Elucidation

of

the

Small

RNA

Component

of

the

Transcriptome

Cheng

Lu

et

al.

Science

-LRB-

-RRB-

DOI

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AAAS

Downloaded

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REPORTS

Elucidation

of

the

Small

RNA

Component

of

the

Transcriptome

Cheng

Lu

Shivakundan

Singh

Tej

Shujun

Luo

Christian

D.

Haudenschild

Blake

C.

Meyers

Pamela

J.

Small

RNAs

play

important

regulatory

roles

in

most

eukaryotes

but

only

a

small

proportion

of

these

molecules

have

been

identified

We

sequenced

more

than

two

million

small

RNAs

from

seedlings

and

the

inflorescence

of

the

model

plant

Arabidopsis

thaliana

Known

and

new

microRNAs

-LRB-

miRNAs

-RRB-

were

among

the

most

abundant

of

the

nonredundant

set

of

more

than

sequences

whereas

more

than

half

represented

lower

abundance

small

interfering

RNAs

-LRB-

siRNAs

-RRB-

that

match

repetitive

sequences

intergenic

regions

and

genes

Individual

or

clusters

of

highly

regulated

small

RNAs

were

readily

observed

Targets

of

antisense

RNA

or

miRNA

did

not

appear

to

be

preferentially

associated

with

siRNAs

Many

genomic

regions

previously

considered

featureless

were

found

to

be

sites

of

numerous

small

RNAs

Small

RNAs

to

nucleotides

-LRB-

nt

-RRB-

function

to

silence

genes

by

multiple

mechanisms

and

are

present

in

diverse

eukaryotic

organisms

Among

these

molecules

small

interfering

RNAs

-LRB-

siRNAs

-RRB-

and

microRNAs

-LRB-

miRNAs

-RRB-

are

the

two

major

types

and

both

are

produced

by

RNase

III

like

enzymes

called

DICERs

-LRB-

-RRB-

Whereas

siRNAs

are

processed

from

longer

double-stranded

RNA

molecules

and

represent

both

strands

of

the

RNA

miRNA

molecules

originate

from

Bhairpin

-LSB-

precursors

transcribed

from

one

strand

of

distinct

genomic

loci

Existing

methods

do

not

sequence

deeply

enough

to

sample

the

full

complexity

of

small

RNAs

in

plant

and

animal

systems

nor

do

they

quantify

small

RNA

abundances

To

investigate

the

complexity

of

small

RNAs

we

adapted

massively

parallel

signature

sequencing

-LRB-

MPSS

-RRB-

for

these

molecules

-LRB-

fig

-RRB-

MPSS

sequences

hundreds

of

thousands

of

molecules

per

reaction

and

provides

quantitative

information

Briefly

small

RNA

molecules

are

isolated

by

size

fractionation

on

a

polyacrylamide

gel

RNA

adapters

are

sequentially

ligated

to

the

and

ends

and

reverse

transcriptase

generates

the

first

strand

of

cDNA

which

is

amplified

and

used

as

the

template

for

MPSS

-LRB-

-RRB-

We

generated

libraries

using

small

RNA

of

Arabidopsis

inflorescence

or

seedlings

resulting

in

-LRB-

distinct

-RRB-

and

-LRB-

distinct

-RRB-

sequences

or

Bsignatures

-LSB-

respectively

-LRB-

Table

see

SOM

for

the

second

round

of

sequencing

on

seedlings

in

Table

-RRB-

For

the

two

libraries

of

the

total

distinct

small

RNA

Delaware

Biotechnology

Institute

Department

of

Plant

and

Soil

Sciences

and

of

Marine

Studies

University

of

Delaware

Newark

DE

USA

Inc.

Industrial

Boulevard

Hayward

CA

USA

To

whom

correspondence

should

be

addressed

E-mail

meyers@dbi.udel.edu

-LRB-

B.C.M.

-RRB-

green@dbi

udel.edu

-LRB-

P.J.G.

-RRB-

sequences

matched

the

genome

Ethe

Institute

for

Genomic

Research

-LRB-

TIGR

-RRB-

version

-LRB-

-RRB-

representing

of

the

nearly

million

total

signatures

-LRB-

Table

-RRB-

and

exceeding

by

more

than

the

total

distinct

sequences

from

all

previous

Arabidopsis

studies

-LRB-

-RRB-

The

unmatched

signatures

may

be

derived

from

genomic

gaps

such

as

ribosomal

RNA

-LRB-

rRNA

-RRB-

repeats

or

centromeres

or

may

result

from

sequencing

errors

-LRB-

-RRB-

Signatures

matching

to

rRNAs

transfer

RNAs

-LRB-

tRNAs

-RRB-

small

nucleolar

RNAs

-LRB-

snoRNAs

-RRB-

or

small

nuclear

RNAs

-LRB-

snRNAs

-RRB-

made

up

of

the

inflorescence

library

and

of

the

seedling

library

-LRB-

table

-RRB-

lower

levels

compared

with

those

previously

reported

-LRB-

-RRB-

Even

after

removing

these

RNAs

from

consideration

the

inflorescence

library

was

proportionally

more

complex

-LRB-

Table

-RRB-

The

increased

levels

and

diversity

of

small

RNAs

in

inflorescence

could

reflect

stronger

silencing

of

transposons

in

the

germline

tissue

similar

to

that

of

Caenorhabditis

elegans

-LRB-

-RRB-

Of

the

distinct

signatures

in

the

inflorescence

and

seedling

libraries

and

respectively

matched

unique

sites

in

the

genome

-LRB-

table

-RRB-

We

examined

the

distribution

of

small

RNAs

on

the

five

Arabidopsis

chromosomes

and

compared

this

with

repeat

and

mRNA

abundance

distributions

-LRB-

Fig.

A

and

B

and

fig

-RRB-

The

small

RNAs

from

both

libraries

were

highly

concentrated

in

the

pericentromeric

regions

of

each

chromosome

similar

to

the

repeats

In

contrast

mRNA

levels

were

greatest

in

the

euchromatic

regions

-LRB-

fig

-RRB-

The

small

RNA

data

for

these

and

other

specific

genomic

locations

are

best

examined

via

the

Web

-LRB-

-RRB-

the

site

provides

detailed

information

about

each

signature

that

can

be

accessed

by

clicking

on

the

corresponding

triangle

More

than

half

of

the

genomic

sequences

matching

the

small

RNAs

in

the

two

libraries

were

transposons

or

retrotransposons

-LRB-

table

-RRB-

However

the

small

RNAs

matching

these

sequences

accounted

for

less

than

half

the

number

of

distinct

small

RNAs

-LRB-

Fig.

-RRB-

because

more

than

of

these

predicted

siRNAs

matched

multiple

locations

in

the

genome

The

corresponding

small

RNA

signatures

were

predominantly

found

at

moderate

abundances

-LRB-

to

TPQ

transcripts

per

quarter

million

-RRB-

At

least

half

of

the

retrotransposon

or

transposon-related

sequences

in

the

Arabidopsis

genome

had

matches

to

small

RNAs

in

each

library

and

small

RNAs

matched

to

of

pseudogenes

-LRB-

table

-RRB-

The

relative

number

of

distinct

small

RNAs

per

megabase

of

sequence

was

lower

for

genes

than

for

other

genomic

features

-LRB-

Fig.

table

a

and

b

-RRB-

About

two-thirds

of

the

genes

that

were

matched

had

relatively

few

small

RNAs

-LRB-

to

TPQ

-RRB-

These

low-abundance

signatures

could

represent

perfectly

matched

miRNAs

or

siRNAs

targeted

to

silenced

genes

unannotated

pseudogenes

unannotated

repeats

or

other

unknown

sources

of

siRNAs

-LRB-

Fig.

-RRB-

Matching

the

small

RNAs

to

genes

in

different

GO

functional

categories

indicated

that

the

small

RNAs

were

well

distributed

among

a

broad

range

of

cellular

processes

and

molecular

functions

-LRB-

table

-RRB-

A

comparison

of

mRNA

and

small

RNA

MPSS

data

for

highly

expressed

genes

suggested

that

Table

Summary

statistics

for

small

RNA

MPSS

libraries

The

signatures

sequenced

for

each

library

reflect

the

sum

of

two

sequencing

reactions

Distinct

refers

to

the

number

of

different

sequences

found

within

the

set

Total

refers

to

the

union

of

the

different

libraries

Genome

matches

refers

to

distinct

signatures

that

perfectly

match

to

at

least

one

location

in

the

genome

and

includes

signatures

matching

to

tRNAs

rRNAs

snRNAs

or

snoRNAs

No

Library

Signatures

sequenced

Distinct

signatures

Genome

matches

A.

Inflorescence

and

seedling

signatures

Inflorescence

Seedling

Total

of

rows

and

B.

Additional

signatures

from

a

second

round

of

sequencing

from

seedlings

Seedling

Combined

seedling

Total

of

all

libraries

SCIENCE

VOL

SEPTEMBER

Downloaded

from

on

May

REPORTS

Fig.

Small

RNAs

map

to

numerous

chromosomal

locations

-LRB-

A

-RRB-

Inflorescence

small

RNAs

matched

to

chromosome

The

height

of

the

vertical

lines

indicates

the

abundance

of

the

small

RNA

Maximum

height

of

black

bar

TPQ

red

bar

maximum

TPQ

-LRB-

B

-RRB-

A

pericentromeric

region

from

Chr

Retrotransposon-related

sequences

identified

by

RepeatMasker

are

highlighted

in

pink

and

this

entire

region

was

found

to

be

repetitive

including

the

spaces

between

annotated

retrotransposons

Black

triangles

above

or

below

the

matching

strands

small

RNAs

hollow

triangles

signatures

mapping

to

more

than

one

location

red

or

blue

boxes

exons

on

top

or

bottom

strands

respectively

colored

triangles

poly

-LRB-

A

-RRB-

-LRB-

MPSS

from

polyadenylated

RNA

-RRB-

MPSS

signatures

retrotransposons

thin

yellow

bars

-LRB-

C

-RRB-

A

typical

genic

region

most

small

RNAs

map

to

intergenic

regions

which

are

often

unannotated

transposon-related

sequences

Yellow

shading

DNA

transposonrelated

sequences

identified

by

RepeatMasker

-LRB-

D

-RRB-

An

intergenic

region

of

chr

Orange

box

small

RNAs

and

poly

-LRB-

A

-RRB-

MPSS

signatures

that

correspond

to

the

small

RNA

data

contained

only

a

very

low

level

of

degradation

products

of

the

longer

mRNAs

The

number

of

distinct

small

RNAs

that

matched

to

intergenic

regions

exceeded

the

numbers

that

matched

to

genes

pseudogenes

transposons

or

retrotransposons

-LRB-

Fig.

