Vol

June

doi

ARTICLES

Identification

and

analysis

of

functional

elements

in

of

the

human

genome

by

the

ENCODE

pilot

project

The

ENCODE

Project

Consortium

We

report

the

generation

and

analysis

of

functional

data

from

multiple

diverse

experiments

performed

on

a

targeted

of

the

human

genome

as

part

of

the

pilot

phase

of

the

ENCODE

Project

These

data

have

been

further

integrated

and

augmented

by

a

number

of

evolutionary

and

computational

analyses

Together

our

results

advance

the

collective

knowledge

about

human

genome

function

in

several

major

areas

First

our

studies

provide

convincing

evidence

that

the

genome

is

pervasively

transcribed

such

that

the

majority

of

its

bases

can

be

found

in

primary

transcripts

including

non-protein-coding

transcripts

and

those

that

extensively

overlap

one

another

Second

systematic

examination

of

transcriptional

regulation

has

yielded

new

understanding

about

transcription

start

sites

including

their

relationship

to

specific

regulatory

sequences

and

features

of

chromatin

accessibility

and

histone

modification

Third

a

more

sophisticated

view

of

chromatin

structure

has

emerged

including

its

inter-relationship

with

DNA

replication

and

transcriptional

regulation

Finally

integration

of

these

new

sources

of

information

in

particular

with

respect

to

mammalian

evolution

based

on

inter

and

intra-species

sequence

comparisons

has

yielded

new

mechanistic

and

evolutionary

insights

concerning

the

functional

landscape

of

the

human

genome

Together

these

studies

are

defining

a

path

for

pursuit

of

a

more

comprehensive

characterization

of

human

genome

function

The

human

genome

is

an

elegant

but

cryptic

store

of

information

The

roughly

three

billion

bases

encode

either

directly

or

indirectly

the

instructions

for

synthesizing

nearly

all

the

molecules

that

form

each

human

cell

tissue

and

organ

Sequencing

the

human

provided

highly

accurate

DNA

sequences

for

each

of

the

chromosomes

However

at

present

we

have

an

incomplete

understanding

of

the

protein-coding

portions

of

the

genome

and

markedly

less

understanding

of

both

non-protein-coding

transcripts

and

genomic

elements

that

temporally

and

spatially

regulate

gene

expression

To

understand

the

human

genome

and

by

extension

the

biological

processes

it

orchestrates

and

the

ways

in

which

its

defects

can

give

rise

to

disease

we

need

a

more

transparent

view

of

the

information

it

encodes

The

molecular

mechanisms

by

which

genomic

information

directs

the

synthesis

of

different

biomolecules

has

been

the

focus

of

much

of

molecular

biology

research

over

the

last

three

decades

Previous

studies

have

typically

concentrated

on

individual

genes

with

the

resulting

general

principles

then

providing

insights

into

transcription

chromatin

remodelling

messenger

RNA

splicing

DNA

replication

and

numerous

other

genomic

processes

Although

many

such

principles

seem

valid

as

additional

genes

are

investigated

they

generally

have

not

provided

genome-wide

insights

about

biological

function

The

first

genome-wide

analyses

that

shed

light

on

human

genome

function

made

use

of

observing

the

actions

of

evolution

The

evergrowing

set

of

vertebrate

genome

is

providing

increasing

power

to

reveal

the

genomic

regions

that

have

been

most

and

least

acted

on

by

the

forces

of

evolution

However

although

these

studies

convincingly

indicate

the

presence

of

numerous

genomic

regions

under

strong

evolutionary

constraint

they

have

less

power

in

identifying

the

precise

bases

that

are

constrained

and

provide

little

if

any

insight

into

why

those

bases

are

biologically

important

Furthermore

although

we

have

good

models

for

how

protein-coding

regions

A

list

of

authors

and

their

affiliations

appears

at

the

end

of

the

paper

evolve

our

present

understanding

about

the

evolution

of

other

functional

genomic

regions

is

poorly

developed

Experimental

studies

that

augment

what

we

learn

from

evolutionary

analyses

are

key

for

solidifying

our

insights

regarding

genome

function

The

Encyclopedia

of

DNA

Elements

-LRB-

ENCODE

-RRB-

aims

to

provide

a

more

biologically

informative

representation

of

the

human

genome

by

using

high-throughput

methods

to

identify

and

catalogue

the

functional

elements

encoded

In

its

pilot

phase

groups

provided

more

than

experimental

and

computational

data

sets

that

examined

in

unprecedented

detail

a

targeted

kilobases

-LRB-

kb

-RRB-

of

the

human

genome

These

roughly

Mb

equivalent

to

of

the

human

genome

are

sufficiently

large

and

diverse

to

allow

for

rigorous

pilot

testing

of

multiple

experimental

and

computational

methods

These

Mb

are

divided

among

genomic

regions

approximately

Mb

reside

in

regions

for

which

there

is

already

substantial

biological

knowledge

whereas

the

other

Mb

reside

in

regions

chosen

by

a

stratified

random-sampling

method

-LRB-

see

-RRB-

The

highlights

of

our

findings

to

date

include

The

human

genome

is

pervasively

transcribed

such

that

the

majority

of

its

bases

are

associated

with

at

least

one

primary

transcript

and

many

transcripts

link

distal

regions

to

established

proteincoding

loci

Many

novel

non-protein-coding

transcripts

have

been

identified

with

many

of

these

overlapping

protein-coding

loci

and

others

located

in

regions

of

the

genome

previously

thought

to

be

transcriptionally

silent

Numerous

previously

unrecognized

transcription

start

sites

have

been

identified

many

of

which

show

chromatin

structure

and

sequence-specific

protein-binding

properties

similar

to

wellunderstood

promoters

Nature

Publishing

Group

ARTICLES

NATURE

Vol

June

Regulatory

sequences

that

surround

transcription

start

sites

are

symmetrically

distributed

with

no

bias

towards

upstream

regions

Chromatin

accessibility

and

histone

modification

patterns

are

highly

predictive

of

both

the

presence

and

activity

of

transcription

start

sites

Distal

DNaseI

hypersensitive

sites

have

characteristic

histone

modification

patterns

that

reliably

distinguish

them

from

promoters

some

of

these

distal

sites

show

marks

consistent

with

insulator

function

DNA

replication

timing

is

correlated

with

chromatin

structure

A

total

of

of

the

bases

in

the

genome

can

be

confidently

identified

as

being

under

evolutionary

constraint

in

mammals

for

approximately

of

these

constrained

bases

there

is

evidence

of

function

on

the

basis

of

the

results

of

the

experimental

assays

performed

to

date

Although

there

is

general

overlap

between

genomic

regions

identified

as

functional

by

experimental

assays

and

those

under

evolutionary

constraint

not

all

bases

within

these

experimentally

defined

regions

show

evidence

of

constraint

Different

functional

elements

vary

greatly

in

their

sequence

variability

across

the

human

population

and

in

their

likelihood

of

residing

within

a

structurally

variable

region

of

the

genome

Surprisingly

many

functional

elements

are

seemingly

unconstrained

across

mammalian

evolution

This

suggests

the

possibility

of

a

large

pool

of

neutral

elements

that

are

biochemically

active

but

provide

no

specific

benefit

to

the

organism

This

pool

may

serve

as

a

warehouse

for

natural

selection

potentially

acting

as

the

source

of

lineage-specific

elements

and

functionally

conserved

but

nonorthologous

elements

between

species

Below

we

first

provide

an

overview

of

the

experimental

techniques

used

for

our

studies

after

which

we

describe

the

insights

gained

from

analysing

and

integrating

the

generated

data

sets

We

conclude

with

a

perspective

of

what

we

have

learned

to

date

about

this

of

the

human

genome

and

what

we

believe

the

prospects

are

for

a

broader

and

deeper

investigation

of

the

functional

elements

in

the

human

genome

To

aid

the

reader

Box

provides

a

glossary

for

many

of

the

abbreviations

used

throughout

this

paper

Experimental

techniques

Table

-LRB-

expanded

in

Supplementary

Information

section

-RRB-

lists

the

major

experimental

techniques

used

for

the

studies

reported

here

relevant

acronyms

and

references

reporting

the

generated

data

sets

These

data

sets

reflect

over

million

experimental

data

points

-LRB-

million

data

points

if

one

includes

comparative

sequencing

bases

-RRB-

In

describing

the

major

results

and

initial

conclusions

we

seek

to

distinguish

biochemical

function

from

biological

role

Biochemical

function

reflects

the

direct

behaviour

of

a

molecule

-LRB-

s

-RRB-

whereas

biological

role

is

used

to

describe

the

consequence

-LRB-

s

-RRB-

of

this

function

for

the

organism

Genome-analysis

techniques

nearly

always

focus

on

biochemical

function

but

not

necessarily

on

biological

role

This

is

because

the

former

is

more

amenable

to

largescale

data-generation

methods

whereas

the

latter

is

more

difficult

to

assay

on

a

large

scale

The

ENCODE

pilot

project

aimed

to

establish

redundancy

with

respect

to

the

findings

represented

by

different

data

sets

In

some

instances

this

involved

the

intentional

use

of

different

assays

that

were

based

on

a

similar

technique

whereas

in

other

situations

different

techniques

assayed

the

same

biochemical

function

Such

redundancy

has

allowed

methods

to

be

compared

and

consensus

data

sets

to

be

generated

much

of

which

is

discussed

in

companion

papers

such

as

the

ChIP-chip

platform

All

ENCODE

data

have

been

released

after

verification

but

before

this

publication

as

befits

a

community

resource

project

-LRB-

see

-RRB-

Verification

is

defined

as

when

the

experiment

is

reproducibly

confirmed

-LRB-

see

Supplementary

Information

section

-RRB-

The

main

portal

for

ENCODE

data

is

provided

by

the

UCSC

Genome

Browser

-LRB-

-RRB-

this

is

Box

Frequently

used

abbreviations

in

this

paper

AR

Ancient

repeat

a

repeat

that

was

inserted

into

the

early

mammalian

lineage

and

has

since

become

dormant

the

majority

of

ancient

repeats

are

thought

to

be

neutrally

evolving

CAGE

tag

A

short

sequence

from

the

end

of

a

transcript

CDS

Coding

sequence

a

region

of

a

cDNA

or

genome

that

encodes

proteins

ChIP-chip

Chromatin

immunoprecipitation

followed

by

detection

of

the

products

using

a

genomic

tiling

array

CNV

Copy

number

variants

regions

of

the

genome

that

have

large

duplications

in

some

individuals

in

the

human

population

CS

Constrained

sequence

a

genomic

region

associated

with

evidence

of

negative

selection

-LRB-

that

is

rejection

of

mutations

relative

to

neutral

regions

-RRB-

DHS

DNaseI

hypersensitive

site

a

region

of

the

genome

showing

a

sharply

different

sensitivity

to

DNaseI

compared

with

its

immediate

locale

EST

Expressed

sequence

tag

a

short

sequence

of

a

cDNA

indicative

of

expression

at

this

point

FAIRE

Formaldehyde-assisted

isolation

of

regulatory

elements

a

method

to

assay

open

chromatin

using

formaldehyde

crosslinking

followed

by

detection

of

the

products

using

a

genomic

tiling

array

FDR

False

discovery

rate

a

statistical

method

for

setting

thresholds

on

statistical

tests

to

correct

for

multiple

testing

GENCODE

Integrated

annotation

of

existing

cDNA

and

protein

resources

to

define

transcripts

with

both

manual

review

and

experimental

testing

procedures

GSC

Genome

structure

correction

a

method

to

adapt

statistical

tests

to

make

fewer

assumptions

about

the

distribution

of

features

on

the

genome

sequence

This

provides

a

conservative

correction

to

standard

tests

HMM

Hidden

Markov

model

a

machine-learning

technique

that

can

establish

optimal

parameters

for

a

given

model

to

explain

the

observed

data

Indel

An

insertion

or

deletion

two

sequences

often

show

a

length

difference

within

alignments

but

it

is

not

always

clear

whether

this

reflects

a

previous

insertion

or

a

deletion

PET

A

short

sequence

that

contains

both

the

and

ends

of

a

transcript

RACE

Rapid

amplification

of

cDNA

ends

a

technique

for

amplifying

cDNA

sequences

between

a

known

internal

position

in

a

transcript

and

its

end

RFBR

Regulatory

factor

binding

region

a

genomic

region

found

by

a

ChIP-chip

assay

to

be

bound

by

a

protein

factor

RFBR-Seqsp

Regulatory

factor

binding

regions

that

are

from

sequence-specific

binding

factors

RT

PCR

Reverse

transcriptase

polymerase

chain

reaction

a

technique

for

amplifying

a

specific

region

of

a

transcript

RxFrag

Fragment

of

a

RACE

reaction

a

genomic

region

found

to

be

present

in

a

RACE

product

by

an

unbiased

tiling-array

assay

SNP

Single

nucleotide

polymorphism

a

single

base

pair

change

between

two

individuals

in

the

human

population

STAGE

Sequence

tag

analysis

of

genomic

enrichment

a

method

similar

to

ChIP-chip

for

detecting

protein

factor

binding

regions

but

using

extensive

short

sequence

determination

rather

than

genomic

tiling

arrays

SVM

Support

vector

machine

a

machine-learning

technique

that

can

establish

an

optimal

classifier

on

the

basis

of

labelled

training

data

A

measure

of

replication

timing

corresponding

to

the

time

in

the

cell

cycle

when

of

the

cells

have

replicated

their

DNA

at

a

specific

genomic

position

TSS

Transcription

start

site

TxFrag

Fragment

of

a

transcript

a

genomic

region

found

to

be

present

in

a

transcript

by

an

unbiased

tiling-array

assay

Un.TxFrag

A

TxFrag

that

is

not

associated

with

any

other

functional

annotation

UTR

Untranslated

region

part

of

a

cDNA

either

at

the

or

end

that

does

not

encode

a

protein

sequence

Nature

Publishing

Group

NATURE

Vol

June

ARTICLES

augmented

by

multiple

other

websites

-LRB-

see

Supplementary

Information

section

-RRB-

A

common

feature

of

genomic

analyses

is

the

need

to

assess

the

significance

of

the

co-occurrence

of

features

or

of

other

statistical

tests

One

confounding

factor

is

the

heterogeneity

of

the

genome

which

can

produce

uninteresting

correlations

of

variables

distributed

across

the

genome

We

have

developed

and

used

a

statistical

framework

that

mitigates

many

of

these

hidden

correlations

by

adjusting

the

appropriate

distribution

of

the

test

statistics

We

term

this

correction

procedure

genome

structure

correction

-LRB-

GSC

-RRB-

-LRB-

see

Supplementary

Information

section

-RRB-

In

the

next

five

sections

we

detail

the

various

biological

insights

of

the

pilot

phase

of

the

ENCODE

Project

Transcription

Overview

RNA

transcripts

are

involved

in

many

cellular

functions

either

directly

as

biologically

active

molecules

or

indirectly

by

encoding

other

active

molecules

In

the

conventional

view

of

genome

organization

sets

of

RNA

transcripts

-LRB-

for

example

messenger

RNAs

-RRB-

are

encoded

by

distinct

loci

with

each

usually

dedicated

to

a

single

biological

role

-LRB-

for

example

encoding

a

specific

protein

-RRB-

However

this

picture

has

substantially

grown

in

complexity

in

recent

Other

forms

of

RNA

molecules

-LRB-

such

as

small

nucleolar

RNAs

and

micro

-LRB-

mi

-RRB-

RNAs

-RRB-

are

known

to

exist

and

often

these

are

encoded

by

regions

that

intercalate

with

protein-coding

genes

These

observations

are

consistent

with

the

well-known

discrepancy

between

the

levels

of

observable

mRNAs

and

large

structural

RNAs

Table

Summary

of

types

of

experimental

techniques

used

in

ENCODE

Feature

class

Experimental

technique

-LRB-

s

-RRB-

Abbreviations

References

Number

of

experimental

data

points

Transcription

Tiling

array

integrated

annotation

Tag

sequencing

Tiling

array

TxFrag

RxFrag

GENCODE

ends

of

transcripts

Histone

modifications

Chromatin

-LCB-

structure

PET

CAGE

Histone

nomenclature

-LCB-

RFBR

DHS

FAIRE

118 19 119 121

SequenceTiling

array

tag

STAGE

specific

factors

sequencing

Chip

ChIP-PET

promoter

assays

RFBR

Replication

Tiling

array

Computational

Computational

CCI

RFBR

cluster

analysis

methods

10 16 126 127

Genomic

Comparative

CS

sequencing

multisequence

sequence

analysis

alignments

computational

analyses

CNV

Polymorphisms

Resequencing

copy

number

variation

QT-PCR

tiling

array

compared

with

the

total

RNA

in

a

cell

suggesting

that

there

are

numerous

RNA

species

yet

to

be

In

addition

studies

of

specific

loci

have

indicated

the

presence

of

RNA

transcripts

that

have

a

role

in

chromatin

maintenance

and

other

regulatory

control

We

sought

to

assay

and

analyse

transcription

comprehensively

across

the

ENCODE

regions

in

an

effort

to

understand

the

repertoire

of

encoded

RNA

molecules

Transcript

maps

We

used

three

methods

to

identify

transcripts

emanating

from

the

ENCODE

regions

hybridization

of

RNA

-LRB-

either

total

or

polyA-selected

-RRB-

to

unbiased

tiling

arrays

-LRB-

see

Supplementary

Information

section

-RRB-

tag

sequencing

of

cap-selected

RNA

at

the

or

joint

ends

-LRB-

see

Supplementary

Information

sections

and

-RRB-

and

integrated

annotation

of

available

complementary

DNA

and

EST

sequences

involving

computational

manual

and

experimental

-LRB-

see

Supplementary

Information

section

-RRB-

We

abbreviate

the

regions

identified

by

unbiased

tiling

arrays

as

TxFrags

the

cap-selected

RNAs

as

CAGE

or

PET

tags

-LRB-

see

Box

-RRB-

and

the

integrated

annotation

as

GENCODE

transcripts

When

a

TxFrag

does

not

overlap

a

GENCODE

annotation

we

call

it

an

Un.TxFrag

Validation

of

these

various

studies

is

described

in

papers

reporting

these

data

-LRB-

see

Supplementary

Information

sections

and

-RRB-

These

methods

recapitulate

previous

findings

but

provide

enhanced

resolution

owing

to

the

larger

number

of

tissues

sampled

and

the

integration

of

results

across

the

three

approaches

-LRB-

see

Table

-RRB-

To

begin

with

our

studies

show

that

of

the

bases

represented

in

the

unbiased

tiling

arrays

are

transcribed

in

at

least

one

tissue

sample

Consistent

with

previous

many

-LRB-

-RRB-

TxFrags

reside

outside

of

GENCODE

annotations

both

in

intronic

-LRB-

-RRB-

and

intergenic

-LRB-

-RRB-

regions

GENCODE

annotations

are

richer

than

the

more-conservative

RefSeq

or

Ensembl

annotations

with

transcripts

clustered

into

loci

leading

to

an

average

of

transcripts

per

locus

Finally

extensive

testing

of

predicted

protein-coding

sequences

outside

of

GENCODE

annotations

was

positive

in

only

of

suggesting

that

GENCODE

annotations

cover

nearly

all

protein-coding

sequences

The

GENCODE

annotations

are

categorized

both

by

likely

function

-LRB-

mainly

the

presence

of

an

open

reading

frame

-RRB-

and

by

classification

evidence

-LRB-

for

example

transcripts

based

solely

on

ESTs

are

distinguished

from

other

scenarios

-RRB-

this

classification

is

not

strongly

correlated

with

expression

levels

-LRB-

see

Supplementary

Information

sections

and

-RRB-

Analyses

of

more

biological

samples

have

allowed

a

richer

description

of

the

transcription

specificity

-LRB-

see

Fig.

