Vol

November

doi

ARTICLES

Discovery

of

functional

elements

in

Drosophila

genomes

using

evolutionary

signatures

Alexander

Michael

F.

Pouya

Jakob

S.

Leopold

Joseph

W.

Madeline

A.

Matthew

D.

Sushmita

Ameya

N.

J.

Graham

Julius

Harvard

FlyBase

curators

-LCB-

Berkeley

Drosophila

Genome

Project

-LCB-

Emily

Angie

S.

Anat

Benedict

Seung-Won

Mira

V.

Morgan

L.

Benjamin

J.

Bryanne

E.

Stein

Jacques

van

Bassem

Donald

G.

Deborah

A.

Michael

Michael

Matthew

W.

Yongkyu

Colin

N.

Lior

W.

James

David

Eric

C.

David

P.

Gregory

J.

Thomas

C.

Michael

B.

Andrew

G.

Douglas

Susan

E.

William

M.

Manolis

Sequencing

of

multiple

related

species

followed

by

comparative

genomics

analysis

constitutes

a

powerful

approach

for

the

systematic

understanding

of

any

genome

Here

we

use

the

genomes

of

Drosophila

species

for

the

de

novo

discovery

of

functional

elements

in

the

fly

Each

type

of

functional

element

shows

characteristic

patterns

of

change

or

evolutionary

signatures

dictated

by

its

precise

selective

constraints

Such

signatures

enable

recognition

of

new

protein-coding

genes

and

exons

spurious

and

incorrect

gene

annotations

and

numerous

unusual

gene

structures

including

abundant

stop-codon

readthrough

Similarly

we

predict

non-protein-coding

RNA

genes

and

structures

and

new

microRNA

-LRB-

miRNA

-RRB-

genes

We

provide

evidence

of

miRNA

processing

and

functionality

from

both

hairpin

arms

and

both

DNA

strands

We

identify

several

classes

of

pre

and

post-transcriptional

regulatory

motifs

and

predict

individual

motif

instances

with

high

confidence

We

also

study

how

discovery

power

scales

with

the

divergence

and

number

of

species

compared

and

we

provide

general

guidelines

for

comparative

studies

The

sequencing

of

the

human

genome

and

the

genomes

of

dozens

of

other

metazoan

species

has

intensified

the

need

for

systematic

methods

to

extract

biological

information

directly

from

DNA

sequence

Comparative

genomics

has

emerged

as

a

powerful

methodology

for

this

Comparison

of

few

-LRB-

two

four

-RRB-

closely

related

genomes

has

proven

successful

for

the

discovery

of

protein-coding

RNA

miRNA

and

catalogues

of

regulatory

The

resolution

and

discovery

power

of

these

studies

should

increase

with

the

number

of

in

principle

enabling

the

systematic

discovery

of

all

conserved

functional

elements

The

fruitfly

Drosophila

melanogaster

is

an

ideal

system

for

developing

and

evaluating

comparative

genomics

methodologies

Over

the

past

century

Drosophila

has

been

a

pioneering

model

in

which

many

of

the

basic

principles

governing

animal

development

and

population

biology

were

In

the

past

decade

the

genome

sequence

of

D.

melanogaster

provided

one

of

the

first

systematic

views

The

Broad

Institute

Massachusetts

Institute

of

Technology

and

Harvard

University

Cambridge

Massachusetts

USA

Science

and

Artificial

Intelligence

Laboratory

MIT

Cambridge

Massachusetts

USA

Bioinformatics

Centre

Department

of

Molecular

Biology

University

of

Copenhagen

Ole

Maaloes

Vej

Copenhagen

N

Denmark

for

Biomolecular

Science

and

Engineering

University

of

California

Santa

Cruz

California

USA

Trust

Sanger

Institute

Wellcome

Trust

Genome

Campus

Hinxton

Cambridge

UK

of

Computer

Science

University

of

Tartu

Estonia

LBNL

Cyclotron

Road

MS

Berkeley

California

USA

The

Biological

Laboratories

Harvard

University

Divinity

Avenue

Cambridge

Massachusetts

USA

of

Computer

Science

University

of

New

Mexico

Albuquerque

New

Mexico

USA

of

Biology

MIT

Cambridge

Massachusetts

USA

Institute

Cambridge

Massachusetts

USA

Spring

Harbor

Laboratory

Watson

School

of

Biological

Sciences

Bungtown

Road

Cold

Spring

Harbor

New

York

USA

of

California

San

Francisco/University

of

California

Berkeley

Joint

Graduate

Group

in

Bioengineering

Berkeley

California

USA

Nucleotide

Sequence

Submissions

European

Bioinformatics

Institute

Wellcome

Trust

Genome

Campus

Hinxton

Cambridge

UK

of

Cell

Biology

and

Molecular

Medicine

MSB

South

Orange

Avenue

UMDNJ-New

Jersey

Medical

School

Newark

New

Jersey

USA

of

Biology

and

School

of

Informatics

Indiana

University

Indiana

USA

of

Biology

Connecticut

College

New

London

Connecticut

USA

of

Neurogenetics

Department

of

Molecular

and

Developmental

Genetics

VIB

Leuven

Belgium

of

Human

Genetics

K.

U.

Leuven

School

of

Medicine

Leuven

Belgium

de

Biologie

Moleculaire

Universite

Libre

de

Bruxelles

Brussels

Belgium

of

Biology

Indiana

University

Bloomington

Indiana

USA

of

Mathematics

and

Computer

Science

Wesleyan

University

Middletown

Connecticut

USA

Department

Wesleyan

University

Middletown

Connecticut

USA

of

Biostatistics

and

Medical

Informatics

University

of

Wisconsin-Madison

Madison

Wisconsin

USA

of

Mathematics

University

of

California

at

Berkeley

Berkeley

California

USA

Department

of

Computer

Science

University

of

California

at

Berkeley

Berkeley

California

USA

of

Developmental

Biology

Memorial

Sloan-Kettering

Cancer

Center

New

York

New

York

USA

Group

in

Biophysics

Department

of

Molecular

and

Cell

Biology

and

Center

for

Integrative

Genomics

University

of

California

Berkeley

California

USA

Berkeley

National

Laboratory

Life

Sciences

Division

Berkeley

California

USA

of

Molecular

Biology

and

Genetics

Cornell

University

Ithaca

New

York

USA

Bioscience

Corporation

Cummings

Center

Suite

Beverly

Massachusetts

USA

Department

of

Molecular

and

Cellular

Biology

Harvard

University

Cambridge

Massachusetts

USA

These

authors

contributed

equally

to

this

work

-LCB-

Lists

of

participants

and

affiliations

appear

at

the

end

of

the

paper

Nature

Publishing

Group

ARTICLES

NATURE

Vol

November

of

a

metazoan

and

the

ongoing

effort

by

the

FlyBase

and

Berkeley

Drosophila

Genome

Project

-LRB-

BDGP

-RRB-

groups

established

a

systematic

high-quality

genome

Moreover

the

fruitfly

benefits

from

extensive

experimental

which

enable

novel

functional

elements

to

be

systematically

tested

and

used

in

the

evaluation

of

genetic

The

fly

research

community

has

sequenced

assembled

and

annotated

the

genomes

of

Drosophila

at

a

range

of

evolutionary

distances

from

D.

melanogaster

-LRB-

Fig.

b

-RRB-

The

analysis

of

these

genomes

was

organized

around

two

complementary

aims

The

first

described

in

an

accompanying

was

to

understand

the

evolution

of

genes

and

chromosomes

on

the

Drosophila

phylogeny

and

how

it

relates

to

speciation

and

adaptation

The

second

goal

described

here

was

to

develop

general

comparative

methodologies

to

discover

and

refine

functional

elements

in

D.

melanogaster

using

the

genomes

and

to

investigate

the

scaling

of

discovery

power

and

its

implications

for

studies

in

vertebrates

-LRB-

Fig.

-RRB-

Here

we

report

genome-wide

alignments

of

the

species

-LRB-

Supplementary

Information

-RRB-

and

the

systematic

discovery

of

euchromatic

functional

elements

in

the

D.

melanogaster

genome

We

predict

and

refine

thousands

of

protein-coding

exons

RNA

genes

and

structures

miRNAs

pre

and

post-transcriptional

regulatory

motifs

and

regulatory

targets

We

validate

many

of

these

elements

using

complementary

DNA

-LRB-

cDNA

-RRB-

sequencing

human

curation

small

RNA

sequencing

and

correlation

with

experimentally

supported

transcription

factor

and

miRNA

targets

In

addition

our

analysis

leads

to

several

specific

biological

findings

listed

below

We

predict

novel

polycistronic

transcripts

genes

with

apparent

stop-codon

readthrough

and

several

candidate

programmed

frameshifts

with

potential

roles

in

regulation

localization

and

function

of

the

corresponding

protein

products

We

make

available

the

first

systematic

prediction

of

general

RNA

genes

and

structures

-LRB-

non-coding

RNAs

-LRB-

ncRNAs

-RRB-

-RRB-

in

Drosophila

including

several

structures

probably

involved

in

translational

regulation

and

adenosine-to-inosine

RNA

editing

-LRB-

A-to-I

editing

-RRB-

We

present

comparative

and

experimental

evidence

that

some

miRNA

loci

yield

multiple

functional

products

from

both

hairpin

arms

or

from

both

DNA

strands

thereby

increasing

the

versatility

and

complexity

of

miRNA-mediated

regulation

We

provide

further

comparative

evidence

for

miRNA

targeting

in

protein-coding

exons

We

report

an

initial

network

of

pre

and

post-transcriptional

regulatory

targets

in

Drosophila

on

the

basis

of

individual

highconfidence

motif

occurrences

Comparative

genomics

and

evolutionary

signatures

Although

multiple

closely

related

genomes

provide

sufficient

neutral

divergence

for

recognition

of

functional

regions

in

stretches

of

highly

conserved

measures

of

nucleotide

conservation

alone

do

not

distinguish

between

different

types

of

functional

elements

Moreover

functional

elements

that

tolerate

abundant

silent

mutations

such

as

protein-coding

exons

and

many

regulatory

motifs

might

not

be

detected

when

searching

on

the

basis

of

strong

nucleotide

conservation

Across

many

genomes

spanning

larger

evolutionary

distances

the

information

in

the

patterns

of

sequence

change

reveals

evolutionary

signatures

-LRB-

Fig.

-RRB-

that

can

be

used

for

systematic

genome

annotation

Protein-coding

regions

show

highly

constrained

codon

substitution

and

insertions

and

deletions

that

are

heavily

a

melanogaster

group

Subgenus

Sophophora

D.

melanogaster

melanogaster

D.

simulans

subgroup

D.

sechellia

D.

yakuba

D.

erecta

D.

ananassae

D.

pseudoobscura

D.

persimilis

D.

willistoni

D.

mojavensis

D.

virilis

D.

grimshawi

substitutions

per

site

b

Subgenus

Drosophila

el

D.s

D.sim

ec

D.mel

D.sim

D.sec

D.yak

D.ere

D.ana

D.pse

D.per

D.wil

D.moj

D.vir

D.gri

D.v

D.gir

ri

D.m

o

D.w

j

il

D.a

D.p

D.per

se

Flies

-LRB-

pairwise

-RRB-

Vertebrates

-LRB-

pairwise

-RRB-

D.m

c

D.e

D.y

re

ak

m

ssu

Pla

ty

Chi

pus

cke

n

Liza

rd

Fro

g

kleb

Hum

D.m

Ch

an

el

Rheimp

sp

B

s

-LRB-

D

T

ushb

us

sim

ree

ab

shr

y

-RRB-

H

ew

Eleporse

sp

han

-LRB-

D

yak

A

D

t

rma

og

-RRB-

dillo

Cat

Cow

Rab

Gui

b

n

Hedea-pi

it

geh

g

Shr

og

Ten

ew

Mo

rec

use

Rat

-LRB-

D

sp.

ana

-RRB-

-LRB-

D

sp.

pse

-RRB-

Stic

Flies

-LRB-

multi-species

-RRB-

Mammals

Hum

an

-LRB-

rhe

sp.

sus

-RRB-

-LRB-

tr

sp.

ees

hrew

-RRB-

-LRB-

m

sp.

ous

e

-RRB-

s

-LRB-

do

p.

g

-RRB-

-LRB-

el

sp.

eph

ant

-RRB-

-LRB-

op

sp.

oss

um

-RRB-

-LRB-

multi-species

-RRB-

Figure

Phylogeny

and

alignment

of

Drosophila

species

a

Phylogenetic

tree

relating

the

Drosophila

species

estimated

from

fourfold

degenerate

sites

-LRB-

Supplementary

Methods

-RRB-

The

species

span

a

total

branch

length

of

substitutions

per

neutral

site

b

Gene

order

conservation

for

a

region

of

chromosome

centred

on

for

which

we

predict

a

new

exon

-LRB-

Fig.

-RRB-

and

spanning

genes

Colour

represents

the

direction

of

transcription

Boxes

represent

full

gene

models

Individual

exons

and

introns

are

not

shown

c

Comparison

of

evolutionary

distances

spanned

by

fly

and

vertebrate

trees

Pairwise

and

multi-species

distances

-LRB-

in

substitutions

per

fourfold

degenerate

site

-RRB-

are

shown

from

D.

melanogaster

and

from

human

as

reference

genomes

Note

that

species

with

longer

branches

-LRB-

for

example

mouse

-RRB-

show

higher

pairwise

distances

not

always

reflecting

the

order

of

divergence

Multi-species

distances

include

all

species

within

a

phylogenetic

clade

Nature

Publishing

Group

-LRB-

pl

sp.

atyp

us

-RRB-

-LRB-

D

sp.

gri

-RRB-

-LRB-

D

sp.

wil

-RRB-

ack

Tet

Fugu

rao

don

Zeb

rafi

sh

Me

dak

a

Opo

NATURE

Vol

November

ARTICLES

biased

to

be

multiples

of

-LRB-

Fig.

-RRB-

RNA

genes

and

structures

tolerate

substitutions

that

preserve

base

-LRB-

Fig.

-RRB-

MicroRNA

hairpins

show

a

characteristic

conservation

profile

with

high

conservation

in

the

stem

and

mutations

in

loop

-LRB-

Fig.

-RRB-

Finally

regulatory

motifs

are

marked

by

high

levels

of

genome-wide

and

post-transcriptional

motifs

show

strand-biased

-LRB-

Fig.

e

-RRB-

We

find

that

these

signatures

can

be

much

more

precise

for

genome

annotation

than

the

overall

level

of

nucleotide

conservation

-LRB-

for

example

Fig.

-RRB-

Revisiting

the

protein-coding

gene

catalogue

The

annotation

of

protein-coding

genes

remains

difficult

in

metazoan

genomes

owing

to

short

exons

and

complex

gene

structures

a

D.mel

D.sim

D.sec

D.yak

D.ere

D.ana

D.pse

D.per

D.wil

D.moj

D.vir

D.gri

G

S

A

A

T

I

Y

Y

E

S

M

P

A

S

A

S

T

G

V

L

S

L

T

T

GGA

AGT

GCT

GCC

ACA

ATC

TAC

TAC

GAA

TCT

ATG

CCA

GCC

TCC

GCC

TCC

ACA

GGC

GTT

CTA

TCA

TTG

ACT

ACG

GGA

AGT

GCT

GCC

ACA

ATC

TAC

TAC

GAA

TCT

ATG

CCA

GCC

TCC

GCC

TCC

ACA

GGC

GTT

CTA

TCA

TTG

ACT

ACG

GGA

AGT

GCT

GCC

ACA

ATC

TAC

TAC

GAA

TCT

ATG

CCA

GCC

TCC

GCC

TCC

ACA

GGC

GTT

CTA

TCA

TTG

ACT

ACT

GGA

AGT

GCT

GCC

ACA

ATC

TAC

TAC

GAG

TCT

ATG

CCA

GCC

TCC

GCC

TCC

ACG

GGC

GTT

CTA

TCA

TTG

ACT

ACG

GGA

AGT

GCT

GCC

ACA

ATC

TAC

TAC

GAG

TCT

ATG

CCA

GCC

TCC

GCC

TCC

ACA

GGC

GTT

CTA

TCA

TTG

ACT

ACG

GGT

AGT

GCA

GCT

ACG

ATC

TAC

TAC

GAA

TCA

ATG

CCG

GCA

TCC

TCG

TCC

ACT

GGC

GTA

CTC

TCG

TTG

ACC

ACC

GGC

AGC

TCT

GCC

ACA

ATC

TAC

TAC

GAA

TCG

ATG

CCC

GCC

TCG

GCC

TCC

ACG

GGC

GTC

CTC

TCG

CTG

ACC

ACA

GGC

AGC

TCT

GCC

ACA

ATC

TAC

TAC

GAA

TCG

ATG

CCC

GCC

TCG

GCC

TCC

ACG

GGC

GTC

CTC

TCG

CTG

ACC

ACA

GGT

GGA

GCT

GCC

ACC

ATT

TAT

TAT

GAA

TCC

ATG

CCA

GCA

TCT

GCC

TCA

ACT

GGA

GTC

CTC

TCG

CTG

ACC

ACC

GGC

AGC

TCA

G

CC

ATC

TAC

TAT

GAA

TCT

ATG

CCG

GC

C

TCA

ACG

GGC

GTT

CTA

TCG

CTG

ACC

ACC

GGC

AGC

TCG

G

CC

ATC

TAC

TAT

GAG

TCC

ATG

CCG

GC

C

TCG

ACG

GGG

GTG

CTC

TCG

CTG

ACC

ACC

GGC

AGC

TCG

G

CC

ATC

TAT

TAC

GAG

TCG

ATG

CCG

GC

G

TCG

ACG

GGC

GTC

CTC

TCA

CTG

ACG

ACG

with

abundant

alternative

splicing

Comparative

information

has

improved

computational

gene

but

their

accuracy

still

falls

far

short

of

well-studied

gene

catalogues

such

as

the

FlyBase

annotation

which

combines

computational

gene

high-throughput

experimental

and

extensive

manual

Recognizing

this

we

set

out

not

only

to

produce

an

independent

computational

annotation

of

protein-coding

genes

in

the

fly

genome

but

also

to

assess

and

refine

its

already

high-quality

Our

analyses

of

D.

melanogaster

coding

genes

are

based

on

two

independent

evolutionary

signatures

unique

to

protein-coding

regions

-LRB-

Fig.

