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Haldane

N.

M.

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in

mice

J.

Genet

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White

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of

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genetic

linkage

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in

man

using

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fragment

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J.

Hum

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Dietrich

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genetic

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the

mouse

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McAleer

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of

a

high

resolution

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the

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Acids

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Weber

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L.

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Hudson

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hybrid

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of

the

mouse

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Nature

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YAC-based

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mouse

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encyclopedia

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-RRB-

Acknowledgements

We

thank

J.

Takahashi

and

M.

Johnston

for

comments

on

the

manuscript

the

Mouse

Liaison

Group

for

strategic

advice

L.

Gaffney

D.

Leja

and

K.-S

Toh

for

graphical

help

B.

Graham

and

G.

Roberts

for

administrative

work

on

sequencing

of

individual

mouse

BACs

and

P.

Kassos

and

M.

McMurtry

for

secretarial

assistance

We

thank

D.

Hill

and

L.

Corbani

of

the

Mouse

Genome

Informatics

Group

for

their

contributions

to

the

GO

analysis

for

mouse

and

human

and

the

members

of

the

Bork

group

at

EMBL

for

discussions

Funding

was

provided

by

the

National

Institutes

of

Health

-LRB-

National

Human

Genome

Research

Institute

National

Cancer

Institute

National

Institute

of

Dental

and

Craniofacial

Research

National

Institute

of

Diabetes

and

Digestive

and

Kidney

Diseases

National

Institute

of

General

Medical

Sciences

National

Eye

Institute

National

Institute

of

Environmental

Health

Sciences

National

Institute

of

Aging

National

Institute

of

Arthritis

and

Musculoskeletal

and

Skin

Diseases

National

Institute

on

Deafness

and

Other

Communication

Disorders

National

Institute

of

Mental

Health

National

Institute

on

Drug

Abuse

National

Center

for

Research

Resources

the

National

Heart

Lung

and

Blood

Institute

and

The

Fogarty

International

Center

-RRB-

the

Wellcome

Trust

the

Howard

Hughes

Medical

Institute

the

United

States

Department

of

Energy

the

National

Science

Foundation

the

Medical

Research

Council

NSERC

BMBF

-LRB-

German

Ministry

for

Research

and

Education

-RRB-

the

European

Molecular

Biology

Laboratory

Plan

Nacional

de

I

D

and

Instituto

Carlos

III

Swiss

National

Science

Foundation

NCCR

Frontiers

in

Genetics

the

Swiss

Cancer

League

and

the

Childcare

and

J.

Lejeune

Foundations

and

the

Ministry

of

Education

Culture

Sports

Science

and

Technology

of

Japan

The

initial

threefold

sequence

coverage

was

partly

supported

by

the

Mouse

Sequencing

Consortium

-LRB-

GlaxoSmithKline

Merck

and

Affymetrix

-RRB-

through

the

Foundation

for

the

National

Institutes

of

Health

We

acknowledge

A.

Holden

for

coordinating

the

Mouse

Sequencing

Consortium

We

thank

the

Sanger

Institute

systems

group

for

maintenance

and

provision

of

the

computer

resource

The

MGSC

also

used

Hewlett-Packard

Company

's

BioCluster

a

configuration

of

HP

AlphaServer

systems

with

CPUs

and

terabyte

of

storage

The

BioCluster

is

housed

in

Hewlett-Packard

's

IQ

Solutions

Center

and

was

accessed

remotely

The

computing

resource

greatly

accelerated

the

analysis

Competing

interests

statement

The

authors

declare

that

they

have

no

competing

financial

interests

Correspondence

and

requests

for

materials

should

be

addressed

to

R.H.W.

-LRB-

e-mail

waterston@gs.washington.edu

-RRB-

K.L.T.

-LRB-

e-mail

kersli@genome.wi.mit.edu

-RRB-

or

E.S.L.

-LRB-

e-mail

lander@genome.wi.mit.edu

-RRB-

Authors

contributions

The

following

authors

contributed

to

project

leadership

R.

H.

Waterston

K.

Lindblad-Toh

E.

Birney

J.

Rogers

M.

R.

Brent

F.

S.

Collins

R.

Guigo

R.

C.

Hardison

D.

Haussler

D.

B.

Jaffe

W.

J.

Kent

W.

Miller

C.

P.

Ponting

A.

Smit

M.

C.

Zody

and

E.

S.

Lander

NATURE

VOL

DECEMBER

Nature

Publishing

Group

articles

Robert

H.