-RRB-

an

observation

that

can

not

be

explained

by

the

fraction

of

the

genome

that

these

entities

comprise

Inflorescence

small

RNAs

matching

intergenic

regions

were

about

four

times

as

complex

as

those

from

seedlings

and

this

complexity

difference

was

evident

to

a

slightly

lesser

extent

for

small

RNAs

in

other

categories

-LRB-

Fig.

table

a

and

b

-RRB-

Small

RNAs

in

the

intergenic

regions

potentially

represent

miRNAs

or

siRNAs

from

unannotated

repeats

such

as

tandem

or

inverted

genomic

repeats

-LRB-

-RRB-

We

observed

good

correlations

for

tandem

repeats

-LRB-

r

-RRB-

and

inverted

repeats

-LRB-

r

-RRB-

and

small

RNAs

based

on

comparisons

of

the

repeat

scores

-LRB-

representing

the

size

and

percent

sequence

identity

-RRB-

versus

the

total

numbers

of

matching

small

RNA

signatures

for

each

repeat

Repetitive

sources

of

siRNAs

should

produce

numerous

small

RNAs

that

match

nearby

sequences

whereas

each

miRNA

derives

from

a

specific

sequence

within

the

corresponding

miR

gene

-LRB-

s

-RRB-

We

developed

a

proximity-based

algorithm

to

build

clusters

of

small

RNAs

so

that

clusters

with

overlapping

genomic

locations

could

be

compared

across

libraries

Moreover

the

characteristics

of

these

clusters

may

help

differentiate

novel

miRNAs

from

siRNAs

as

sparse

clusters

may

characterize

miRNAs

and

dense

clusters

may

characterize

siRNAs

Genes

matched

by

small

RNAs

contained

an

average

of

one

sparse

cluster

-LRB-

table

-RRB-

Fig.

Small

RNAs

matching

classes

of

genomic

features

Stippled

bars

indicate

the

total

number

of

base

pairs

of

the

Arabidopsis

genome

-LRB-

scale

on

the

right

-RRB-

that

are

found

in

the

indicated

genomic

features

Retrotransposon

and

transposon

categories

are

from

RepeatMasker

Gray

vertical

bars

total

number

of

distinct

small

RNAs

matched

from

the

inflorescence

library

black

vertical

bars

total

number

of

distinct

small

RNAs

matched

from

the

seedling

library

the

scale

for

distinct

small

RNAs

is

on

the

left

In

contrast

many

transposons

contained

more

than

one

cluster

typically

dense

In

the

intergenic

unannotated

regions

of

the

Arabidopsis

genome

more

than

clusters

of

small

RNAs

were

identified

in

the

inflorescence

library

alone

which

suggests

a

previously

unrecognized

activity

for

a

large

proportion

of

the

intergenic

space

A

comparison

of

genes

with

and

without

antisense

transcripts

for

small

RNAs

indicated

no

correlation

-LRB-

table

-RRB-

consistent

with

and

extending

previous

arguments

against

small

RNA

involvement

in

general

antisense

control

-LRB-

-RRB-

Nevertheless

the

impact

of

small

RNAs

may

be

far

greater

than

this

analysis

of

perfectly

matching

signatures

reflects

because

small

RNAs

are

active

against

imperfectly

matched

targets

-LRB-

-RRB-

and

such

interactions

may

be

numerous

-LRB-

table

-RRB-

Of

the

genes

matched

by

small

RNAs

-LRB-

-RRB-

contained

small

RNAs

found

in

only

one

of

the

two

libraries

-LRB-

table

-RRB-

Four

times

as

many

of

these

sequences

were

specific

to

inflorescence

as

to

seedling

-LRB-

SOM

file

-RRB-

which

may

reflect

a

greater

variety

of

specialSCIENCE

ized

cell

types

or

an

increased

use

of

small

RNAs

in

all

cell

types

within

the

inflorescence

We

selected

representative

known

miRNAs

or

new

small

RNAs

for

validation

by

RNA

gelblot

analysis

Figure

includes

examples

of

signatures

that

were

specific

to

or

highly

preferential

for

inflorescence

or

seedlings

-LRB-

signal

in

only

one

library

or

greater

in

one

library

-RRB-

These

include

which

is

a

known

siRNA

-LRB-

-RRB-

and

five

new

small

RNAs

-LRB-

and

-RRB-

Clear

differential

expression

recapitulating

the

MPSS

results

was

detected

for

all

probes

We

also

examined

several

small

RNAs

that

exhibited

to

differences

in

accumulation

represented

in

Fig.

by

a

known

miRNA

and

The

correlation

between

RNA

gel

blots

and

MPSS

was

strong

but

not

always

perfectly

proportional

particularly

for

small

differences

or

low

abundances

-LRB-

see

SOM

-RRB-

Most

siRNAs

in

Arabidopsis

are

dependent

on

the

RNA-dependent

RNA

polymerase

and

are

absent

in

an

mutant

-LRB-

-RRB-

RNA

isolated

from

the

inflorescence

of

the

mutant

was

also

included

in

our

SEPTEMBER

VOL

Downloaded

from

on

May

REPORTS

blots

-LRB-

Im

in

Fig.

and

fig

-RRB-

to

help

distinguish

new

siRNAs

and

miRNAs

As

expected

siRNA

is

lacking

in

this

mutant

as

reported

previously

-LRB-

-RRB-

as

are

and

-LRB-

Fig.

left

-RRB-

The

other

new

small

RNAs

in

Fig.

and

fig

are

not

diminished

in

These

are

presumably

miRNAs

although

it

is

possible

that

they

belong

to

a

specialized

class

of

siRNAs

dependent

on

another

RDR

such

as

-LRB-

-RRB-

Indeed

of

these

small

RNAs

several

derive

from

regions

that

can

form

typical

pre-miRNA

hairpin

structures

-LRB-

see

fig

for

examples

-RRB-

and

thus

fit

the

requirements

for

annotation

as

new

miRNAs

-LRB-

-RRB-

Most

of

the

miRNAs

known

at

the

time

of

this

analysis

-LRB-

-RRB-

were

found

in

our

data

set

-LRB-

of

-RRB-

Signatures

exactly

matching

miRNAs

accounted

for

of

the

total

abundance

of

genome-matched

signatures

from

the

inflorescence

library

and

known

miRNAs

accounted

for

of

seedling

signatures

-LRB-

SOM

file

Fig.

-RRB-

We

examined

known

or

predicted

mRNA

targets

of

Arabidopsis

miRNAs

for

evidence

of

transitivity

the

production

of

secondary

siRNAs

that

match

a

target

gene

outside

the

sequence

originally

targeted

Although

transitivity

is

common

for

transgene

miRNA

targets

-LRB-

-RRB-

most

endogenous

targets

had

no

matching

small

RNAs

other

than

miRNAs

or

the

only

matching

small

RNAs

were

few

of

very

low

abundance

or

corresponded

to

repeats

-LRB-

see

SOM

-RRB-

To

enrich

for

new

miRNAs

we

developed

a

set

of

filters

that

captured

the

majority

of

the

known

miRNAs

present

in

the

small

RNA

MPSS

data

Our

abundance

and

sparse

cluster

filters

captured

and

respectively

and

the

Bpaired

-LSB-

filter

designed

to

identify

small

RNAs

near

another

small

RNA

that

could

be

a

miRNA

-LRB-

the

complementary

molecule

produced

from

the

opposite

arm

of

the

miRNA

precursor

-RRB-

identified

known

miRNAs

-LRB-

table

-RRB-

These

filters

were

applied

in

combination

with

hairpin

folding

-LRB-

-RRB-

and

rice

conservation

-LRB-

-RRB-

data

sets

-LRB-

-RRB-

to

generate

the

five-way

Venn

diagram

in

Fig.

Among

those

that

form

hairpins

the

sequences

in

box

were

retained

by

all

of

the

three

filters

and

represent

good

candidates

for

novel

miRNAs

-LRB-

right

Fig.

-RRB-

were

lacking

in

the

inflorescence

of

the

mutant

as

expected

This

is

also

true

for

representatives

of

box

retained

by

the

sparse

and

abundance

filters

and

box

which

had

paired

and

sparse

configurations

but

was

not

identified

by

the

filter

-LRB-

fig

-RRB-

The

absence

of

these

sequences

in

indicates

that

filters

based

on

MPSS

data

can

enhance

miRNA

prediction

capability

even

when

cross-species

conservation

is

lacking

Our

data

indicate

that

the

small

RNA

component

of

the

genome

and

its

regulatory

role

is

more

extensive

and

complex

than

previously

demonstrated

Many

regions

of

the

genome

considered

inactive

or

featureless

were

found

in

our

analyses

to

be

sites

of

considerable

small

RNA

activity

Insight

into

the

functional

basis

for

this

complexity

will

result

from

detailed

analyses

of

the

Arabidopsis

small

RNAs

and

application

of

this

approach

in

diverse

treatments

small

RNA

mutants

and

other

species

SCIENCE

VOL

References

and

Notes

E.

Bernstein

A.

A.

Caudy

S.

M.

Hammond

G.

J.

Hannon

Nature

-LRB-

-RRB-

A.

Grishok

et

al.

Cell

-LRB-

-RRB-

S.

Brenner

et

al.

Nat

Biotechnol

-LRB-

-RRB-

J.

R.

Wortman

et

al.

Plant

Physiol

-LRB-

-RRB-

A.

M.

Gustafson

et

al.

Nucleic

Acids

Res

-LRB-

-RRB-

B.

C.

Meyers

et

al.

Genome

Res

-LRB-

-RRB-

W.

Park

J.

Li

R.

Song

J.

Messing

X.

Chen

Curr

Biol

-LRB-

-RRB-

R.

Sunkar

J.

K.

Zhu

Plant

Cell

-LRB-

-RRB-

T.

Sijen

R.

H.

Plasterk

Nature

-LRB-

-RRB-

R.

A.

Martienssen

Nat

Genet

-LRB-

-RRB-

C.

H.

Jen

I.

Michalopoulos

D.

R.

Westhead

P.

Meyer

Genome

Biol

-LRB-

-RRB-

X.

J.

Wang

T.

Gaasterland

N.

H.

Chua

Genome

Biol

-LRB-

-RRB-

A.

L.

Jackson

P.

S.

Linsley

Trends

Genet

-LRB-

-RRB-

L.

P.

Lim

et

al.

Nature

-LRB-

-RRB-

Z.

Xie

et

al.

PLoS

Biol

-LRB-

-RRB-

V.

Ambros

et

al.

RNA

-LRB-

-RRB-

S.

Griffiths-Jones

Nucleic

Acids

Res

-LRB-

-RRB-

E.

A.

Parizotto

P.

Dunoyer

N.

Rahm

C.

Himber

O.

Voinnet

Genes

Dev

-LRB-

-RRB-

M.

W.

Jones-Rhoades

D.

P.

Bartel

Mol

Cell

-LRB-

-RRB-

We

are

grateful

to

M.

Nakano

and

D.

Lee

for

the

web

interface

J.

Carrington

for

the

mutant

F.

Souret

and

B.-C

Yoo

for

comments

on

the

manuscript

and

S.

Jacobsen

for

helpful

discussions

This

work

was

supported

primarily

by

NSF

SGER

with

additional

support

from

the

NSF

Plant

Genome

Program

-LRB-

B.C.M.

-RRB-

DOE

-LRB-

P.J.G.