-RRB-

-LRB-

-RRB-

reading

frame

conservation

-LRB-

RFC

-RRB-

which

observes

the

tendency

of

nucleotide

insertions

and

deletions

to

preserve

the

codon

reading

frame

and

-LRB-

-RRB-

codon

substitution

frequencies

Characteristic

protein-preserving

events

Codon

substitution

typical

of

protein-coding

regions

L

AAC

CGC

CTT

CCC

CCT

GGA

CTC

GTC

CCA

CTC

TCT

GCT

CCT

TCT

CCA

CCA

GCG

ATG

CAA

ACT

TTG

CGA

ATC

ACT

AGC

CGC

CTT

CCC

CCC

GGA

CTC

GTC

CCA

CTA

CCT

GCT

CCT

TCT

CCA

CCA

GCG

ATG

CAA

ACT

TTG

CGA

ATC

ACT

AGC

CGC

CTT

CCC

CCC

GGA

CTC

GCC

CCA

CTA

CCT

GCT

CCT

TCT

CCA

CCA

GCG

ATG

CAA

ACT

TTG

CGA

ATC

ACT

AGC

CGC

CTT

CCC

TCT

G

CAT

GCT

CCT

TCT

CCT

CCA

GCG

ATG

CAA

ACT

TTG

CGA

ATC

ACT

AGC

CGC

CTT

CCC

CCT

GGA

CTC

GTC

CCA

CTA

CCT

GCT

CCT

GCT

CCT

CCA

ACG

ATG

CAA

ACT

TTG

CGA

ATC

ACT

GGC

CAT

CCT

CCT

CCT

GGC

AGC

CC

CAA

CTG

CCT

CCG

TTT

TGT

CTG

TGT

GTG

TTG

GTA

ACT

TTG

CAA

ATC

ACT

GTT

CAC

GTC

CTT

TGT

GGC

CAG

TTC

TCC

TCT

CCT

TTT

CTC

TCT

CGG

TGC

GTG

TTG

GAA

ACT

TTG

CAA

ATC

ACT

GTT

CAC

GTC

CTT

TGT

GGC

CAG

TTC

TCC

TCT

CCT

TTT

CTC

TCT

CGG

TGC

GTG

TTG

GAA

ACT

TTG

CAA

ATC

ACT

ACT

CGC

CAG

CTT

TGT

GGC

CAG

TAG

TTC

TCT

GCT

CTT

GT

GTG

TTG

GAA

AAC

TTG

CAA

ATC

ACT

AGC

TTA

CGT

CCA

AGT

GAG

CGT

GTG

CGT

ATA

CCT

GTT

GTG

TTG

GCT

TGC

CTG

TTG

AAA

ATT

TTT

CCC

AAC

ACT

AGC

TAA

CGT

CCA

AGT

GTG

CAT

GTG

CAT

GTA

CGT

GTG

GTG

TTT

GTA

TGT

CTG

TTG

AAA

ATT

TTG

CCC

AAC

ACT

AGC

TAA

CGT

TCA

GCT

GTG

TGTG

TGT

GTG

TGT

TCG

TTG

AAA

ATT

TTG

CCA

AAC

ACT

Frame-preserving

gap

-LRB-

length

L

a

multiple

of

-RRB-

Characteristic

non-coding

region

events

Triplet

substitution

typical

of

non-coding

regions

Nonsense

mutation

introducing

a

stop

codon

L

Frame-shifting

gap

-LRB-

length

L

not

a

multiple

of

-RRB-

Protein-coding

exon

Non-coding

region

No

change

Conserved

paired

nucleotide

Conserved

unpaired

nucleotide

b

AA

AC

G

U

G

U

C

G

A

U

C

G

U

A

G

C

G

C

G

C

U

A

U

G

U

A

G

C

A

A

A

U

C

G

U

A

A

C

U

G

C

G

G

C

A

U

G

G

C

G

C

U

A

U

A

U

A

A

U

G

U

C

G

G

C

D.mel

GCGAUUUGGAGCUCUCAAGUUUGGGUCACUUAAAC-GGGUGACCCAGACAUGAAGGCUGCCAAAUUGC

D.sim

GCGAUUUGGAGCUCUCAAGUUUGGGUCACUUAAAG-GGGUGACCCAGACAUGAAAGCUGCCAAAUCGC

D.sec

GCGAUUUGGAGCUCUCAAGUUUGGGUCACUUAAAG-GGGUGACCCAGACAUGAAGGCUGCCAAAUUGC

D.yak

GCGAUUUGGAGCCCUUAAGUUUGGGUCAUUUAAAG-GGGUGACCCAGACAUGAGGGCUGCCAAGUUGC

D.ere

GCGAUUUGGAGCCAUUAAGUUUGGGUCAUUUAAAG-GGGUGACCCAGACAUGAGGGCUGCCAAGUUGC

D.ana

GCGAUUUGGAGCCCUCAAGUUUGGGUCACUUUAAC-GCGUGUCCCAGACAUGAUGGCUGCCAAAUUGC

D.pse

GCGAUUUGGAGCCCUCAAGUUUGGGUCACUUAAAU-GGGUGACCCAGACAUGAUGGCUACUAGAUC-D

per

wil

GCAAUUUCGAACUAUUAAGUUUGGAUCACUUAAAGCACGUGAUCCAGACAUAAUAGAUCUGAGAUUUU

D.moj

AACAUUUGG-CCUGUCAAGUCUGCGCCAUUUAAAU-GCGUGGCCCAGACAUGACAAGCUACAAAUGUU

D.vir

AGCAUUUGG-UUUGCCAAGUCUGUGGCAUUUGAAU-GUAUGUCGCAGACAUGACAAUC-GCAAAUGCU

D.gri

AGCAUUUGG-UUUGUUAAGUCUGCGUCAUUUCAAU-GUGUGCCGCAGACAUGACAAAUUCCAAAUGUU

Silent

changes

characteristic

of

RNA

evolution

Silent

G

U

substitution

Silent

substitution

in

unpaired

base

Silent

base-preserving

double

substitution

Changes

disruptive

of

RNA

structures

Disruptive

double

substitution

Disruptive

single

substitution

Disruptive

insertion

or

deletion

RNA

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

abcdefgh

iklm

nop

qrstuvwxyzA

Azyxwvutsrq

pon

mlki

hgfedcba

c

U

U

C

U

U

U

G

U

C

A

G

U

U

U

G

U

G

C

U

U

U

A

U

G

U

C

G

A

U

U

A

G

A

C

A

A

G

U

G

A

U

miRNA

D.mel

D.sim

D.sec

D.yak

D.ere

D.ana

D.pse

D.per

D.wil

D.moj

D.vir

D.gri

miRNA

miRNA

U

U

U

U

A

A

A

G

GGGGATGTGGGGAAGGATGCTCTTTTCTGACTCTATTTTGTCGGCGAACATGGATCTAGTGCACGGTGG-TTCATGATTAAGTTCGTGACTAGATTTCATGCTCGTCTATTAAGTTGGGTCAGCACA-ACGAAGA

GAGCGGAGCT

GAGCGCAGCT

GAGCGGAGCT

GGGGATGTGGGGAAGGATGCTCTTTTCTGACTCTATTTTGTCGGCGAACATGGATCTAGTGCACGGTGG-TTCATGATTAAGTTCGTGACTAGATTTCATGCTCGTCTATTAAGTTGGGTCAGCACT-ACGAAGA

GAG

CT

GGAGAAGTGGGGAAGGATGCTCTTTTCTGACTCTATTTTGTCGGCGAACATGGATCTAGTGCACGGTGG-TTCATGATTAAGTTCGTGACTAGATTTCATGCTCGTCTATTAAGTTGGGTCAGCACT-ACGAAGA

GAG

CT

GAAAAGG

ATTTGGGGTCTTTTTCTGACTCTATTTTGTCGGCGAACATGGATCTAGTGCACGGTGT-TTCATGATTAAGTTCGTGACTAGATTTCATGCTCGTCTATTAAGTTGGGTCAGCACA-CCAAAGAGTCGGATAGTGGAG

TCTGATCCGGCAGCGTTTGCTCTTCTCTGACTCTATTTTGTCGGCGAACATGGATCTAGTGCACGGTTG-TTCATGATTAAGTTCGTGACTAGATTTCATGCTCGTCTATTAAGTTGGGTCAACACA-ACGAACCGAAAGAGCAGAGCA

GAGTCCTTTCTATGTGGCAGCGTCTCTTGACTCTATTTTGTCGGCGAACATGGATCTAGTGCACGGTTTGTTCATGATTAAGTTCGTGACTAGATTTCATGCTCGTCTATTAAGTTGGGTCAGCACA-ACAAGAG

CGCAGCGGAGAG

ATTTCTTTT

TTTTGCTCTTCTCTGACTCTATTTTGTCGGCGAACATGGATCTAGTGCACGGTTG-TTCATGATTAAGTTCGTGACTAGATTTCATGCTCGTCTATTAAGTTGGGTCAATACACACA-GCGAAAACATGGCCAAG

GTTTCGCTC

TTTTGCTCTTCTCTGACTCTATTTTGTCGGCGAACATGGATCTAGTGCACGGTTG-TTCATGATTAAGTTCGTGACTAGATTTCATGCTCGTCTATTAAGTTGGGTCAACACACACACACACACACATAAAAGAA

ACTGCAACTGCAACTGCTGCTCTTTTCTGACTCTATTTTGTCGGCGAACATGGATCTAGTGCACGGTTG-TTCATGATTAAGTTCGTGACTAGATTTCATGCTCGTCTATTAAGTTGGGTCAACACACA-ACACAAAAAAAAAAGAGGA