and

Supplementary

Information

section

-RRB-

We

found

that

of

TxFrags

are

present

in

only

one

sample

whereas

only

are

present

in

all

samples

Although

exon-containing

TxFrags

are

more

likely

-LRB-

-RRB-

to

be

expressed

in

more

than

one

sample

of

unannotated

TxFrags

are

also

expressed

in

multiple

samples

GENCODE

annotations

of

separate

loci

often

-LRB-

-RRB-

overlap

with

respect

to

their

genomic

coordinates

in

particular

on

opposite

strands

-LRB-

of

loci

-RRB-

Further

analysis

of

GENCODE-annotated

sequences

with

respect

to

the

positions

of

open

reading

frames

revealed

that

some

component

exons

do

not

have

the

expected

synonymous

versus

non-synonymous

substitution

patterns

of

protein-coding

sequence

-LRB-

see

Supplement

Information

section

-RRB-

and

some

have

deletions

incompatible

with

Table

Bases

detected

in

processed

transcripts

either

as

a

GENCODE

exon

a

TxFrag

or

as

either

a

GENCODE

exon

or

a

TxFrag

GENCODE

exon

TxFrag

Either

GENCODE

exon

or

TxFrag

Not

all

data

generated

by

the

ENCODE

Project

-LCB-

Histone

code

nomenclature

follows

the

Brno

nomenclature

as

described

in

ref

-LCB-

Also

contains

histone

modification

Total

detectable

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

transcripts

-LRB-

bases

-RRB-

Transcripts

detected

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

in

tiled

regions

of

arrays

-LRB-

bases

-RRB-

Percentages

are

of

total

bases

in

ENCODE

in

the

first

row

and

bases

tiled

in

arrays

in

the

second

row

Nature

Publishing

Group

ARTICLES

NATURE

Vol

June

Annotated

transcripts

Tiling

array

nucleotides

-LRB-

-RRB-

Novel

transcripts

Figure

Annotated

and

unannotated

TxFrags

detected

in

different

cell

lines

The

proportion

of

different

types

of

transcripts

detected

in

the

indicated

number

of

cell

lines

-LRB-

from

at

the

far

left

to

at

the

far

right

-RRB-

is

shown

The

data

for

annotated

and

unannotated

TxFrags

are

indicated

separately

and

also

split

into

different

categories

based

on

GENCODE

classification

exonic

intergenic

-LRB-

proximal

being

within

kb

of

a

gene

and

distal

being

otherwise

-RRB-

intronic

-LRB-

proximal

being

within

kb

of

an

intron

and

distal

being

otherwise

-RRB-

and

matching

other

ESTs

not

used

in

the

GENCODE

annotation

-LRB-

principally

because

they

were

unspliced

-RRB-

The

y

axis

indicates

the

per

cent

of

tiling

array

nucleotides

present

in

that

class

for

that

number

of

samples

-LRB-

combination

of

cell

lines

and

tissues

-RRB-

Figure

Length

of

genomic

extensions

to

GENCODE-annotated

genes

on

the

basis

of

RACE

experiments

followed

by

array

hybridizations

-LRB-

RxFrags

-RRB-

The

indicated

bars

reflect

the

frequency

of

extension

lengths

among

different

length

classes

The

solid

line

shows

the

cumulative

frequency

of

extensions

of

that

length

or

greater

Most

of

the

extensions

are

greater

than

kb

from

the

annotated

gene

-LRB-

see

text

for

details

-RRB-

Nature

Publishing

Group

Extension

length

-LRB-

kb

-RRB-

GENCODE

exonic

Other

ESTs

Intergenic

distal

Intergenic

proximal

Intronic

distal

Intronic

proximal

Per

cent

of

RxFrag

extensions

-LRB-

shaded

boxes

-RRB-

cell

lines

Cumulative

per

cent

of

extensions

this

length

or

greater

-LRB-

line

-RRB-

protein

Such

exons

are

on

average

less

expressed

-LRB-

versus

by

RT

PCR

see

Supplementary

Information

section

-RRB-

than

exons

involved

in

more

than

one

transcript

-LRB-

see

Supplementary

Information

section

-RRB-

but

when

expressed

have

a

tissue

distribution

comparable

to

well-established

genes

Critical

questions

are

raised

by

the

presence

of

a

large

amount

of

unannotated

transcription

with

respect

to

how

the

corresponding

sequences

are

organized

in

the

genome

do

these

reflect

longer

transcripts

that

include

known

loci

do

they

link

known

loci

or

are

they

completely

separate

from

known

loci

We

further

investigated

these

issues

using

both

computational

and

new

experimental

techniques

Unannotated

transcription

Consistent

with

previous

findings

the

Un.TxFrags

did

not

show

evidence

of

encoding

proteins

-LRB-

see

Supplementary

Information

section

-RRB-

One

might

expect

Un.TxFrags

to

be

linked

within

transcripts

that

exhibit

coordinated

expression

and

have

similar

conservation

profiles

across

species

To

test

this

we

clustered

Un.TxFrags

using

two

methods

The

first

used

expression

levels

in

cell

lines

or

conditions

dinucleotide

composition

location

relative

to

annotated

genes

and

evolutionary

conservation

profiles

to

cluster

TxFrags

-LRB-

both

unannotated

and

annotated

-RRB-

By

this

method

of

Un.TxFrags

could

be

assigned

to

annotated

loci

and

could

be

clustered

into

novel

loci

-LRB-

with

an

average

of

TxFrags

per

locus

-RRB-

We

experimentally

examined

these

novel

loci

to

study

the

connectivity

of

transcripts

amongst

Un.TxFrags

and

between

Un.TxFrags

and

known

exons

Overall

about

of

the

connections

-LRB-

out

of

-RRB-

were

validated

by

RT

PCR

The

second

clustering

method

involved

analysing

a

time

course

-LRB-

and

h

-RRB-

of

expression

changes

in

human

cells

following

retinoic-acid

stimulation

There

is

a

coordinated

program

of

expression

changes

from

annotated

loci

which

can

be

shown

by

plotting

Pearson

correlation

values

of

the

expression

levels

of

exons

inside

annotated

loci

versus

unrelated

exons

-LRB-

see

Supplementary

Information

section

-RRB-

Similarly

there

is

coordinated

expression

of

nearby

Un.TxFrags

albeit

lower

though

still

significantly

different

from

randomized

sets

Both

clustering

methods

indicate

that

there

is

coordinated

behaviour

of

many

Un.TxFrags

consistent

with

them

residing

in

connected

transcripts

Transcript

connectivity

We

used

a

combination

of

RACE

and

tiling

to

investigate

the

diversity

of

transcripts

emanating

from

protein-coding

loci

Analogous

to

TxFrags

we

refer

to

transcripts

detected

using

RACE

followed

by

hybridization

to

tiling

arrays

as

RxFrags

We

performed

RACE

to

examine

protein-coding

loci

-LRB-

those

loci

found

entirely

in

ENCODE

regions

-RRB-

using

RNA

derived

from

tissues

and

were

able

to

unambiguously

detect

RxFrags

for

loci

-LRB-

see

Supplementary

Information

section

-RRB-

Almost

half

of

these

RxFrags

-LRB-

-RRB-

do

not

overlap

a

GENCODE

exon

and

most

-LRB-

-RRB-

loci

have

at

least

one

novel

RxFrag

which

often

extends

a

considerable

distance

beyond

the

end

of

the

locus

Figure

shows

the

distribution

of

distances

between

these

new

RACE-detected

ends

and

the

previously

annotated

TSS

of

each

locus

The

average

distance

of

the

extensions

is

between

kb

and

kb

with

many

extensions

-LRB-

-RRB-

being

more

than

kb

Consistent

with

the

known

presence

of

overlapping

genes

in

the

human

genome

our

findings

reveal

evidence

for

an

overlapping

gene

at

loci

with

transcripts

from

of

these

loci

-LRB-

of

the

RACE-positive

loci

-RRB-

appearing

to

have

incorporated

at

least

one

exon

from

an

upstream

gene

To

characterize

further

the

RxFrag

extensions

we

performed

RT

PCR

followed

by

cloning

and

sequencing

for

of

the

RxFrags

-LRB-

including

the

longest

extensions

identified

for

each

locus

-RRB-

The

approach

of

mapping

RACE

products

using

microarrays

is

a

combination

method

previously

described

and

validated

in

several

Hybridization

of

the

RT

PCR

products

to

tiling

arrays

confirmed

connectivity

in

almost

of

the

cases

Sequenced

clones

confirmed

transcript

extensions

Longer

extensions

were

harder

to

clone

and

sequence

but

out

of

RT

PCR-positive

extensions

over

kb

were

verified

by

sequencing

-LRB-

see

Supplementary

Information

section

and

ref

-RRB-

The

detection

of

numerous

RxFrag

extensions

coupled

with

evidence

of

considerable

intronic

transcription

indicates

that

protein-coding

loci

are

more

transcriptionally

complex

than

previously

thought

Instead

of

the

traditional

view

that

many

genes

have

one

or

more

alternative

transcripts

that

code

for

alternative

proteins

our

data

suggest

that

a

given

gene

may

both

encode

multiple

protein

products

and

produce

other

transcripts

that

include

sequences

from

both

strands

and

from

neighbouring

loci

-LRB-

often

without

encoding

a

different

protein

-RRB-

Figure

illustrates

such

a

case

in

which

a

new

fusion

transcript

is

expressed

in

the

small

intestine

and

consists

of

at

least

three

coding

exons

from

the

gene

and

at

least

two

coding

exons

from

the

DONSON

NATURE

Vol

June

ARTICLES

Chr

RxFrag

PETs

-LRB-

-RRB-

strand

GENCODE

-LRB-

-RRB-

strand

reference

genes

-LRB-

-RRB-

strand

DONSON

DONSON

Cloned

RT-PCR

product

Figure

Overview

of

RACE

experiments

showing

a

gene

fusion

Transcripts

emanating

from

the

region

between

the

DONSON

and

genes

A

interval

of

human

chromosome

-LRB-

within

-RRB-

is

shown

which

contains

four

annotated

genes

DONSON

and

The

RACE

products

generated

from

small

intestine

RNA

and

detected

by

tiling-array

analyses

-LRB-

RxFrags

-RRB-

are

shown

along

the

top

Along

the

bottom

is

shown

the

placement

of

a

cloned

and

sequenced

RT

PCR

product

that

has

two

exons

from

the

DONSON

gene

followed

by

three

exons

from

the

gene

these

sequences

are

separated

by

a

kb

intron

in

the

genome

A

PET

tag

shows

the

termini

of

a

transcript

consistent

with

this

RT

PCR

product

gene

with

no

evidence

of

sequences

from

two

intervening

proteincoding

genes

-LRB-

and

-RRB-

Pseudogenes

Pseudogenes

reviewed

in

refs

and

are

generally

considered

non-functional

copies

of

genes

are

sometimes

transcribed

and

often

complicate

analysis

of

transcription

owing

to

close

sequence

similarity

to

functional

genes

We

used

various

computational

methods

to

identify

pseudogenes

-LRB-

processed

and

non-processed

-RRB-

in

the

ENCODE

regions

-LRB-

see

Supplementary

Information

section

and

ref

-RRB-

Tiling-array

analysis

of

of

these

revealed

that

overlapped

at

least

one

TxFrag

However

possible

cross-hybridization

between

the

pseudogenes

and

their

corresponding

parent

genes

may

have

confounded

such

analyses

To

assess

better

the

extent

of

pseudogene

transcription

pseudogenes

-LRB-

processed

and

non-processed

-RRB-

were

examined

for

expression

using

RACE/tiling-array

analysis

-LRB-

see

Supplementary

Information

section

-RRB-

Transcripts

were

detected

for

pseudogenes

-LRB-

processed

and

non-processed

-RRB-

in

at

least

one

of

the

tested

RNA

sources

the

majority

-LRB-

-RRB-

being

in

testis

-LRB-

see

ref

-RRB-

Additionally

there

was

evidence

for

the

transcription

of

pseudogenes

on

the

basis

of

their

proximity

-LRB-

within

bp

of

a

pseudogene

end

-RRB-

to

CAGE

tags

-LRB-

-RRB-

PETs

-LRB-

-RRB-

or

cDNAs/ESTs

-LRB-

-RRB-

Overall

we

estimate

that

at

least

of

the

pseudogenes

in

the

ENCODE

regions

are

transcribed

which

is

consistent

with

previous

Non-protein-coding

RNA

Non-protein-coding

RNAs

-LRB-

ncRNAs

-RRB-

include

structural

RNAs

-LRB-

for

example

transfer

RNAs

ribosomal

RNAs

and

small

nuclear

RNAs

-RRB-

and

more

recently

discovered

regulatory

RNAs

-LRB-

for

example

miRNAs

-RRB-

There

are

only

wellcharacterized

ncRNA

genes

within

the

ENCODE

regions

-LRB-

and

-RRB-

whereas

representatives

of

other

classes

-LRB-

for

example

box

C/D

snoRNAs

tRNAs

and

functional

snRNAs

-RRB-

seem

to

be

completely

absent

in

the

ENCODE

regions

Tiling-array

data

provided

evidence

for

transcription

in

at

least

one

of

the

assayed

RNA

samples

for

all

of

these

ncRNAs

with

the

exception

of

-LRB-

expression

of

might

be

specific

to

fetal

liver

which

was

not

tested

-RRB-

There

is

also

evidence

for

the

transcription

of

out

of

pseudogenes

of

ncRNAs

-LRB-

mainly

snoRNA-derived

-RRB-

Similar

to

the

analysis

of

proteinpseudogenes

the

hybridization

results

could

also

originate

from

the

known

snoRNA

gene

elsewhere

in

the

genome

Many

known

ncRNAs

are

characterized

by

a

well-defined

RNA

secondary

structure

We

applied

two

de

novo

ncRNA

prediction

algorithms

EvoFold

and

RNAz

to

predict

structured

ncRNAs

-LRB-

as

well

as

functional

structures

in

mRNAs

-RRB-

using

the

multi-species

sequence

alignments

-LRB-

see

below

Supplementary

Information

section

and

ref

-RRB-

Using

a

sensitivity

threshold

capable

of

detecting

all

known

miRNAs

and

snoRNAs

we

identified

and

candidate

ncRNA

loci

with

EvoFold

and

RNAz

respectively

Only