-RRB-

and

NIH

Supporting

Online

Material

SOM

Text

Materials

and

Methods

Figs

to

Tables

to

References

and

Notes

Data

files

and

April

accepted

July

science

SEPTEMBER

Downloaded

from

on

May

Fig.

Regulation

and

identification

of

new

miRNA

candidates

-LRB-

A

-RRB-

Differential

control

of

miRNAs

and

siRNA

RNA

gel

blots

of

low-molecularweight

RNA

isolated

from

inflorescence

tissues

-LRB-

I

-RRB-

and

seedlings

-LRB-

S

-RRB-

were

probed

with

labeled

oligonucleotides

The

blots

also

included

RNA

from

inflorescence

tissues

of

the

mutant

-LRB-

Im

-RRB-

The

normalized

abundance

level

from

the

MPSS

data

for

each

small

RNA

is

listed

above

the

blots

and

ethidium

bromide

staining

of

the

region

of

the

gels

is

shown

below

-LRB-

B

-RRB-

Five-way

Venn

diagram

of

selection

criteria

for

miRNAs

The

number

of

distinct

signatures

matching

the

criteria

is

indicated

in

each

box

numbered

in

upper

right

as

mentioned

in

the

text

and

table

The

figure

excludes

distinct

signatures

that

did

not

pass

any

of

the

criteria

See

SOM

and

text

for

descriptions

of

the

paired

sparse

and

abundance

filters

and

application

of

the

and

data

sets

-LRB-

-RRB-

Probes

for

the

right-hand

blots

in

-LRB-

A

-RRB-

are

from

box

O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


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from

on

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Published

by

Cold

Spring

Harbor

Laboratory

Press

Close

sequence

comparisons

are

sufficient

to

identify

human

cis

regulatory

elements

Shyam

Prabhakar

Francis

Poulin

Malak

Shoukry

Veena

Afzal

Edward

M.

Rubin

Olivier

Couronne

and

Len

A.

Pennacchio

Genome

Res

originally

published

online

Jun

Access

the

most

recent

version

at

doi

gr

Supplementary

data

References

Supplemental

Research

Data

This

article

cites

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of

which

can

be

accessed

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Notes

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subscribe

to

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Research

go

to

Cold

Spring

Harbor

Laboratory

Press

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Letter

Close

sequence

comparisons

are

sufficient

to

identify

human

cis-regulatory

elements

Shyam

Prabhakar

Francis

Poulin

Malak

Shoukry

Veena

Afzal

Edward

M.

Rubin

Olivier

Couronne

and

Len

A.

Genomics

Division

Lawrence

Berkeley

National

Laboratory

Berkeley

California

USA

S.

Department

of

Energy

Joint

Genome

Institute

Walnut

Creek

California

USA

Cross-species

DNA

sequence

comparison

is

the

primary

method

used

to

identify

functional

noncoding

elements

in

human

and

other

large

genomes

However

little

is

known

about

the

relative

merits

of

evolutionarily

close

and

distant

sequence

comparisons

To

address

this

problem

we

identified

evolutionarily

conserved

noncoding

regions

in

primate

mammalian

and

more

distant

comparisons

using

a

uniform

approach

-LRB-

Gumby

-RRB-

that

facilitates

unbiased

assessment

of

the

impact

of

evolutionary

distance

on

predictive

power

We

benchmarked

computational

predictions

against

previously

identified

cis-regulatory

elements

at

diverse

genomic

loci

and

also

tested

numerous

extremely

conserved

human

rodent

sequences

for

transcriptional

enhancer

activity

using

an

in

vivo

enhancer

assay

in

transgenic

mice

Human

regulatory

elements

were

identified

with

acceptable

sensitivity

-LRB-

-RRB-

and

true-positive

rate

-LRB-

-RRB-

by

comparison

with

one

to

five

other

eutherian

mammals

or

six

other

simian

primates

More

distant

comparisons

-LRB-

marsupial

avian

amphibian

and

fish

-RRB-

failed

to

identify

many

of

the

empirically

defined

functional

noncoding

elements

Our

results

highlight

the

practical

utility

of

close

sequence

comparisons

and

the

loss

of

sensitivity

entailed

by

more

distant

comparisons

We

derived

an

intuitive

relationship

between

ancient

and

recent

noncoding

sequence

conservation

from

whole-genome

comparative

analysis

that

explains

most

of

the

observations

from

empirical

benchmarking

Lastly

we

determined

that

in

addition

to

strength

of

conservation

genomic

location

and/or

density

of

surrounding

conserved

elements

must

also

be

considered

in

selecting

candidate

enhancers

for

in

vivo

testing

at

embryonic

time

points

-LSB-

Supplemental

material

is

available

online

at

-RSB-

The

majority

of

long-range

cis-regulatory

elements

in

the

human

genome

have

yet

to

be

identified

These

gene

regulatory

modules

which

likely

number

in

the

tens

or

hundreds

of

thousands

are

hard

to

detect

since

they

lack

any

obvious

distinguishing

features

analogous

to

codon

structure

splicing

motifs

open

reading

frames

or

other

hallmarks

of

protein-coding

genes

Furthermore

functional

sequences

of

this

class

are

mostly

unique

in

the

genome

-LRB-

Bejerano

et

al.

-RRB-

which

largely

rules

out

paralogybased

identification

The

possibility

that

regulatory

elements

could

lie

more

than

Mb

from

their

target

genes

-LRB-

Lettice

et

al.

Nobrega

et

al.

-RRB-

presents

another

challenge

Consequently

cross-species

sequence

comparisons

which

rely

upon

the

slow

substitution

rate

of

many

categories

of

functional

DNA

relative

to

neutral

sequence

have

emerged

as

the

pre-eminent

means

of

identifying

candidate

cis-regulatory

elements

in

large

genomes

such

as

human

-LRB-

Pennacchio

and

Rubin

Nobrega

et

al.

Ahituv

et

al.

Chapman

et

al.

de

la

CalleMustienes

et

al.

Hughes

et

al.

King

et

al.

Woolfe

et

al.

-RRB-

Distant

comparisons

such

as

human

fugu

-LRB-

Brenner

et

al.

Nobrega

et

al.

de

la

Calle-Mustienes

et

al.

Woolfe

et

al.

-RRB-

have

proven

especially

powerful

at

highspecificity

prediction

of

functional

elements

since

neutral

sequences

have

had

sufficient

time

to

diverge

beyond

recognition

Present

address

Department

of

Integrative

Biology

University

of

California

Berkeley

CA

USA

Corresponding

authors

E-mail

SPrabhakar@lbl.gov

fax

-LRB-510-RRB- 486-4229

E-mail

LAPennacchio@lbl.gov

fax

-LRB-510-RRB- 486-4229

Article

published

online

before

print

Article

and

publication

date

are

at

http

However

despite

the

similar

gene

content

of

the

human

and

fugu

genomes

even

functional

noncoding

elements

are

likely

to

have

diverged

over

such

great

distances

as

demonstrated

by

the

small

number

-LRB-

thousands

-RRB-

of

human

fugu

conserved

noncoding

sequences

-LRB-

CNSs

-RRB-

in

the

genome

At

the

other

extreme

primate

sequence

comparisons

-LRB-

Boffelli

et

al.

-RRB-

are

likely

to

capture

most

functional

components

of

the

human

genome

due

to

shared

biology

but

suffer

from

low

resolution

due

to

insufficient

neutral

divergence

among

primate

taxa

-LRB-

Eddy

-RRB-

Mammalian

genome

comparisons

have

been

proposed

as

a

compromise

between

the

requirements

of

sequence

divergence

and

biological

similarity

-LRB-

Cooper

et

al.

-RRB-

and

efforts

are

under

way

to

sequence

additional

mammalian

genomes

albeit

at

low

coverage

-LRB-

Margulies

et

al.

-RRB-

Here

we

present

an

empirical

and

genomic

evaluation

of

the

relative

merits

of

close

and

distant

sequence

comparisons

at

detecting

functional

noncoding

regions

in

the

human

genome

Our

study

complements

recent

theoretical

analyses

of

this

problem

-LRB-

Eddy

Stone

et

al.

-RRB-

and

is

directly

relevant

to

the

choice

of

species

for

whole-genome

sequencing

and

comparative

analysis

Additionally

given

that

regulatory

divergence

is

often

proposed

as

a

primary

mechanism

of

phenotypic

variation

it

is

important

to

characterize

the

rate

of

decline

of

noncoding

sequence

conservation

with

increasing

evolutionary

distance

To

impartially

assess

the

effect

of

evolutionary

distance

on

the

predictive

power

of

noncoding

conservation

we

used

a

uniform

computational

approach

-LRB-

Gumby

-RRB-

to

detect

CNSs

in

primate

mammalian

and

more

distant

sequence

alignments

Close

and

distant

comparisons

were

tested

at

three

diverse

genomic

loci

for

which

numerous

cis-regulatory

elements

have

been

characterized

by

Cold

Spring

Harbor

Laboratory

Press

ISSN

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Prabhakar

et

al.

experimentally

To

complement

these

empirical

results

we

performed

a

whole-genome

meta-analysis

of

human

rodent

human

mouse

chicken

human

mouse

frog

and

human

mouse

fish

whole-genome

CNS

sets

and

uncovered

a

general

principle

linking

shallow

and

deep

evolutionary

constraint

Finally

we

performed

systematic

in

vivo

testing

of

extremely

conserved

human

rodent

CNSs

in

an

in

vivo

transgenic

mouse

enhancer

assay

and

identified

with

high

specificity

developmental

enhancers

missed

by

human

fish

comparative

analysis

and

in

the

process

determined

that

genomic

context

is

a

critical

factor

in

identifying

such

enhancers

man

rodent

-RRB-

to

bp

at

its

deep

end

-LRB-

human

mouse

fish

-RRB-

Close-species

CNSs

are

longer

and

have

stronger

P-values

when

they

are

also

conserved

in

distant

species

For

example

while

human

rodent

CNSs

conserved

in

chicken

frog

and

fish

have

median

length

bp

and

P-value

human

rodent

CNSs

with

no

significant

nonmammalian

conservation

have

median

length

bp

and

P-value

Since

they

arise

from

the

characteristic

funnel

shape

of

ESPs

we

refer

to

these

two

trends

collectively

as

the

funnel

principle

of

noncoding

conservation

The

funnel

principle

suggests

that

highly

conserved

human

rodent

CNSs

should

be

enriched

for

conservation

in

nonmammals

To

quantify

this

enrichment

we

sorted

whole-genome

human

rodent

CNSs

by

P-value

and

calculated

the

fraction

of

human

mouse

chicken

human

mouse

frog

and

human

mouse

fish

CNSs

overlapped

by

various

quantiles

of

the

human

rodent

set

-LRB-

Fig.