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

miRNA

G

C

G

U

C

C

A

A

A

U

U

U

G

A

G

U

miRNA

d

D.mel

D.sim

D.sec

D.yak

D.ere

D.ana

D.pse

D.per

D.wil

D.moj

D.vir

D.gri

GATTAGT

TCATCATTTATTAT

T

ATT

AATTAATGGCGTT

TCGCAGC-GGCTGG-C

TGTTTATTATTAACCATTATTT

A-ACA

CC

GATTAGT

TCATCATTTATTAT

T

ATT

AATTAATGGCGTT

TCGCAGC

GCTGG-C

TGTTTATTATTAACCATTATTT

A-ACA

CC

GATTAGT

TCATCATTTATTAT

T

ATT

AATTAATGGCGTT

TCGCAGC

GCTGG-C

TTTTTATTATTAACCATTATTT

A-ATA

CC

GATTAGT

TCATCATTTATTAT

T

ATT

AATTAATGGCGTT

TCGCAGC

GCTGG-CTG

TGTTTATTATTTATCATTATTA

A-ACA

CC

GATTAGT

TCATCGTTTATTAT

T

ATC

AATTAATGGCGTT

TCGCAGC

GGTGG-C

TGTTTATTATTAACCATTACTA

A-ACA

CC

GATTTGT

TCATCATTTATTAT

T

AATTAATGGTATT

TCTTGACTGGCTGC-CTGCC

TGCCTGTTA

TTTGTTGTTTATTATTAAGCATTATTA

A-ACA

CA

GATATGC

TCATCATTTATTAT

T

GAT

AATTAATGGAACTTTGGTCAGTT-TTGCTGCCTGCCTG-TTGCCTGCTGCCTGTTGCTTTTGCTGTTTATTATTAACTATTATTG

A-GCAGCGCCA

GATATGC

TCCCCATTTTTTCT

T

GAT

AATTAATGGAAATTTGGTCACTTATTACTGCCTGCCGG-T

CACCTCTCGCTTCTGCTGTTTATTATTAACTATTATTG

A-GCAGCGCCA

GATTAGT

TCATCATTTATTAT

TATTTATATT

AATTAATGAAGTTT

TCGTTTC

G-T

TTCGTATGGTT

TCGTTT

G-ATG

GATTAGTCGTTCATCAATATTAATTATGTAT

ATAATTAATTAATGAAGTT

TT

C

GCTTTAT

CGTTTATCGACAGCTATTTTTAAT

A-ACA

AC

GATTAGTTGATCATCATCATTAATTAT

T

ATA

AATTAATGAAGTT

GCGTT-T

CGTTTATCGACAGCTATTTTTAAT

A-ACA

AC

GATTAGTTGCTCATCATCATTAATTATGAGT

ATT

AATTAATGAAGTT

T

GCTCT-T

CGCTCACCGATAGCTATTTTTAATACCAA-ACA

AC

e

Number

of

conserved

instances

200 160 120

Random

motifs

Known

motifs

Confidence

Regulatory

motifs

-LRB-

BLS

-RRB-

YTAWWWWTAR

-LRB-

BLS

-RRB-

BLS

-LRB-

of

tree

-RRB-

Figure

Distinct

evolutionary

signatures

for

diverse

classes

of

functional

elements

a

Protein-coding

genes

tolerate

mutations

that

preserve

the

amino-acid

translation

leading

to

abundant

conservative

codon

substitutions

-LRB-

green

-RRB-

Insertions

and

deletions

are

largely

constrained

to

be

a

multiple

of

three

-LRB-

grey

-RRB-

In

contrast

non-coding

regions

show

abundant

non-conservative

triplet

substitutions

-LRB-

red

-RRB-

nonsense

mutations

-LRB-

blue

-RRB-

and

frame-shifting

insertions

and

deletions

-LRB-

orange

-RRB-

b

RNA

genes

tolerate

mutations

that

preserve

the

secondary

structure

-LRB-

for

example

single

substitutions

involving

G

U

base

pairs

and

compensatory

changes

-RRB-

and

exclude

structure-disrupting

mutations

Matching

parentheses

and

matching

letters

of

the

alphabet

indicate

paired

bases

c

MicroRNA

genes

in

contrast

generally

do

not

show

changes

in

stem

regions

but

tolerate

substitutions

in

loop

regions

and

flanking

unpaired

regions

leading

to

a

distinctive

conservation

profile

Asterisks

denote

the

number

of

informant

species

matching

the

melanogaster

sequence

at

each

position

d

Regulatory

motifs

tolerate

local

movement

and

nucleotide

substitutions

consistent

with

their

degeneracy

patterns

and

show

increased

conservation

across

the

phylogenetic

tree

measured

as

the

branch

length

score

-LRB-

BLS

Supplementary

Methods

-RRB-

e

Increasing

BLS

thresholds

select

for

instances

of

known

motifs

-LRB-

black

-RRB-

at

increasing

confidence

-LRB-

red

-RRB-

as

the

number

of

conserved

instances

of

control

motifs

-LRB-

grey

-RRB-

drops

significantly

faster

Nature

Publishing

Group

Confidence

level

ARTICLES

NATURE

Vol

November

Chr

Predicted

exons

Protein-coding

evolutionary

signal

FlyBase

genes

FlyBase

curation

a

b

rejected

genes

Removed

from

FlyBase

protein-coding

genes

predicted

new

exons

Flagged

as

uncertain

No

action

562 192 174

Modify

existing

annotation

New

gene

No

action

-LRB-

see

panel

a

-RRB-

c

Known

splice

form

Inverse

PCR

primers

cDNA

validation

Conservation

New

exon

-LRB-

see

panel

c

-RRB-

High

conservation

No

protein-coding

signal

Low

conservation

High

protein-coding

signal

d

A

V

A

A

A

E

Q

Q

H

Y

H

A

Q

H

H

H

H

P

Q

X

Y

K

P

H

G

K

L

K

S

R

D

Y

T

L

H

W

Q

N

Y

X

GCA

GTC

GCT

GCC

GCC

GAG

CAG

CAG

CAC

TAT

CAC

GCC

CAG

CaC

CAT

CAC

CAT

cCG

CaA

TGA

TAC

AAG

CCC

CAC

GGA

AAG

CTC

AAA

TCA

CGC

GAC

TAT

ACC

CTT

CAC

TGG

CAG

AAC

TAT

TAG

TTA

AAG

TTC

ATT

CAT

ATT

CaT

CGC

ACA

TTG

GCC

ATA

TCC

CGA

Protein-coding

evolution

Stop

Continued

protein-coding

evolution

Stop

Non-coding

evolution

Codon

substitutions

e

P

P

R

F

V

P

P

P

P

P

P

R

R

L

L

L

T

Q

T

GAC

TAT

TTC

AAC

AAT

CAG

CAG

CGC

GAG

CGA

CAC

TAC

CAG

CTC

CGG

CGG

CAG

AGC

CAG

CGG

CAG

A

CCT

CCG

AGA

TTT

GTA

CCG

CCG

CCA

CCG

CCT

CCG

CGT

CGC

TTG

CTC

CTC

ACG

CAG

ACC

A

A

A

A

G

G

G

G

G

G

G

D

Y

F

N

N

Q

Q

R

E

R

H

Y

Q

L

R

R

Q

S

Q

R

Q

Conservative

substitution

Disruptive

substitution

Gaps

Frame-preserving

-LRB-

multiple

of

-RRB-

Frame-shifting

-LRB-

not

a

multiple

of

-RRB-

Protein-coding

evolution

-LRB-

frame

-RRB-

Frameshift

Protein-coding

evolution

-LRB-

frame

-RRB-

Exon

boundaries

Stop

codon

Figure

Revisiting

the

protein-coding

gene

catalogue

and

revealing

unusual

gene

structures

a

Protein-coding

evolutionary

signatures

correlate

with

annotated

protein-coding

exons

more

precisely

than

the

overall

conservation

level

-LRB-

phastCons

-RRB-

for

example

excluding

highly

conserved

yet

non-coding

elements

Asterisk

denotes

new

predicted

exon

which

we

validate

with

cDNA

sequencing

-LRB-

see

panel

c

-RRB-

The

height

of

the

black

tracks

indicates

protein-coding

potential

according

to

evolutionary

signatures

-LRB-

top

-RRB-

and

overall

sequence

conservation

-LRB-

bottom

-RRB-

Blue

and

green

boxes

indicate

predicted

coding

exons

-LRB-

top

-RRB-

and

the

current

FlyBase

annotation

-LRB-

bottom

-RRB-

The

region

shown

represents

the

central

kb

of

Fig.

rendered

by

the

UCSC

genome

b

Results

of

FlyBase

curation

of

genes

rejected

by

evolutionary

signatures

-LRB-

Table

-RRB-

and

predicted

new

exons

c

Experimental

validation

of

predicted

new

exon

from

panel

a.

Inverse

PCR

with

primers

in

the

predicted

exon

-LRB-

green

-RRB-

results

in

a

fulllength

cDNA

clone

confirming

the

predicted

exon

and

revealing

a

new

alternative

splice

form

for

d

Protein-coding

evolution

continues

downstream

of

a

conserved

stop

codon

in

genes

suggesting

translational

readthrough

e

Codon-based

evolutionary

signatures

-LRB-

CSF

score

-RRB-

abruptly

shift

from

one

reading

frame

to

another

within

a

protein-coding

exon

suggesting

a

conserved

programmed

frameshift

-LRB-

CSF

see

Supplementary

Methods

-RRB-

which

observes

mutational

biases

towards

synonymous

codon

substitutions

and

conservative

amino

acid

changes

similar

to

the

non-synonymous/synonymous

substitution

ratio

and

other

Assessing

and

refining

existing

gene

annotations

We

first

assessed

the

euchromatic

genes

in

release

Using

the

above

measures

we

defined

tests

that

confirmed

genes

supported

by

the

evolutionary

evidence

rejected

genes

inconsistent

with

protein-coding

selection

or

abstained

for

genes

that

were

not

aligned

or

with

ambiguous

comparative

evidence

-LRB-

Supplementary

Methods

-RRB-

Of

the

genes

with

descriptive

names

we

confirmed

rejected

and

abstained

for

whereas

the

same

criteria

applied

to

random

non-coding

regions

nucleotides

rejected

of

candidates

and

confirmed

virtually

-LRB-

Table

-RRB-

Together

these

results

illustrate

the

high

sensitivity

and

specificity

of

our

criteria

Applying

the

same

criteria

to

the

genes

lacking

a

descriptive

name

-LRB-

genes

designated

only

by

a

CG

identifier

referred

to

hereafter

as

CGid-only

genes

-RRB-

our

tests

accepted

rejected

-LRB-

genes

-RRB-

and

abstained

for

This

provides

strong

evidence

that

most

CGidonly

genes

encode

proteins

but

also

suggests

that

they

may

be

less

and/or

may

include

incorrect

annotations

Indeed

on

manual

review

-LRB-

-RRB-

of

the

rejected

CGid-only

genes

were

re-categorized

as

non-protein-coding

or

deleted

-LRB-

of

which

were

due

to

genomically

primed

clones

-RRB-

-LRB-

-RRB-

were

flagged

as

being

of

uncertain

quality

and

the

remaining

-LRB-

-RRB-

were

kept

unchanged

-LRB-

Fig.

-RRB-

Some

of

these

are

probably

rapidly

evolving

protein-coding

genes

but

others

may

also

prove

to

be

non-proteincoding

genes

or

spurious

in

fact

of

these

had

any

functional

gene

ontology

-LRB-

GO

-RRB-

In

addition

we

proposed

specific

corrections

and

adjustments

to

hundreds

of

existing

transcript

models

including

translation

start

site

adjustments

-LRB-

Supplementary

Fig.

-RRB-

alternative

splice

boundaries

-LRB-

Supplementary

Fig.

-RRB-

recent

nonsense

mutations

-LRB-

Supplementary

Fig.

-RRB-

and

alternative

translational

reading

Identifying

new

genes

and

exons

To

predict

new

protein-coding

exons

we

integrated

our

metrics

into

a

probabilistic

algorithm

that

determines

an

optimal

segmentation

of

the

genome

into

proteincoding

and

non-coding

regions

-LRB-

Fig.

-RRB-

on

the

basis

of

wholegenome

sequence

alignments

of

the

fly

species

-LRB-

Supplementary

Table

Assessment

of

FlyBase

euchromatic

protein-coding

gene

annotations

Regions

evaluated

Total

Confirm

Abstain

Reject

Named

genes

CGid-only

genes

Non-coding

regions

-LCB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

A

minority

of

rejected

genes

are

false

rejections

see

Fig.

and

text

for

details

-LCB-

Regions

nucleotides

in

length

randomly

chosen

from

the

non-coding

part

of

the

genome

-LRB-

see

Supplementary

Methods

-RRB-

Nature

Publishing

Group

NATURE

Vol

November

ARTICLES

Methods

-RRB-

Our

genome-wide

search

predicted

new

proteincoding

exons

mostly

in

euchromatic

regions

annotated

as

intergenic

-LRB-

-RRB-

intronic

-LRB-

-RRB-

or

untranslated

region

-LRB-

UTR

-RRB-

in

FlyBase

annotation

release

We

manually

reviewed

of

these

predictions

according

to

FlyBase

-LRB-

Supplementary

Methods

-RRB-

leading

to

new

gene

models

-LRB-

incorporating

predictions

-RRB-

and

revised

gene

models

-LRB-

incorporating

predictions

-RRB-

-LRB-

Fig.

-RRB-

In

parallel

we

tested

predictions

-LRB-

intergenic

intronic

-RRB-

by

directed

cDNA

sequencing

using

inverse

polymerase

chain

reaction

-LRB-

inverse

PCR

-RRB-

of

circularized

full-length

-LRB-

Fig.

-RRB-

which

validated

targeted

predictions

-LRB-

-RRB-

and

an

additional

predictions

not

directly

targeted

but

contained

within

the

recovered

transcripts

Predictions

in

intergenic

regions

yielded

full-length

cDNAs

providing

evidence

for

new

genes

and

modification

of

gene

models

Predictions

within

introns

of

existing

annotations

yielded

fulllength

cDNAs

of

which

only

-LRB-

-RRB-

represent

new

splice

variants

of

the

surrounding

gene

whereas

the

remaining

revealed

nested

or

interleaved

gene

structures

This

provides

additional

evidence

that

such

complex

gene

structures

are

not

rare

in

Overall

of

the

predicted

exons

that

we

assessed

by

manual

curation

or

cDNA

sequencing

were

incorporated

into

FlyBase

resulting

in

new

genes

and

modifications

to

hundreds

of

existing

gene

models

Finally

the

predictions

that

we

did

not

assess

were

in

non-coding

regions

of

existing

transcript

models

or

were

already

included

in

FlyBase

independent

of

our

study

In

an

independent

we

predicted

new

genes

on

the

basis

of

inferred

homology

to

predicted

genes

in

the

informant

of

which

matched

the

above

predictions

Discovering

unusual

features

of

protein-coding

genes

Our

analysis

also

predicted

an

abundance

of

unusual

protein-coding

genes

that

call

for

follow-up

experimental

investigation

First

we

found

open

reading

frames

with

clear

protein-coding

signatures

and

conserved

start

and

stop

sites

on

the

transcribed

strand

of

annotated

UTRs

indicative

of

polycistronic

These

include

of

annotated

dicistronic

transcripts

and

new

candidate

cistrons

of

genes

-LRB-

Supplementary

Fig.

-RRB-

Second

we

predicted

that

genes

undergo

stop

codon

readthrough

with

protein-coding

selection

continuing

past

a

deeply

conserved

stop

codon

-LRB-

Fig.

-RRB-

in

some

cases

for

hundreds

of

amino

acids

It

is

unlikely

that

these

genes

are

selenoproteins

as

they

appear

to

lack

SECIS

elements

a

that

direct

selenocysteine

Other

mechanisms

may

instead

be

at

work

such

as

regulation

of

ribosomal

release

A-to-I

alternative

splicing

or

other

less-characterized

In

fact

these

genes

are

significantly

enriched

in

neuronal

proteins

-LRB-

P

-RRB-

which

frequently

undergo

A-to-I

Third

we

found

four

genes

in

which

CSF

signatures

abruptly

shift

from

one

reading

frame

to

another

in

the

absence

of

nearby

intron

exon

boundaries

or

insertions

and

deletions

-LRB-

Fig.

-RRB-

These

are

suggestive

of

conserved

programmed

which

are

thought

to

be

rare

in

eukaryotes

Overall

our

results

affected

over

of

protein-coding

genes

and

will

be

available

in

future

releases

of

FlyBase

They

also

suggest

that

several

types

of

unusual

protein-coding

gene

structure

may

be

more

prevalent

in

the

fly

than

previously

appreciated

RNA

genes

and

structures

Several

comparative

approaches

to

RNA

gene

identification

have

been

that

recognize

their

characteristic

properties

compensatory

double

substitutions

of

paired

nucleotides

-LRB-

for

example

A

U

C

G

-RRB-

structure-preserving

single-nucleotide

mutations

involving

G

U

base

pairs

-LRB-

G

U

G

C

and

G

U

A

U

-RRB-

and

few

nucleotide

substitutions

disrupting

functional

base

pairs

-LRB-

Fig.

-RRB-

To

predict

new

structures

we

applied

in

highly

conserved

segments

of

the

Drosophila

species

and

focused

on

highstringency

candidates

with

strong

support

by

compensatory

changes

-LRB-

Supplementary

Methods

-RRB-

Our

search

led

to

predictions

recovering

known

RNA

structures

-LRB-

primarily

transfer

RNA

genes

-RRB-

in

of

the

genome

-LRB-

enrichment

-RRB-

The

novel

candidates

consisted

of

structures

in

intergenic

regions

-LRB-

-RRB-

in

introns

-LRB-

-RRB-

in

UTRs

-LRB-

-RRB-

and

in

UTRs

-LRB-

-RRB-

In

addition

we

predicted

structures

in

protein-coding

regions

-LRB-

Supplementary

Methods

-RRB-

Notably

of

UTR

structures

and

of

UTR

structures

were

predicted

on

the

transcribed

strand

suggesting

that

they

are

frequently

part

of

the

messenger

RNA

In

contrast

only

of

intronic

structures

are

on

the

transcribed

strand

suggesting

that

they

are

largely

independent

of

the

surrounding

genes

Known

and

novel

types

of

RNA

genes

Of

the

predicted

intergenic

structures

were

detected

in

a

tiling-array

expression

This

fraction

-LRB-

-RRB-

is

significantly

above

that

for

all

conserved

intergenic

regions

-LRB-

P

-RRB-

but

lower

than

that

of

known

C

GU

C

C

U

G

A

U

A

U

A

U

A

U

A

U

A

U

U

G

G

C

G

C

A

U

A

U

A

Protein

translation

U

A

U

M

R

I

G

U

G

C

G

C

A

U

U

U

U

U

Start

V

E

G

E

C

T

L

I

L

Exon

M

F

G

N

Intron

L

A

CUUUGC

A

U

G

A

A

G

C

G

G

A

A

G

C

G

U

A

C

A

G

U

C

A

U

A

G

U

A

A

U

C

U

U

U

A

G

G

U

A

A

A

U

C

C

C

G

G

CU

U

C

U

U

U

G

C

C

A

C

G

C

A

F

F

A

T

H

b

C

A

G

A

U

C

G

A

G

A

G

C

S

I

E

S

Y

G

UU

A

A

G

C

C

A

U

C

G

G

U

G

G

G

C

U

G

G

C

U

G

G

C

U

G

G

C

A

U

G

C

U

G

C

G

C

A

U

c

D.mel

D.sim

D.sec

D.yak

D.ere

D.ana

D.pse

D.per

D.wil

D.moj

D.vir

D.gri

UGGCAGUCGCUGGGCACGAGUUACUGUGCGGUGGCUGUCCA

UGGAAGUCGCUGGGCACGAGUUACUGUGCGGUGGCUGUCCA

UGGGAGUCGCUGGGCACGAGUUACUGUGCGGUGGCUGUCCA

UGGGAGUCGCUGGGCACGAGUUACUGUGCGGCGGCUGU

UCA

UGGGAGCCGCUGGGCACGAGUUACUGUGCGGCGGCUGU

UCA

UG-GG

GUCGUUGGGCACGAGUUACUGUGCGACGGUCAU

CA

UGGGGGUCGCUGGGCACGAGUUACUGUGCGGCGCCUGU

UUA

UGGGGGUCGCUGGGCACGAGUUACUGUGCGGCGCCUGU

UUA

UGAUUAAUUCCUGGCACGAGUUACUGUGCGGAAUUCGA

UCA

UGUUGGCGCCUGGGCACGAGUUACUGUGCGGGCGCAUA

ACA

UGUUGGCGGCCAGGCACGAGUUACUGUGCGGACGUCUU

UCA

UGUCUGCGCCUGGGCACGAGUUAAUGUGCGGGCGCCUG

GCA

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

abc

defghik

lmnop

ponmlkihgfed

cba

Intron

lodestar

Exon

C

U

A

U

U

U

G

G

Intron

G

G

UU

U

CAU

CA

GGUUUGA

L

G

FHQ

spineless

Exon

Splice

Splice

Figure

Novel

RNA

structures

a

New

exonic

RNA

structure

spanning

of

nucleotides

of

spineless

exon

b

New

intronic

RNA

structure

in

lodestar

shows

compensatory

substitutions

and

silent

G

U

substitutions

providing

strong

evidence

of

structural

selection

-LRB-

colours

as

in

Fig.