Kerstin

Ewan

Jane

Josep

F.

Pankaj

Richa

Rachel

Marina

Peter

Stylianos

E.

John

Robert

Jonathon

Karen

Stephan

Eric

Bruce

Toby

Peer

Marc

Nicolas

Michael

R.

Daniel

G.

Stephen

D.

Carol

John

Jonathan

Robert

D.

Piero

Simon

Francesca

Asif

T.

Deanna

M.

Michele

Christopher

Francis

S.

Lisa

L.

Richard

R.

Alan

Olivier

James

Val

Tim

Mark

Robert

Joy

Kimberly

D.

Justin

Emmanouil

T.

Colin

Nicholas

J.

Mark

Sheila

Inna

Diane

M.

Sean

R.

Laura

Richard

D.

Pallavi

Eduardo

Adam

Ginger

A.

Paul

Karen

Wayne

N.

Lucinda

A.

Robert

S.

Terrence

S.

Diane

Richard

A.

Gustavo

Sante

Nick

Leo

Darren

Tina

A.

Eric

D.

Simon

Roderic

Mark

Ross

C.

David

Yoshihide

LaDeana

W.

Angela

Wratko

Timothy

Fan

Axin

Tim

Adrienne

Ian

David

B.

L.

Steven

Matthew

Thomas

A.

Ann

Michael

Elinor

K.

Donna

Arkadiusz

Jun

Evan

Cristyn

W.

James

Andrew

Diana

L.

Ian

Raju

S.

Edward

J.

Kulbokas

David

Tom

J.

P.

Steven

Ivica

Rosie

Jia

Ming

Christine

Susan

Bin

Donna

R.

Elaine

R.

Lucy

Evan

John

H.

Megan

W.

Richard

Stuart

Kirsten

John

D.

Jim

Beverley

Jill

P.

Webb

Tracie

L.

Emmanuel

Kate

T.

Michael

Richard

James

C.

Donna

M.

William

E.

Joanne

O.

Michael

N.

Robert

Zemin

Chad

Michael

J.

Yasushi

Karen

Emma

Lior

Genis

Kymberlie

H.

Jane

Pavel

Robert

Craig

S.

Alex

Tracy

C.

Chris

P.

Simon

Michael

Alexandre

Bruce

A.

Krishna

M.

Edward

M.

Alistair

G.

Ralph

Victor

Brian

Jorg

Matthias

S.

Scott

Carol

Steven

Steve

Ted

Andrew

Ratna

Sarah

Jonathan

B.

Guy

Arian

Douglas

R.

Brian

Arne

Nicole

Charles

Mikita

Glenn

Johanna

David

Evanne

John

Catherine

Abel

Jade

P.

Andrew

C.

von

Claire

M.

Melanie

Ryan

J.

Robert

B.

Michael

C.

Anthony

P.

Kris

Raymond

Simon

Jamey

David

Sophie

Richard

K.

Eitan

Kim

C.

Dudley

Shan

Shiaw-Pyng

Evgeny

M.

Michael

C.

Eric

S.