-RRB-

Remarkably

we

see

that

the

top

of

the

human

rodent

CNSs

overlap

of

the

CNSs

in

the

human

mouse

fish

set

of

the

human

mouse

frog

set

and

of

the

human

mouse

chicken

set

indicating

that

a

large

fraction

of

deeply

conserved

CNSs

can

paradoxically

be

identified

through

shallow

evolutionary

comparisons

Similarly

almost

half

-LRB-

-RRB-

of

the

human

mouse

fish

CNSs

are

contained

in

the

top

of

the

human

rodent

set

In

terms

of

probabilities

the

highestscoring

human

rodent

CNSs

have

an

chance

of

being

significantly

conserved

in

chicken

while

the

lowest-scoring

ones

have

less

than

a

chance

of

being

in

the

human

mouse

chicken

set

-LRB-

Fig.

-RRB-

The

corresponding

likelihoods

are

and

in

human

mouse

frog

and

and

in

human

mouse

fish

-LRB-

see

Supplemental

Methods

for

analyses

of

potential

artifacts

-RRB-

Although

we

see

a

significant

correlation

between

ancient

and

recent

noncoding

conservation

there

are

also

important

differences

between

the

two

phenomena

While

half

-LRB-

-RRB-

of

the

whole-genome

human

mouse

fish

CNSs

have

a

human

rodent

P-value

there

are

human

rodent

CNSs

that

meet

the

same

P-value

criterion

and

yet

lack

significant

conservation

in

fish

These

data

indicate

that

human

rodent

analysis

is

capable

of

identifying

a

much

larger

set

of

unambiguously

constrained

noncoding

elements

than

are

obtainable

from

mammal

fish

comparison

Results

Assessment

of

whole-genome

noncoding

conservation

among

mammals

and

more

distant

species

Coding

exons

are

known

to

retain

sequence

similarity

across

great

evolutionary

distances

such

as

that

between

human

and

pufferfish

-LRB-

Aparicio

et

al.

-RRB-

In

contrast

intronic

and

intergenic

conserved

elements

evaporate

much

more

rapidly

thus

limiting

the

sensitivity

of

distant

noncoding

sequence

comparisons

To

quantify

the

decay

of

noncoding

sequence

conservation

with

evolutionary

distance

we

generated

whole-genome

CNS

sets

through

Gumby

analysis

-LRB-

see

Methods

-RRB-

of

the

following

three-way

genome

alignments

human

mouse

rat

-LRB-

HMR

-RRB-

human

mouse

chicken

-LRB-

HMG

-RRB-

human

mouse

frog

-LRB-

HMX

-RRB-

and

human

mouse

fish

-LRB-

HMF

-RRB-

-LRB-

see

Methods

-RRB-

In

the

human

rodent

alignment

we

identified

CNSs

representing

of

the

human

genome

-LRB-

Gumby

P-value

-RRB-

At

the

same

P-value

threshold

the

corresponding

CNS

statistics

were

and

for

human

mouse

chicken

and

for

human

mouse

frog

and

and

for

human

mouse

fish

As

expected

the

more

distantly

related

genomes

exhibit

markedly

less

conservation

relative

to

human

suggesting

a

reduction

in

sensitivity

that

offsets

their

increased

specificity

in

detecting

functional

noncoding

regions

In

order

to

correlate

mammalian

and

more

ancient

noncoding

conservation

we

classified

whole-genome

CNSs

into

four

primary

categories

Category

consists

of

human

rodent

CNSs

that

overlap

human

mouse

chicken

human

mouse

frog

and

human

mouse

fish

CNSs

Category

extends

only

to

human

mouse

frog

Category

to

human

mouse

chicken

and

Category

is

restricted

to

human

rodent

alone

Median

length

and

log

-LRB-

P-value

-RRB-

of

CNSs

within

a

given

category

and

phylogenetic

scope

-LRB-

for

instance

Category

and

human

mouse

frog

-RRB-

were

represented

by

the

dimensions

of

a

single

rectangular

block

Building

on

such

blocks

we

define

shapes

generated

by

stacking

blocks

of

the

same

category

as

evolutionary

stacking

patterns

-LRB-

ESPs

Fig.

D

-RRB-

Although

there

is

considerable

variation

Figure

Whole-genome

noncoding

conservation

and

the

funnel

principle

correlation

between

within

each

category

the

four

ESPs

char

ancient

and

recent

noncoding

conservation

-LRB-

funnel

principle

-RRB-

illustrated

by

four

evolutionary

stacking

acterizing

the

four

CNS

categories

illus

patterns

-LRB-

ESPs

-RRB-

HMR/G/X

F

human

mouse

rat/chicken/frog

fish

The

four

ESPs

depict

four

sets

of

whole-genome

HMR

CNS

categorized

by

their

most

ancient

overlapping

nonmammalian

CNS

trate

two

general

trends

CNSs

shrink

as

evolutionary

distance

increases

with

the

tallest

stacking

pattern

tapering

from

bp

at

its

phylogenetically

shallow

end

-LRB-

huStacked

below

the

rectangular

blocks

representing

HMR

CNSs

are

blocks

depicting

the

corresponding

ancient

CNSs

-LRB-

A

-RRB-

Category

CNSs

extend

to

HMF

-LRB-

B

-RRB-

Category

to

HMX

-LRB-

C

-RRB-

Category

to

HMG

and

-LRB-

D

-RRB-

Category

is

limited

to

HMR

Block

width

is

proportional

to

median

CNS

length

in

human

and

block

area

is

proportional

to

the

median

of

log

-LRB-

P-value

-RRB-

Block

height

thus

represents

degree

of

evolutionary

constraint

at

the

basepair

level

Error

bars

mark

the

range

from

the

to

the

percentile

of

CNS

length

The

funnel

principle

takes

its

name

from

the

funnel-like

shape

of

ESPs

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Close

comparisons

detect

human

regulatory

elements

dard

criterion

of

sequence

identity

over

bp

thus

necessitating

some

criterion

for

prioritizing

the

abundance

of

human

rodent

elements

It

has

previously

been

demonstrated

that

human

fish

conservation

constitutes

one

such

criterion

for

prioritizing

human

rodent

elements

in

that

restricting

the

analysis

to

human

fish

CNSs

facilitated

the

identification

of

transcriptional

enhancers

with

a

high

true-positive

rate

-LRB-

Nobrega

et

al.

-RRB-

In

the

present

study

we

attempted

to

achieve

a

comparable

truepositive

rate

and

yet

greater

sensitivity

by

focusing

instead

on

the

human

rodent

CNSs

with

the

most

extreme

-LRB-

i.e.

very

low

-RRB-

P-values

We

first

assessed

the

overlap

between

the

most

conserved

human

rodent

CNSs

and

human

fish

CNSs

in

the

locus

Consistent

with

the

above-described

funnel

principle

we

found

that

of

the

strongest

human

mouse

rat

CNSs

in

this

locus

-LRB-

Pvalue

-RRB-

are

conserved

in

at

least

one

of

the

three

available

fish

genomes

These

include

five

of

the

seven

human

fish

CNSs

previously

validated

through

in

vivo

enhancer

testing

-LRB-

Nobrega

et

al.

-RRB-

Thus

in

addition

to

having

a

high

likelihood

of

ancient

conservation

in

distant

species

the

human

rodent

CNSs

with

extreme

P-values

Figure

Whole-genome

noncoding

conservation

and

the

funnel

principle

strong

enrichment

for

are

also

enriched

for

known

developnonmammalian

conservation

in

top-ranked

-LRB-

lowest-P-value

-RRB-

human

mouse

rat

CNSs

-LRB-

A

-RRB-

Cumulative

mental

enhancers

relative

to

the

entire

fraction

of

nonmammalian

CNSs

overlapped

by

various

quantiles

of

human

mouse

rat

CNSs

The

top

set

of

human

mouse

CNSs

in

the

of

human

rodent

CNSs

-LRB-

by

P-value

-RRB-

constitute

a

set

of

sequences

with

a

high

degree

of

recent

evolutionary

conservation

that

encompass

of

whole-genome

human

mouse

fish

CNSs

vicinity

of

of

human

mouse

frog

CNSs

and

of

human

mouse

chicken

CNSs

-LRB-

B

-RRB-

The

To

demonstrate

the

independent

human

rodent

CNSs

are

binned

by

P-value

Vertical

bars

represent

the

fraction

of

human

rodent

CNSs

predictive

power

of

extreme

human

in

each

bin

that

overlap

more

ancient

CNSs

rodent

conservation

we

focused

on

the

CNSs

within

the

aforementioned

set

of

that

exhibited

no

conservation

in

fish

and

randomly

seIt

is

conceivable

that

the

funnel

principle

could

merely

relected

six

of

them

to

assay

for

transcriptional

enhancer

activity

in

flect

biases

in

the

algorithm

used

to

identify

CNSs

as

would

vivo

Our

assay

fuses

the

human

conserved

element

to

a

galacindeed

be

the

case

if

close

and

distant

comparisons

were

not

tosidase

reporter

vector

and

assesses

the

ability

of

the

conserved

performed

uniformly

or

if

the

CNS

sets

were

corrupted

with

fragment

to

drive

tissue-specific

expression

in

transgenic

mice

at

significant

numbers

of

false-positive

predictions

We

therefore

embryonic

day

-LRB-

-RRB-

-LRB-

Kothary

et

al.

-RRB-

-LRB-

see

performed

evolutionary

simulations

and

further

statistical

analySupplemental

Methods

-RRB-

For

three

of

the

six

extreme

human

ses

which

confirmed

that

such

artifacts

are

unlikely

-LRB-

see

Supplerodent

elements

tested

we

found

reproducible

galactosidase

mental

Methods

-RRB-

expression

localized

to

the

limbs

eyes

and

forebrain

consistent

In

vivo

experimental

validation

and

assessment

Four

with

aspects

of

the

endogenous

developmental

expression

pathuman-genome

loci

tern

of

-LRB-

Caubit

et

al.

Davis

et

al.

-RRB-

-LRB-

Fig.

-RRB-

galactosidase

expression

was

not

reproducibly

localized

to

any

In

vivo

testing

of

the

top-scoring

human

rodent

conserved

other

anatomical

structure

of

the

embryos

elements

To

test

the

predictive

power

of

human

rodent

conservation

P-values

on

a

larger

scale

we

retrospectively

analyzed

huIn

order

to

empirically

evaluate

the

power

of

extreme

human

man

fugu

and

ultra-conserved

-LRB-

Bejerano

et

al.

-RRB-

CNSs

that

rodent

conservation

to

identify

developmental

enhancers

we

were

tested

for

in

vivo

enhancer

function

as

part

of

a

separate

analyzed

the

locus

of

a

transcription-cofactor

gene

inwhole-genome

survey

-LRB-

L.

Pennacchio

N.

Ahituv

A.

Moses

M.

volved

in

limb

eye

and

brain

development

-LRB-

Davis

et

al.

-RRB-

Nobrega

S.

Prabhakar

M.

Shoukry

S.

Minovitsky

A.

Visel

I

The

genomic

region

containing

and

most

of

its

Dubchak

A.