-RRB-

c

New

UTR

structure

that

overlaps

the

translation

start

site

of

the

fly

orthologue

of

yeast

ribosomal

protein

suggesting

a

potential

role

in

translational

regulation

a

c

Structure

shown

corresponds

to

shaded

region

in

the

gene

model

Nature

Publishing

Group

ARTICLES

NATURE

Vol

November

intergenic

ncRNAs

-LRB-

-RRB-

suggesting

that

these

candidates

may

be

of

lower

abundance

temporally

or

spatially

constrained

or

might

include

false

positives

Two

predictions

were

expressed

throughout

development

one

extending

the

annotation

of

a

previously

reported

but

uncharacterized

and

the

other

probably

representing

a

novel

type

of

ncRNA

The

predictions

also

included

nine

novel

H/ACA-box

small

nucleolar

RNA

candidates

in

introns

of

ribosomal

genes

known

to

frequently

contain

small

nucleolar

RNAs

that

guide

post-transcriptional

base

modifications

of

Likely

A-to-I

editing

structures

Many

of

the

intronic

candidates

on

the

transcribed

strand

and

many

of

the

hairpins

in

coding

sequence

are

probably

involved

in

A-to-I

editing

or

post-transcriptional

regulation

-LRB-

Fig.

-RRB-

Hairpins

in

coding

sequence

were

associated

with

of

the

known

editing

sites

-LRB-

enrichment

-RRB-

and

both

intronic

and

coding-sequence

hairpins

showed

a

strong

enrichment

for

ion-channel

genes

-LRB-

P

and

P

respectively

-RRB-

known

to

be

frequent

editing

targets

Editing

is

known

to

occur

at

multiple

sites

in

the

same

and

we

find

an

additional

hairpins

in

known

editing

targets

as

well

as

additional

hairpins

clustered

in

genes

not

previously

known

to

be

edited

-LRB-

for

example

which

harbours

four

predicted

hairpins

more

than

any

other

gene

-RRB-

Intronic

predictions

also

showed

the

highest

abundance

of

compensatory

substitutions

for

example

Resistant

to

dieldrin

-LRB-

Fig.

-RRB-

contained

a

-LRB-

bp

-RRB-

intronic

hairpin

flanked

by

exons

known

to

be

with

a

striking

compensatory

changes

lodestar

showed

one

hairpin

with

compensatory

changes

and

Inverted

repeat-binding

protein

showed

one

hairpin

with

compensatory

substitutions

-LRB-

Fig.

-RRB-

Likely

regulatory

UTR

structures

We

predicted

structures

in

UTRs

a

density

twofold

higher

than

the

genomic

average

whereas

fewer

than

such

examples

are

currently

A

considerable

fraction

of

these

lies

in

regulatory

genes

-LRB-

out

of

P

-RRB-

including

several

transcriptional

regulators

-LRB-

for

example

cas

spen

and

Alh

-RRB-

the

tyrosine

phosphatase

PTP-ER

and

the

translation

initiation

factor

This

suggests

that

many

regulatory

genes

may

themselves

be

regulated

post-transcriptionally

through

these

structures

UTR

structures

were

also

enriched

for

genes

involved

in

mRNA

localization

-LRB-

out

of

P

-RRB-

including

RNA-binding

protein

-LRB-

orb

-RRB-

and

staufen

-LRB-

stau

-RRB-

both

of

which

contain

doublestranded

RNA-binding

domains

are

involved

in

axis

specification

during

oogenesis

and

interact

with

the

mRNA

of

maternal

effect

protein

oskar

The

hairpin

in

orb

is

known

to

be

important

for

mRNA

transport

and

whereas

the

highly

similar

stau

hairpin

has

not

been

previously

described

to

our

knowledge

The

ten

structures

found

in

UTRs

probably

contain

binding

sites

for

factors

that

regulate

translation

For

example

the

fly

homologue

of

yeast

ribosomal

protein

contains

a

hairpin

structure

overlapping

its

start

codon

-LRB-

Fig.

-RRB-

This

is

interesting

in

light

of

high

conservation

upstream

of

the

start

codon

in

yeast

ribosomal

and

findings

that

ribosomal

proteins

bind

to

their

mRNAs

and

control

translation

in

Conserved

RNA

structures

in

recruit

MSL

In

an

independent

we

searched

for

conserved

regions

in

the

non-coding

and

-LRB-

RNA

on

the

X

-RRB-

genes

to

gain

insights

into

their

function

Both

RNAs

are

components

of

the

MSL

-LRB-

Male-specific

lethal

-RRB-

complex

and

are

crucial

for

dosage

compensation

in

male

flies

inducing

lysine

acetylation

of

histone

leading

to

upregulation

of

hundreds

of

genes

on

the

X

We

identified

several

stemloop

structures

with

repeated

sequence

motifs

-LRB-

for

example

GUUNUACG

-RRB-

and

found

that

tandem

repeats

of

one

of

these

were

sufficient

to

recruit

MSL

complexes

to

the

X

chromosome

and

to

induce

acetylation

of

lysine

of

histone

Although

this

structure

could

not

fully

rescue

roX-deficient

males

our

results

suggest

that

it

mediates

MSL

recruitment

during

chromatin

modification

and

dosage

compensation

illustrating

the

power

of

evolutionary

evidence

for

directing

experimental

studies

Prediction

and

characterization

of

miRNA

genes

Focusing

on

specific

classes

of

RNA

genes

markedly

increases

the

accuracy

of

RNA

gene

prediction

reviewed

in

refs

and

illustrated

here

for

Drosophila

miRNA

genes

The

common

biogenesis

and

function

of

lead

to

evolutionary

and

structural

signatures

-LRB-

Fig.

-RRB-

that

can

be

used

for

their

systematic

de

novo

Using

such

signatures

in

the

fly

genomes

-LRB-

Supplementary

Methods

b

-RRB-

we

predicted

-LRB-

Supplementary

Table

-RRB-

which

include

of

the

verified

Rfam

miRNAs

-LRB-

-RRB-

while

spanning

less

than

of

the

fly

genome

-LRB-

nucleotide

enrichment

-RRB-

Comparison

of

our

predictions

with

high-throughput

sequencing

data

of

short

RNA

libraries

from

different

stages

and

tissues

of

D.

revealed

that

of

the

predictions

-LRB-

-RRB-

including

of

the

novel

predictions

-LRB-

-RRB-

were

authentic

miRNA

genes

-LRB-

Fig.

and

Supplementary

Table

-RRB-

An

independent

computational

had

of

its

novel

predictions

validated

when

used

across

six

Drosophila

species

Additional

candidates

may

represent

genuine

miRNAs

whose

temporal

or

spatial

expression

pattern

does

not

overlap

with

the

surveyed

libraries

Several

of

the

validated

miRNAs

were

on

the

transcribed

strand

of

introns

or

clustered

with

other

miRNAs

For

example

and

-LRB-

the

vertebrate

homologue

of

which

has

been

implicated

in

-RRB-

were

both

found

in

the

last

intron

of

and

might

be

involved

in

cell-cycle

regulation

-LRB-

Fig.

-RRB-

Notably

two

predictions

overlapped

exons

of

previously

annotated

protein-coding

genes

that

were

independently

rejected

above

-LRB-

Fig.

-RRB-

providing

an

explanation

for

the

previously

observed

transcripts

of

these

annotations

and

highlighting

the

importance

of

specific

signatures

for

genome

annotation

High-throughput

sequencing

data

discovered

an

additional

miRNAs

not

found

thereby

illustrating

the

limitations

of

purely

computational

approaches

Some

of

these

had

precursor

structures

not

seen

previously

for

animal

miRNAs

including

unusually

long

and

hairpins

corresponding

to

short

introns

-LRB-

mirtrons

-RRB-

The

remaining

were

often

less

broadly

conserved

or

showed

unusual

conservation

properties

Signatures

for

mature

miRNA

annotation

The

exact

position

of

cleavage

of

mature

miRNAs

is

important

because

it

dictates

the

core

of

the

target

recognition

This

leads

to

unique

structural

and

evolutionary

signatures

including

direct

signals

present

at

the

cleavage

site

and

indirect

signals

stemming

from

the

relationship

of

miRNAs

with

their

target

genes

-LRB-

Supplementary

Methods

c

-RRB-

Combined

into

a

computational

these

signatures

predicted

the

exact

start

position

in

of

the

cloned

Rfam

miRNAs

-LRB-

-RRB-

and

were

within

bp

in

cases

-LRB-

-RRB-

The

method

disagreed

with

the

previous

annotation

in

of

the

Rfam

miRNAs

that

were

not

previously

cloned

of

which

were

confirmed

by

sequencing

leading

to

marked

changes

in

the

inferred

target

spectrum

-LRB-

Fig.

-RRB-

Prediction

accuracy

was

significantly

lower

-LRB-

exact

within

nucleotide

-RRB-

for

novel

miRNAs

which

however

also

showed

less

accurate

processing

in

New

insights

into

miRNA

function

and

biogenesis

We

predicted

targets

for

all

conserved

miRNAs

identified

by

high-throughput

searching

for

conserved

matches

to

the

seed

region

-LRB-

similar

to

ref

-RRB-

evaluated

using

the

branch

length

score

-LRB-

Supplementary

Methods

-RRB-

a

new

scoring

scheme

described

below

Whereas

the

resulting

miRNA

targeting

network

changed

we

found

that

the

novel

and

revised

miRNAs

shared

many

of

their

predicted

targets

with

previously

known

miRNAs

resulting

in

a

denser

network

with

increased

potential

for

combinatorial

For

ten

miRNA

hairpins

the

mature

miRNA

and

the

corresponding

miRNA

star

sequence

-LRB-

miRNA

the

small

RNA

from

the

opposite

arm

of

the

hairpin

-RRB-

both

appeared

to

be

functional

both

reached

high

computational

scores

and

were

frequently

often

exceeding

the

abundance

of

many

mature

miRNAs

-LRB-

Supplementary

Table

-RRB-

The

Hox

miRNA

showed

a

particularly

striking

example

of

a

functional

star

sequence

-LRB-

Fig.

-RRB-

both

arms

showed

Nature

Publishing

Group

NATURE

Vol

November

ARTICLES

abundant

reads

high

scores

and

highly

conserved

Hox

gene

suggesting

a

key

role

in

Hox

regulation

In

addition

for

miRNA

loci

the

anti-sense

strand

also

folded

into

a

high-scoring

hairpin

suggestive

of

a

functional

-LRB-

Supplementary

Table

-RRB-

Indeed

sequencing

reads

confirmed

that

four

of

these

anti-sense

hairpins

are

processed

into

small

RNAs

in

Thus

a

single

genomic

miRNA

locus

may

produce

up

to

four

miRNAs

each

with

distinct

targets

Regulatory

motif

discovery

and

characterization

Regulatory

motifs

recognized

by

proteins

and

RNAs

to

control

gene

expression

have

been

difficult

to

identify

due

to

their

short

length

a

rhea

their

many

weakly

specified

positions

and

the

varying

distances

at

which

they

can

Recent

studies

have

shown

that

comparative

genomics

of

a

small

number

of

species

can

be

used

for

motif

on

the

basis

of

hundreds

of

conserved

instances

across

the

genome

-LRB-

Fig.

-RRB-

Many

related

genomes

should

lead

to

increased

discovery

power

but

also

pose

new

challenges

arising

from

sequencing

assembly

or

alignment

artefacts

and

from

movement

or

loss

of

motif

instances

in

individual

species

To

account

for

the

unique

properties

of

regulatory

motifs

we

developed

a

phylogenetic

framework

to

assess

the

conservation

of

each

motif

instance

across

many

Briefly

we

searched

for

motif

instances

in

each

of

the

aligned

genomes

and

based

on

the

set

U

U

C

A

A

G

A

A

U

C

A

miRNA

U

U

U

G

A

G

G

A

C

C

C

A

C

U

miRNA

U

U

GATGGTTCCAGTGAGATATGTTTGATATTCTTGGTTGTTTCATTCAAAAGTTCACCCAGGAATCAAACATATTATTACTGTGACCCT

C

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-LRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

-RRB-

AGATATGTTTGATATTCTT

AGATATGTTTGATATTCTTG

AGATATGTTTGATATTCTTGG

AGATATGTTTGATATTCTTGGT

AGATATGTTTGATATTCTTGGTT

AGATATGTTTGATATTCTTGGTTG

AGATATGTTTGATATTCTTGGTTGT

AGATATGTTTGATATTCTTGGTTGTT

GATATGTTTGATATTCTTGGT

GATATGTTTGATATTCTTGGTTG

ATATGTTTGATATTCTTGGTT

TATGTTTGATATTCTTGGTTG

ATGTTTGATATTCTTGGTTG

ATGTTTGATATTCTTGGT

TGTTTGATATTCTTGGTTG

ACCCAGGAATCAAACATATTATTA

CCCAGGAATCAAACATATTA

CCCAGGAATCAAACATATTAT

CCCAGGAATCAAACATATTATT

CCCAGGAATCAAACATATTATT

CCAGGAATCAAACATATTATTA

miRNA

G

A

U

G

A

U

A

U

G

U

U

U

A

U

A

C

U

A

U

C

U

U

G

G

No

of

reads

miRNA

G

U

G

A

C

A

U

U

A

U

U

A

U

A

C

A

A

No

of

reads

b

c

seed

conservation

d

Target

overlap

e

U

C

A

U

A

A

G

C

U

U

U

A

U

U

U

U

G

U

U

U

A

A

G

C

C

U

A

G

A

A

G

G

A

C

A

A

A

U

U

C

G

G

U

U

C

U

A

G

A

G

A

G

G

U

U

U

G

Average

miRNA

U

U

C

G

U

seed

conservation

U

U

U

U

U

U

G

G

U

U

G

G

U

G

U

C

C

C

A

A

C

Target

overlap

U

C

U

G

C

A

C

U

G

U

G

G

C

Mature

score

No

of

reads

No

of

targets

iab

pb

Dfd

scr

Antp

Ubx

abd-A

abd-B

C

A

G

U

U

A

A

A

A

U

U

G

G

G

G

C

A

Figure

MicroRNA

gene

identification

and

functional

implications

a

New

predicted

miRNA

-LRB-

-RRB-

and

its

validation

by

sequencing

reads

Total

read

counts

for

mature

miRNA

-LRB-

red

-RRB-

and

miRNA

-LRB-

blue

-RRB-

show

a

characteristic

pattern

of

processing

indicative

of

miRNAs

Highlighted

regions

indicate

most

abundant

processing

products

b

Example

of

clustered

known

-LRB-

-RRB-

and

new

-LRB-

-RRB-

miRNAs

in

the

intron

of

cellcycle

regulator

c

Example

of

a

new

miRNA

-LRB-

-RRB-

in

the

transcript

of

a

spurious

gene

was

rejected

by

our

protein-coding

analysis

its

transcript

probably

representing

the

precursor

of

with

no

proteincoding

function

d

Revisions

to

the

end

of

and

are

proposed

on

the

basis

of

evolutionary

evidence

-LRB-

for

example

seed

conservation

black

curve

-RRB-

and

confirmed

by

sequencing

reads

Changes

at

the

end

of

more

than

one

nucleotide

results

in

marked

changes

to

the

predicted

target

spectra

-LRB-

venn

diagrams

-RRB-

e

Evidence

from

evolutionary

signals

-LRB-

mature

score

-RRB-

sequencing

reads

and

target

predictions

suggests

that

both

and

are

functional

each

targeting

distinct

Hox

genes

Nature

Publishing

Group

ARTICLES

NATURE

Vol

November

of

species

that

contained

them

we

evaluated

the

total

branch

length

over

which

the

D.

melanogaster

motif

instance

appears

to

be

conserved

-LRB-

Supplementary

Methods

b

-RRB-

which

we

call

the

branch

length

score

-LRB-

BLS

-RRB-

We

used

BLS

for

the

discovery

of

novel

motifs

-LRB-

this

section

-RRB-

and

for

the

prediction

of

individual

functional

motif

instances

-LRB-

next

section

-RRB-

Predicted

motifs

recover

known

regulators

To

discover

motifs

we

estimated

the

conservation

level

of

candidate

sequence

patterns

with

a

motif

excess

conservation

-LRB-

MEC

-RRB-

score

compared

to

overall

conservation

levels

in

promoters

UTRs

introns

protein-coding

exons

and

intergenic

regions

-LRB-

Supplementary

Methods

-RRB-

Our

search

in

regions

with

roles

in

pre-transcriptional

regulation

resulted

in

distinct

motifs

-LRB-

Table

-RRB-

obtained

by

collapsing

variants

across

motifs

discovered

in

promoters

in

enhancers

in

UTRs

in

core

promoters

in

introns

and

in

the

remaining

intergenic

regions

Motifs

discovered

in

each

region

showed

similar

properties

and

large

overlap

-LRB-

-RRB-

were

discovered

independently

in

at

least

two

regions

and

-LRB-

-RRB-

in

at

least

three

consistent

with

shared

regulatory

elements

in

these

The

discovered

motifs

match

-LRB-

-RRB-

of

the

known

transcription

factors

in

Drosophila

-LRB-

Supplementary

Table

-RRB-

compared

to

expected

at

random

-LRB-

P

-RRB-

Several

of

the

nondiscovered

known

motifs

are

involved

in

early

anterior

posterior

segmentation

of

the

embryo

consistent

with

reports

that

they

are

largely

indeed

of

these

did

not

exceed

the

conservation

expected

by

chance

in

promoter

regions

Other

Table

Pre-transcriptional

motifs

Name

Motif

consensus

MEC

MCS

Region

non-discovered

motifs

often

lacked

characteristics

expected

for

transcription

factor

motifs

suggesting

that

some

may

be

spurious

were

unusually

long

-LRB-

nucleotides

-RRB-

compared

to

of

recovered

ones

and

showed

only

one

or

a

few

total

instances

genomewide

suggestive

of

individual

regulatory

sites

rather

than

motifs

Tissue-specific

and

functional

enrichment

of

novel

motifs

The

discovered

motifs

showed

strong

signals

with

respect

to

embryonic

expression

patterns

-LRB-

Fig.