loci

-LRB-

and

respectively

-RRB-

were

found

with

both

programs

representing

a

enrichment

over

that

expected

by

chance

the

lack

of

more

extensive

overlap

is

due

to

the

two

programs

having

optimal

sensitivity

at

different

levels

of

GC

content

and

conservation

We

experimentally

examined

of

these

targets

using

RACE

tiling-array

analysis

for

brain

and

testis

tissues

-LRB-

see

Supplementary

Information

sections

and

-RRB-

the

predictions

were

validated

at

a

and

rate

for

Evofold

RNAz

and

dual

predictions

respectively

Primary

transcripts

The

detection

of

numerous

unannotated

transcripts

coupled

with

increasing

knowledge

of

the

general

complexity

of

transcription

prompted

us

to

examine

the

extent

of

primary

-LRB-

that

is

unspliced

-RRB-

transcripts

across

the

ENCODE

regions

Three

data

sources

provide

insight

about

these

primary

transcripts

the

GENCODE

annotation

PETs

and

RxFrag

extensions

Figure

summarizes

the

fraction

of

bases

in

the

ENCODE

regions

that

overlap

transcripts

identified

by

these

technologies

Remarkably

of

bases

are

represented

in

a

primary

transcript

identified

by

at

least

two

independent

observations

-LRB-

but

potentially

using

the

same

technology

-RRB-

this

figure

is

reduced

to

in

the

case

of

primary

transcripts

detected

by

at

least

two

different

technologies

These

increased

spans

are

not

mainly

due

to

cell

line

rearrangements

because

they

were

present

in

multiple

tissue

experiments

that

confirmed

the

spans

-LRB-

see

Supplementary

Information

section

-RRB-

These

estimates

assume

that

the

presence

of

PETs

or

RxFrags

defining

the

terminal

ends

of

a

transcript

imply

that

the

entire

intervening

DNA

is

transcribed

and

then

processed

Other

mechanisms

thought

to

be

unlikely

in

the

human

genome

such

as

trans-splicing

or

polymerase

jumping

would

also

produce

these

long

termini

and

potentially

should

be

reconsidered

in

more

detail

Previous

studies

have

suggested

a

similar

broad

amount

of

transcription

across

the

and

genomes

Our

studies

confirm

these

results

and

have

investigated

the

genesis

of

these

transcripts

in

greater

detail

confirming

the

presence

of

substantial

intragenic

and

intergenic

transcription

At

the

same

time

many

of

the

resulting

transcripts

are

neither

traditional

protein-coding

One

technology

one

observation

No

coverage

One

technology

two

observations

All

three

technologies

Two

technologies

Figure

Coverage

of

primary

transcripts

across

ENCODE

regions

Three

different

technologies

-LRB-

integrated

annotation

from

GENCODE

RACE-array

experiments

-LRB-

RxFrags

-RRB-

and

PET

tags

-RRB-

were

used

to

assess

the

presence

of

a

nucleotide

in

a

primary

transcript

Use

of

these

technologies

provided

the

opportunity

to

have

multiple

observations

of

each

finding

The

proportion

of

genomic

bases

detected

in

the

ENCODE

regions

associated

with

each

of

the

following

scenarios

is

depicted

detected

by

all

three

technologies

by

two

of

the

three

technologies

by

one

technology

but

with

multiple

observations

and

by

one

technology

with

only

one

observation

Also

indicated

are

genomic

bases

without

any

detectable

coverage

of

primary

transcripts

Nature

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Group

ARTICLES

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Vol

June

transcripts

nor

easily

explained

as

structural

non-coding

RNAs

Other

studies

have

noted

complex

transcription

around

specific

loci

or

chimaeric-gene

structures

-LRB-

for

example

refs

-RRB-

but

these

have

often

been

considered

exceptions

our

data

show

that

complex

intercalated

transcription

is

common

at

many

loci

The

results

presented

in

the

next

section

show

extensive

amounts

of

regulatory

factors

around

novel

TSSs

which

is

consistent

with

this

extensive

transcription

The

biological

relevance

of

these

unannotated

transcripts

remains

unanswered

by

these

studies

Evolutionary

information

-LRB-

detailed

below

-RRB-

is

mixed

in

this

regard

for

example

it

indicates

that

unannotated

transcripts

show

weaker

evolutionary

conservation

than

many

other

annotated

features

As

with

other

ENCODEdetected

elements

it

is

difficult

to

identify

clear

biological

roles

for

the

majority

of

these

transcripts

such

experiments

are

challenging

to

perform

on

a

large

scale

and

furthermore

it

seems

likely

that

many

of

the

corresponding

biochemical

events

may

be

evolutionarily

neutral

-LRB-

see

below

-RRB-

Regulation

of

transcription

Overview

A

significant

challenge

in

biology

is

to

identify

the

transcriptional

regulatory

elements

that

control

the

expression

of

each

transcript

and

to

understand

how

the

function

of

these

elements

is

coordinated

to

execute

complex

cellular

processes

A

simple

commonplace

view

of

transcriptional

regulation

involves

five

types

of

cis-acting

regulatory

sequences

promoters

enhancers

silencers

insulators

and

locus

control

Overall

transcriptional

regulation

involves

the

interplay

of

multiple

components

whereby

the

availability

of

specific

transcription

factors

and

the

accessibility

of

specific

genomic

regions

determine

whether

a

transcript

is

However

the

current

view

of

transcriptional

regulation

is

known

to

be

overly

simplified

with

many

details

remaining

to

be

established

For

example

the

consensus

sequences

of

transcription

factor

binding

sites

-LRB-

typically

to

bases

-RRB-

have

relatively

little

information

content

and

are

present

numerous

times

in

the

genome

with

the

great

majority

of

these

not

participating

in

transcriptional

regulation

Does

chromatin

structure

then

determine

whether

such

a

sequence

has

a

regulatory

role

Are

there

complex

inter-factor

interactions

that

integrate

the

signals

from

multiple

sites

How

are

signals

from

different

distal

regulatory

elements

coupled

without

affecting

all

neighbouring

genes

Meanwhile

our

understanding

of

the

repertoire

of

transcriptional

events

is

becoming

more

complex

with

an

increasing

appreciation

of

alternative

and

the

presence

of

and

anti-sense

To

better

understand

transcriptional

regulation

we

sought

to

begin

cataloguing

the

regulatory

elements

residing

within

the

ENCODE

regions

For

this

pilot

project

we

mainly

focused

on

the

binding

of

regulatory

proteins

and

chromatin

structure

involved

in

transcriptional

regulation

We

analysed

over

data

sets

mainly

from

ChIP-PET

and

studies

-LRB-

see

Supplementary

Information

section

and

-RRB-

These

methods

use

chromatin

immunoprecipitation

with

specific

antibodies

to

enrich

for

DNA

in

physical

contact

with

the

targeted

epitope

This

enriched

DNA

can

then

be

analysed

using

either

microarrays

-LRB-

ChIP-chip

-RRB-

or

high-throughput

sequencing

-LRB-

ChIP-PET

and

STAGE

-RRB-

The

assays

included

sequence-specific

transcription

factors

and

components

of

the

general

transcription

machinery

-LRB-

for

example

RNA

polymerase

II

-LRB-

Pol

II

-RRB-

and

-RRB-

In

addition

we

tested

more

than

potential

promoter

fragments

for

transcriptional

activity

by

transient-transfection

reporter

assays

that

used

human

cell

We

also

examined

chromatin

structure

by

studying

the

ENCODE

regions

for

DNaseI

sensitivity

-LRB-

by

quantitative

and

tiling

see

Supplementary

Information

section

-RRB-

histone

histone

modifications

-LRB-

using

ChIP-chip

assays

-RRB-

and

histone

displacement

-LRB-

using

FAIRE

see

Supplementary

Information

section

-RRB-

Below

we

detail

these

analyses

starting

with

the

efforts

to

define

and

classify

the

ends

of

transcripts

with

respect

to

their

associated

regulatory

signals

Following

that

are

summaries

of

generated

data

about

sequence-specific

transcription

factor

binding

and

clusters

of

regulatory

elements

Finally

we

describe

how

this

information

can

be

integrated

to

make

predictions

about

transcriptional

regulation

Transcription

start

site

catalogue

We

analysed

two

data

sets

to

catalogue

TSSs

in

the

ENCODE

regions

the

ends

of

GENCODE-annotated

transcripts

and

the

combined

results

of

two

technologies

CAGE

and

PET-tagging

The

initial

results

suggested

the

potential

presence

of

unique

TSSs

However

in

many

cases

multiple

TSSs

resided

within

a

single

small

segment

-LRB-

up

to

bases

-RRB-

this

was

due

to

some

promoters

containing

TSSs

with

many

very

close

precise

initiation

To

normalize

for

this

effect

we

grouped

TSSs

that

were

or

fewer

bases

apart

into

a

single

cluster

and

in

each

case

considered

the

most

frequent

CAGE

or

PET

tag

-LRB-

or

the

TSS

in

the

case

of

TSSs

identified

only

from

GENCODE

data

-RRB-

as

representative

of

that

cluster

for

downstream

analyses

The

above

effort

yielded

TSS

clusters

in

the

ENCODE

regions

We

classified

these

TSSs

into

three

categories

known

-LRB-

present

at

the

end

of

GENCODE-defined

transcripts

-RRB-

novel

-LRB-

supported

by

other

evidence

-RRB-

and

unsupported

The

novel

TSSs

were

further

subdivided

on

the

basis

of

the

nature

of

the

supporting

evidence

-LRB-

see

Table

and

Supplementary

Information

section

-RRB-

with

all

four

of

the

resulting

subtypes

showing

significant

overlap

with

experimental

evidence

using

the

GSC

statistic

Although

there

is

a

larger

relative

proportion

of

singleton

tags

in

the

novel

category

when

analysis

is

restricted

to

only

singleton

tags

the

novel

TSSs

continue

to

have

highly

significant

overlap

with

supporting

evidence

-LRB-

see

Supplementary

Information

section

-RRB-

Correlating

genomic

features

with

chromatin

structure

and

transcription

factor

binding

By

measuring

relative

sensitivity

to

DNaseI

digestion

-LRB-

see

Supplementary

Information

section

-RRB-

we

identified

DNaseI

hypersensitive

sites

throughout

the

ENCODE

regions

DHSs

and

TSSs

both

reflect

genomic

regions

thought

to

be

enriched

for

regulatory

information

and

many

DHSs

reside

at

or

near

TSSs

We

partitioned

DHSs

into

those

within

kb

of

a

TSS

-LRB-

-RRB-

and

the

remaining

ones

which

were

classified

as

distal

-LRB-

-RRB-

We

then

cross-analysed

the

TSSs

and

DHSs

with

data

sets

relating

to

histone

modifications

chromatin

accessibility

and

sequence-specific

transcription

factor

binding

by

summarizing

these

signals

in

aggregate

relative

to

the

distance

from

TSSs

or

DHSs

Figure

shows

representative

profiles

of

specific

histone

modifications

Pol

II

and

selected

transcription

factor

binding

for

the

different

categories

of

TSSs

Further

profiles

and

statistical

analysis

of

these

studies

can

be

found

in

Supplementary

Information

In

the

case

of

the

three

TSS

categories

-LRB-

known

novel

and

unsupported

-RRB-

known

and

novel

TSSs

are

both

associated

with

similar

signals

for

multiple

factors

-LRB-

ranging

from

histone

modifications

through

DNaseI

accessibility

-RRB-

whereas

unsupported

TSSs

are

not

Table

Different

categories

of

TSSs

defined

on

the

basis

of

support

from

different

transcript-survey

methods

Category

Transcript

survey

method

Number

of

TSS

P

value

-LCB-

clusters

-LRB-

non-redundant

-RRB-

Singleton

clusters

-LCB-

-LRB-

-RRB-

Known

Novel

GENCODE

ends

GENCODE

sense

exons

GENCODE

antisense

exons

Unbiased

transcription

survey

CpG

island

Unsupported

521 639 164

-LRB-

overall

-RRB-

Number

of

TSS

clusters

with

this

support

excluding

TSSs

from

higher

categories

-LCB-

Probability

of

overlap

between

the

transcript

support

and

the

PET/CAGE

tags

as

calculated

by

the

Genome

Structure

Correction

statistic

-LRB-

see

Supplementary

Information

section

-RRB-

-LCB-

Per

cent

of

clusters

with

only

one

tag

For

the

known

category

this

was

calculated

as

the

per

cent

of

GENCODE

ends

with

tag

support

-LRB-

-RRB-

or

overall

-LRB-

-RRB-

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ARTICLES

The

enrichments

seen

with

chromatin

modifications

and

sequencespecific

factors

along

with

the

significant

clustering

of

this

evidence

indicate

that

the

novel

TSSs

do

not

reflect

false

positives

and

probably

use

the

same

biological

machinery

as

other

promoters

Sequencespecific

transcription

factors

show

a

marked

increase

in

binding

across

the

broad

region

that

encompasses

each

TSS

This

increase

is

notably

symmetric

with

binding

equally

likely

upstream

or

downstream

of

a

TSS

-LRB-

see

Supplementary

Information

section

for

an

explanation

of

why

this

symmetrical

signal

is

not

an

artefact

of