Holt

et

al.

in

prep

-RRB-

Each

of

the

CNSs

was

flanking

intergenic

DNA

-LRB-

human

assigned

a

Gumby

human

rodent

conservation

score

-LRB-

log

-LRB-

PNCBI

Build

-RRB-

contains

human

mouse

CNSs

by

the

stan

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Prabhakar

et

al.

tive

results

since

of

the

CNSs

lie

within

Mb

of

a

RefSeq

transcription

start

site

It

is

possible

that

these

CNSs

are

not

transcriptional

enhancers

despite

extreme

human

rodent

conservation

or

that

their

activities

are

beyond

the

resolution/sensitivity

of

this

assay

or

perhaps

even

that

they

are

enhancers

at

a

different

embryonic

time

point

To

determine

if

extreme

human

mouse

rat

CNSs

are

better

able

to

capture

transcriptional

enhancers

flanking

key

developmental

transcription

factor

and

signaling

genes

we

tested

of

the

extreme

human

rodent

CNSs

-LRB-

Pvalue

-RRB-

that

lie

within

the

four

gene-poor

regions

on

chromosome

containing

the

aforementioned

developmental

genes

Of

these

CNSs

five

-LRB-

-RRB-

drove

reproducible

galactosidase

gene

expression

a

success

rate

Figure

locus

Identification

of

long-range

embryonic

enhancers

by

extreme

human

comparable

to

the

rate

of

observed

rodent

CNSs

with

no

conservation

in

fish

The

Gumby

human

mouse

rat

conservation

plot

in

the

lower

in

systematic

tests

of

human

fish

CNSs

half

of

the

figure

depicts

CNSs

-LRB-

red

vertical

bars

-RRB-

in

a

genomic

region

containing

the

on

the

same

chromosome

-LRB-

L.

Pennacgene

many

of

which

have

extremely

low

P-values

Blue

ticks

below

the

line

mark

exons

Six

of

chio

unpubl.

the

human

rodent

CNSs

with

P-value

and

no

conservation

in

fish

were

tested

for

enhancer

activity

at

embryonic

day

the

resulting

positives

and

negatives

are

marked

by

-LRB-

-RRB-

and

-LRB-

-RRB-

lbl.gov

-RRB-

The

disparity

in

success

rate

symbols

respectively

The

identified

enhancers

drove

reproducible

galactosidase

expression

in

limbs

among

different

loci

on

chromosome

eyes

and

brain

consistent

with

the

endogenous

expression

domains

of

suggests

that

genomic

context

must

be

considered

in

addition

to

conservation

score

in

selecting

candidate

enhancers

for

testing

at

this

embryvalue

-RRB-

-RRB-

We

found

that

positive

enhancers

had

significantly

onic

time

point

higher

conservation

scores

than

negatives

-LRB-

t-test

P-value

-RRB-

which

further

confirms

the

validity

of

using

human

rodent

Pvalues

to

prioritize

candidate

embryonic

enhancers

SCL

benchmark

Benefits

of

multiple-eutherian

comparison

Human

chromosome

Genomic

distribution

of

enhancers

active

at

embryonic

day

Though

reliable

indicators

of

function

human

fish

CNSs

tend

to

be

limited

in

number

and

strongly

clustered

in

genomic

regions

containing

a

handful

of

developmental

genes

and

transcription

factors

-LRB-

Sandelin

et

al.

Woolfe

et

al.

-RRB-

For

example

the

human

mouse

fish

CNSs

on

human

chromosome

are

highly

skewed

towards

four

gene-poor

loci

-LRB-

Fig.

-RRB-

containing

the

developmentally

regulated

genes

and

WWOX

The

density

graph

of

human

rodent

CNSs

on

this

chromosome

displays

peaks

at

the

same

locations

as

human

mouse

fish

and

also

additional

peaks

absent

in

human

mouse

fish

On

the

basis

of

positive

results

from

the

pilot

study

we

hypothesized

that

human

rodent

CNSs

with

extreme

P-values

could

also

identify

developmental

transcriptional

enhancers

in

these

additional

loci

to

compensate

for

their

poor

coverage

by

human

mouse

fish

CNSs

To

test

this

hypothesis

we

focused

on

the

top-scoring

non-fish

human

mouse

rat

CNSs

on

chromosome

-LRB-

Pvalue

-RRB-

of

which

were

located

outside

the

four

developmental

loci

encompassed

by

human

mouse

fish

conservation

We

tested

of

these

for

in

vivo

enhancer

function

through

our

mouse

enhancer

transgenesis

assay

and

found

to

our

surprise

that

not

a

single

one

of

these

CNSs

drove

a

reproducible

embryonic

expression

pattern

in

contrast

to

our

experience

at

the

locus

Excessive

distance

between

the

tested

CNSs

and

their

flanking

genes

is

an

unlikely

explanation

for

the

nega

The

human

stem

cell

leukemia

-LRB-

SCL

-RRB-

locus

provides

an

excellent

benchmark

for

evaluating

the

effect

of

phylogenetic

scope

on

comparative

sequence

analysis

based

on

the

detailed

experimental

definition

of

nine

nonexonic

murine

DNaseI-hypersensitive

sites

-LRB-

DHSs

-RRB-

and

one

additional

enhancer

in

the

genomic

region

containing

the

SCL

gene

and

its

flanking

intergenic

segments

-LRB-

Chapman

et

al.

-RRB-

Weak

homology

with

chicken

has

been

reported

-LRB-

G

ttgens

et

al.

-RRB-

for

a

subset

of

these

functional

elements

in

alignments

generated

using

DIALIGN

-LRB-

Brudno

et

al.

-RRB-

However

with

the

exception

of

one

of

the

SCL

promoters

of

the

experimentally

defined

elements

shows

significant

conservation

in

LAGAN

alignments

to

nonmammalian

species

such

as

chicken

frog

fugu

tetraodon

or

zebrafish

exemplifying

the

loss

of

sensitivity

entailed

by

distant

sequence

comparisons

-LRB-

data

not

shown

-RRB-

Given

the

poor

sensitivity

of

distant

sequence

comparisons

we

aligned

the

available

human

mouse

rat

and

dog

sequences

from

this

locus

-LRB-

human

NCBI

Build

-RRB-

using

MLAGAN

which

yielded

a

total

divergence

of

substitutions/site

At

the

default

P-value

threshold

of

Gumby

conservation

analysis

-LRB-

see

Supplemental

Methods

-RRB-

detected

eight

of

the

experimentally

defined

noncoding

elements

with

new

predictions

-LRB-

Fig.

-RRB-

Thus

as

in

the

case

of

P-value

prioritization

of

CNSs

in

just

a

few

eutherian

genomes

is

sufficient

to

identify

with

acceptable

true-positive

rate

functional

elements

missed

by

distant

sequence

comparison

Of

the

eight

functional

regions

detected

by

human

mouse

rat

dog

analysis

seven

have

P-value

whereas

only

one

of

the

new

pre

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Close

comparisons

detect

human

regulatory

elements

sequence

data

from

vertebrate

species

spanning

a

broad

range

of

evolutionary

distances

from

old

world

monkeys

to

teleost

fish

The

analyzed

genomic

region

-LRB-

human

NCBI

Build

-RRB-

spans

the

block

of

conserved

synteny

telomeric

to

the

globin

genes

the

globin

genes

themselves

and

the

gene

To

ensure

accurate

alignment

we

split

the

locus

into

four

subregions

and

aligned

each

subregion

separately

-LRB-

see

Supplemental

Methods

-RRB-

As

was

the

case

with

the

SCL

locus

conservation

of

the

benchmark

functional

elements

in

distant

species

is

extremely

limited

frog

and

fish

have

no

apparent

sequence

homology

with

any

of

the

noncoding

functional

elements

and

chicken

shows

homology

with

only

two

of

the

Sequence

conservation

is

also

limited

in

opossum

and

even

hedgehog

-LRB-

which

is

the

most

diverged

of

the

eutherians

considered

here

-RRB-

shows

no

similarity

for

five

of

the

benchmark

sequence

elements

-LRB-

see

Hughes

et

al.

-LSB-

-RSB-

for

a

detailed

breakFigure

Extreme

human

rodent

CNSs

with

no

conservation

in

fish

identify

enhancers

at

down

of

conservation

by

species

-RRB-

only

in

certain

genomic

regions

Upper

and

lower

plots

human

rodent

-LRB-

blue

-RRB-

and

human

mouse

To

assess

the

relative

power

of

varifish

-LRB-

scaled

by

a

factor

of

red

-RRB-

CNS

density

on

the

p

-LRB-

upper

-RRB-

and

q

-LRB-

lower

-RRB-

arms

of

human

chromoous

eutherian

comparisons

at

this

locus

some

We

tested

human

rodent

CNSs

with

P-value

and

no

conservation

in

fish

-LRB-

locations

marked

by

vertical

lines

-RRB-

for

enhancer

activity

at

embryonic

day

The

five

enhancers

we

selected

the

following

three

species

thus

identified

were

located

exclusively

in

two

of

the

four

loci

with

the

highest

human

mouse

fish

CNS

sets

-LRB-

-RRB-

simians

-LRB-

human

baboon

colodensity

on

the

chromosome

-LRB-

yellow

bands

-RRB-

These

loci

contain

developmentally

regulated

genes

all

bus

squirrel

monkey

owl

monkey

marbut

one

of

which

are

transcription

factors

moset

dusky

titi

-RRB-

-LRB-

-RRB-

primates

-LRB-

simian

group

plus

the

prosimian

galago

-RRB-

and

-LRB-

-RRB-

eutherians

minus

nonhuman

primates

and

hedgehog

-LRB-

hudictions

meets

the

same

criterion

suggesting

that

most

of

the

man

mouse

rat

cat

cow

pig

-RRB-

In

the

eutherian

set

which

had

known

functional

elements

in

this

region

are

clearly

distinguisha

total

branch

length

ranging

from

to

substitutions/site

able

from

neutrally

evolving

DNA

Indeed

when

we

reduced

the

across

the

four

subregions

Gumby

identified

benchmark

statistical

power

of

the

sequence

comparison

by

restricting

our

elements

with

new

predictions

-LRB-

P-value

-RRB-

Primate

and

analysis

to

human

and

mouse

alone

-LRB-

branch

length

substisimian

comparisons

were

performed

with

P-value

thresholds

adtutions/site

-RRB-

Gumby

still

identified

only

one

new

prediction

justed

to

yield

the

same

number

of

new

predictions

-LRB-

-RRB-

as

in

the

within

the

P-value

range

of

the

seven

prominent

benchmark

eleutherian

comparison

so

as

to

fairly

assess

relative

sensitivity

of

ements

Remarkably

even

mouse

rat

pairwise

comparison

-LRB-

Pthe

three

species

sets

The

resulting

sensitivity

of

the

primate

value

-RRB-

succeeded

in

identifying

six

of

the

benchmark

elecomparison

-LRB-

substitutions/site

-RRB-

was

while

the

ments

with

only

three

new

predictions

despite

the

minimal

neusimian

comparison

-LRB-

substitutions/site

-RRB-

had

a

sensitivtral

divergence

-LRB-

substitutions/site

-RRB-

between

the

two

rodents

ity

of

demonstrating

that

predictive

power

declines

as

evoThese

results

demonstrate

that

very

low

levels

of

neutral

selutionary

divergence

decreases

in

closely

related

species

Alquence

divergence

are

sufficient

for

identification

of

wellthough

the

simian

comparison

displayed

the

lowest

statistical

conserved

enhancers

and

DHSs

though

functional

elements

power

it

is

notable

that

a

sensitivity

of

-LRB-

-RRB-

and

a

truemarked

by

marginal

levels

of

sequence

conservation

are

better

positive

rate

of

at

least

-LRB-

-LRB-

-RRB-

-RRB-

were

achieved

by

comdetected

when

total

branch

length

is

augmented

by

introduction

paring

no

more

than

six

simian

genomes

with

human

in

any

of

of

additional

eutherians

to

the

species

set

the

four

subregions

globin

benchmark

Simians

primates

mammals

The

human

globin

locus

is

another

well-characterized

genomic

region

with

a

recent

synthesis

of

extensive

computational

and

empirical

analyses

cataloging

functional

noncoding

DNA

elements

-LRB-

Hughes

et

al.