-RRB-

Overall

-LRB-

-RRB-

were

either

enriched

or

depleted

in

genes

expressed

in

at

least

one

tissue

compared

to

of

known

motifs

and

of

random

controls

Motif

depletion

may

represent

either

specific

repressors

for

individual

tissues

or

activators

excluded

from

these

tissues

Motif

depletion

was

found

more

generally

in

ubiquitously

expressed

genes

-LRB-

of

discovered

and

of

known

motifs

compared

with

expected

at

random

-RRB-

similar

to

findings

for

in

vivo

binding

and

probably

reflecting

less

complex

regulation

We

also

found

significant

motif

enrichment

in

groups

of

genetically

interacting

genes

-LRB-

collected

by

FlyBase

-RRB-

that

often

function

in

common

developmental

contexts

or

signalling

pathways

genes

of

metabolic

pathways

-LRB-

Kyoto

Encyclopedia

of

Genes

and

Genomes

-RRB-

and

genes

with

shared

functions

-LRB-

GO

-RRB-

In

total

of

discovered

and

of

known

motifs

were

enriched

or

depleted

in

one

of

the

functional

categories

-LRB-

random

-RRB-

Noteworthy

examples

include

motif

-LRB-

GCAACA

-RRB-

which

was

more

highly

enriched

in

neuroblasts

-LRB-

P

-RRB-

than

either

of

the

two

well-known

regulators

of

neuroblast

development

prospero

and

asense

-LRB-

P

and

respectively

-RRB-

Similarly

Known

transcription

factor

-LCB-

Multiplicity

score

-LCB-

ImaGO

ImaGO

GTCACGTD

AWNTGGGTCA

BCATAAATYA

HAATTAYGCRH

STATAWAWR

VATTWGCAT

BYAATTARH

HRTCAATCA

TGACANNNNNNTGACA

RCGTGNNNNGCAT

MATTAAWNATGCR

TTAATGATG

WTGACANBT

YGACMTTGA

AATTRNNNNCAATT

TGACGTCAT

MAATTNAATT

MRYTTCCGYY

MATTRRCACNY

YTAATGAVS

TAATTRANNTTNATG

WAATGCGCNT

MATTWRTCA

YAATTWNRYGC

TTAYGTAA

YGCGTHAATTR

AATTRYGWCA

GCGCATGH

WAATCARCGC

AATTAANNNNNCATNA

GCGTSAAA

YGCGYRTCAWT

GCGTTGAYA

AAATKKCATTA

RACASCTGY

TGTCAATTG

WAATKNNNNNCRCGY

CASGTAR

WCACGTGC

CATTANNNWAATT

PIG

PIG

PCEIG

C

PIG

G

PIG

PIG

PG

PIG

PG

PEIG

PIG

PEIG

G

G

PEIG

PG

PEG

PEIG

PCEG

G

G

PG

G

PG

PG

PCEG

PG

PEG

PEG

G

Caudal

Engrailed

TATA

Engrailed

CrebA

Dorsal

Giant

Antennapedia

Scute

Single-minded

Enhancer

of

split

Oesophagus

-LRB-

-RRB-

Ubiquitous

-LRB-

-RRB-

Ventral

nerve

cord

-LRB-

-RRB-

Ubiquitous

-LRB-

-RRB-

Ubiquitous

-LRB-

-RRB-

Dorsal

pharyngeal

muscle

PR

-LRB-

-RRB-

Tracheal

PR

-LRB-

-RRB-

Ubiquitous

-LRB-

-RRB-

Midgut

-LRB-

-RRB-

Ubiquitous

-LRB-

-RRB-

Ubiquitous

-LRB-

-RRB-

Foregut

PR

-LRB-

-RRB-

Dorsal

epidermis

PR

-LRB-

-RRB-

Ubiquitous

-LRB-

-RRB-

Midgut

-LRB-

-RRB-

Pericardial

cell

-LRB-

-RRB-

Ventral

nerve

cord

PR

-LRB-

-RRB-

Ventral

sensory

complex

SA

-LRB-

-RRB-

Tracheal

system

-LRB-

-RRB-

Ventral

epidermis

PR

-LRB-

-RRB-

The

top

of

are

shown

MEC

motif

excess

conservation

MCS

motif

conservation

score

See

Supplementary

Table

for

the

full

table

Region

where

the

motif

was

found

P

promoter

C

core

promoter

E

enhancers

UTR

I

intron

G

intergenic

genome

-LCB-

The

known

transcription

factor

motif

matching

the

consensus

sequence

-LCB-

A

multiplicity

score

is

reported

for

motifs

with

many

repeated

occurrences

Tissue

where

motif

is

most

strongly

enriched

or

depleted

and

corresponding

score

-LRB-

positive

enrichment

negative

depletion

-RRB-

PR

primordium

SA

specific

anlage

Nature

Publishing

Group

NATURE

Vol

November

ARTICLES

motifs

-LRB-

CACRCAC

-RRB-

-LRB-

MATTAAWNATGCR

-RRB-

and

-LRB-

MAAMNNCAA

-RRB-

were

highly

enriched

in

malpighian

tubule

-LRB-

P

-RRB-

trachea

-LRB-

P

-RRB-

and

surface

glia

-LRB-

-RRB-

respectively

in

each

case

ranking

above

motifs

for

factors

known

to

be

important

in

these

tissues

-LRB-

Supplementary

Table

-RRB-

These

presumably

correspond

to

as-yet-unknown

regulators

for

these

tissues

Exclusion

clustering

and

positional

constraints

A

large

number

of

motifs

were

depleted

in

coding

sequence

-LRB-

of

discovered

versus

of

known

and

of

random

motifs

P

-RRB-

and

in

UTRs

-LRB-

versus

and

P

-RRB-

suggesting

specific

exclusion

similar

to

in

vivo

Many

of

the

intergenic

or

intronic

instances

occurred

in

clusters

a

property

of

motifs

that

has

been

used

to

identify

enhancer

We

assessed

increased

conservation

of

motifs

when

found

near

other

instances

of

the

same

motif

-LRB-

whether

conserved

or

not

to

correct

for

regional

conservation

biases

-RRB-

and

found

significant

multiplicity

for

of

the

discovered

motifs

-LRB-

compared

to

of

known

and

of

random

motifs

-RRB-

In

addition

of

the

discovered

motifs

-LRB-

-RRB-

were

significantly

enriched

near

transcription

start

sites

-LRB-

compared

to

of

known

and

of

random

motifs

-RRB-

Several

were

enriched

at

precise

positions

and

preferred

orientations

-LRB-

Fig.

-RRB-

including

close

matches

to

several

known

core

promoter

motifs

involved

in

transcription

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

ME

M

E

ME

ME

ME

ME

ME

ME

For

example

-LRB-

STATAWAWR

-RRB-

which

matches

the

TATA-box

motif

displayed

a

sharp

peak

on

the

transcribed

strand

nucleotides

upstream

of

the

transcription

start

site

Similarly

-LRB-

TCAGTT

-RRB-

corresponding

to

the

known

initiator

motif

-LRB-

Inr

-RRB-

strongly

peaked

directly

on

the

transcription

start

site

and

-LRB-

RCGYRCGY

-RRB-

which

matches

a

known

downstream

promoter

element

-LRB-

DPE

-RRB-

peaked

nucleotides

downstream

of

the

transcription

start

site

Regulatory

motifs

involved

in

post-transcriptional

regulation

We

also

used

BLS/MEC

to

discover

motifs

involved

in

post-transcriptional

regulation

and

developed

methods

to

distinguish

motifs

acting

at

the

DNA

level

motifs

acting

at

the

RNA

level

and

motifs

stemming

from

protein-coding

codon

biases

-LRB-

Supplementary

Methods

-RRB-

Motifs

acting

post-transcriptionally

at

the

RNA

level

generally

showed

highly

asymmetric

as

functional

instances

can

only

occur

on

the

transcribed

strand

Indeed

of

motifs

-LRB-

-RRB-

discovered

in

UTRs

showed

strand-specific

conservation

-LRB-

compared

with

only

of

UTR

motifs

and

of

intron

motifs

suggesting

that

these

act

primarily

in

pre-transcriptional

regulation

-RRB-

Overall

motifs

discovered

in

UTRs

were

complementary

to

the

end

of

Rfam

miRNAs

recovering

of

known

miRNAs

-LRB-

of

unique

miRNA

families

-RRB-

An

additional

motifs

matched

to

ends

of

novel

miRNAs

predicted

above

of

which

STATAWAWR

-LRB-

TATA

box

-RRB-

a

Germ

cell

Central

nervous

system

Ventral

nerve

cord

Brain

Procephalon

Amnioserosa

Cuprophilic

cell

Central

brain

neuron

Central

brain

Central

brain

surface

glia

Central

brain

glia

Lateral

cord

surface

glia

Ventral

midline

Sensory

nervous

system

PR

Labial

sensory

complex

Ventral

apodeme

Maxillary

sensory

complex

Epipharynx

Hypopharynx

Salivary

gland

common

duct

Head

epidermis

dorsal

Optic

lobe

Visual

system

Ventral

sensory

complex

PR

Sensory

system

head

Salivary

gland

duct

Salivary

gland

Apoptotic

amnioserosa

Proventriculus

inner

layer

Visceral

branch

Dorsal

trunk

Oesophagus

Tracheal

system

Posterior

spiracle

Head

epidermis

Ventral

epidermis

Dorsal

epidermis

Atrium

Clypeolabrum

Foregut

Hindgut

Lateral

cord

neuron

Lateral

cord

glia

Midgut

interstitial

cell

Labral

sensory

complex

Corpus

allatum

Ring

gland

Large

intestine

Dorsal

apodeme

Visceral

muscle

Oenocyte

Antennal

sense

organ

Circulatory

system

Dorsal

vessel

Small

intestine

Rectum

Proventriculus

intermediate

layer

Proventriculus

Anal

pad

Malpighian

tubule

main

segment

Midgut

chamber

Malpighian

tubule

Midgut

Dorsal

prothoracic

pharyngeal

muscle

Gastric

caecum

Somatic

muscle

Gonad

Ubiquitous

Muscle

system

Plasmatocytes

Crystal

cell

Dorsal-lateral

sensory

complex

No

staining

b

TCAGTT

-LRB-

Inr

-RRB-

RCGYRCGY

-LRB-

DPE

-RRB-

TATCGATA

-LRB-

Dref

-RRB-

nt

nt

TSS

-LRB-

-RRB-

nt

nt

c

Motif

excess

conservation

-LRB-

-RRB-

d

Motif

excess

conservation

miRNA

conservation

In

coding

exons

In

UTRs

All

Fs

Reading

frame

Position

relative

to

mature

miRNA

end

Figure

Regulatory

motif

discovery

a

Discovered

motifs

show

enrichment

-LRB-

red

-RRB-

or

depletion

-LRB-

blue

-RRB-

in

genes

expressed

in

a

given

tissue

-LRB-

log

colour

range

from

P

enrichment

to

P

depletion

-RRB-

Biclustering

reveals

groups

of

motifs

with

similar

tissue

enrichment

and

groups

of

tissues

with

similar

motif

content

Full

matrix

and

randomized

control

is

shown

in

Supplementary

Fig.

b

Positional

bias

of

discovered

motifs

relative

to

transcription

start

sites

-LRB-

TSS

-RRB-

Peaks

with

highly

specific

distances

from

the

transcription

start

site

-LRB-

for

example

first

three

plots

-RRB-

are

characteristic

of

core

promoter

elements

and

broad

peaks

-LRB-

for

example

fourth

plot

-RRB-

are

characteristic

of

transcription

factors

For

non-palindromic

motifs

colours

indicate

forward-strand

-LRB-

red

-RRB-

and

reverse-strand

-LRB-

blue

-RRB-

instances

Curves

denote

the

density

of

all

instances

and

individual

segments

denote

individual

motif

instances

summed

across

groups

of

genes

-LRB-

each

line

-RRB-

c

Coding

regions

show

reading-frame-invariant

conservation

for

miRNA

motifs

-LRB-

red

-RRB-

and

reading-frame-biased

conservation

for

protein

motifs

-LRB-

grey

-RRB-

MEC

scores

are

evaluated

for

each

of

the

three

reading

frame

offsets

-LRB-

-RRB-

and

also

without

frame

correction

-LRB-

all

Fs

-RRB-

Plots

show

average

MEC

for

all

miRNA

motifs

and

top-scoring

protein-coding

motifs

-LRB-

based

on

MEC

without

frame

correction

-RRB-

d

Motif

excess

conservation

-LRB-

MEC

-RRB-

of

complements

at

different

offsets

with

respect

to

miRNA

end

averaged

across

all

Rfam

miRNAs

MEC

scores

evaluated

in

protein-coding

regions

and

UTRs

show

a

highly

similar

profile

-LRB-

correlation

coefficient

-RRB-

suggesting

similar

evolutionary

constraints

Nature

Publishing

Group

ARTICLES

NATURE

Vol

November

were

validated

and

motifs

matched

uniquely

to

miRNA

star

sequences

all

of

which

were

abundantly

expressed

in

vivo

-LRB-

Supplementary

Table

-RRB-

We

found

additional

motifs

in

UTRs

that

were

apparently

not

associated

with

miRNAs

-LRB-

TGTANWTW

-RRB-

closely

matches

the

Puf-family

Pumilio

-LRB-

AATAAA

-RRB-

corresponds

to

the

polyadenylation

signal

and

displays

both

very

strong

conservation

and

a

sharply

defined

distance

preference

with

respect

to

the

end

of

the

annotated

UTR

-LRB-

P

-RRB-

Finally

several

motifs

-LRB-

for

example

TAATTTAT

TTATTTT

-RRB-

are

variants

of

known

AU-rich

elements

which

are

known

to

mediate

mRNA

instability

and

MicroRNA

targeting

in

protein-coding

regions

Protein-coding

regions

can

also

harbour

functional

regulatory

motifs

such

as

exonic

splicing

regulatory

However

motif

conservation

is

difficult

to

assess

within

protein-coding

regions

because

of

the

overlapping

selective

pressures

Indeed

the

most

highly

conserved

nucleotide

sequence

patterns

of

length

seven

-LRB-

-RRB-

in

coding

sequence

showed

strong

reading-frame-biased

conservation

suggesting

that

they

reflect

protein-coding

constraints

rather

than

regulatory

roles

at

the

DNA

or

RNA

level

-LRB-

Fig.

-RRB-

MicroRNA

motifs

which

function

at

the

RNA

level

instead

showed

high

conservation

in

all

three

reading

frames

suggesting

that

they

are

specifically

selected

within

coding

regions

for

their

RNAlevel

function

Indeed

previous

studies

have

shown

that

miRNA

motifs

in

coding

regions

are

preferentially

conserved

in

that

they

can

lead

to

repression

in

experimental

and

that

they

are

avoided

in

genes

co-expressed

with

the

Frameinvariant

conservation

allows

us

to

demonstrate

the

coding-region

targeting

of

individual

miRNAs

and

also

enables

the

de

novo

discovery

of

miRNA

motifs

in

coding

regions

Using

frame-invariant

conservation

we

recovered

miRNA

motifs

within

the

top

coding-region

motifs

-LRB-

Supplementary

Table

-RRB-

whereas

using

overall

conservation

required

several

hundred

candidates

to

recover

miRNA

motifs

Moreover

complementary

to

different

positions

in

the

mature

miRNA

show

a

distinctive

conservation

pattern

indicative

of

functional

targeting

in

coding

regions

-LRB-

Fig.

-RRB-

and

similar

to

that

found

in

-LRB-

correlation

coefficient

-RRB-

Finally

complementary

to

miRNA

ends

were

depleted

in

coding

exons

of

a

anti-target

genes

-LRB-

Supplementary

Fig.