the

analysis

of

the

signals

-RRB-

Furthermore

there

is

enrichment

of

binding

-LRB-

a

member

of

the

SWI/SNF

chromatinmodifying

complex

-RRB-

which

persists

across

a

broader

extent

than

other

factors

The

broad

signals

with

this

factor

indicate

that

the

ChIP-chip

results

reflect

both

specific

enrichment

at

the

TSS

and

broader

enrichments

across

regions

-LRB-

this

is

not

due

to

technical

issues

see

Supplementary

Information

section

-RRB-

We

selected

GENCODE-defined

TSSs

at

the

ends

of

a

protein-coding

transcript

with

over

exons

to

assess

expression

status

Each

transcript

was

classified

as

-LRB-

-RRB-

active

-LRB-

gene

on

-RRB-

or

inactive

-LRB-

gene

off

-RRB-

on

the

basis

of

the

unbiased

transcript

surveys

and

-LRB-

-RRB-

residing

near

a

CpG

island

or

not

-LRB-

non-CpG

island

-RRB-

-LRB-

see

Supplementary

Information

section

-RRB-

As

expected

the

aggregate

a

GENCODE

TSS

Aggregate

normalized

intensity

Aggregate

normalized

intensity

signal

of

histone

modifications

is

mainly

attributable

to

active

TSSs

-LRB-

Fig.

-RRB-

in

particular

those

near

CpG

islands

Pronounced

doublet

peaks

at

the

TSS

can

be

seen

with

these

large

signals

-LRB-

similar

to

previous

work

in

-RRB-

owing

to

the

chromatin

accessibility

at

the

TSS

Many

of

the

histone

marks

and

Pol

II

signals

are

now

clearly

asymmetrical

with

a

persistent

level

of

Pol

II

into

the

genic

region

as

expected

However

the

sequence-specific

factors

remain

largely

symmetrically

distributed

TSSs

near

CpG

islands

show

a

broader

distribution

of

histone

marks

than

those

not

near

CpG

islands

-LRB-

see

Supplementary

Information

section

-RRB-

The

binding

of

some

transcription

factors

-LRB-

and

MYC

-RRB-

is

extensive

in

the

case

of

active

genes

and

is

lower

-LRB-

or

absent

-RRB-

in

the

case

of

inactive

genes

Chromatin

signature

of

distal

elements

Distal

DHSs

show

characteristic

patterns

of

histone

modification

that

are

the

inverse

of

TSSs

with

high

accompanied

by

lower

levels

of

and

-LRB-

Fig.

-RRB-

Many

factors

with

high

occupancy

at

TSSs

-LRB-

for

example

-RRB-

show

little

enrichment

at

distal

DHSs

whereas

other

factors

-LRB-

for

example

MYC

-RRB-

are

enriched

at

both

TSSs

and

distal

A

particularly

interesting

observation

is

the

relative

enrichment

of

the

insulator-associated

factor

at

both

distal

DHSs

and

TSSs

this

contrasts

with

SWI/SNF

components

and

which

are

TSS-centric

Such

differential

b

Novel

TSS

Aggregate

normalized

intensity

Aggregate

normalized

intensity

FAIRE

DNaseI

MYC

CTCF

Pol

II

FAIRE

DNaseI

MYC

CTCF

Pol

II

Distance

to

nearest

TSS

Distance

to

nearest

TSS

Distance

to

nearest

TSS

Distance

to

nearest

TSS

c

Unsupported

tags

Aggregate

normalized

intensity

Aggregate

normalized

intensity

d

Distal

DHS

Aggregate

normalized

intensity

FAIRE

DNaseI

MYC

CTCF

Pol

II

FAIRE

DNaseI

Aggregate

normalized

intensity

MYC

CTCF

Pol

II

Distance

to

nearest

TSS

Distance

to

nearest

TSS

Distance

to

nearest

Distance

to

nearest

DHS

e

Gene

on

CpG

Aggregate

normalized

intensity

Aggregate

normalized

intensity

f

Gene

off

CpG

Aggregate

normalized

intensity

FAIRE

DNaseI

MYC

CTCF

Pol

II

FAIRE

DNaseI

Aggregate

normalized

intensity

MYC

CTCF

Pol

II

Distance

to

nearest

TSS

Distance

to

nearest

TSS

Distance

to

nearest

TSS

Distance

to

nearest

TSS

Figure

Aggregate

signals

of

tiling-array

experiments

from

either

ChIPchip

or

chromatin

structure

assays

represented

for

different

classes

of

TSSs

and

DHS

For

each

plot

the

signal

was

first

normalized

with

a

mean

of

and

standard

deviation

of

and

then

the

normalized

scores

were

summed

at

each

position

for

that

class

of

TSS

or

DHS

and

smoothed

using

a

kernel

density

method

-LRB-

see

Supplementary

Information

section

-RRB-

For

each

class

of

sites

there

are

two

adjacent

plots

The

left

plot

depicts

the

data

for

general

factors

FAIRE

and

DNaseI

sensitivity

as

assays

of

chromatin

accessibility

and

and

histone

modifications

-LRB-

as

indicated

-RRB-

the

right

plot

shows

the

data

for

additional

factors

namely

MYC

CTCF

and

Pol

II

The

columns

provide

data

for

the

different

classes

of

TSS

or

DHS

-LRB-

unsmoothed

data

and

statistical

analysis

shown

in

Supplementary

Information

section

-RRB-

Nature

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Vol

June

behaviour

of

sequence-specific

factors

points

to

distinct

biological

differences

mediated

by

transcription

factors

between

distal

regulatory

sites

and

TSSs

Unbiased

maps

of

sequence-specific

regulatory

factor

binding

The

previous

section

focused

on

specific

positions

defined

by

TSSs

or

DHSs

We

then

analysed

sequence-specific

transcription

factor

binding

data

in

an

unbiased

fashion

We

refer

to

regions

with

enriched

binding

of

regulatory

factors

as

RFBRs

RFBRs

were

identified

on

the

basis

of

ChIP-chip

data

in

two

ways

first

each

investigator

developed

and

used

their

own

analysis

method

-LRB-

s

-RRB-

to

define

high-enrichment

regions

and

second

-LRB-

and

independently

-RRB-

a

stringent

false

discovery

rate

-LRB-

FDR

-RRB-

method

was

applied

to

analyse

all

data

using

three

cut-offs

-LRB-

and

-RRB-

The

laboratory-specific

and

FDR-based

methods

were

highly

correlated

particularly

for

regions

with

strong

For

consistency

we

used

the

results

obtained

with

the

FDR-based

method

-LRB-

see

Supplementary

Information

section

-RRB-

These

RFBRs

can

be

used

to

find

sequence

motifs

-LRB-

see

Supplementary

Information

section

-RRB-

RFBRs

are

associated

with

the

ends

of

transcripts

The

distribution

of

RFBRs

is

non-random

-LRB-

see

ref

-RRB-

and

correlates

with

the

positions

of

TSSs

We

examined

the

distribution

of

specific

RFBRs

relative

to

the

known

TSSs

Different

transcription

factors

and

histone

modifications

vary

with

respect

to

their

association

with

TSSs

-LRB-

Fig.

see

Supplementary

Information

section

for

modelling

of

random

expectation

-RRB-

Factors

for

which

binding

sites

are

most

enriched

at

the

ends

of

genes

include

histone

modifications

and

RNA

Pol

II

with

a

hypo-phosphorylated

carboxy-terminal

confirming

previous

expectations

Surprisingly

we

found

that

a

sequence-specific

factor

that

regulates

the

expression

of

many

genes

at

the

to

S

is

also

tightly

associated

with

this

association

is

as

strong

as

that

of

the

well-known

TATA

box-binding

protein

associated

factor

-LRB-

ref

-RRB-

These

results

suggest

that

has

a

more

general

role

in

transcription

than

previously

suspected

similar

to

that

for

In

contrast

the

largescale

assays

did

not

support

the

promoter

binding

that

was

found

in

smaller-scale

studies

-LRB-

for

example

on

and

-LRB-

-RRB-

-RRB-

Integration

of

data

on

sequence-specific

factors

We

expect

that

regulatory

information

is

not

dispersed

independently

across

the

genome

but

rather

is

clustered

into

distinct

We

refer

to

regions

that

contain

multiple

regulatory

elements

as

regulatory

clusters

We

sought

to

predict

the

location

of

regulatory

clusters

by

Pol

II

Fraction

of

RFBRs

near

TSSs

Sequence-specific

General

MYC

CTCF

Fraction

of

TSSs

near

RFBRs

Figure

Distribution

of

RFBRs

relative

to

GENCODE

TSSs

Different

RFBRs

from

sequence-specific

factors

-LRB-

red

-RRB-

or

general

factors

-LRB-

blue

-RRB-

are

plotted

showing

their

relative

distribution

near

TSSs

The

x

axis

indicates

the

proportion

of

TSSs

close

-LRB-

within

kb

-RRB-

to

the

specified

factor

The

y

axis

indicates

the

proportion

of

RFBRs

close

to

TSSs

The

size

of

the

circle

provides

an

indication

of

the

number

of

RFBRs

for

each

factor

A

handful

of

representative

factors

are

labelled

cross-integrating

data

generated

using

all

transcription

factor

and

histone

modification

assays

including

results

falling

below

an

arbitrary

threshold

in

individual

experiments

Specifically

we

used

four

complementary

methods

to

integrate

the

data

from

ChIP-chip

data

sets

-LRB-

see

Supplementary

Information

section

and

ref

These

four

methods

detect

different

classes

of

regulatory

clusters

and

as

a

whole

identified

clusters

Of

these

were

identified

by

all

four

methods

with

another

found

by

three

methods

-LRB-

see

Supplementary

Information

section

-RRB-

of

the

regulatory

clusters

identified

by

all

four

methods

-LRB-

or

of

the

full

set

of

-RRB-

reside

within

kb

of

a

known

or

novel

TSS

-LRB-

as

defined

above

see

Table

and

Supplementary

Information

section

for

a

breakdown

by

category

-RRB-

Restricting

this

analysis

to

previously

annotated

TSSs

-LRB-

for

example

RefSeq

or

Ensembl

-RRB-

reveals

that

roughly

of

the

regulatory

clusters

are

close

to

a

previously

identified

TSS

These

results

suggest

that

many

of

the

regulatory

clusters

identified

by

integrating

the

ChIP-chip

data

sets

are

undiscovered

promoters

or

are

somehow

associated

with

transcription

in

another

fashion

To

test

these

possibilities

sets

of

and

non-GENCODE-based

regulatory

clusters

were

tested

for

promoter

activity

-LRB-

see

Supplementary

Information

section

-RRB-

and

by

RACE

respectively

These

studies

revealed

that

of

the

tested

regulatory

clusters

had

promoter

activity

and

that

of

the

regulatory

clusters

analysed

by

RACE

yielded

products

consistent

with

a

The

ChIP-chip

data

sets

were

generated

on

a

mixture

of

cell

lines

predominantly

HeLa

and

and

were

different

from

the

CAGE

PET

data

meaning

that

tissue

specificity

contributes

to

the

presence

of

unique

TSSs

and

regulatory

clusters

The

large

increase

in

promoter

proximal

regulatory

clusters

identified

by

including

the

additional

novel

TSSs

coupled

with

the

positive

promoter

and

RACE

assays

suggests

that

most

of

the

regulatory

regions

identifiable

by

these

clustering

methods

represent

bona

fide

promoters

-LRB-

see

Supplementary

Information

-RRB-

Although

the

regulatory

factor

assays

were

more

biased

towards

regions

associated

with

promoters

many

of

the

sites

from

these

experiments

would

have

previously

been

described

as

distal

to

promoters

This

suggests

that

commonplace

use

of

RefSeq

or

Ensembl-based

gene

definition

to

define

promoter

proximity

will

dramatically

overestimate

the

number

of

distal

sites

Predicting

TSSs

and

transcriptional

activity

on

the

basis

of

chromatin

structure

The

strong

association

between

TSSs

and

both

histone

modifications

and

DHSs

prompted

us

to

investigate

whether

the

location

and

activity

of

TSSs

could

be

predicted

solely

on

the

basis

of

chromatin

structure

information

We

trained

a

support

vector

machine

-LRB-

SVM

-RRB-

by

using

histone

modification

data

anchored

around

DHSs

to

discriminate

between

DHSs

near

TSSs

and

those

distant

from

TSSs

We

used

a

selected

DHSs

split

roughly

between

TSSproximal

DHSs

and

TSS-distal

DHSs

as

a

training

set

The

SVM

performed

well

with

an

accuracy

of

-LRB-

see

Supplementary

Information

section

-RRB-

Using

this

SVM

we

then

predicted

new

TSSs

using

information

about

DHSs

and

histone

modifications

of

high-scoring

predicted

TSSs

resided

within

kb

of

a

novel

TSS

As

expected

these

show

a

significant

overlap

to

the

novel

TSS

groups

-LRB-

defined

above

-RRB-

but

without

a

strong

bias

towards

any

particular

category

-LRB-

see

Supplementary

Information

section

-RRB-

To

investigate

the

relationship

between

chromatin

structure

and

gene

expression

we

examined

transcript

levels

in

two

cell

lines

using

a

transcript-tiling

array

We

compared

this

transcript

data

with

the

results

of

ChIP-chip

experiments

that

measured

histone

modifications

across

the

ENCODE

regions

From

this

we

developed

a

variety

of

predictors

of

expression

status

using

chromatin

modifications

as

variables

these

were

derived

using

both

decision

trees

and

SVMs

-LRB-

see

Supplementary

Information

section

-RRB-

The

best

of

these

correctly

predicts

expression

status

-LRB-

transcribed

versus

non-transcribed

-RRB-

in

of

cases

This

success

rate

did

not

decrease

dramatically

when

the

predicting

algorithm

incorporated

the

results

from

one

cell

line

to

predict

the

expression

status

of

another

cell

line

Interestingly

despite

Nature

Publishing

Group

NATURE

Vol

June

ARTICLES

the

striking

difference

in

histone

modification

enrichments

in

TSSs

residing

near

versus

those

more

distal

to

CpG

islands

-LRB-

see

Fig.