-RRB-

The

elements

comprise

promoters

four

nonpromoter

DHSs

involved

in

transcriptional

regulation

and

two

putative

regulators

of

alternative

splicing

In

addition

this

locus

is

well

suited

to

evaluating

the

relative

merits

of

close

and

distant

sequence

comparisons

due

to

the

availability

of

Discussion

Ancient

human

fish

noncoding

conservation

has

been

the

mainstay

of

searches

for

enhancers

of

key

developmental

genes

whereas

mammalian

sequence

comparisons

have

been

considered

insufficiently

specific

especially

in

large

highly

conserved

intergenic

regions

harboring

hundreds

of

human

rodent

CNSs

This

dichotomy

disappears

in

light

of

the

funnel

principle

established

by

whole-genome

meta-analysis

of

close

and

distant

se

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Prabhakar

et

al.

is

limited

to

genes

with

complex

tissuespecific

expression

at

and

produces

false

negatives

in

loci

that

are

silent

or

regulated

in

a

relatively

simple

manner

at

this

embryonic

time

point

Since

such

in

vivo

tests

are

expensive

and

time-consuming

one

strategy

for

large-scale

functional

genomics

would

be

to

prioritize

loci

that

are

known

to

require

tissue-specific

regulation

at

the

selected

developmental

stage

Alternatively

one

could

prioritize

loci

of

unknown

function

that

nevertheless

have

a

high

density

of

ancient

-LRB-

human

mouse

fish

or

perhaps

even

human

mouse

frog

or

human

mouse

chicken

-RRB-

CNSs

since

the

loci

on

human

chromosome

yielding

high

success

rates

in

the

embryonic

enhancer

assay

were

originally

identified

solely

on

the

basis

Figure

SCL

locus

Benchmarking

eutherian

sequence

comparison

against

empirical

data

The

of

their

high

human

mouse

fish

CNS

displayed

genomic

region

contains

the

SCL

gene

and

its

flanking

intergenic

regions

Human

density

-LRB-

Fig.

-RRB-

For

instance

the

secmouse

rat

dog

CNSs

-LRB-

red

-RRB-

and

exonic

-LRB-

blue

-RRB-

conserved

regions

identified

by

Gumby

-LRB-

P-value

-RRB-

are

shown

as

vertical

bars

The

nine

CNSs

marked

with

asterisks

identify

benchmark

sequence

ond-largest

peak

of

human

mouse

fish

elements

with

empirical

evidence

of

cis-regulatory

function

-LRB-

green

rectangles

-RRB-

Additionally

there

are

CNS

density

in

the

human

genome

new

predictions

at

this

P-value

threshold

-LRB-

data

not

shown

-RRB-

occurs

between

the

first

exons

of

the

uncharacterized

huquence

comparisons

The

longer

and

more

constrained

a

mamman

genes

and

making

this

gene

a

candidate

malian

CNS

the

deeper

its

evolutionary

conservation

in

distant

for

deeply

conserved

and

highly

specific

early

developmental

vertebrates

-LRB-

on

average

-RRB-

and

vice

versa

Thus

extreme

human

regulation

rodent

P-values

serve

as

a

proxy

for

human

fish

conservation

Results

from

whole-genome

conservation

analysis

from

and

most

human

fish

CNSs

can

be

identified

through

human

benchmarking

using

pre-existing

functional

data

sets

from

three

rodent

analysis

alone

The

correspondence

between

recent

and

diverse

genomic

loci

and

from

systematic

in

vivo

characterizaancient

sequence

conservation

is

likely

to

grow

even

stronger

tion

of

new

enhancer

predictions

all

indicate

the

considerwhen

more

mammalian

genomes

are

added

to

the

human

able

statistical

power

of

sequence

comparisons

involving

just

a

mouse

rat

trio

-LRB-

Margulies

et

al.

-RRB-

A

relationship

between

few

-LRB-

three

to

six

-RRB-

eutherian

mammals

Another

consistent

theme

ancient

and

recent

noncoding

conservation

consistent

with

the

is

the

loss

of

sensitivity

when

more

distant

species

such

as

marfunnel

principle

has

been

reported

earlier

-LRB-

Ovcharenko

et

al.

supials

chicken

frog

and

fish

are

compared

with

human

either

-RRB-

though

the

correspondence

described

between

the

two

because

they

have

diverged

in

their

cis-regulatory

programs

or

evolutionary

scales

was

significantly

weaker

than

is

evident

from

because

of

stabilizing

selection

that

allows

the

regulatory

seour

results

Although

human

fish

sequence

comparison

identiquence

to

diverge

while

retaining

the

same

tissue

and

temporal

fies

developmental

enhancers

with

high

specificity

only

a

small

specificity

-LRB-

Ludwig

et

al.

Oda-Ishii

et

al.

-RRB-

At

the

other

fraction

of

the

expected

tens

or

hundreds

of

thousands

of

funcextreme

simian

sequence

comparisons

in

the

globin

locus

pertional

noncoding

elements

in

the

human

genome

are

conserved

formed

surprisingly

well

despite

their

low

total

divergence

-LRB-

in

fish

Extreme

P-value

mammalian

CNSs

form

a

superset

substitutions/site

-RRB-

achieving

a

sensitivity

of

and

a

trueering

the

majority

of

human

fish

as

well

as

a

much

larger

fracpositive

rate

of

at

least

The

funnel

principle

provides

one

tion

of

noncoding

functional

elements

in

the

human

genome

possible

explanation

Since

the

length

of

a

CNS

in

human

while

still

maintaining

a

low

false-positive

rate

More

generally

mouse

chicken

is

on

average

greater

than

that

of

its

equivalent

in

identifying

candidates

for

experimental

testing

the

tradeoff

in

human

mouse

fish

and

the

corresponding

mammalian

CNS

between

sensitivity

and

specificity

can

be

tuned

simply

by

adis

longer

still

the

size

of

constrained

blocks

in

simians

should

by

justing

the

threshold

conservation

score

-LRB-

P-value

-RRB-

in

contrast

to

extrapolation

be

greater

than

that

in

mammals

thus

offering

the

more

common

technique

of

varying

the

evolutionary

dissimian

comparisons

a

larger

target

and

partially

compensating

tance

between

the

compared

species

for

their

lower

neutral

divergence

In

vivo

assays

of

extreme

human

rodent

CNSs

with

no

It

is

common

in

analyses

of

the

statistical

power

of

sequence

conservation

in

fish

yielded

eight

positive

enhancers

at

comparisons

to

fix

the

size

of

an

individual

constrained

block

as

close

to

the

success

rate

of

human

fish

comparison

This

the

total

branch

length

-LRB-

neutral

divergence

-RRB-

is

varied

-LRB-

Margulies

experimentally

confirms

the

theoretical

prediction

that

extreme

et

al.

Eddy

Stone

et

al.

-RRB-

However

the

funnel

conservation

in

relatively

close

species

is

on

a

par

with

conserprinciple

implies

that

the

two

variables

are

not

independent

and

vation

between

highly

diverged

species

In

terms

of

genomic

disthat

constrained

blocks

shrink

when

more

distant

species

are

tribution

the

success

rate

was

among

human

rodent

CNSs

compared

Thus

for

a

given

total

branch

length

one

could

maxiin

the

neighborhood

of

developmental

genes

such

as

mize

block

size

and

consequently

statistical

power

by

choosing

and

WWOX

and

it

was

at

multiple

extremely

close

species

-LRB-

say

simian

primates

-RRB-

over

a

few

other

loci

on

human

chromosome

most

likely

since

the

assay

more

diverged

species

-LRB-

mammals

-RRB-

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Close

comparisons

detect

human

regulatory

elements

In

this

study

we

have

focused

on

the

identification

of

large

sequence

elements

with

empirical

evidence

of

cis-regulatory

function

such

as

enhancers

promoters

and

DNaseIhypersensitive

sites

which

have

typical

lengths

of

bp

or

more

Previous

theoretical

studies

-LRB-

Cooper

et

al.

Eddy

-RRB-

have

shown

that

higher-resolution

functional

prediction

at

the

level

of

a

transcription-factor

binding

site

-LRB-

bp

-RRB-

or

even

a

single

basepair

is

likely

to

require

sequence

from

more

than

ten

mammals

spread

across

the

clade

However

a

more

accessible

initial

strategy

might

be

to

use

the

existing

mammalian

genome

sequences

for

prediction

of

larger

higher-level

functional

elements

many

of

which

show

little

or

no

sequence

conservation

in

distant

species

It

should

also

be

possible

to

use

sequence

data

from

multiple

primates

to

identify

distant

regulatory

elements

that

evolve

too

rapidly

to

be

detected

in

mammalian

sequence

comparisons

We

have

demonstrated

that

such

strategies

are

highly

effective

based

on

systematic

benchmarking

of

sequence

comparisons

across

a

broad

range

of

phylogenetic

scopes

against

empirical

data

from

a

diverse

array

of

genomic

loci

While

the

sequencing

of

additional

mammalian

genomes

will

incrementally

facilitate

identification

of

large

regulatory

modules

in

the

human

genome

it

is

likely

that

the

greatest

strength

of

deep

mammalian

genomic

alignments

will

be

in

computationally

dissecting

their

internal

structure

value

-RRB-

constrained

regions

are

expected

to

evolve

at

times

the

neutral

rate

until

sequence

divergence

begins

to

saturate

The

log-odds

mismatch

score

for

the

sequence

pair

is

then

given

by

log

-LRB-

-LRB-

pN/R

-RRB-

pN

-RRB-

log

-LRB-

R

-RRB-

and

the

match

score

is

log

-LRB-

-LRB-

pN/R

-RRB-

-LRB-

pN

-RRB-

-RRB-

The

default

R-ratio

-LRB-

-RRB-

was

selected

to

optimize

the

sensitivity

specificity

tradeoff

in

detecting

empirically

defined

regulatory

elements

in

the

SCL

locus

-LRB-

Supplemental

Fig.