-RRB-

similar

to

findings

for

these

genes

Overall

these

results

together

with

findings

in

suggest

that

important

miRNA

targets

have

been

overlooked

by

many

target

prediction

that

have

traditionally

focused

exclusively

on

UTR

sequences

Prediction

of

individual

regulator

binding

sites

Previous

methods

for

regulatory

motif

integrated

conservation

information

over

hundreds

of

motif

instances

across

the

genome

leading

to

an

exceedingly

clear

signal

for

motif

discovery

even

if

many

of

these

instances

are

only

marginally

conserved

In

contrast

the

reliable

identification

of

individual

motif

instances

has

been

hampered

by

lack

of

neutral

divergence

and

would

require

many

related

In

the

absence

of

such

data

previous

studies

have

relied

on

motif

or

other

sequence

to

predict

regulatory

targets

or

regions

With

the

availability

of

the

fly

genomes

we

inferred

high-confidence

instances

of

regulatory

motifs

by

mapping

the

BLS

of

each

motif

instance

to

a

confidence

value

-LRB-

Supplementary

Methods

-RRB-

This

value

represents

the

probability

that

a

motif

instance

is

functional

on

the

basis

of

the

conservation

level

of

appropriate

control

motifs

evaluated

in

the

same

type

of

region

-LRB-

promoter

UTR

coding

and

so

on

-RRB-

Because

the

number

of

conserved

instances

decreases

much

more

rapidly

for

control

motifs

than

for

real

motifs

the

many

genomes

allowed

us

to

reach

high

confidence

values

for

many

transcription

factors

and

miRNAs

even

at

relatively

modest

BLS

thresholds

-LRB-

Fig.

-RRB-

Conserved

motif

instances

identify

functional

in

vivo

targets

We

found

that

increasing

confidence

levels

selected

for

functional

instances

for

both

transcription

factor

and

miRNA

motifs

the

normalized

fraction

of

transcription

factor

motif

instances

within

promoter

regions

rose

from

to

that

of

miRNA

motif

instances

within

UTRs

rose

from

to

and

the

fraction

of

miRNA

motif

instances

on

the

transcribed

strand

of

UTRs

rose

from

-LRB-

uniform

-RRB-

to

-LRB-

Fig.

-RRB-

in

each

case

selecting

the

regions

and

strands

where

the

motifs

are

known

to

be

functional

We

further

assessed

how

predicted

motif

instances

compared

with

in

vivo

targets

in

promoter

regions

defined

experimentally

-LRB-

without

comparative

information

-RRB-

We

used

a

set

of

highconfidence

direct

CrebA

and

three

genome-wide

chromatin

d

Motif

instance

recovery

Enrichment/depletion

of

muscle

genes

Known

motif

Expected

random

Fraction

of

motif

instances

conserved

Bound

region

enrichment

-LRB-

fold

-RRB-

Scaled

fraction

of

motif

instances

b

c

rs

s

s

TF

motifs

in

bound

regions

ote

ing

rons

TR

TR

d

U

om

U

Int

Co

Confidence

Pr

Twist

Baseline

Snail

TF

motifs

miRNA

UTRs

-LRB-

strand

-RRB-

UTRs

-LRB-

strand

-RRB-

Relative

functional

enrichment

Twist

Snail

Twist

Snail

ChIP

cons

ChIP

only

ChIP

all

Cons

all

Cons

only

CrebA

CrebA

miRNAs

Twist

Snail

Baseline

miRNA

Confidence

-LRB-

-RRB-

ChIP

enhancer

Figure

Identification

of

individual

motif

instances

a

Increasing

confidence

levels

select

for

motif

instances

in

regions

they

are

known

to

be

functional

conserved

transcription

factor

-LRB-

TF

-RRB-

motifs

enrich

for

promoters

miRNA

motifs

for

and

specifically

the

transcribed

strand

Regions

are

normalized

for

their

overall

length

measured

by

the

number

of

motif

instances

without

conservation

-LRB-

confidence

baseline

-RRB-

b

Increasing

confidence

levels

select

for

transcription

factor

motif

instances

with

experimental

support

for

each

factor

tested

c

The

high

fraction

of

experimentally

supported

motif

instances

that

are

recovered

at

confidence

for

transcription

factors

and

confidence

for

miRNAs

illustrates

the

high

sensitivity

of

the

BLS

approach

d

Comparison

of

chromatin

immunoprecipitation

-LRB-

ChIP

-RRB-

and

conservation

in

their

ability

to

identify

functional

motif

instances

Motif

instances

that

are

both

ChIPbound

and

conserved

-LRB-

purple

-RRB-

show

the

strongest

functional

enrichment

in

muscle

genes

for

and

Twist

-LRB-

depletion

for

Snail

-RRB-

whereas

motif

instances

derived

by

ChIP

alone

-LRB-

light

blue

-RRB-

show

substantially

reduced

enrichment

levels

Comparing

the

enrichment

of

all

instances

recovered

by

ChIP

-LRB-

blue

-RRB-

and

all

instances

recovered

by

conservation

-LRB-

red

-RRB-

suggests

that

the

two

approaches

perform

comparably

Even

the

sites

recovered

by

conservation

alone

outside

bound

regions

-LRB-

pink

-RRB-

show

enrichment

levels

comparable

to

ChIP

suggesting

that

they

are

also

functional

Nature

Publishing

Group

NATURE

Vol

November

ARTICLES

immunoprecipitation

-LRB-

ChIP

-RRB-

data

sets

for

Snail

and

and

in

each

case

found

that

the

enrichment

between

conserved

motif

instances

and

known

in

vivo

regions

increased

sharply

for

increasing

confidence

values

-LRB-

Fig.

-RRB-

We

also

found

that

a

large

fraction

of

motif

instances

in

experimentally

determined

target

regions

was

conserved

-LRB-

Fig.

-RRB-

of

motif

instances

in

direct

CrebA

targets

and

of

motif

instances

in

experimentally

supported

miRNA

were

recovered

at

confidence

Although

many

of

the

miRNA

targets

stem

from

comparative

predictions

and

are

expected

to

be

well

conserved

their

high

recovery

rate

illustrates

the

increased

sensitivity

of

the

BLS

measure

compared

to

perfect

conservation

-LRB-

Supplementary

Fig.

-RRB-

Similar

results

were

found

for

motifs

in

known

enhancers

that

were

determined

to

be

bound

by

ChIP

-LRB-

ChIP-bound

-RRB-

of

motifs

of

Snail

motifs

and

of

Twist

motifs

were

conserved

-LRB-

Fig.

-RRB-

ChIP-determined

and

conservation-determined

targets

show

similar

enrichment

To

determine

whether

ChIP-bound

motifs

that

lack

conservation

are

biologically

meaningful

we

studied

their

enrichment

in

muscle

gene

promoters

We

found

that

motifs

that

were

both

bound

and

evolutionarily

conserved

showed

very

strong

correlation

with

muscle

genes

for

all

three

factors

showed

eightfold

enrichment

Twist

showed

sevenfold

enrichment

and

Snail

a

mesodermal

repressor

showed

threefold

depletion

for

muscle

genes

However

when

only

non-conserved

sites

were

considered

the

correlation

dropped

significantly

to

for

all

three

factors

suggesting

that

non-conserved

ChIP-bound

sites

may

be

of

decreased

biological

significance

-LRB-

Fig.

-RRB-

We

also

used

the

correlation

with

muscle

genes

to

compare

ChIPon-chip

and

evolutionary

conservation

as

two

complementary

methods

for

target

identification

-LRB-

Fig.

-RRB-

We

found

that

the

enrichment

of

conservation-inferred

targets

was

consistently

higher

than

the

enrichment

of

ChIP-inferred

targets

for

each

of

the

three

factors

Finally

we

assessed

the

functional

significance

of

motif

instances

that

were

only

found

by

the

conservation

approach

specifically

excluding

those

in

ChIP-bound

regions

and

found

that

these

were

also

enriched

in

the

same

functional

categories

as

ChIP-bound

sites

with

comparable

or

higher

functional

correlations

-LRB-

Fig.

-RRB-

This

suggests

that

the

additional

conserved

instances

are

indeed

functional

probably

reflecting

the

higher

coverage

of

conservation-based

approaches

which

are

not

restricted

to

the

experimental

conditions

surveyed

or

that

they

may

be

bound

in

vivo

yet

missed

by

ChIP-on-chip

In

an

independent

we

compared

several

strategies

for

the

prediction

of

motif

instances

and

cis-regulatory

modules

and

found

that

using

the

fly

genomes

led

to

substantial

improvements

In

another

study

we

reported

the

recovery

of

conserved

motifs

for

a

Exon

sensitivity

at

specificity

-LRB-

-RRB-

several

known

regulators

including

Suppressor

of

Hairless

in

genes

of

the

Enhancer

of

split

A

regulatory

network

of

D.

melanogaster

at

confidence

Having

established

the

accuracy

of

conserved

motif

instances

we

present

an

initial

regulatory

network

for

D.

melanogaster

at

confidence

-LRB-

Supplementary

Fig.

-RRB-

containing

regulatory

connections

between

transcription

factors

and

genes

and

connections

between

cloned

miRNAs

-LRB-

clustered

in

families

with

unique

seed

sequences

-RRB-

and

genes

The

distribution

of

predicted

sites

per

target

gene

is

highly

nonuniform

and

indicative

of

varying

levels

of

regulatory

control

Genes

with

the

highest

number

of

sites

appeared

to

be

enriched

in

morphogenesis

organogenesis

neurogenesis

and

a

variety

of

tissues

whereas

ubiquitously

expressed

genes

and

maternal

genes

with

housekeeping

functions

had

the

fewest

Interestingly

transcription

factors

appeared

to

be

more

heavily

targeted

than

other

genes

both

by

transcription

factors

-LRB-

sites

versus

on

average

P

-RRB-

and

by

miRNAs

-LRB-

versus

miRNAs

P

-RRB-

Moreover

genes

with

many

transcription

factor

sites

also

had

many

miRNA

sites

and

conversely

genes

with

few

transcription

factor

sites

also

had

few

miRNA

sites

-LRB-

P

and

P

respectively

-RRB-

Several

of

the

predicted

regulatory

connections

have

independent

experimental

support

-LRB-

Supplementary

Table

-RRB-

including

direct

regulation

of

achaete

by

of

giant

by

of

Enhancer

of

split

complex

genes

by

Suppressor

of

and

of

bagpipe

by

Tinman

-LRB-

known

to

cooperate

in

mesoderm

induction

and

heart

-RRB-

More

generally

when

tissue-specific

expression

data

were

available

we

found

that

on

average

of

all

targets

were

co-expressed

with

their

factor

in

at

least

one

tissue

-LRB-

Supplementary

Fig.

-RRB-

which

is

significantly

higher

than

expected

by

chance

-LRB-

P

-RRB-

Scaling

of

comparative

genomics

power

Theoretical

considerations

and

pilot

studies

on

selected

genomic

regions

showed

that

the

discovery

power

of

comparative

methods

scales

with

the

number

and

phylogenetic

distance

of

the

species

We

extended

these

analyses

by

investigating

the

scaling

of

genome-wide

discovery

power

using

evolutionary

signatures

for

each

class

of

functional

elements

-LRB-

Fig.

-RRB-

on

the

basis

of

the

recovery

of

known

elements

using

different

subsets

of

informant

species

-LRB-

at

a

fixed

stringency

-RRB-

We

found

that

recovery

consistently

increased

with

the

total

number

of

informant

species

and

that

multi-species

comparisons

outperformed

pairwise

comparisons

within

the

same

phylogenetic

clade

When

we

examined

subsets

of

informants

with

similar

total

branch

length

-LRB-

for

example

several

close

species

versus

one

distant

species

-RRB-

c

Cloned

miRNAs

recovered

Pairwise

Multiple

Motifs

reaching

confidence

-LRB-

-RRB-

b

Known

ncRNAs

recovered

d

Pairwise

Multiple

Pairwise

Multiple

No

of

species

Best

pair

Branch

Species

used

length

D.mel

D.ere

D.sim

D.sec

D.yak

D.ana

D.pse

D.per

D.wil

D.moj

D.vir

D.gri

D.mel

D.ana

60 100 200 300

400 500 600

Sequence

length

-LRB-

nucleotide

-RRB-

Branch

length

Branch

length

Branch

length

Figure

Scaling

of

discovery

power

with

the

number

and

distance

of

informant

species

a

Discriminatory

power

of

CSF

protein-coding

evolutionary

metric

for

varying

exon

lengths

and

using

different

numbers

of

informant

species

Sensitivity

is

shown

for

known

exons

at

a

fixed

falsepositive

rate

based

on

random

non-coding

regions

Mean

length

is

shown

for

each

exon

length

quantile

Multi-species

comparisons

increase

discovery

power

especially

among

short

exons

b

Recovery

of

known

ncRNAs

-LRB-

among

the

top

predictions

-RRB-

for

pairwise

-LRB-

blue

-RRB-

and

multi-species

-LRB-

red

-RRB-

comparisons

c

Recovery

of

cloned

miRNAs

-LRB-

among

the

top

predictions

-RRB-

d

Recovery

of

transcription

factor

and

miRNA

motifs

with

instances

at

confidence

Nature

Publishing

Group

ARTICLES

NATURE

Vol

November

multi-species

comparisons

sometimes

performed

better

-LRB-

proteincoding

exons

ncRNAs

-RRB-

comparably

-LRB-

motifs

-RRB-

or

worse

-LRB-

miRNAs

-RRB-

than

pairwise

comparisons

This

complex

relationship

between

total

branch

length

and

actual

discovery

power

probably

reflects

imperfect

genome

assemblies/alignments

characteristics

of

each

class

of

functional

elements

and

the

specific

methods

we

used

For

example

ncRNA

discovery

probably

benefits

from

observing

more

compensatory

changes

across

more

genomes

whereas

miRNA

discovery

may

be

more

sensitive

to

artefacts

in

low-coverage

genomes

given

the

expected

high

conservation

of

miRNA

arms

As

expected

longer

elements

were

easier

to

discover

than

shorter

elements

Long

protein-coding

exons

-LRB-

nucleotides

-RRB-

were

recovered

at

very

high

rates

even

with

few

species

at

close

distances

-LRB-

leaving

little

room

for

improvement

with

additional

species

-RRB-

In

contrast

more

informant

species

and

larger

distances

were

crucial

for

recovering

short

exons

miRNAs

and

regulatory

motifs

Notably

the

optimal

evolutionary

distance

for

pairwise

comparisons

to

D.

melanogaster

also

seemed

to

depend

on

element

length

for

long

protein-coding

exons

the

best

pairwise

informant

was

the

closely

related

D.

erecta

for

exons

of

intermediate

lengths

D.

ananassae

and

for

the

shortest

exons

the

distant

D.

willistoni

-LRB-

Supplementary

Table

-RRB-

Distant

species

were

also

optimal

for

other

classes

of

short

elements

-LRB-

ncRNAs

miRNAs

and

motifs

Fig.

d

-RRB-

This

suggests

that

a

small

number

of

species

at

close

evolutionary

distances

may

generally

allow

the

discovery

of

long

elements

possibly

including

clade-specific

elements

whereas

short

clade-specific

elements

may

not

be

reliably

detectable

without

many

genomes

at

close

distances

Finally

we

investigated

the

effect

of

alignment

choice

on

our

results

-LRB-

Supplementary

Fig.

-RRB-

We

found

high

similarity

between

different

alignment

strategies

for

longer

elements

-LRB-

agreement

for

exons

-RRB-

whereas

shorter

elements

showed

larger

discrepancies

between

alignments

-LRB-

and

agreement

for

miRNA

and

motif

instances

respectively

-RRB-

Although

factors

such

as

genome

size

repeat

density

pseudogene

abundance

and

physiological

differences

might

confound

a

simple

analogy

to

the

vertebrate

phylogeny

based

on

neutral

branch

length

-LRB-

Fig.