and

Supplementary

Information

section

-RRB-

including

information

about

the

proximity

to

CpG

islands

did

not

improve

the

predictors

This

suggests

that

despite

the

marked

differences

in

histone

modifications

among

these

TSS

classes

a

single

predictor

can

be

made

using

the

interactions

between

the

different

histone

modification

levels

In

summary

we

have

integrated

many

data

sets

to

provide

a

more

complete

view

of

regulatory

information

both

around

specific

sites

-LRB-

TSSs

and

DHSs

-RRB-

and

in

an

unbiased

manner

From

analysing

multiple

data

sets

we

find

known

and

novel

TSSs

in

the

ENCODE

regions

almost

tenfold

more

than

the

number

of

established

genes

This

large

number

of

TSSs

might

explain

the

extensive

transcription

described

above

it

also

begins

to

change

our

perspective

about

regulatory

information

without

such

a

large

TSS

catalogue

many

of

the

regulatory

clusters

would

have

been

classified

as

residing

distal

to

promoters

In

addition

to

this

revelation

about

the

abundance

of

promoter-proximal

regulatory

elements

we

also

identified

a

considerable

number

of

putative

distal

regulatory

elements

particularly

on

the

basis

of

the

presence

of

DHSs

Our

study

of

distal

regulatory

elements

was

probably

most

hindered

by

the

paucity

of

data

generated

using

distal-element-associated

transcription

factors

nevertheless

we

clearly

detected

a

set

of

distal-DHS-associated

segments

bound

by

CTCF

or

MYC

Finally

we

showed

that

information

about

chromatin

structure

alone

could

be

used

to

make

effective

predictions

about

both

the

location

and

activity

of

TSSs

Replication

Overview

DNA

replication

must

be

carefully

coordinated

both

across

the

genome

and

with

respect

to

development

On

a

larger

scale

early

replication

in

S

phase

is

broadly

correlated

with

gene

density

and

transcriptional

however

this

relationship

is

not

universal

as

some

actively

transcribed

genes

replicate

late

and

vice

Importantly

the

relationship

between

transcription

and

DNA

replication

emerges

only

when

the

signal

of

transcription

is

averaged

over

a

large

window

-LRB-

kb

-RRB-

suggesting

that

largerscale

chromosomal

architecture

may

be

more

important

than

the

activity

of

specific

a

Enrichment

The

ENCODE

Project

provided

a

unique

opportunity

to

examine

whether

individual

histone

modifications

on

human

chromatin

can

be

correlated

with

the

time

of

replication

and

whether

such

correlations

support

the

general

relationship

of

active

open

chromatin

with

early

replication

Our

studies

also

tested

whether

segments

showing

interallelic

variation

in

the

time

of

replication

have

two

different

types

of

histone

modifications

consistent

with

an

interallelic

variation

in

chromatin

state

DNA

replication

data

set

We

mapped

replication

timing

across

the

ENCODE

regions

by

analysing

Brd-U-labelled

fractions

from

synchronized

HeLa

cells

-LRB-

collected

at

h

intervals

throughout

S

phase

-RRB-

on

tiling

arrays

-LRB-

see

Supplementary

Information

section

-RRB-

Although

the

HeLa

cell

line

has

a

considerably

altered

karyotype

correlation

of

these

data

with

other

cell

line

data

-LRB-

see

below

-RRB-

suggests

the

results

are

relevant

to

other

cell

types

The

results

are

expressed

as

the

time

at

which

of

any

given

genomic

position

is

replicated

-LRB-

-RRB-

with

higher

values

signifying

later

replication

times

In

addition

to

the

five

activating

histone

marks

we

also

correlated

the

with

a

modification

associated

with

polycomb-mediated

transcriptional

To

provide

a

consistent

comparison

framework

the

histone

data

were

smoothed

to

resolution

and

then

correlated

with

the

data

by

a

sliding

window

correlation

analysis

-LRB-

see

Supplementary

Information

section

-RRB-

The

continuous

profiles

of

the

activating

marks

histone

mono

di

and

tri-methylation

and

histone

and

acetylation

are

generally

anti-correlated

with

the

signal

-LRB-

Fig.

and

Supplementary

Information

section

-RRB-

In

contrast

marks

show

a

predominantly

positive

correlation

with

late-replicating

segments

-LRB-

Fig.

see

Supplementary

Information

section

for

additional

analysis

-RRB-

Although

most

genomic

regions

replicate

in

a

temporally

specific

window

in

S

phase

other

regions

demonstrate

an

atypical

pattern

of

replication

-LRB-

Pan-S

-RRB-

where

replication

signals

are

seen

in

multiple

parts

of

S

phase

We

have

suggested

that

such

a

pattern

of

replication

stems

from

interallelic

variation

in

the

chromatin

If

one

allele

is

in

active

chromatin

and

the

other

in

repressed

chromatin

both

types

of

modified

histones

are

expected

to

be

enriched

in

the

Pan-S

segments

An

ENCODE

region

was

classified

as

non-specific

-LRB-

or

Pan-S

-RRB-

regions

when

of

the

probes

in

a

window

b

Mb

-LRB-

-RRB-

Early

Mid

Late

Pan-S

Per

cent

enrichment

Genomic

position

-LRB-

kb

-RRB-

Enrichment

Figure

Correlation

between

replication

timing

and

histone

modifications

a

Comparison

of

two

histone

modifications

-LRB-

and

-RRB-

plotted

as

enrichment

ratio

from

the

Chip-chip

experiments

and

the

time

for

of

the

DNA

to

replicate

-LRB-

-RRB-

indicated

for

ENCODE

region

The

colours

on

the

curves

reflect

the

correlation

strength

in

a

sliding

window

b

Differing

levels

of

histone

modification

for

different

partitions

The

amounts

of

enrichment

or

depletion

of

different

histone

modifications

in

various

cell

lines

are

depicted

-LRB-

indicated

along

the

bottom

as

histone

mark.cell

line

GM

-RRB-

Asterisks

indicate

enrichments/depletions

that

are

not

significant

on

the

basis

of

multiple

tests

Each

set

has

four

partitions

on

the

basis

of

replication

timing

early

mid

late

and

Pan-S

Nature

Publishing

Group

m

He

L

m

a

H

eL

a

me

m

eL

a

He

ac

La

HeL

ac

a

H

e

m

La

G

m

M

G

m

M

GM

ac

G

M

ac

G

M

ARTICLES

NATURE

Vol

June

replicated

in

multiple

intervals

in

S

phase

The

remaining

regions

were

sub-classified

into

early

mid

or

late-replicating

based

on

the

average

of

the

temporally

specific

probes

within

a

For

regions

of

each

class

of

replication

timing

we

determined

the

relative

enrichment

of

various

histone

modification

peaks

in

HeLa

cells

-LRB-

Fig.

Supplementary

Information

section

-RRB-

The

correlations

of

activating

and

repressing

histone

modification

peaks

with

are

confirmed

by

this

analysis

-LRB-

Fig.

-RRB-

Intriguingly

the

Pan-S

segments

are

unique

in

being

enriched

for

both

activating

-LRB-

and

-RRB-

and

repressing

-LRB-

-RRB-

histones

consistent

with

the

suggestion

that

the

Pan-S

replication

pattern

arises

from

interallelic

variation

in

chromatin

structure

and

time

of

This

observation

is

also

consistent

with

the

Pan-S

replication

pattern

seen

for

the

locus

a

known

imprinted

region

with

differential

epigenetic

modifications

across

the

two

The

extensive

rearrangements

in

the

genome

of

HeLa

cells

led

us

to

ask

whether

the

detected

correlations

between

and

chromatin

state

are

seen

with

other

cell

lines

The

histone

modification

data

with

cells

allowed

us

to

test

whether

the

time

of

replication

of

genomic

segments

in

HeLa

cells

correlated

with

the

chromatin

state

in

cells

Early

and

late-replicating

segments

in

HeLa

cells

are

enriched

and

depleted

respectively

for

activating

marks

in

cells

-LRB-

Fig.

-RRB-

Thus

despite

the

presence

of

genomic

rearrangements

-LRB-

see

Supplementary

Information

section

-RRB-

the

and

chromatin

state

in

HeLa

cells

are

not

far

from

a

constitutive

baseline

also

seen

with

a

cell

line

from

a

different

lineage

The

enrichment

of

multiple

activating

histone

modifications

and

the

depletion

of

a

repressive

modification

from

segments

that

replicate

early

in

S

phase

extends

previous

work

in

the

field

at

a

level

of

detail

and

scale

not

attempted

before

in

mammalian

cells

The

duality

of

histone

modification

patterns

in

Pan-S

areas

of

the

HeLa

genome

and

the

concordance

of

chromatin

marks

and

replication

time

across

two

disparate

cell

lines

-LRB-

HeLa

and

-RRB-

confirm

the

coordination

of

histone

modifications

with

replication

in

the

human

genome

Chromatin

architecture

and

genomic

domains

Overview

The

packaging

of

genomic

DNA

into

chromatin

is

intimately

connected

with

the

control

of

gene

expression

and

other

chromosomal

processes

We

next

examined

chromatin

structure

over

a

larger

scale

to

ascertain

its

relation

to

transcription

and

other

processes

Large

domains

-LRB-

to

kb

-RRB-

of

generalized

DNaseI

sensitivity

have

been

detected

around

developmentally

regulated

gene

prompting

speculation

that

the

genome

is

organized

a

Mb

-LRB-

-RRB-

into

open

and

closed

chromatin

territories

that

represent

higherorder

functional

domains

We

explored

how

different

chromatin

features

particularly

histone

modifications

correlate

with

chromatin

structure

both

over

short

and

long

distances

Chromatin

accessibility

and

histone

modifications

We

used

histone

modification

studies

and

DNaseI

sensitivity

data

sets

-LRB-

introduced

above

-RRB-

to

examine

general

chromatin

accessibility

without

focusing

on

the

specific

DHS

sites

-LRB-

see

Supplementary

Information

sections

and

-RRB-

A

fundamental

difficulty

in

analysing

continuous

data

across

large

genomic

regions

is

determining

the

appropriate

scale

for

analysis

-LRB-

for

example

kb

kb

kb

and

so

on

-RRB-

To

address

this

problem

we

developed

an

approach

based

on

wavelet

analysis

a

mathematical

tool

pioneered

in

the

field

of

signal

processing

that

has

recently

been

applied

to

continuous-value

genomic

analyses

Wavelet

analysis

provides

a

means

for

consistently

transforming

continuous

signals

into

different

scales

enabling

the

correlation

of

different

phenomena

independently

at

differing

scales

in

a

consistent

manner

Global

correlations

of

chromatin

accessibility

and

histone

modifications

We

computed

the

regional

correlation

between

DNaseI

sensitivity

and

each

histone

modification

at

multiple

scales

using

a

wavelet

approach

-LRB-

Fig.

and

Supplementary

Information

section

-RRB-

To

make

quantitative

comparisons

between

different

histone

modifications

we

computed

histograms

of

correlation

values

between

DNaseI

sensitivity

and

each

histone

modification

at

several

scales

and

then

tested

these

for

significance

at

specific

scales

Figure

shows

the

distribution

of

correlation

values

at

a

scale

which

is

considerably

larger

than

individual

cis-acting

regulatory

elements

At

this

scale

and

show

similarly

high

correlation

However

they

are

significantly

distinguished

from

and

modifications

-LRB-

P

see

Supplementary

Information

section

-RRB-

which

show

lower

correlation

with

DNaseI

sensitivity

These

results

suggest

that

larger-scale

relationships

between

chromatin

accessibility

and

histone

modifications

are

dominated

by

sub-regions

in

which

higher

average

DNaseI

sensitivity

is

accompanied

by

high

levels

of

and

modifications

Local

correlations

of

chromatin

accessibility

and

histone

modifications

Narrowing

to

a

scale

of

kb

revealed

a

more

complex

situation

in

which

is

the

histone

modification

that

is

best

correlated

with

DNaseI

sensitivity

However

there

is

no

clear

combination

of

marks

that

correlate

with

DNaseI

sensitivity

in

a

way

that

is

analogous

to

that

seen

at

a

larger

scale

-LRB-

see

Supplementary

Information

section

-RRB-

One

explanation

for

the

increased

c

Signal/control

scale

DNaseI

sensitivity

Density

b

Scale

-LRB-

kb

-RRB-

Genomic

position

-LRB-

kb

-RRB-

DNaseI

correlation

by

scale

Correlation

value

Negative

Positive

Genomic

position

-LRB-

kb

-RRB-

Correlation

Figure

Wavelet

correlations

of

histone

marks

and

DNaseI

sensitivity

As

an

example

correlations

between

DNaseI

sensitivity

and

-LRB-

both

in

the

cell

line

-RRB-

over

a

region

on

chromosome

-LRB-

ENCODE

region

-RRB-

are

shown

a

The

relationship

between

histone

modification

-LRB-

upper

plot

-RRB-

and

DNaseI

sensitivity

-LRB-

lower

plot

-RRB-

is

shown

for

ENCODE

region

The

curves

are

coloured

with

the

strength

of

the

local

correlation

at

the

scale

-LRB-

top

dashed

line

in

panel

b

-RRB-

b

The

same

data

as

in

a

are

represented

as

a

wavelet

correlation

The

y

axis

shows

the

differing

scales

decomposed

by

the

wavelet

analysis

from

large

to

small

scale

-LRB-

in

kb

-RRB-

the

colour

at

each

point

in

the

heatmap

represents

the

level

of

correlation

at

the

given

scale

measured

in

a

kb

window

centred

at

the

given

position

c

Distribution

of

correlation

values

at

the

kb

scale

between

the

indicated

histone

marks

The

y

axis

is

the

density

of

these

correlation

values

across

ENCODE

all

modifications

show

a

peak

at

a

positive-correlation

value

Nature

Publishing

Group

NATURE

Vol

June

ARTICLES

complexity

at

smaller

scales

is

that

there

is

a

mixture

of

different

classes

of

accessible

chromatin

regions

each

having

a

different

pattern

of

histone

modifications

To

examine

this

we

computed

the

degree

to

which

local

peaks

in

histone

methylation

or

acetylation

occur

at

DHSs

-LRB-

see

Supplementary

Information

section

-RRB-

We

found

that

and

of

significant

peaks

in

mono

di

and

tri-methylation

respectively

and

and

of

significant

peaks

in

and

acetylation

respectively

coincided

with

DHSs

-LRB-

see

Supplementary

Information

section

-RRB-

Conversely

a

proportion

of

DHSs

seemed

not

to

be

associated

with

significant

peaks

in

mono

di

or

tri-methylation

-LRB-

and

respectively

-RRB-

nor

with

peaks

in

or

acetylation

-LRB-

both

-RRB-

Because

only

a

limited

number

of

histone

modification

marks

were

assayed

the

possibility

remains

that

some

DHSs

harbour

other

histone

modifications

The

absence

of

a

more

complete

concordance

between

DHSs

and

peaks

in

histone

acetylation

is

surprising

given

the

widely

accepted

notion

that

histone

acetylation

has

a

central

role

in

mediating

chromatin

accessibility

by

disrupting

higher-order

chromatin

folding

DNA

structure

at

DHSs

The

observation

that

distinctive

hydroxyl

radical

cleavage

patterns

are

associated

with

specific

DNA

prompted

us

to

investigate

whether

DHS

subclasses

differed

with

respect

to

their

local

DNA

structure

Conversely

because

different

DNA

sequences

can

give

rise

to

similar

hydroxyl

radical

cleavage

genomic

regions

that

adopt

a

particular

local

structure

do

not

necessarily

have

the

same

nucleotide

sequence

Using

a

Gibbs

sampling

algorithm

on

hydroxyl

radical

cleavage

patterns

of

we

discovered

an

segment

with

a

conserved

cleavage

signature

-LRB-

CORCS

see

Supplementary

Information

section

-RRB-

The

underlying

DNA

sequences

that

give

rise

to

this

pattern

have

little

primary

sequence

similarity

despite

this

similar

structural

pattern

Furthermore

this

structural

element

is

strongly

enriched

in

promoterproximal

DHSs

-LRB-

enrichment

compared

to

the

rest

of

the

ENCODE

regions

-RRB-

relative

to

promoter-distal

DHSs

-LRB-

enrichment

-RRB-

this

element

is

enriched

in

CpG

islands

but

is

higher

still

-LRB-

-RRB-

in

CpG

islands

that

overlap

a

DHS

Large-scale

domains

in

the

ENCODE

regions

The

presence

of

extensive

correlations

seen

between

histone

modifications

DNaseI

a

sensitivity

replication

transcript

density

and

protein

factor

binding

led

us

to

investigate

whether

all

these

features

are

organized

systematically

across

the

genome

To

test

this

we

performed

an

unsupervised

training

of

a

two-state

HMM

with

inputs

from

these

different

features

-LRB-

see

Supplementary

Information

section

and

ref

-RRB-

No

other

information

except

for

the

experimental

variables

was

used

for

the

HMM

training

routines

We

consistently

found

that

one

state

-LRB-

active

-RRB-

generally

corresponded

to

domains

with

high

levels

of

and

RNA

transcription

low

levels

of

marks

and

early

replication

timing

whereas

the

other

state

-LRB-

repressed

-RRB-

reflected

domains

with

low

and

RNA

high

and

late

replication

-LRB-

see

Fig.