-RRB-

However

other

values

of

R

gave

very

similar

results

perhaps

because

seven

of

the

benchmark

regulatory

elements

are

very

significantly

conserved

and

therefore

robustly

distinguishable

from

the

rest

of

the

locus

Gap

characters

in

the

alignment

are

assigned

a

weighted

average

of

mismatch

and

match

scores

SG

-LRB-

pN

-RRB-

This

gap

score

is

guaranteed

to

be

negative

and

has

the

effect

of

lightly

penalizing

indels

a

compromise

between

treating

them

as

mismatches

which

is

the

usual

practice

for

algorithms

implementing

phylogenetic

shadowing

and

ignoring

them

altogether

as

is

common

when

the

species

set

is

sufficiently

diverged

Missing

data

i.e.

N

nucleotides

are

given

a

zero

score

However

one

drawback

of

any

scoring

scheme

that

penalizes

gaps

is

that

failure

to

flag

sequencing

gaps

with

N

characters

results

in

spurious

alignment

gaps

which

artificially

lower

the

conservation

score

of

the

corresponding

region

in

the

alignment

Each

alignment

column

is

scored

as

a

sum

of

pairwise

log-odds

scores

along

a

circular

tour

of

the

phylogenetic

tree

For

example

for

the

phylogenetic

tree

shown

in

Supplemental

Figure

the

circular-tour

column

score

is

S

S

-LRB-

human

mouse

-RRB-

S

-LRB-

mouse

cow

-RRB-

S

-LRB-

cow

dog

-RRB-

S

-LRB-

dog

lemur

-RRB-

S

-LRB-

lemur

human

-RRB-

This

averaging

scheme

traverses

each

branch

in

the

phylogenetic

tree

the

same

number

of

times

and

thus

permits

simple

phylogenetic

scoring

of

multiple

alignments

while

avoiding

the

drawbacks

of

sum-of-pairs

and

consensus

schemes

The

resulting

conservation

score

fulfills

the

requirements

of

Karlin-Altschul

statistics

in

that

positive

column

scores

are

possible

though

the

average

column

score

is

negative

-LRB-

Karlin

and

Altschul

-RRB-

Conserved

regions

appear

as

stretches

of

alignment

columns

with

a

high

aggregate

score

Gumby

traverses

the

alignment

from

left

to

right

initiating

a

conserved

segment

at

each

new

alignment

column

with

a

positive

score

and

extending

the

segment

until

the

aggregate

score

becomes

negative

at

which

point

the

right

edge

of

the

segment

is

retraced

to

the

alignment

column

yielding

the

maximal

aggregate

segment

score

This

procedure

guarantees

that

both

boundaries

of

each

segment

are

maximal

with

respect

to

segment

score

thus

eliminating

the

need

for

setting

arbitrary

window

sizes

and

allowing

detection

of

long

weakly

conserved

regions

as

well

as

short

strongly

conserved

elements

This

feature

is

also

important

in

achieving

generality

across

close

and

distant

sequence

comparisons

since

short

conserved

elements

are

not

likely

to

be

statistically

significant

in

sequence

comparisons

with

low

total

neutral

divergence

-LRB-

for

example

primate

shadowing

-RRB-

The

aggregate

score

of

the

alignment

columns

in

each

conserved

region

is

translated

into

a

P-value

using

Karlin-Altschul

statistics

As

is

the

case

with

the

BLAST

algorithm

-LRB-

Altschul

et

al.

-RRB-

the

P-value

of

a

given

conserved

element

varies

with

the

size

of

the

search

space

since

one

is

more

likely

to

find

a

given

degree

of

conservation

by

random

chance

in

a

long

alignment

than

in

a

short

alignment

To

make

the

P-values

comparable

across

alignments

of

different

lengths

Gumby

normalizes

them

to

refer

to

a

fictitious

fixed-length

alignment

with

the

same

statistical

properties

as

the

true

alignment

In

other

words

the

P-value

answers

the

question

What

is

the

likelihood

of

seeing

such

a

high

conservation

score

in

a

Methods

Development

of

a

uniform

statistical

scoring

scheme

for

sequence

conservation

-LRB-

Gumby

-RRB-

In

order

to

uniformly

evaluate

the

benefits

and

limitations

of

close

versus

distant

sequence

comparisons

we

sought

a

computational

algorithm

general

enough

to

process

alignments

at

all

evolutionary

distances

identify

conserved

regions

of

any

size

and

most

importantly

quantify

their

statistical

significance

-LRB-

Pvalue

-RRB-

For

generality

and

convenience

we

further

stipulated

that

the

method

should

require

no

training

data

no

prior

estimates

of

evolutionary

rates

-LRB-

branch

lengths

-RRB-

and

only

one

arbitrary

parameter

which

could

remain

fixed

across

all

evolutionary

distances

The

Gumby

program

meets

these

design

goals

by

making

minimal

assumptions

about

the

statistical

features

of

conserved

noncoding

regions

and

treating

the

sequence

alignment

as

its

own

training

set

Gumby

takes

its

name

from

the

Gumbel

distribution

which

is

the

extreme

value

distribution

underlying

Karlin-Altschul

statistics

The

input

data

are

an

alignment

a

phylogenetic

tree

-LRB-

topology

only

without

branch

lengths

-RRB-

and

annotations

of

coding

regions

-LRB-

optional

-RRB-

The

algorithm

proceeds

through

five

steps

Noncoding

regions

in

the

input

alignment

are

used

to

estimate

the

neutral

mismatch

frequency

pN

between

each

pair

of

aligned

sequences

This

is

done

simply

by

counting

the

number

of

mismatches

in

nonexonic

positions

and

dividing

by

the

number

of

aligned

nonexonic

positions

Failure

to

provide

exon

annotations

introduces

a

bias

in

the

mismatch

frequency

that

is

proportional

to

the

fraction

of

genomic

DNA

contained

in

exons

which

is

generally

small

in

vertebrates

A

log-odds

scoring

scheme

for

constrained

versus

neutral

evolution

is

then

independently

initialized

for

each

pair

of

sequences

based

on

the

assumption

that

the

mismatch

frequency

pC

in

constrained

regions

equals

pN/R

where

the

ratio

R

is

an

arbitrary

parameter

For

example

if

R

-LRB-

default

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Prabhakar

et

al.

pseudo-alignment

of

length

kb

that

is

generated

by

randomly

selecting

columns

from

the

given

alignment

The

P-value

is

related

to

the

expected

number

of

false

positives

in

a

region

-LRB-

i.e.

the

E-value

-RRB-

as

follows

P

exp

-LRB-

E

-RRB-

When

P

P

E.

Thus

the

P-value

also

doubles

as

an

estimate

of

the

false-positive

rate

One

pitfall

in

applying

Karlin-Altschul

statistics

to

global

alignments

is

the

fact

that

column

scores

are

not

identically

distributed

For

example

the

distribution

of

scores

at

positions

that

are

aligned

in

say

five

of

species

is

different

from

that

at

positions

that

are

unique

to

a

single

species

Also

the

number

of

aligned

species

is

highly

correlated

between

neighboring

alignment

columns

due

to

the

block-like

structure

generated

by

long

indels

The

fictitious

randomly

permuted

alignments

modeled

by

Karlin-Altschul

statistics

do

not

have

this

correlation

structure

and

are

therefore

less

likely

to

contain

local

high-scoring

segments

than

are

neutral

regions

in

real

alignments

Thus

a

straightforward

application

of

the

permutation-based

model

generates

unrealistically

strong

P-values

To

compensate

for

this

effect

Gumby

takes

the

conservative

approach

of

estimating

the

Karlin-Altschul

and

parameters

only

on

the

basis

of

columns

that

are

aligned

in

at

least

k

species

-LRB-

k

-RRB-

As

k

is

increased

the

number

of

columns

in

the

model

decreases

Gumby

sets

k

to

the

maximum

value

such

that

the

number

of

columns

in

the

model

is

at

least

of

the

length

of

the

base

sequence

Another

source

of

spurious

P-values

is

the

suppression

of

null-model

scores

by

large

indels

that

are

artifacts

of

missing

sequence

data

Gumby

again

takes

the

conservative

approach

of

penalizing

indels

only

after

the

model

has

been

generated

and

human

chromosome

Identifying

candidate

embryonic

enhancers

Due

to

the

extreme

levels

of

noncoding

constraint

observed

in

the

locus

we

identified

human

mouse

rat

CNSs

in

this

genomic

region

using

a

Gumby

R-ratio

of

and

a

P-value

threshold

of

However

in

the

larger

subsequent

analysis

of

extremely

conserved

human

mouse

rat

CNSs

on

human

chromosome

we

retained

the

default

R-ratio

of

and

relaxed

the

P-value

threshold

to

so

as

to

obtain

a

larger

and

more

representative

set

of

conserved

elements

In

addition

to

the

aforementioned

filters

for

transcriptional

evidence

extreme

human

rodent

CNSs

with

more

than

bp

of

overlap

with

human

or

nonhuman

unspliced

ESTs

from

more

than

one

library

were

discarded

as

were

CNSs

overlapping

single

unspliced

ESTs

with

BLASTX

matches

-LRB-

E-value

-RRB-

in

peptide

sequence

databases

-LRB-

Altschul

et

al.

-RRB-

CNSs

within

bp

of

exons

of

known

genes

were

also

removed

so

as

to

eliminate

potential

regulators

of

pre-mRNA

splicing

The

remaining

human

rodent

CNSs

were

filtered

for

overlap

with

Gumby

human

mouse

fish

CNSs

or

with

human

fugu

human

tetraodon

or

human

zebrafish

net

alignments

in

the

UCSC

Genome

Browser

-LRB-

ucsc.edu

-RRB-

Acknowledgments

We

thank

Alexander

Poliakov

and

Inna

Dubchak

for

incorporating

Gumby

into

the

VISTA

web

server

Nadav

Ahituv

for

the

vector

Jim

Hughes

for

feedback

regarding

globin

sequence

analysis

Qian-fei

Wang

Jan-Fang

Cheng

and

Michael

Brudno

for

numerous

productive

conversations

during

development

of

Gumby

and

James

Noonan

Nadav

Ahituv

Qian-fei

Wang

and

Dario

Boffelli

for

comments

on

the

manuscript

F.P.

was

supported

by

a

fellowship

from

the

Canadian

Institutes

of

Health

Research

Research

was

conducted

at

the

E.O.

Lawrence

Berkeley

National

Laboratory

supported

by

the

Grant

Berkeley-PGA

under

the

Programs

for

Genomic

Application

funded

by

National

Heart

Lung

and

Blood

Institute

USA

and

performed

under

Department

of

Energy

Contract

University

of

California

Gumby

availability

Gumby

conservation

analysis

is

automatically

performed

when

DNA

sequences

are

submitted

to

the

VISTA

server

-LRB-

Frazer

et

al.

-RRB-

-LRB-

-RRB-

and

conserved

regions

are

graphically

displayed

using

RankVISTA

Pre-computed

wholegenome

Gumby

results

based

on

pairwise

alignments

are

available

as

RankVISTA

tracks

on

the

VISTA

Browser

-LRB-

http

pipeline.lbl.gov

-RRB-

Gumby

source

code

is

available

at

http

pga.lbl.gov

gumby

Generation

of

whole-genome

CNS

sets

Whole-genome

CNS

sets

were

generated

as

described

in

-LRB-

Ahituv

et

al.