Affiliations

for

authors

Genome

Sequencing

Center

Washington

University

School

of

Medicine

Campus

Box

Forest

Park

Avenue

St

Louis

Missouri

USA

Whitehead

Institute/MIT

Center

for

Genome

Research

Charles

Street

Cambridge

Massachusetts

USA

European

Bioinformatics

Institute

Wellcome

Trust

Genome

Campus

Hinxton

Cambridge

UK

Wellcome

Trust

Sanger

Institute

Wellcome

Trust

Genome

Campus

Hinxton

Cambridge

UK

Research

Group

in

Biomedical

Informatics

Institut

Municipal

d'Investigacio

Medica/Universitat

Pompeu

Fabra

Centre

de

Regulacio

Genomica

Barcelona

Catalonia

Spain

Bioinformatics

GlaxoSmithKline

Swedeland

Road

King

of

Prussia

Pennsylvania

USA

National

Center

for

Biotechnology

Information

National

Institutes

of

Health

Bethesda

Maryland

USA

Department

of

Mathematics

University

of

California

at

Berkeley

Evans

Hall

Berkeley

California

USA

Division

of

Medical

Genetics

University

of

Geneva

Medical

School

rue

Michel-Servet

Geneva

Switzerland

Center

for

Biomolecular

Science

and

Engineering

University

of

California

Santa

Cruz

California

USA

EMBL

Meyerhofstrasse

Heidelberg

Germany

UK

MRC

Mouse

Sequencing

Consortium

MRC

Mammalian

Genetics

Unit

Harwell

UK

Lawrence

Berkeley

National

Laboratory

Cyclotron

Road

Mailstop

Berkeley

California

USA

Department

of

Computer

Science

Washington

University

Box

St

Louis

Missouri

USA

School

of

Computer

Science

University

of

Waterloo

Waterloo

Ontario

Canada

The

Jackson

Laboratory

Main

Street

Bar

Harbor

Maine

USA

Laboratory

for

Genome

Exploration

RIKEN

Genomic

Sciences

Center

Yokohama

Institute

Suchiro-cho

Tsurumi-ku

Yokohama

Kanagawa

Japan

Affymetrix

Inc.

Emeryville

California

USA

Departments

of

Statistics

and

Health

Evaluation

Sciences

The

Pennsylvania

State

University

University

Park

Pennsylvania

USA

National

Human

Genome

Research

Institute

National

Institutes

of

Health

Center

Drive

Room

Bethesda

Maryland

USA

Wellcome

Trust

Centre

for

Human

Genetics

University

of

Oxford

Roosevelt

Drive

Oxford

UK

Department

of

Electrical

Engineering

University

of

California

Berkeley

Cory

Hall

Berkeley

California

USA

Department

of

Human

Anatomy

and

Genetics

MRC

Functional

Genetics

Unit

University

of

Oxford

South

Parks

Road

Oxford

UK

Department

of

Human

Genetics

University

of

Utah

Salt

Lake

City

Utah

USA

Howard

Hughes

Medical

Institute

and

Department

of

Genetics

Washington

University

School

of

Medicine

St

Louis

Missouri

USA

Departments

of

Biochemistry

and

Molecular

Biology

and

Computer

Science

and

Engineering

The

Pennsylvania

State

University

University

Park

Pennsylvania

USA

Department

of

Computer

Science

and

Engineering

The

Pennsylvania

State

University

University

Park

Pennsylvania

USA

Baylor

College

of

Medicine

Human

Genome

Sequencing

Center

One

Baylor

Plaza

Houston

Texas

USA

The

Institute

for

Systems

Biology

North

Street

Seattle

Washington

USA

National

Human

Genome

Research

Institute

National

Institutes

of

Health

South

Drive

Building

Room

Bethesda

Maryland

USA

Department

of

Biochemistry

and

Molecular

Biology

The

Pennsylvania

State

University

University

Park

Pennsylvania

USA

Howard

Hughes

Medical

Institute

University

of

California

Santa

Cruz

California

USA

Department

of

Chemistry

and

Biochemistry

University

of

Oklahoma

Advanced

Center

for

Genome

Technology

University

of

Oklahoma

Parrington

Oval

Room

Norman

Oklahoma

USA

Departments

of

Genetics

and

Medicine

and

Harvard-Partners

Center

for

Genetics

and

Genomics

Harvard

Medical

School

Boston

Massachusetts

USA

Department

of

Statistics

The

Pennsylvania

State

University

University

Park

Pennsylvania

USA

Department

of

Computer

Science

University

of

California

Santa

Barbara

California

USA

US

DOE

Joint

Genome

Institute

Mitchell

Drive

Walnut

Creek

California

USA

Department

of

Computer

Science

University

of

Western

Ontario

London

Ontario

Canada

Cold

Spring

Harbor

Laboratory

PO

Box

Bungtown

Road

Cold

Spring

Harbor

New

York

USA

Wellcome

Trust

Euston

Road

London

UK

Department

of

Computer

Science

and

Engineering

University

of

California

San

Diego

Gilman

Drive

La

Jolla

California

USA

Max

Planck

Institute

for

Molecular

Genetics

Ihnestrasse

Berlin

Germany

Genome

Therapeutics

Corporation

Beaver

Street

Waltham

Massachusetts

USA

Bioinformatics

Solutions

Inc.

Columbia

Street

W

Waterloo

Ontario

Canada

Department

of

Molecular

and

Human

Genetics

Baylor

College

of

Medicine

Mailstop

Room

One

Baylor

Plaza

Houston

Texas

USA

Department

of

Biology

Massachusetts

Institute

of

Technology

Cambridge

Massachusetts

USA

Members

of

the

Mouse

Genome

Analysis

Group

Nature

Publishing

Group

NATURE

VOL

DECEMBER

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