-RRB-

In

total

we

identified

active

regions

spanning

Mb

and

inactive

regions

spanning

Mb

-LRB-

median

size

kb

versus

kb

respectively

-RRB-

The

active

domains

are

markedly

enriched

for

GENCODE

TSSs

CpG

islands

and

Alu

repetitive

elements

-LRB-

P

for

each

-RRB-

whereas

repressed

regions

are

significantly

enriched

for

and

LTR

transposons

-LRB-

P

-RRB-

Taken

together

these

results

demonstrate

remarkable

concordance

between

ENCODE

functional

data

types

and

provide

a

view

of

higher-order

functional

domains

defined

by

a

broader

range

of

factors

at

a

markedly

higher

resolution

than

was

previously

Evolutionary

constraint

and

population

variability

Overview

Functional

genomic

sequences

can

also

be

identified

by

examining

evolutionary

changes

across

multiple

extant

species

and

within

the

human

population

Indeed

such

studies

complement

experimental

assays

that

identify

specific

functional

Evolutionary

constraint

-LRB-

that

is

the

rejection

of

mutations

at

a

particular

location

-RRB-

can

be

measured

by

either

-LRB-

i

-RRB-

comparing

observed

substitutions

to

neutral

rates

calculated

from

multi-sequence

or

-LRB-

ii

-RRB-

determining

the

presence

and

frequency

of

intra-species

polymorphisms

Importantly

both

approaches

are

indifferent

to

any

specific

function

that

the

constrained

sequence

might

confer

Previous

studies

comparing

the

human

mouse

rat

and

dog

genomes

examined

bulk

evolutionary

properties

of

all

nucleotides

in

the

genome

and

provided

little

insight

about

the

precise

positions

of

constrained

bases

Interestingly

these

studies

indicated

that

the

b

Mb

Repressed

Active

Depletion

Enrichment

RNA

Gencode

TSSs

CpG

islands

RFBR

Active

Repressed

GENCODE

genes

Figure

Higher-order

functional

domains

in

the

genome

The

general

concordance

of

multiple

data

types

is

shown

for

an

illustrative

ENCODE

region

-LRB-

-RRB-

a

Domains

were

determined

by

simultaneous

HMM

segmentation

of

replication

time

-LRB-

black

-RRB-

bulk

RNA

transcription

-LRB-

blue

-RRB-

-LRB-

purple

-RRB-

-LRB-

orange

-RRB-

DHS

density

-LRB-

green

-RRB-

and

RFBR

density

-LRB-

light

blue

-RRB-

measured

continuously

across

the

All

data

were

generated

using

HeLa

cells

The

histone

RNA

DHS

and

RFBR

signals

are

wavelet-smoothed

to

an

approximately

scale

-LRB-

see

Supplementary

Information

section

-RRB-

The

HMM

segmentation

is

shown

as

the

blocks

labelled

active

and

repressed

and

the

structure

of

GENCODE

genes

-LRB-

not

used

in

the

training

-RRB-

is

shown

at

the

end

b

Enrichment

or

depletion

of

annotated

sequence

features

-LRB-

GENCODE

TSSs

CpG

islands

repeats

Alu

repeats

and

non-exonic

constrained

sequences

-LRB-

CSs

-RRB-

-RRB-

in

active

versus

repressed

domains

Note

the

marked

enrichment

of

TSSs

CpG

islands

and

Alus

in

active

domains

and

the

enrichment

of

LINE

and

LTRs

in

repressed

domains

Nature

Publishing

Group

LINEs

-LRB-

-RRB-

LTRs

SINE

Alus

All

repeat

DHS

ARTICLES

NATURE

Vol

June

majority

of

constrained

bases

reside

within

the

non-coding

portion

of

the

human

genome

Meanwhile

increasingly

rich

data

sets

of

polymorphisms

across

the

human

genome

have

been

used

extensively

to

establish

connections

between

genetic

variants

and

disease

but

far

fewer

analyses

have

sought

to

use

such

data

for

assessing

functional

The

ENCODE

Project

provides

an

excellent

opportunity

for

more

fully

exploiting

inter

and

intra-species

sequence

comparisons

to

examine

genome

function

in

the

context

of

extensive

experimental

studies

on

the

same

regions

of

the

genome

We

consolidated

the

experimentally

derived

information

about

the

ENCODE

regions

and

focused

our

analyses

on

major

classes

of

genomic

elements

These

classes

are

listed

in

Table

and

include

two

non-experimentally

derived

data

sets

ancient

repeats

-LRB-

ARs

mobile

elements

that

inserted

early

in

the

mammalian

lineage

have

subsequently

become

dormant

and

are

assumed

to

be

neutrally

evolving

-RRB-

and

constrained

sequences

-LRB-

CSs

regions

that

evolve

detectably

more

slowly

than

neutral

sequences

-RRB-

Comparative

sequence

data

sets

and

analysis

We

generated

Mb

of

genomic

sequence

orthologous

to

the

ENCODE

regions

from

mammalian

species

using

a

targeted

strategy

that

involved

and

individual

bacterial

artificial

chromosome

clones

For

an

additional

vertebrate

species

we

used

Mb

of

orthologous

genomic

sequence

derived

from

genome-wide

sequencing

The

orthologous

sequences

were

aligned

using

three

alignment

programs

and

Four

independent

methods

that

generated

highly

concordant

were

then

used

to

identify

sequences

under

constraint

-LRB-

and

-RRB-

From

these

analyses

we

developed

a

high-confidence

set

of

constrained

sequences

that

correspond

to

of

the

nucleotides

in

the

ENCODE

regions

The

threshold

for

determining

constraint

was

set

using

a

FDR

rate

of

-LRB-

see

ref

-RRB-

this

level

is

similar

to

previous

estimates

of

the

fraction

of

the

human

genome

under

mammalian

but

the

FDR

rate

was

not

chosen

to

fit

this

result

The

median

length

of

these

constrained

sequences

is

bases

with

the

minimum

being

bases

roughly

the

size

of

a

typical

transcription

factor

binding

site

These

analyses

therefore

provide

a

resolution

of

constrained

sequences

that

is

substantially

better

than

that

currently

available

using

only

whole-genome

vertebrate

Intra-species

variation

studies

mainly

used

SNP

data

from

Phases

I

and

II

and

the

re-sequenced

regions

in

ENCODE

regions

with

individuals

of

the

HapMap

nucleotide

insertion

or

deletion

-LRB-

indel

-RRB-

data

were

from

the

SNP

Consortium

and

HapMap.We

also

examined

the

ENCODE

regions

for

the

presence

of

overlaps

with

known

segmental

and

CNVs

Experimentally

identified

functional

elements

and

constrained

sequences

We

first

compared

the

detected

constrained

sequences

Table

Eleven

classes

of

genomic

elements

subjected

to

evolutionary

and

population-genetics

analyses

Abbreviation

Description

with

the

positions

of

experimentally

identified

functional

elements

A

total

of

of

the

constrained

bases

reside

within

protein-coding

exons

and

their

associated

untranslated

regions

-LRB-

Fig.

-RRB-

and

in

agreement

with

previous

genome-wide

estimates

the

remaining

constrained

bases

do

not

overlap

the

mature

transcripts

of

proteincoding

When

we

included

the

other

experimental

annotations

we

found

that

an

additional

of

the

constrained

bases

overlap

experimentally

identified

non-coding

functional

regions

although

far

fewer

of

these

regions

overlap

constrained

sequences

compared

to

coding

exons

-LRB-

see

below

-RRB-

Most

experimental

annotations

are

significantly

different

from

a

random

expectation

for

both

base-pair

or

element-level

overlaps

-LRB-

using

the

GSC

statistic

see

Supplementary

Information

section

-RRB-

with

a

more

striking

deviation

when

considering

elements

-LRB-

Fig.

-RRB-

The

exceptions

to

this

are

pseudogenes

Un.TxFrags

and

RxFrags

The

increase

in

significance

moving

from

base-pair

measures

to

the

element

level

suggests

that

discrete

islands

of

constrained

sequence

exist

within

experimentally

identified

functional

elements

with

the

surrounding

bases

apparently

not

showing

evolutionary

constraint

This

notion

is

discussed

in

greater

detail

in

ref

We

also

examined

measures

of

human

variation

-LRB-

heterozygosity

derived

allele-frequency

spectra

and

indel

rates

-RRB-

within

the

sequences

of

the

experimentally

identified

functional

elements

-LRB-

Fig.

-RRB-

For

these

studies

ARs

were

used

as

a

marker

for

neutrally

evolving

sequence

Most

experimentally

identified

functional

elements

are

associated

with

lower

heterozygosity

compared

to

ARs

and

a

few

have

lower

indel

rates

compared

with

ARs

Striking

outliers

are

UTRs

which

have

dramatically

increased

indel

rates

without

an

obvious

cause

This

is

discussed

in

more

depth

in

ref

These

findings

indicate

that

the

majority

of

the

evolutionarily

constrained

experimentally

identified

functional

elements

show

evidence

of

negative

selection

both

across

mammalian

species

and

within

the

human

population

Furthermore

we

have

assigned

at

least

one

molecular

function

to

the

majority

-LRB-

-RRB-

of

all

constrained

bases

in

the

ENCODE

regions

Conservation

of

regulatory

elements

The

relationship

between

individual

classes

of

regulatory

elements

and

constrained

sequences

varies

considerably

ranging

from

cases

where

there

is

strong

evolutionary

constraint

-LRB-

for

example

pan-vertebrate

ultraconserved

-RRB-

to

examples

of

regulatory

elements

that

are

not

conserved

between

orthologous

human

and

mouse

Within

the

ENCODE

regions

of

RFBRs

overlap

the

high-confidence

All

ENCODE

regions

-LRB-

kb

-RRB-

Non-constrained

Unannotated

CDS

UTR

UTR

Un.TxFrag

RxFrag

Pseudogene

RFBR

RFBR-SeqSp

DHS

FAIRE

TSS

AR

CS

Coding

exons

as

annotated

by

GENCODE

untranslated

region

as

annotated

by

GENCODE

untranslated

region

as

annotated

by

GENCODE

Unannotated

region

detected

by

RNA

hybridization

to

tiling

array

-LRB-

that

is

unannotated

TxFrag

-RRB-

Region

detected

by

RACE

and

analysis

on

tiling

array

Pseudogene

identified

by

consensus

pseudogene

analysis

Regulatory

factor

binding

region

identified

by

ChIP-chip

assay

Regulatory

factor

binding

region

identified

only

by

ChIP-chip

assays

for

factors

with

known

sequence-specificity

DNaseI

hypersensitive

sites

found

in

multiple

tissues

Region

of

open

chromatin

identified

by

the

FAIRE

assay

Transcription

start

site

Ancient

repeat

inserted

early

in

the

mammalian

lineage

and

presumed

to

be

neutrally

evolving

Constrained

sequence

identified

by

analysing

multi-sequence

alignments

Constrained

Other

ENCODE

experimental

annotations

UTRs

Coding

Figure

Relative

proportion

of

different

annotations

among

constrained

sequences

The

of

bases

in

the

ENCODE

regions

identified

as

constrained

is

subdivided

into

the

portions

that

reflect

known

coding

regions

UTRs

other

experimentally

annotated

regions

and

unannotated

sequence

Nature

Publishing

Group

NATURE

Vol

June

ARTICLES

constrained

sequences

As

expected

RFBRs

have

many

unconstrained

bases

presumably

owing

to

the

small

size

of

the

specific

binding

site

We

investigated

whether

the

binding

sites

in

RFBRs

could

be

further

delimited

using

information

about

evolutionary

constraint

For

out

of

factors

with

either

known

TRANSFAC

or

Jaspar

motifs

our

ChIP-chip

data

revealed

a

marked

enrichment

of

the

appropriate

motif

within

the

constrained

versus

the

unconstrained

portions

of

the

RFBRs

-LRB-

see

Supplementary

Information

section

-RRB-

This

enrichment

was

seen

for

levels

of

stringency

used

for

defining

ChIP-chip-positive

sites

-LRB-

and

FDR

level

-RRB-

indicating

that

combining

sequence

constraint

and

ChIP-chip

data

may

provide

a

highly

sensitive

means

for

detecting

factor

binding

sites

in

the

human

genome

Experimentally

identified

functional

elements

and

genetic

variation

The

above

studies

focus

on

purifying

-LRB-

negative

-RRB-

selection

We

used

nucleotide

variation

to

detect

potential

signals

of

adaptive

-LRB-

positive

-RRB-

selection

We

modified

the

standard

McDonald

Kreitman

test

-LRB-

-RRB-

and

the

Hudson

Kreitman

Aguade

-LRB-

HKA

-RRB-

test

-LRB-

see

Supplementary

Information

section

-RRB-

to

examine

whether

an

entire

set

of

sequence

elements

shows

an

excess

of

polymorphisms

or

an

excess

of

inter-species

divergence

We

found

that

constrained

sequences

and

coding

exons

have

an

excess

of

polymorphisms

-LRB-

consistent

with

purifying

selection

-RRB-

whereas

UTRs

a

Constrained

sequence

Experimental

annotation

Overlap

Bases

Regions

Yes

Yes

No

Yes

Overall

-LRB-

out

of

-RRB-

b

Bases

Fraction

of

experimental

annotation

overlapping

constrained

sequence

Regions

show

evidence

of

an

excess

of

divergence

-LRB-

with

a

portion

probably

reflecting

positive

selection

-RRB-

In

general

non-coding

genomic

regions

show

more

variation

with

both

a

large

number

of

segments

that

undergo

purifying

selection

and

regions

that

are

fast

evolving

We

also

examined

structural

variation

-LRB-

that

is

CNVs

inversions

and

see

Supplementary

Information

section

-RRB-

Within

these

polymorphic

regions

we

encountered

significant

overrepresentation

of

CDSs

TxFrags

and

intra-species

constrained

sequences

-LRB-

P

Fig.