-RRB-

Syntenic

blocks

between

the

compared

genomes

were

defined

by

PARAGON

globally

aligned

using

MLAGAN

-LRB-

Brudno

et

al.

-RRB-

and

scanned

for

statistically

significant

conserved

regions

using

Gumby

This

procedure

minimizes

false

alignments

since

only

syntenic

conservation

is

allowed

As

in

previous

large-scale

analyses

-LRB-

Ahituv

et

al.

-RRB-

we

sought

to

improve

alignment

accuracy

by

performing

three-way

-LRB-

human

mouse

nonmammal

-RRB-

instead

of

pairwise

-LRB-

human

nonmammal

-RRB-

genome

alignments

-LRB-

Brudno

et

al.

-RRB-

Consequently

we

analyzed

alignments

between

the

human

and

mouse

genomes

and

those

of

rat

-LRB-

HMR

-RRB-

chicken

-LRB-

HMG

-RRB-

frog

-LRB-

HMX

-RRB-

and

fish

-LRB-

HMF

union

of

human

mouse

fugu

human

mouse

tetraodon

and

human

mouse

zebrafish

-RRB-

CNSs

were

defined

as

conserved

regions

-LRB-

P-value

-RRB-

that

do

not

overlap

known

genes

mRNAs

or

spliced

ESTs

in

any

of

the

aligned

genomes

This

P-value

threshold

is

a

factor

of

below

the

default

threshold

of

and

is

therefore

not

suitable

for

high-sensitivity

identification

of

noncoding

regulatory

elements

However

it

severely

limits

false-positive

predictions

and

therefore

facilitates

reliable

characterization

of

the

relations

between

recent

and

ancient

noncoding

conservation

References

Ahituv

N.

Rubin

E.M.

and

Nobrega

M.A.

Exploiting

human

fish

genome

comparisons

for

deciphering

gene

regulation

Hum

Mol

Genet

Ahituv

N.

Prabhakar

S.

Poulin

F.

Rubin

E.M.

and

Couronne

O.

Mapping

cis-regulatory

domains

in

the

human

genome

using

multi-species

conservation

of

synteny

Hum

Mol

Genet

Altschul

S.F.

Gish

W.

Miller

W.

Myers

E.W.

and

Lipman

D.J.

Basic

local

alignment

search

tool

J.

Mol

Biol

Aparicio

S.

Chapman

J.

Stupka

E.

Putnam

N.

Chia

J.M.

Dehal

P.

Christoffels

A.

Rash

S.

Hoon

S.

Smit

A.

et

al.

Whole-genome

shotgun

assembly

and

analysis

of

the

genome

of

Fugu

rubripes

Science

Bejerano

G.

Haussler

D.

and

Blanchette

M.

Into

the

heart

of

darkness

Large-scale

clustering

of

human

non-coding

DNA

Bioinformatics

-LRB-

Suppl

-RRB-

Bejerano

G.

Pheasant

M.

Makunin

I.

Stephen

S.

Kent

W.J.

Mattick

J.S.

and

Haussler

D.

Ultraconserved

elements

in

the

human

genome

Science

Boffelli

D.

McAuliffe

J.

Ovcharenko

D.

Lewis

K.D.

Ovcharenko

I.

Pachter

L.

and

Rubin

E.M.

Phylogenetic

shadowing

of

primate

sequences

to

find

functional

regions

of

the

human

genome

Science

Brenner

S.

Elgar

G.

Sandford

R.

Macrae

A.

Venkatesh

B.

and

Aparicio

S.

Characterization

of

the

pufferfish

-LRB-

Fugu

-RRB-

genome

as

a

compact

model

vertebrate

genome

Nature

Genome

Research

Downloaded

from

on

May

Published

by

Cold

Spring

Harbor

Laboratory

Press

Close

comparisons

detect

human

regulatory

elements

Brudno

M.

Chapman

M.

G

ttgens

B.

Batzoglou

S.

and

Morgenstern

B.

Fast

and

sensitive

multiple

alignment

of

large

genomic

sequences

BMC

Bioinformatics

Brudno

M.

Do

C.B.

Cooper

G.M.

Kim

M.F.

Davydov

E.

Green

E.D.

Sidow

A.

and

Batzoglou

S.

LAGAN

and

Multi-LAGAN

Efficient

tools

for

large-scale

multiple

alignment

of

genomic

DNA

Genome

Res

Caubit

X.

Thangarajah

R.

Theil

T.

Wirth

J.

Nothwang

H.G.

Ruther

U.

and

Krauss

S.

Mouse

Dac

a

novel

nuclear

factor

with

homology

to

Drosophila

dachshund

shows

a

dynamic

expression

in

the

neural

crest

the

eye

the

neocortex

and

the

limb

bud

Dev

Dyn

Chapman

M.A.

Donaldson

I.J.

Gilbert

J.

Grafham

D.

Rogers

J.

Green

A.R.

and

G

ttgens

B.

Analysis

of

multiple

genomic

sequence

alignments

A

web

resource

online

tools

and

lessons

learned

from

analysis

of

mammalian

SCL

loci

Genome

Res

Cooper

G.M.

Brudno

M.

Green

E.D.

Batzoglou

S.

and

Sidow

A.

Quantitative

estimates

of

sequence

divergence

for

comparative

analyses

of

mammalian

genomes

Genome

Res

Cooper

G.M.

Stone

E.A.

Asimenos

G.

Green

E.D.

Batzoglou

S.

and

Sidow

A.

Distribution

and

intensity

of

constraint

in

mammalian

genomic

sequence

Genome

Res

Davis

R.J.

Shen

W.

Heanue

T.A.

and

Mardon

G.

Mouse

Dach

a

homologue

of

Drosophila

dachshund

is

expressed

in

the

developing

retina

brain

and

limbs

Dev

Genes

Evol

de

la

Calle-Mustienes

E.

Feijoo

C.G.

Manzanares

M.

Tena

J.J.

Rodriguez-Seguel

E.

Letizia

A.

Allende

M.L.

and

Gomez-Skarmeta

J.L.

A

functional

survey

of

the

enhancer

activity

of

conserved

non-coding

sequences

from

vertebrate

Iroquois

cluster

gene

deserts

Genome

Res

Eddy

S.R.

A

model

of

the

statistical

power

of

comparative

genome

sequence

analysis

PLoS

Biol

Frazer

K.A.

Pachter

L.

Poliakov

A.

Rubin

E.M.

and

Dubchak

I.

VISTA

Computational

tools

for

comparative

genomics

Nucleic

Acids

Res

G

ttgens

B.

Barton

L.M.

Gilbert

J.G.

Bench

A.J.

Sanchez

M.J.

Bahn

S.

Mistry

S.

Grafham

D.

McMurray

A.

Vaudin

M.

et

al.

Analysis

of

vertebrate

SCL

loci

identifies

conserved

enhancers

Nat

Biotechnol

Hughes

J.R.

Cheng

J.F.

Ventress

N.

Prabhakar

S.

Clark

K.

Anguita

E.

De

Gobbi

M.

de

Jong

P.

Rubin

E.

and

Higgs

D.R.

Annotation

of

cis-regulatory

elements

by

identification

subclassification

and

functional

assessment

of

multispecies

conserved

sequences

Proc

Natl.

Acad

Sci

Karlin

S.

and

Altschul

S.F.

Methods

for

assessing

the

statistical

significance

of

molecular

sequence

features

by

using

general

scoring

schemes

Proc

Natl.

Acad

Sci

King

D.C.

Taylor

J.

Elnitski

L.

Chiaromonte

F.

Miller

W.

and

Hardison

R.C.

Evaluation

of

regulatory

potential

and

conservation

scores

for

detecting

cis-regulatory

modules

in

aligned

mammalian

genome

sequences

Genome

Res

Kothary

R.

Clapoff

S.

Darling

S.

Perry

M.D.

Moran

L.A.

and

Rossant

J.

Inducible

expression

of

an

hybrid

gene

in

transgenic

mice

Development

Lettice

L.A.

Heaney

S.J.

Purdie

L.A.

Li

L.

de

Beer

P.

Oostra

B.A.

Goode

D.

Elgar

G.

Hill

R.E.

and

de

Graaff

E.

A

long-range

Shh

enhancer

regulates

expression

in

the

developing

limb

and

fin

and

is

associated

with

preaxial

polydactyly

Hum

Mol

Genet

Ludwig

M.Z.

Bergman

C.

Patel

N.H.

and

Kreitman

M.

Evidence

for

stabilizing

selection

in

a

eukaryotic

enhancer

element

Nature

Margulies

E.H.

Blanchette

M.

Haussler

D.

and

Green

E.D.

Identification

and

characterization

of

multi-species

conserved

sequences

Genome

Res

Margulies

E.H.

Vinson

J.P.

Miller

W.

Jaffe

D.B.

Lindblad-Toh

K.

Chang

J.L.

Green

E.D.

Lander

E.S.

Mullikin

J.C.

and

Clamp

M.

An

initial

strategy

for

the

systematic

identification

of

functional

elements

in

the

human

genome

by

low-redundancy

comparative

sequencing

Proc

Natl.

Acad

Sci

Nobrega

M.A.

Ovcharenko

I.

Afzal

V.

and

Rubin

E.M.

Scanning

human

gene

deserts

for

long-range

enhancers

Science

Oda-Ishii

I.

Bertrand

V.

Matsuo

I.

Lemaire

P.

and

Saiga

H.

Making

very

similar

embryos

with

divergent

genomes

Conservation

of

regulatory

mechanisms

of

Otx

between

the

ascidians

Halocynthia

roretzi

and

Ciona

intestinalis

Development

Ovcharenko

I.

Stubbs

L.

and

Loots

G.G.

Interpreting

mammalian

evolution

using

Fugu

genome

comparisons

Genomics

Pennacchio

L.A.

and

Rubin

E.M.

Genomic

strategies

to

identify

mammalian

regulatory

sequences

Nat

Rev.

Genet

Sandelin

A.

Bailey

P.

Bruce

S.

Engstrom

P.G.

Klos

J.M.

Wasserman

W.W.

Ericson

J.

and

Lenhard

B.

Arrays

of

ultraconserved

non-coding

regions

span

the

loci

of

key

developmental

genes

in

vertebrate

genomes

BMC

Genomics

Stone

E.A.

Cooper

G.M.

and

Sidow

A.

Trade-offs

in

detecting

evolutionarily

constrained

sequence

by

comparative

genomics

Annu

Rev.

Genomics

Hum

Genet

Woolfe

A.

Goodson

M.

Goode

D.K.

Snell

P.

McEwen

G.K.

Vavouri

T.

Smith

S.F.

North

P.

Callaway

H.

Kelly

K.

et

al.

Highly

conserved

non-coding

sequences

are

associated

with

vertebrate

development

PLoS

Biol

Received

September

accepted

in

revised

form

April

Genome

Research

constraint prediction annotation evolution rate human conserved mammals transcription alignment selection