-RRB-

our

results

suggest

that

comparisons

spanning

marsupials

birds

and

reptiles

may

prove

surprisingly

useful

for

biological

signal

discovery

in

the

human

genome

Discussion

Our

results

demonstrate

the

potential

of

comparative

genomics

for

the

systematic

characterization

of

functional

elements

in

a

complete

genome

Even

in

a

species

as

intensely

studied

as

D.

melanogaster

our

methods

predicted

several

thousand

new

functional

elements

including

protein-coding

genes

and

exons

novel

RNA

genes

and

structures

miRNA

genes

regulatory

motifs

and

regulator

targets

Our

novel

predictions

have

overwhelming

statistical

support

often

surpassing

that

of

known

functional

elements

and

are

additionally

supported

by

experimental

evidence

in

hundreds

of

cases

The

common

underlying

methodology

in

this

study

has

been

the

recognition

of

specific

evolutionary

signatures

associated

with

each

class

of

functional

elements

which

can

be

much

more

informative

for

genome

annotation

than

overall

measures

of

nucleotide

conservation

These

signatures

are

general

and

are

immediately

relevant

to

the

analysis

of

the

human

genome

and

more

generally

of

any

species

In

addition

to

the

many

new

elements

we

gained

specific

biological

insights

and

formulated

hypotheses

that

we

hope

will

guide

follow-up

experiments

We

found

genes

with

potential

translational

readthrough

showing

protein-like

evolution

downstream

of

a

highly

conserved

stop

codon

and

possibly

encoding

additional

protein

domains

or

peptides

specific

to

certain

developmental

contexts

We

also

found

several

candidate

programmed

frameshifts

which

might

be

part

of

regulatory

circuits

-LRB-

as

for

ODC/Oda

-RRB-

or

help

expand

the

diversity

of

protein

products

generated

from

one

mRNA

similar

to

their

role

in

We

also

presented

evidence

of

miRNA

processing

from

both

arms

of

a

miRNA

hairpin

and

from

both

DNA

strands

of

a

miRNA

locus

in

some

cases

potentially

leading

to

as

many

as

four

functional

miRNAs

per

locus

As

miRNA/miRNA

pairs

are

expressed

from

a

single

precursor

and

thus

co-regulated

whereas

sense/anti-sense

pairs

are

expressed

from

distinct

promoters

the

use

of

both

arms

or

both

strands

provides

compelling

general

building

blocks

for

higher-level

miRNAmediated

regulation

The

newly

discovered

elements

did

not

dramatically

increase

the

total

number

of

annotated

nucleotides

Known

and

predicted

elements

explain

of

nucleotides

in

phastCons

compared

to

for

previous

annotations

-LRB-

Supplementary

Fig.

-RRB-

an

increase

-LRB-

mostly

owing

to

conserved

motif

instances

-RRB-

The

remaining

phastCons

elements

and

independent

estimates

based

on

transcriptional

would

suggest

that

a

much

higher

fraction

of

the

genome

may

be

functional

-LRB-

Supplementary

Fig.

-RRB-

Although

it

is

possible

that

these

estimates

are

artificially

high

and

that

we

are

in

fact

converging

on

a

complete

annotation

of

the

fly

genome

they

might

instead

indicate

that

much

remains

to

be

discovered

which

may

require

the

recognition

of

as-yet-unknown

classes

of

functional

elements

with

distinct

evolutionary

signatures

Our

results

also

allowed

us

to

compare

and

contrast

evolutionary

and

experimental

methods

for

the

recovery

of

functional

elements

particularly

for

the

identification

of

regulator

targets

We

found

that

comparative

genomics

resulted

in

many

functionally

meaningful

sites

for

transcription

factors

Twist

and

Snail

outside

ChIPbound

regions

probably

representing

targets

from

diverse

conditions

not

surveyed

experimentally

Similarly

ChIP

resulted

in

many

additional

sites

outside

those

recovered

by

comparative

genomics

some

of

these

may

have

been

replaced

by

functionally

equivalent

non-orthologous

sequence

rendering

them

apparently

non-conserved

in

sequence

others

may

have

species

or

lineagespecific

roles

thus

lacking

sufficient

signal

for

their

comparative

detection

finally

some

bound

sites

may

be

biochemically

active

yet

selectively

It

is

worth

noting

however

that

ChIP-bound

motifs

that

were

not

conserved

showed

decreased

enrichment

in

muscle

mesoderm

development

where

the

factors

are

known

to

act

suggesting

that

potential

lineage-specific

roles

may

lie

outside

the

regulators

conserved

functions

To

resolve

these

questions

comparative

genomics

studies

would

benefit

greatly

from

experimental

studies

in

several

related

species

in

parallel

Overall

comparative

genomics

and

species-specific

experimental

studies

provide

complementary

approaches

to

biological

signal

discovery

Comparative

studies

help

pinpoint

evolutionarily

selected

functional

elements

across

diverse

conditions

whereas

experimental

studies

reveal

stage

and

tissue-specific

information

as

well

as

species-specific

sites

Ultimately

their

integration

is

a

necessary

step

towards

a

comprehensive

understanding

of

animal

genomes

METHODS

SUMMARY

The

Methods

are

described

in

Supplementary

Information

with

more

details

found

in

the

cited

companion

papers

for

each

section

The

sections

of

the

Supplementary

Methods

are

arranged

in

the

same

order

as

the

manuscript

to

facilitate

cross-referencing

with

an

index

on

the

first

page

to

aid

navigation

Received

July

accepted

October

Miller

W.

Makova

K.

D.

Nekrutenko

A.

Hardison

R.

C.

Comparative

genomics

Annu

Rev.

Genomics

Hum

Genet

-LRB-

-RRB-

Ureta-Vidal

A.

Ettwiller

L.

Birney

E.

Comparative

genomics

genome-wide

analysis

in

metazoan

eukaryotes

Nature

Rev.

Genet

-LRB-

-RRB-

Kellis

M.

et

al.

Sequencing

and

comparison

of

yeast

species

to

identify

genes

and

regulatory

elements

Nature

-LRB-

-RRB-

Cliften

P.

et

al.

Finding

functional

features

in

Saccharomyces

genomes

by

phylogenetic

footprinting

Science

-LRB-

-RRB-

Brent

M.

R.

Genome

annotation

past

present

and

future

how

to

define

an

ORF

at

each

locus

Genome

Res

-LRB-

-RRB-

Washietl

S.

Hofacker

I.

L.

Stadler

P.

F.

Fast

and

reliable

prediction

of

noncoding

RNAs

Proc

Natl

Acad

Sci

USA

-LRB-

-RRB-

Pedersen

J.

S.

et

al.

Identification

and

classification

of

conserved

RNA

secondary

structures

in

the

human

genome

PLoS

Comput

Biol

-LRB-

-RRB-

Nature

Publishing

Group

NATURE

Vol

November

ARTICLES

Zhang

L.

Pavlovic

V.

Cantor

C.

R.

Kasif

S.

Human-mouse

gene

identification

by

comparative

evidence

integration

and

evolutionary

analysis

Genome

Res

-LRB-

-RRB-

-LRB-

-RRB-

Crosby

M.

A.

et

al.

FlyBase

genomes

by

the

dozen

Nucleic

Acids

Res

-LRB-

Database

issue

-RRB-

-LRB-

-RRB-

Ashburner

M.

et

al.

Gene

ontology

tool

for

the

unification

of

biology

The

Gene

Ontology

Consortium

Nature

Genet

-LRB-

-RRB-

Ochman

H.

Ajioka

J.

W.

Garza

D.

Hartl

D.

L.

Inverse

polymerase

chain

reaction

Bio/Technology

-LRB-

-RRB-

Hoskins

R.

A.

et

al.

Rapid

and

efficient

cDNA

library

screening

by

self-ligation

of

inverse

PCR

products

-LRB-

SLIP

-RRB-

Nucleic

Acids

Res

-LRB-

-RRB-

Wan

K.

H.

et

al.

High-throughput

plasmid

cDNA

library

screening

Nature

Protocols

-LRB-

-RRB-

Hahn

M.

W.

Han

M.

V.

Han

S.-G

Gene

family

evolution

across

Drosophila

genomes

PLoS

Genet

-LRB-

-RRB-

Andrews

J.

et

al.

The

stoned

locus

of

Drosophila

melanogaster

produces

a

dicistronic

transcript

and

encodes

two

distinct

polypeptides

Genetics

-LRB-

-RRB-

Brogna

S.

Ashburner

M

The

Adh-related

gene

of

Drosophila

melanogaster

is

expressed

as

a

functional

dicistronic

messenger

RNA

multigenic

transcription

in

higher

organisms

EMBO

J.

-LRB-

-RRB-

Hatfield

D.

L.

Gladyshev

V.

N

How

selenium

has

altered

our

understanding

of

the

genetic

code

Mol

Cell

Biol

-LRB-

-RRB-

Kryukov

G.

V.

et

al.

Characterization

of

mammalian

selenoproteomes

Science

-LRB-

-RRB-

Copeland

P.

R.

Regulation

of

gene

expression

by

stop

codon

recoding

selenocysteine

Gene

-LRB-

-RRB-

Castellano

S.

et

al.

In

silico

identification

of

novel

selenoproteins

in

the

Drosophila

melanogaster

genome

EMBO

Rep.

-LRB-

-RRB-

von

der

Haar

T.

Tuite

M.

F.

Regulated

translational

bypass

of

stop

codons

in

yeast

Trends

Microbiol

-LRB-

-RRB-

Luo

G.

X.

et

al.

A

specific

base

transition

occurs

on

replicating

hepatitis

delta

virus

RNA

J.

Virol

-LRB-

-RRB-

Casey

J.

L.

Gerin

J.

L.

Hepatitis

D

virus

RNA

editing

specific

modification

of

adenosine

in

the

antigenomic

RNA

J.

Virol

-LRB-

-RRB-

Steneberg

P.

et

al.

Translational

readthrough

in

the

hdc

mRNA

generates

a

novel

branching

inhibitor

in

the

Drosophila

trachea

Genes

Dev

-LRB-

-RRB-

Bass

B.

L.

RNA

editing

by

adenosine

deaminases

that

act

on

RNA

Annu

Rev.

Biochem

-LRB-

-RRB-

Ivanov

I.

P.

et

al.

The

Drosophila

gene

for

antizyme

requires

ribosomal

frameshifting

for

expression

and

contains

an

intronic

gene

for

snRNP

Sm

on

the

opposite

strand

Mol

Cell

Biol

-LRB-

-RRB-

Eddy

S.

R.

Non-coding

RNA

genes

and

the

modern

RNA

world

Nature

Rev.

Genet

-LRB-

-RRB-

Yuan

G.

et

al.

RNomics

in

Drosophila

melanogaster

identification

of

candidates

for

novel

non-messenger

RNAs

Nucleic

Acids

Res

-LRB-

-RRB-

Lestrade

L.

Weber

M.

J.

snoRNA-LBME-db

a

comprehensive

database

of

human

H/ACA

and

C/D

box

snoRNAs

Nucleic

Acids

Res

-LRB-

Database

issue

-RRB-

-LRB-

-RRB-

Bier

E.

Drosophila

the

golden

bug

emerges

as

a

tool

for

human

genetics

Nature

Rev.

Genet

-LRB-

-RRB-

Hoopengardner

B.

Bhalla

T.

Staber

C.

Reenan

R.

Nervous

system

targets

of

RNA

editing

identified

by

comparative

genomics

Science

-LRB-

-RRB-

Mignone

F.

et

al.

UTRdb

and

UTRsite

a

collection

of

sequences

and

regulatory

motifs

of

the

untranslated

regions

of

eukaryotic

mRNAs

Nucleic

Acids

Res

-LRB-

Database

issue

-RRB-

-LRB-

-RRB-

Cohen

R.

S.

Zhang

S.

Dollar

G.

L

The

positional

structural

and

sequence

requirements

of

the

Drosophila

TLS

RNA

localization

element

RNA

-LRB-

-RRB-

Allemand

F.

et

al.

Escherichia

coli

ribosomal

protein

binds

as

a

single

monomer

to

its

own

mRNA

bearing

two

potential

binding

sites

Nucleic

Acids

Res

-LRB-

-RRB-

Okumura

T.

Matsumoto

A.

Tanimura

T.

Murakami

R

An

endoderm-specific

GATA

factor

gene

dGATAe

is

required

for

the

terminal

differentiation

of

the

Drosophila

endoderm

Dev

Biol

-LRB-

-RRB-

Park

S.

W.

et

al.

An

evolutionarily

conserved

domain

of

RNA

is

sufficient

for

induction

of

acetylation

on

the

Drosophila

X

chromosome

Genetics

-LRB-

in

the

press

-RRB-

Park

Y.

Kuroda

M.

I.

Epigenetic

aspects

of

X-chromosome

dosage

compensation

Science

-LRB-

-RRB-

Berezikov

E.

Cuppen

E.

Plasterk

R.

H.

Approaches

to

microRNA

discovery

Nature

Genet

-LRB-

Suppl

-RRB-

-LRB-

-RRB-

Bartel

D.

P.

MicroRNAs

genomics

biogenesis

mechanism

and

function

Cell

-LRB-

-RRB-

Stark

A.

et

al.

Systematic

discovery

and

characterization

of

fly

microRNAs

using

Drosophila

genomes

Genome

Res

doi

gr

-LRB-

in

the

press

-RRB-

Ruby

J.

G.

et

al.

Evolution

biogenesis

expression

and

target

predictions

of

a

substantially

expanded

set

of

Drosophila

microRNAs

Genome

Res

doi

gr

-LRB-

in

the

press

-RRB-

Pekarsky

Y.

et

al.

expression

in

chronic

lymphocytic

leukemia

is

regulated

by

and

Cancer

Res

-LRB-

-RRB-

Ruby

J.

G.

Jan

C.

H.

Bartel

D.

P.

Intronic

microRNA

precursors

that

bypass

Drosha

processing

Nature

-LRB-

-RRB-

Lim

L.

P.

et

al.

The

microRNAs

of

Caenorhabditis

elegans

Genes

Dev

-LRB-

-RRB-

Lim

L.

P.

et

al.

Vertebrate

microRNA

genes

Science

-LRB-

-RRB-

Lai

E.

C.

Tomancak

P.

Williams

R.

W.

Rubin

G.

M.

Computational

identification

of

Drosophila

microRNA

genes

Genome

Biol

-LRB-

-RRB-

Berezikov

E.

et

al.

Phylogenetic

shadowing

and

computational

identification

of

human

microRNA

genes

Cell

-LRB-

-RRB-

Xie

X.

et

al.

Systematic

discovery

of

regulatory

motifs

in

human

promoters

and

UTRs

by

comparison

of

several

mammals

Nature

-LRB-

-RRB-

Ettwiller

L.

et

al.

The

discovery

positioning

and

verification

of

a

set

of

transcription-associated

motifs

in

vertebrates

Genome

Biol

-LRB-

-RRB-

Chan

C.

S.

Elemento

O.

Tavazoie

S.

Revealing

posttranscriptional

regulatory

elements

through

network-level

conservation

PLoS

Comput

Biol

-LRB-

-RRB-

Boffelli

D.

et

al.

Phylogenetic

shadowing

of

primate

sequences

to

find

functional

regions

of

the

human

genome

Science

-LRB-

-RRB-

Cooper

G.

M.

et

al.

Distribution

and

intensity

of

constraint

in

mammalian

genomic

sequence

Genome

Res

-LRB-

-RRB-

Margulies

E.

H.

Blanchette

M.

Haussler

D.

Green

E.

D.

Identification

and

characterization

of

multi-species

conserved

sequences

Genome

Res

-LRB-

-RRB-

Thomas

J.

W.

et

al.

Comparative

analyses

of

multi-species

sequences

from

targeted

genomic

regions

Nature

-LRB-

-RRB-

Eddy

S.

R

A

model

of

the

statistical

power

of

comparative

genome

sequence

analysis

PLoS

Biol

-LRB-

-RRB-

Bergman

C.

M.

et

al.

Assessing

the

impact

of

comparative

genomic

sequence

data

on

the

functional

annotation

of

the

Drosophila

genome

Genome

Biol

-LRB-

-RRB-

Rubin

G.

M.

Lewis

E.

B

A

brief

history

of

Drosophila

's

contributions

to

genome

research

Science

-LRB-

-RRB-

Adams

M.

D.

et

al.

The

genome

sequence

of

Drosophila

melanogaster

Science

-LRB-

-RRB-

Misra

S.

et

al.

Annotation

of

the

Drosophila

melanogaster

euchromatic

genome

a

systematic

review

Genome

Biol

-LRB-

-RRB-

Celniker

S.

E.

Rubin

G.

M

The

Drosophila

melanogaster

genome

Annu

Rev.

Genomics

Hum

Genet

-LRB-

-RRB-

Ashburner

M.

Bergman

C.

M.

Drosophila

melanogaster

a

case

study

of

a

model

genomic

sequence

and

its

consequences

Genome

Res

-LRB-

-RRB-

Matthews

K.

A.

Kaufman

T.

C.

Gelbart

W.

M.

Research

resources

for

Drosophila

the

expanding

universe

Nature

Rev.

Genet

-LRB-

-RRB-

Venken

K.

J.

He

Y.

Hoskins

R.

A.

Bellen

H.

J.

P

-LSB-

acman

-RSB-

a

BAC

transgenic

platform

for

targeted

insertion

of

large

DNA

fragments

in

D.

melanogaster

Science

-LRB-

-RRB-

Dietzl

G.

et

al.