-RRB-

and

also

detected

a

statistically

significant

under-representation

of

ARs

-LRB-

P

-RRB-

A

similar

overrepresentation

of

CDSs

and

intra-species

constrained

sequences

was

found

within

non-polymorphic

segmental

duplications

Unexplained

constrained

sequences

Despite

the

wealth

of

complementary

data

of

the

ENCODE-region

sequences

identified

as

constrained

are

not

associated

with

any

experimental

evidence

of

function

There

is

no

evidence

indicating

that

mutational

cold

spots

account

for

this

constraint

they

have

similar

measures

of

constraint

to

experimentally

identified

elements

and

harbour

equal

proportions

of

SNPs

To

characterize

further

the

unexplained

constrained

sequences

we

examined

their

clustering

and

phylogenetic

distribution

These

sequences

are

not

uniformly

distributed

across

most

ENCODE

regions

and

even

in

most

ENCODE

regions

the

distribution

is

different

from

constrained

sequences

within

experimentally

identified

functional

elements

-LRB-

see

Supplementary

Information

section

-RRB-

The

large

fraction

of

constrained

sequence

that

does

not

match

any

experimentally

identified

elements

is

not

surprising

considering

that

only

a

limited

set

of

transcription

factors

cell

lines

and

biological

conditions

have

thus

far

been

examined

Unconstrained

experimentally

identified

functional

elements

In

contrast

an

unexpectedly

large

fraction

of

experimentally

identified

functional

elements

show

no

evidence

of

evolutionary

constraint

ranging

from

for

Un.TxFrags

to

for

CDS

For

most

types

of

non-coding

functional

elements

roughly

of

the

individual

elements

seemed

to

be

unconstrained

across

all

mammals

There

are

two

methodological

reasons

that

might

explain

the

apparent

excess

of

unconstrained

experimentally

identified

functional

elements

the

underestimation

of

sequence

constraint

or

overestimation

of

experimentally

identified

functional

elements

We

do

not

believe

that

either

of

these

explanations

fully

accounts

for

the

large

and

varied

levels

of

unconstrained

experimentally

functional

sequences

The

set

of

constrained

bases

analysed

here

is

highly

accurate

and

complete

due

to

the

depth

of

the

multiple

alignment

Both

by

bulk

fitting

procedures

and

by

comparison

of

SNP

frequencies

to

constraint

there

is

clearly

a

proportion

of

constrained

bases

not

captured

in

the

defined

of

constrained

sequences

but

it

is

small

-LRB-

see

Un.TxFrags

ARs

UTRs

TSSs

DHSs

RFBRs

FA

IR

E

UTRs

RFBRs-SeqSp

Heterozygosity

-LRB-

-RRB-

FAIRE

RFBRs-SeqSp

RxFrags

DHSs

Pseudogenes

UTRs

Un.TxFrags

UTRs

RFBRs

CDSs

ARs

CDSs

RxFrags

RNA

transcription

Open

chromatin

DNA/protein

CSs

Figure

Overlap

of

constrained

sequences

and

various

experimental

annotations

a

A

schematic

depiction

shows

the

different

tests

used

for

assessing

overlap

between

experimental

annotations

and

constrained

sequences

both

for

individual

bases

and

for

entire

regions

b

Observed

fraction

of

overlap

depicted

separately

for

bases

and

regions

The

results

are

shown

for

selected

experimental

annotations

The

internal

bars

indicate

confidence

intervals

of

randomized

placement

of

experimental

elements

using

the

GSC

methodology

to

account

for

heterogeneity

in

the

data

sets

When

the

bar

overlaps

the

observed

value

one

can

not

reject

the

hypothesis

that

these

overlaps

are

consistent

with

random

placements

Rate

of

polymorphic

indels

Figure

Relationship

between

heterozygosity

and

polymorphic

indel

rate

for

a

variety

of

experimental

annotations

UTRs

are

an

expected

outlier

for

the

indel

measures

owing

to

the

presence

of

low-complexity

sequence

-LRB-

leading

to

a

higher

indel

rate

-RRB-

Nature

Publishing

Group

ARTICLES

NATURE

Vol

June

Supplementary

Information

section

and

-RRB-

More

aggressive

schemes

to

detect

constraint

only

marginally

increase

the

overlap

with

experimentally

identified

functional

elements

and

do

so

with

considerably

less

specificity

Similarly

all

experimental

findings

have

been

independently

validated

and

for

the

least

constrained

experimentally

identified

functional

elements

-LRB-

Un.TxFrags

and

binding

sites

of

sequence-specific

factors

-RRB-

there

is

both

internal

validation

and

cross-validation

from

different

experimental

techniques

This

suggests

that

there

is

probably

not

a

significant

overestimation

of

experimentally

identified

functional

elements

Thus

these

two

explanations

may

contribute

to

the

general

observation

about

unconstrained

functional

elements

but

can

not

fully

explain

it

Instead

we

hypothesize

five

biological

reasons

to

account

for

the

presence

of

large

amounts

of

unconstrained

functional

elements

The

first

two

are

particular

to

certain

biological

assays

in

which

the

elements

being

measured

are

connected

to

but

do

not

coincide

with

the

analysed

region

An

example

of

this

is

the

parent

transcript

of

an

miRNA

where

the

current

assays

detect

the

exons

-LRB-

some

of

which

are

not

under

evolutionary

selection

-RRB-

whereas

the

intronic

miRNA

actually

harbours

the

constrained

bases

Nevertheless

the

transcript

sequence

provides

the

critical

coupling

between

the

regulated

promoter

and

the

miRNA

The

sliding

of

transcription

factors

-LRB-

which

might

bind

a

specific

sequence

but

then

migrate

along

the

DNA

-RRB-

or

the

processivity

of

histone

modifications

across

chromatin

are

more

exotic

examples

of

this

A

related

second

hypothesis

is

that

delocalized

behaviours

of

the

genome

such

as

general

chromatin

accessibility

may

be

maintained

by

some

biochemical

processes

-LRB-

such

as

transcription

of

intergenic

regions

or

specific

factor

binding

-RRB-

without

the

requirement

for

specific

sequence

elements

These

two

explanations

of

both

connected

components

and

diffuse

components

related

to

but

not

coincident

with

constrained

sequences

are

particularly

relevant

for

the

considerable

amount

of

unannotated

and

unconstrained

transcripts

The

other

three

hypotheses

may

be

more

general

the

presence

of

neutral

-LRB-

or

near

neutral

-RRB-

biochemical

elements

of

lineagespecific

functional

elements

and

of

functionally

conserved

but

non-orthologous

elements

We

believe

there

is

a

considerable

proportion

of

neutral

biochemically

active

elements

that

do

not

confer

a

selective

advantage

or

disadvantage

to

the

organism

This

neutral

pool

of

sequence

elements

may

turn

over

during

evolutionary

time

Fr

ag

s

Ss

BR

s-S

U

TR

eqS

C

s

p

S

no

RF

nC

BR

D

S

s

emerging

via

certain

mutations

and

disappearing

by

others

The

size

of

the

neutral

pool

would

largely

be

determined

by

the

rate

of

emergence

and

extinction

through

chance

events

low

informationcontent

elements

such

as

transcription

factor-binding

will

have

larger

neutral

pools

Second

from

this

neutral

pool

some

elements

might

occasionally

acquire

a

biological

role

and

so

come

under

evolutionary

selection

The

acquisition

of

a

new

biological

role

would

then

create

a

lineage-specific

element

Finally

a

neutral

element

from

the

general

pool

could

also

become

a

peer

of

an

existing

selected

functional

element

and

either

of

the

two

elements

could

then

be

removed

by

chance

If

the

older

element

is

removed

the

newer

element

has

in

essence

been

conserved

without

using

orthologous

bases

providing

a

conserved

function

in

the

absence

of

constrained

sequences

For

example

a

common

binding

site

in

the

human

and

mouse

genomes

may

not

reflect

orthologous

human

and

mouse

bases

though

the

presence

of

an

site

in

that

region

was

evolutionarily

selected

for

in

both

lineages

Note

that

both

the

neutral

turnover

of

elements

and

the

functional

peering

of

elements

has

been

suggested

for

cis-acting

regulatory

elements

in

and

Our

data

support

these

hypotheses

and

we

have

generalized

this

idea

over

many

different

functional

elements

The

presence

of

conserved

function

encoded

by

conserved

orthologous

bases

is

a

commonplace

assumption

in

comparative

genomics

our

findings

indicate

that

there

could

be

a

sizable

set

of

functionally

conserved

but

non-orthologous

elements

in

the

human

genome

and

that

these

seem

unconstrained

across

mammals

Functional

data

akin

to

the

ENCODE

Project

on

other

related

species

such

as

mouse

would

be

critical

to

understanding

the

rate

of

such

functionally

conserved

but

non-orthologous

elements

Conclusion

The

generation

and

analyses

of

over

experimental

data

sets

from

studies

examining

the

ENCODE

regions

provide

a

rich

source

of

functional

information

for

Mb

of

the

human

genome

The

first

conclusion

of

these

efforts

is

that

these

data

are

remarkably

informative

Although

there

will

be

ongoing

work

to

enhance

existing

assays

invent

new

techniques

and

develop

new

data-analysis

methods

the

generation

of

genome-wide

experimental

data

sets

akin

to

the

ENCODE

pilot

phase

would

provide

an

impressive

platform

for

future

genome

exploration

efforts

This

now

seems

feasible

in

light

of

throughput

improvements

of

many

of

the

assays

and

the

everdeclining

costs

of

whole-genome

tiling

arrays

and

DNA

sequencing

Such

genome-wide

functional

data

should

be

acquired

and

released

openly

as

has

been

done

with

other

large-scale

genome

projects

to

ensure

its

availability

as

a

new

foundation

for

all

biologists

studying

the

human

genome

It

is

these

biologists

who

will

often

provide

the

critical

link

from

biochemical

function

to

biological

role

for

the

identified

elements

The

scale

of

the

pilot

phase

of

the

ENCODE

Project

was

also

sufficiently

large

and

unbiased

to

reveal

important

principles

about

the

organization

of

functional

elements

in

the

human

genome

In

many

cases

these

principles

agree

with

current

mechanistic

models

For

example

we

observe

trimethylation

of

enriched

near

active

genes

and

have

improved

the

ability

to

accurately

predict

gene

activity

based

on

this

and

other

histone

modifications

However

we

also

uncovered

some

surprises

that

challenge

the

current

dogma

on

biological

mechanisms

The

generation

of

numerous

intercalated

transcripts

spanning

the

majority

of

the

genome

has

been

repeatedly

but

this

phenomenon

has

been

met

with

mixed

opinions

about

the

biological

importance

of

these

transcripts

Our

analyses

of

numerous

orthogonal

data

sets

firmly

establish

the

presence

of

these

transcripts

and

thus

the

simple

view

of

the

genome

as

having

a

defined

set

of

isolated

loci

transcribed

independently

does

not

seem

to

be

accurate

Perhaps

the

genome

encodes

a

network

of

transcripts

many

of

which

are

linked

to

protein-coding

transcripts

and

to

the

majority

of

which

we

can

not

-LRB-

yet

-RRB-

assign

a

biological

role

Our

perspective

of

transcription

and

genes

may

have

to

evolve

and

also

poses

DH

Relative

enrichment

Tx

CS

Fr

s

ag

s

C

DS

s

Rx

RF

AR

FA

s

IR

E

TS

ge

ne

s

U

TR

s

Figure

CNV

enrichment

The

relative

enrichment

of

different

experimental

annotations

in

the

ENCODE

regions

associated

with

CNVs

are

constrained

sequences

outside

of

coding

regions

A

value

of

or

less

indicates

no

enrichment

and

values

greater

than

show

enrichment

Starred

columns

are

cases

that

are

significant

on

the

basis

of

this

enrichment

being

found

in

less

than

of

randomizations

that

matched

each

element

class

for

length

and

density

of

features

Ps

eu

do

Ss

Nature

Publishing

Group

NATURE

Vol

June

ARTICLES

some

interesting

mechanistic

questions

For

example

how

are

splicing

signals

coordinated

and

used

when

there

are

so

many

overlapping

primary

transcripts

Similarly

to

what

extent

does

this

reflect

neutral

turnover

of

reproducible

transcripts

with

no

biological

role

We

gained

subtler

but

equally

important

mechanistic

findings

relating

to

transcription

replication

and

chromatin

modification

Transcription

factors

previously

thought

to

primarily

bind

promoters

bind

more

generally

and

those

which

do

bind

to

promoters

are

equally

likely

to

bind

downstream

of

a

TSS

as

upstream

Interestingly

many

elements

that

previously

were

classified

as

distal

enhancers

are

in

fact

close

to

one

of

the

newly

identified

TSSs

only

about

of

sites

showing

evidence

of

binding

by

multiple

transcription

factors

are

actually

distal

to

a

TSS

This

need

not

imply

that

most

regulatory

information

is

confined

to

classic

promoters

but

rather

it

does

suggest

that

transcription

and

regulation

are

coordinated

actions

beyond

just

the

traditional

promoter

sequences

Meanwhile

although

distal

regulatory

elements

could

be

identified

in

the

ENCODE

regions

they

are

currently

difficult

to

classify

in

part

owing

to

the

lack

of

a

broad

set

of

transcription

factors

to

use

in

analysing

such

elements

Finally

we

now

have

a

much

better

appreciation

of

how

DNA

replication

is

coordinated

with

histone

modifications

At

the

outset

of

the

ENCODE

Project

many

believed

that

the

broad

collection

of

experimental

data

would

nicely

dovetail

with

the

detailed

evolutionary

information

derived

from

comparing

multiple

mammalian

sequences

to

provide

a

neat

dictionary

of

conserved

genomic

elements

each

with

a

growing

annotation

about

their

biochemical

function

-LRB-

s

-RRB-

In

one

sense

this

was

achieved

the

majority

of

constrained

bases

in

the

ENCODE

regions

are

now

associated

with

at

least

some

experimentally

derived

information

about

function

However

we

have

also

encountered

a

remarkable

excess

of

experimentally

identified

functional

elements

lacking

evolutionary

constraint

and

these

can

not

be

dismissed

for

technical

reasons

This

is

perhaps

the

biggest

surprise

of

the

pilot

phase

of

the

ENCODE

Project

and

suggests

that

we

take

a

more

neutral

view

of

many

of

the

functions

conferred

by

the

genome

METHODS

The

methods

are

described

in

the

Supplementary

Information

with

more

technical

details

for

each

experiment

often

found

in

the

references

provided

in

Table

The

Supplementary

Information

sections

are

arranged

in

the

same

order

as

the

manuscript

-LRB-

with

similar

headings

to

facilitate

cross-referencing

-RRB-

The

first

page

of

Supplementary

Information

also

has

an

index

to

aid

navigation

Raw

data

are

available

in

ArrayExpress

GEO

or

EMBL/GenBank

archives

as

appropriate

as

detailed

in

Supplementary

Information

section

Processed

data

are

also

presented

in

a

user-friendly

manner

at

the

UCSC

Genome

Browser

's

ENCODE

portal

-LRB-

-RRB-

Received

March

accepted

April

International

Human

Genome

Sequencing

Consortium

Initial

sequencing

and

analysis

of

the

human

genome

Nature

-LRB-

-RRB-

Venter

J.