A

genome-wide

transgenic

RNAi

library

for

conditional

gene

inactivation

in

Drosophila

Nature

-LRB-

-RRB-

Spradling

A.

C.

et

al.

The

Berkeley

Drosophila

Genome

Project

gene

disruption

project

Single

P-element

insertions

mutating

of

vital

Drosophila

genes

Genetics

-LRB-

-RRB-

St

Johnston

D

The

art

and

design

of

genetic

screens

Drosophila

melanogaster

Nature

Rev.

Genet

-LRB-

-RRB-

Richards

S.

et

al.

Comparative

genome

sequencing

of

Drosophila

pseudoobscura

chromosomal

gene

and

cis-element

evolution

Genome

Res

-LRB-

-RRB-

Drosophila

Genomes

Consortium

Evolution

of

genes

and

genomes

on

the

Drosophila

phylogeny

Nature

doi

-LRB-

this

issue

-RRB-

-LRB-

-RRB-

Siepel

A.

et

al.

Evolutionarily

conserved

elements

in

vertebrate

insect

worm

and

yeast

genomes

Genome

Res

-LRB-

-RRB-

Nekrutenko

A.

Makova

K.

D.

Li

W.

H

The

KA/KS

ratio

test

for

assessing

the

protein-coding

potential

of

genomic

regions

an

empirical

and

simulation

study

Genome

Res

-LRB-

-RRB-

Eddy

S.

R.

Computational

genomics

of

noncoding

RNA

genes

Cell

-LRB-

-RRB-

Bompfuenewerer

A.

F.

et

al.

Evolutionary

patterns

of

non-coding

RNAs

Theor

Biosci

-LRB-

-RRB-

Reese

M.

G.

et

al.

Genome

annotation

assessment

in

Drosophila

melanogaster

Genome

Res

-LRB-

-RRB-

Rubin

G.

M.

et

al.

A

Drosophila

complementary

DNA

resource

Science

-LRB-

-RRB-

Stapleton

M.

et

al.

A

Drosophila

full-length

cDNA

resource

Genome

Biol

-LRB-

-RRB-

Hild

M.

et

al.

An

integrated

gene

annotation

and

transcriptional

profiling

approach

towards

the

full

gene

content

of

the

Drosophila

genome

Genome

Biol

-LRB-

-RRB-

Yandell

M.

et

al.

A

computational

and

experimental

approach

to

validating

annotations

and

gene

predictions

in

the

Drosophila

melanogaster

genome

Proc

Natl

Acad

Sci

USA

-LRB-

-RRB-

Manak

J.

R.

et

al.

Biological

function

of

unannotated

transcription

during

the

early

development

of

Drosophila

melanogaster

Nature

Genet

-LRB-

-RRB-

Lin

M.

F.

et

al.

Revisiting

the

protein-coding

gene

catalog

of

Drosophila

melanogaster

using

twelve

fly

genomes

Genome

Res

doi

gr

-LRB-

in

the

press

-RRB-

Yang

Z.

Bielawski

J.

P.

Statistical

methods

for

detecting

molecular

adaptation

Trends

Ecol

Evol

-LRB-

-RRB-

Mignone

F.

Grillo

G.

Liuni

S.

Pesole

G.

Computational

identification

of

protein

coding

potential

of

conserved

sequence

tags

through

cross-species

evolutionary

analysis

Nucleic

Acids

Res

-LRB-

-RRB-

Nature

Publishing

Group

ARTICLES

NATURE

Vol

November

Okamura

K.

et

al.

The

mirtron

pathway

generates

microRNA-class

regulatory

RNAs

in

Drosophila

Cell

-LRB-

-RRB-

Lewis

B.

P.

et

al.

Prediction

of

mammalian

microRNA

targets

Cell

-LRB-

-RRB-

Stark

A.

Brennecke

J.

Russell

R.

B.

Cohen

S.

M.

Identification

of

Drosophila

microRNA

targets

PLoS

Biol

-LRB-

-RRB-

Lai

E.

C.

Micro

RNAs

are

complementary

to

UTR

sequence

motifs

that

mediate

negative

post-transcriptional

regulation

Nature

Genet

-LRB-

-RRB-

Lewis

B.

P.

Burge

C.

B.

Bartel

D.

P.

Conserved

seed

pairing

often

flanked

by

adenosines

indicates

that

thousands

of

human

genes

are

microRNA

targets

Cell

-LRB-

-RRB-

Tompa

M.

Identifying

functional

elements

by

comparative

DNA

sequence

analysis

Genome

Res

-LRB-

-RRB-

Stormo

G.

D.

DNA

binding

sites

representation

and

discovery

Bioinformatics

-LRB-

-RRB-

Kheradpour

P.

Stark

A.

Roy

S.

Kellis

M.

Reliable

prediction

of

regulator

targets

using

Drosophila

genomes

Genome

Res

doi

gr

-LRB-

in

the

press

-RRB-

Stathopoulos

A.

Levine

M.

Genomic

regulatory

networks

and

animal

development

Dev

Cell

-LRB-

-RRB-

Schroeder

M.

D.

et

al.

Transcriptional

control

in

the

segmentation

gene

network

of

Drosophila

PLoS

Biol

-LRB-

-RRB-

Zeitlinger

J.

et

al.

Whole-genome

ChIP-chip

analysis

of

Dorsal

Twist

and

Snail

suggests

integration

of

diverse

patterning

processes

in

the

Drosophila

embryo

Genes

Dev

-LRB-

-RRB-

Kanehisa

M.

et

al.

The

KEGG

resource

for

deciphering

the

genome

Nucleic

Acids

Res

-LRB-

Database

issue

-RRB-

-LRB-

-RRB-

Berman

B.

P.

et

al.

Exploiting

transcription

factor

binding

site

clustering

to

identify

cis-regulatory

modules

involved

in

pattern

formation

in

the

Drosophila

genome

Proc

Natl

Acad

Sci

USA

-LRB-

-RRB-

Markstein

M.

et

al.

A

regulatory

code

for

neurogenic

gene

expression

in

the

Drosophila

embryo

Development

-LRB-

-RRB-

Philippakis

A.

A.

et

al.

Expression-guided

in

silico

evaluation

of

candidate

cis

regulatory

codes

for

Drosophila

muscle

founder

cells

PLoS

Comput

Biol

-LRB-

-RRB-

Smale

S.

T.

Kadonaga

J.

T

The

RNA

polymerase

II

core

promoter

Annu

Rev.

Biochem

-LRB-

-RRB-

Gerber

A.

P.

et

al.

Genome-wide

identification

of

mRNAs

associated

with

the

translational

regulator

PUMILIO

in

Drosophila

melanogaster

Proc

Natl

Acad

Sci

USA

-LRB-

-RRB-

Zubiaga

A.

M.

Belasco

J.

G.

Greenberg

M.

E

The

nonamer

UUAUUUAUU

is

the

key

AU-rich

sequence

motif

that

mediates

mRNA

degradation

Mol

Cell

Biol

-LRB-

-RRB-

Fairbrother

W.

G.

Yeh

R.

F.

Sharp

P.

A.

Burge

C.

B.

Predictive

identification

of

exonic

splicing

enhancers

in

human

genes

Science

-LRB-

-RRB-

Kloosterman

W.

P.

Wienholds

E.

Ketting

R.

F.

Plasterk

R.

H.

Substrate

requirements

for

function

in

the

developing

zebrafish

embryo

Nucleic

Acids

Res

-LRB-

-RRB-

Grimson

A.

et

al.

MicroRNA

targeting

specificity

in

mammals

determinants

beyond

seed

pairing

Mol

Cell

-LRB-

-RRB-

Farh

K.

K.

et

al.

The

widespread

impact

of

mammalian

MicroRNAs

on

mRNA

repression

and

evolution

Science

-LRB-

-RRB-

Stark

A.

et

al.

Animal

microRNAs

confer

robustness

to

gene

expression

and

have

a

significant

impact

on

UTR

evolution

Cell

-LRB-

-RRB-

Rajewsky

N.

microRNA

target

predictions

in

animals

Nature

Genet

-LRB-

suppl

-RRB-

-LRB-

-RRB-

Elnitski

L.

et

al.

Distinguishing

regulatory

DNA

from

neutral

sites

Genome

Res

-LRB-

-RRB-

Abrams

E.

W.

Andrew

D.

J.

CrebA

regulates

secretory

activity

in

the

Drosophila

salivary

gland

and

epidermis

Development

-LRB-

-RRB-

Sandmann

T.

et

al.

A

temporal

map

of

transcription

factor

activity

directly

regulates

target

genes

at

all

stages

of

muscle

development

Dev

Cell

-LRB-

-RRB-

Sandmann

T.

et

al.

A

core

transcriptional

network

for

early

mesoderm

development

in

Drosophila

melanogaster

Genes

Dev

-LRB-

-RRB-

Sethupathy

P.

Corda

B.

Hatzigeorgiou

A.

G.

TarBase

A

comprehensive

database

of

experimentally

supported

animal

microRNA

targets

RNA

-LRB-

-RRB-

Lee

T.

I.

et

al.

Control

of

developmental

regulators

by

Polycomb

in

human

embryonic

stem

cells

Cell

-LRB-

-RRB-

Boyer

L.

A.

et

al.

Core

transcriptional

regulatory

circuitry

in

human

embryonic

stem

cells

Cell

-LRB-

-RRB-

Aerts

S.

van

Helden

J.

Sand

O.

Hassan

B.

Fine-tuning

enhancer

models

to

predict

transcriptional

targets

across

multiple

genomes

PLoS

ONE

-LRB-

-RRB-

-LRB-

-RRB-

Maeder

M.

Polansky

B.

Robson

B.

Eastman

D.

Phylogenetic

footprinting

analysis

in

the

upstream

regulatory

regions

of

the

Drosophila

Enhancer

of

split

genes

Genetics

-LRB-

in

the

press

-RRB-

Van

Doren

M.

et

al.

Negative

regulation

of

proneural

gene

activity

hairy

is

a

direct

transcriptional

repressor

of

achaete

Genes

Dev

-LRB-

-RRB-

Kraut

R.

Levine

M.

Spatial

regulation

of

the

gap

gene

giant

during

Drosophila

development

Development

-LRB-

-RRB-

Bailey

A.

M.

Posakony

J.

W.

Suppressor

of

hairless

directly

activates

transcription

of

enhancer

of

split

complex

genes

in

response

to

Notch

receptor

activity

Genes

Dev

-LRB-

-RRB-

Yin

Z.

Frasch

M.

Regulation

and

function

of

tinman

during

dorsal

mesoderm

induction

and

heart

specification

in

Drosophila

Dev

Genet

-LRB-

-RRB-

Margulies

E.

H.

et

al.

An

initial

strategy

for

the

systematic

identification

of

functional

elements

in

the

human

genome

by

low-redundancy

comparative

sequencing

Proc

Natl

Acad

Sci

USA

-LRB-

-RRB-

Margulies

E.

H.

Chen

C.

W.

Green

E.

D.

Differences

between

pair-wise

and

multi-sequence

alignment

methods

affect

vertebrate

genome

comparisons

Trends

Genet

-LRB-

-RRB-

Farabaugh

P.

J.

Programmed

translational

frameshifting

Annu

Rev.

Genet

-LRB-

-RRB-

Odom

D.

T.

et

al.

Tissue-specific

transcriptional

regulation

has

diverged

significantly

between

human

and

mouse

Nature

Genet

-LRB-

-RRB-

Ludwig

M.

Z.

Kreitman

M.

Evolutionary

dynamics

of

the

enhancer

region

of

even-skipped

in

Drosophila

Mol

Biol

Evol

-LRB-

-RRB-

Ludwig

M.

Z.

et

al.

Functional

evolution

of

a

cis-regulatory

module

PLoS

Biol

-LRB-

-RRB-

The

ENCODE

Project

Consortium

Identification

and

analysis

of

functional

elements

in

of

the

human

genome

by

the

ENCODE

pilot

project

Nature

-LRB-

-RRB-

Kent

W.

J.

et

al.

The

human

genome

browser

at

UCSC

Genome

Res

-LRB-

-RRB-

Supplementary

Information

is

linked

to

the

online

version

of

the

paper

at

Acknowledgements

We

thank

the

National

Human

Genome

Research

Institute

-LRB-

NHGRI

-RRB-

for

continued

support

A.S.

was

supported

in

part

by

the

Schering

AG

Ernst

Schering

Foundation

and

in

part

by

the

Human

Frontier

Science

Program

Organization

-LRB-

HFSPO

-RRB-

P.K.

was

supported

in

part

by

a

National

Science

Foundation

Graduate

Research

Fellowship

J.S.P.

thanks

B.

Raney

and

R.

Baertsch

and

the

Danish

Medical

Research

Council

and

the

National

Cancer

Institute

for

support

J.B.

thanks

the

Schering

AG/Ernst

Schering

Foundation

for

a

postdoctoral

fellowship

L.Parts

thanks

J.

Vilo

S.R.

was

supported

by

a

HHMI-NIH/NIBIB

Interfaces

Training

Grant

and

thanks

T.

Lane

and

M.

Werner-Washburne

D.H.

D.P.B.

G.J.H.

and

T.C.K.

are

Investigators

of

the

Howard

Hughes

Medical

Institute

and

B.P.

J.G.R.

E.H.

and

J.B.

are

affiliated

with

these

investigators

J.W.C.

and

S.E.C.

were

supported

by

the

NHGRI

M.K.

was

supported

by

start-up

funds

from

the

MIT

Electrical

Engineering

and

Computer

Science

Laboratory

the

Broad

Institute

of

MIT

and

Harvard

and

the

MIT

Computer

Science

and

Artificial

Intelligence

Laboratory

and

by

the

Distinguished

Alumnus

-LRB-

-RRB-

Career

Development

Professorship

Author

Contributions

Organizing

committee

Manolis

Kellis

William

Gelbart

Doug

Smith

Andrew

G.

Clark

Michael

E.

Eisen

Thomas

C.

Kaufman

protein-coding

gene

prediction

Michael

F.

Lin

Ameya

N.

Deoras

Mira

V.

Han

Matthew

W.

Hahn

Donald

G.

Gilbert

Michael

Weir

Michael

Rice

Manolis

Kellis

manual

curation

of

protein-coding

genes

Madeline

A.

Crosby

Harvard

FlyBase

curators

William

M.

Gelbart

validation

of

protein-coding

genes

Joseph

W.

Carlson

Berkeley

Drosophila

Genome

Project

Susan

E.

Celniker

non-coding

RNA

gene

prediction

Jakob

S.

Pedersen

David

Haussler

Yongkyu

Park

Seung-Won

Park

Manolis

Kellis

microRNA

gene

prediction

Alexander

Stark

Pouya

Kheradpour

Leopold

Parts

Manolis

Kellis

microRNA

cloning

and

sequencing

Julius

Brennecke

Emily

Hodges

Gregory

J.

Hannon

microRNA

target

prediction

Alexander

Stark

J.

Graham

Ruby

Manolis

Kellis

Eric

C.

Lai

David

P.

Bartel

motif

identification

Alexander

Stark

Pouya

Kheradpour

Manolis

Kellis

motif

instance

prediction

Alexander

Stark

Pouya

Kheradpour

Sushmita

Roy

Morgan

L.

Maeder

Benjamin

J.

Polansky

Bryanne

E.

Robson

Deborah

A.

Eastman

Stein

Aerts

Bassem

Hassan

Jacques

van

Helden

Manolis

Kellis

genome

alignments

Angie

S.

Hinrichs

W.

James

Kent

Anat

Caspi

Lior

Pachter

Colin

N.

Dewey

Benedict

Paten

phylogeny

and

branch

length

estimation

Matthew

D.

Rasmussen

Manolis

Kellis

final

manuscript

preparation

Alexander

Stark

Michael

F.

Lin

Pouya

Kheradpour

Jakob

Pedersen

Manolis

Kellis

Author

Information

Reprints

and

permissions

information

is

available

at

Correspondence

and

requests

for

materials

should

be

addressed

to

M.K.

-LRB-

manoli@mit.edu

-RRB-

Harvard

FlyBase

curators

Madeline

A.

Beverley

B.

Andrew

J.

L.

Sian

Susan

E.

St

Margaret

Kenneth

L.

Wiley

Rob

J.

Peili

Kyl

V.

Jerry

V.

William

M.

Berkeley

Drosophila

Genome

Project

Joseph

W.

Charles

Soo

Kenneth

H.

Susan

E.

FlyBase

The

Biological

Laboratories

Harvard

University

Divinity

Avenue

Cambridge

Massachusetts

USA

LBNL

Cyclotron

Road

MS

Berkeley

California

USA

Nature

Publishing

Group