C.

et

al.

The

sequence

of

the

human

genome

Science

-LRB-

-RRB-

International

Human

Genome

Sequencing

Consortium

Finishing

the

euchromatic

sequence

of

the

human

genome

Nature

-LRB-

-RRB-

Mouse

Genome

Sequencing

Consortium

Initial

sequencing

and

comparative

analysis

of

the

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Chiou

Yu

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Ugrappa

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Program

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of

Medicine

Human

Genome

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Center

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Institute

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Hospital

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Jonghwan

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Kuo

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Giresi

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FormaldehydeAssisted

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improvements

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Acknowledgements

We

thank

D.

Leja

for

providing

graphical

expertise

and

support

Funding

support

is

acknowledged

from

the

following

sources

National

Institutes

of

Health

The

European

Union

BioSapiens

NoE

Affymetrix

Swiss

National

Science

Foundation

the

Spanish

Ministerio

de

Educacion

y

Ciencia

Spanish

Ministry

of

Education

and

Science

CIBERESP

Genome

Spain

and

Generalitat

de

Catalunya

Ministry

of

Education

Culture

Sports

Science

and

Technology

of

Japan

the

NCCR

Frontiers

in

Genetics

the

Jerome

Lejeune

Foundation

the

Childcare

Foundation

the

Novartis

Foundations

the

Danish

Research

Council

the

Swedish

Research

Council

the

Knut

and

Alice

Wallenberg

Foundation

the

Wellcome

Trust

the

Howard

Hughes

Medical

Institute

the

Bio-X

Institute

the

RIKEN

Institute

the

US

Army

National

Science

Foundation

the

Deutsche

Forschungsgemeinschaft

the

Austrian

Gen-AU

program

the

BBSRC

and

The

European

Molecular

Biology

Laboratory

We

thank

the

Barcelona

SuperComputing

Center

and

the

NIH

Biowulf

cluster

for

computer

facilities

The

Consortium

thanks

the

ENCODE

Scientific

Advisory

Panel

for

their

advice

on

the

project

G.

Weinstock

M.

Cherry

G.

Churchill

M.

Eisen

S.

Elgin

J.

Lis

J.

Rine

M.

Vidal

and

P.

Zamore

Author

Information

Reprints

and

permissions

information

is

available

at

The

authors

declare

no

competing

financial

interests

The

list

of

individual

authors

is

divided

among

the

six

main

analysis

groups

and

five

organizational

groups

Correspondence

and

requests

for

materials

should

be

addressed

to

the

co-chairs

of

the

ENCODE

analysis

groups

-LRB-

listed

in

the

Analysis

Coordination

group

-RRB-

E.

Birney

-LRB-

birney@ebi.ac.uk

-RRB-

J.

A.

Stamatoyannopoulos

-LRB-

jstam@u.washington.edu

-RRB-

A.

Dutta

-LRB-

-RRB-

R.

Guigo

-LRB-

rguigo@imim.es

-RRB-

T.

R.

Gingeras

-LRB-

-RRB-

E.

H.

Margulies

-LRB-

elliott@nhgri.nih.gov

-RRB-

Z.

Weng

-LRB-

zhiping@bu.edu

-RRB-

M.

Snyder

-LRB-

michael.snyder@yale.edu

-RRB-

E.

T.

Dermitzakis

-LRB-

-RRB-

or

collectively

-LRB-

-RRB-

The

ENCODE

Project

Consortium

Analysis

Coordination

Ewan

John

A.

Anindya

Roderic

Thomas

R.

Elliott

H.

Zhiping

Michael

Emmanouil

T.

Chromatin

and

Replication

John

A.

Robert

E.

Michael

S.

Christopher

M.

Shane

Christoph

M.

Saurabh

Ankit

Ivan

Jason

A.

Robert

M.

Paul

Patrick

J.

Hua

Nigel

P.

Gayle

K.

Sean

Nathan

Pawandeep

Shane

C.

Michael

O.

Heike

Paul

G.

Jeff

Michael

Andrew

Richard

Keith

D.

Brett

E.

Ericka

M.

Tristan

T.

Elizabeth

R.

Neerja

Kirsten

Gregory

C.

Patrick

A.

Fidencio

Stephen

C.

J.

Peter

J.

Richard

Anthony

David

Molly

Sarah

Nature

Publishing

Group

ARTICLES

Variation

Emmanouil

T.

Lluis

Christine

P.

Taane

G.

Gregory

M.

-LCB-

Paul

I.

W.

de

Andrew

D.

Nuria

Joel

D.

Barbara

E.

Daryl

J.

Abigail

Serafim

Eugene

Antigone

Eduardo

Ross

C.

Julian

Arend

Ingileif

B.

Hallgri

James

Heather

Michael

C.

Roderic

James

C.

Goncalo

R.

Xavier

Ewan

NISC

Comparative

Sequencing

Program

Gerard

G.

Xiaobin

Nancy

F.

Jacquelyn

R.

Valerie

V.B.

Baishali

Jennifer

C.

Morgan

Pamela

J.

Alice

C.

Robert

W.

Baylor

College

of

Medicine

Human

Genome

Sequencing

Center

Donna

M.

Erica

David

A.

Kim

C.

Huaiyang

George

M.

Richard

A.

Washington

University

Genome

Sequencing

Center

Tina

Robert

Elaine

R.

Richard

K.

Broad

Institute

Michele

James

Sante

David

B.

Jean

L.

Kerstin

Eric

S.

Children

's

Hospital

Oakland

Research

Institute

Maxim

Mikhail

Kazutoyo

Yuko

Baoli

Pieter

J.

de

Affiliations

for

participants

Bioinformatics

Institute

Wellcome

Trust

Genome

Campus

Hinxton

Cambridge

UK

of

Genome

Sciences

NE

Pacific

Street

Box

University

of

Washington

Seattle

Washington

USA

of

Biochemistry

and

Molecular

Genetics

Jordan

Box

Jefferson

Park

Ave

University

of

Virginia

School

of

Medicine

Charlottesville

Virginia

USA

Bioinformatics

Program

Center

for

Genomic

Regulation

Group

in

Biomedical

Informatics

Institut

Municipal

d'Investigacio

Medica/Universitat

Pompeu

Fabra

c/o

Dr.

Aiguader

Barcelona

Biomedical

Research

Park

Building

Barcelona

Catalonia

Spain

Inc.

Santa

Clara

California

USA

Technology

Branch

National

Human

Genome

Research

Institute

National

Institutes

of

Health

Bethesda

Maryland

USA

Program

Boston

University

Cummington

St.

Boston

Massachusetts

USA

Engineering

Department

Boston

University

Cummington

St.

Boston

Massachusetts

USA

of

Molecular

Cellular

and

Developmental

Biology

Yale

University

New

Haven

Connecticut

USA

of

Molecular

Biophysics

and

Biochemistry

Yale

University

PO

Box

New

Haven

Connecticut

USA

Wellcome

Trust

Sanger

Institute

Wellcome

Trust

Genome

Campus

Hinxton

Cambridge

UK

of

Medical

Genetics

NE

Pacific

Street

Box

University

of

Washington

Seattle

Washington

USA

of

Genetics

Brigham

and

Women

's

Hospital

and

Harvard

Medical

School

Avenue

Louis

Pasteur

Boston

Massachusetts

USA

of

Chemistry

and

Program

in

Bioinformatics

Boston

University

Commonwealth

Avenue

Boston

Massachusetts

USA

Branch

Center

for

Cancer

Research

National

Cancer

Institute

National

Institutes

of

Health

Bethesda

Maryland

USA

of

Biology

and

Carolina

Center

for

Genome

Sciences

CB

Fordham

Hall

The

University

of

North

Carolina

at

Chapel

Hill

Chapel

Hill

North

Carolina

USA

Institute

for

Brain

Sciences

North

Street

Seattle

Washington

USA

in

Gene

Function

and

Expression

and

Department

of

Biochemistry

and

Molecular

Pharmacology

University

of

Massachusetts

Medical

School

Plantation

Street

Worcester

Massachusetts

USA

for

Genome

Sciences

Policy

and

Department

of

Pediatrics

Science

Drive

Duke

University

Durham

North

Carolina

USA

for

Integrative

Genomics

University

of

Lausanne

Genopode

building

Lausanne

Switzerland

of

Genetic

Medicine

and

Development

University

of

Geneva

Medical

School

Geneva

Switzerland

Exploration

Research

Group

RIKEN

Genomic

Sciences

Center

-LRB-

GSC

-RRB-

RIKEN

Yokohama

Institute

Suehiro-cho

Tsurumi-ku

Yokohama

Kanagawa

Japan

for

Theoretical

Chemistry

University

of

Vienna

Wahringerstra

e

Wien

Austria

Department

of

Biomolecular

Engineering

University

of

California

Santa

Cruz

High

Street

Santa

Cruz

California

USA

for

Biomolecular

Science

and

Engineering

Engineering

Suite

Mail

Stop

CBSE/ITI

University

of

California

Santa

Cruz

California

USA

of

Biological

Chemistry

Molecular

Pharmacology

Harvard

Medical

School

Longwood

Avenue

Boston

Massachusetts

USA

of

Genetics

Facultat

de

Biologia

Universitat

de

Barcelona

Av

Diagonal

Barcelona

Catalonia

Spain

Institute

Biopolis

Street

Matrix

Singapore

Singapore

Group

Department

of

Computer

Science

Center

of

Bioinformatics

University

of

Leipzig

Hartelstra

e

Leipzig

Germany

Institut

fur

Zelltherapie

und

Immunologie

IZI

Deutscher

Platz

Leipzig

Germany

of

Genetics

Stanford

University

School

of

Medicine

Stanford

NATURE

Vol

June

California

USA

Institute

of

Singapore

Biopolis

Street

Singapore

Singapore

for

Computational

Genomics

Washington

University

Campus

Box

Saint

Louis

Missouri

USA

of

Mathematics

and

Computer

Science

University

of

California

Berkeley

California

USA

National

Cancer

Research

Centre

CNIO

Madrid

Spain

of

Epidemiology

and

Public

Health

Imperial

College

St

Mary

's

Campus

Norfolk

Place

London

UK

of

Applied

Science

Technology

University

of

California

Berkeley

California

USA

Science

Laboratory

Discovery

and

Research

Institute

RIKEN

Wako

Institute

Hirosawa

Wako

Saitama

Japan

of

Genetics

Yale

University

School

of

Medicine

Cedar

Street

New

Haven

Connecticut

USA

of

Pediatrics

University

of

Massachusetts

Medical

School

Lake

Avenue

North

Worcester

Massachusetts

USA

of

Statistics

University

of

California

Berkeley

California

USA

for

Molecular

Bioscience

University

of

Queensland

St.

Lucia

QLD

Australia

Santa

Fe

Institute

Hyde

Park

Road

Santa

Fe

New

Mexico

USA

of

Computer

Science

Yale

University

PO

Box

New

Haven

Connecticut

USA

in

Computational

Biology

Bioinformatics

Yale

University

PO

Box

New

Haven

Connecticut

USA

Intramural

Sequencing

Center

National

Human

Genome

Research

Institute

National

Institutes

of

Health

Bethesda

Maryland

USA

Department

of

Computer

Science

and

Engineering

The

Pennsylvania

State

University

University

Park

Pennsylvania

USA

for

Comparative

Genomics

and

Bioinformatics

Huck

Institutes

for

Life

Sciences

The

Pennsylvania

State

University

University

Park

Pennsylvania

USA

of

Extramural

Research

National

Human

Genome

Research

Institute

National

Institute

of

Health

Fishers

Lane

Suite

Bethesda

Maryland

USA

of

the

Director

National

Human

Genome

Research

Institute

National

Institute

of

Health

Center

Drive

Suite

Bethesda

Maryland

USA

of

Computer

Science

Stanford

University

Stanford

California

USA

of

Biostatistics

and

Medical

Informatics

University

of

Wisconsin-Madison

MSC

University

Ave

Madison

Wisconsin

USA

of

Zoology

and

Animal

Biology

Faculty

of

Sciences

University

of

Geneva

Geneva

Switzerland

of

Statistics

Stanford

University

Stanford

California

USA

of

Bioengineering

University

of

California

Berkeley

California

USA

Center

for

Biotechnology

Information

National

Institutes

of

Health

Bethesda

Maryland

USA

of

Biochemistry

and

Molecular

Biology

Huck

Institutes

of

Life

Sciences

The

Pennsylvania

State

University

University

Park

Pennsylvania

USA

Howard

Hughes

Medical

Institute

University

of

California

Santa

Cruz

California

USA

of

Biology

The

Pennsylvania

State

University

University

Park

Pennsylvania

USA

of

Pathology

Stanford

University

School

of

Medicine

Stanford

California

USA

Genome

Sequencing

Center

and

Department

of

Molecular

and

Human

Genetics

Baylor

College

of

Medicine

Houston

Texas

USA

Sequencing

Center

Washington

University

School

of

Medicine

Campus

Box

Forest

Park

Avenue

Saint

Louis

Missouri

USA

Institute

of

Harvard

University

and

Massachusetts

Institute

of

Technology

Charles

Street

Cambridge

Massachusetts

USA

Institute

for

Biomedical

Research

Cambridge

Center

Cambridge

Massachusetts

USA

Children

's

Hospital

Oakland

Research

Institute

BACPAC

Resources

Street

Oakland

California

USA

Institute

for

Cancer

Research

Gilman

Drive

La

Jolla

California

USA

Linnaeus

Centre

for

Bioinformatics

Uppsala

University

BMC

Box

Uppsala

Sweden

of

Pharmacology

and

the

Genome

Center

University

of

California

Davis

California

USA

for

Cellular

Molecular

Biology

The

University

of

Texas

at

Austin

University

Station

Austin

Texas

USA

Systems

Inc.

Science

Court

Madison

Wisconsin

USA

of

Wisconsin

Medical

School

Madison

Wisconsin

USA

of

Genetics

and

Pathology

Rudbeck

Laboratory

Uppsala

University

Uppsala

Sweden

of

California

San

Diego

School

of

Medicine

Gilman

Drive

La

Jolla

California

USA

and

Disease

Program

Center

for

Genomic

Regulation

c/o

Dr.

Aiguader

Barcelona

Biomedical

Research

Park

Building

Barcelona

Catalonia

Spain

Broad

Institute

of

MIT

and

Harvard

Cambridge

Center

Cambridge

Massachusetts

USA

for

Statistical

Genetics

Department

of

Biostatistics

SPH

II

Washington

Heights

Ann

Arbor

Michigan

USA

of

Statistics

University

of

Oxford

Oxford

UK

Pompeu

Fabra

c/o

Dr.

Aiguader

Barcelona

Biomedical

Research

Park

Building

Barcelona

Catalonia

Spain

-LCB-

Present

addresses

Department

of

Genome

Sciences

University

of

Washington

School

of

Medicine

Seattle

Washington

USA

-LRB-

G.M.C.

-RRB-

Department

of

Biological

Statistics

Computational

Biology

Cornell

University

Ithaca

New

York

USA

-LRB-

A.S.

-RRB-

Faculty

of

Life

Sciences

University

of

Manchester

Michael

Smith

Building

Oxford

Road

Manchester

PT

UK

-LRB-

S.W.

-RRB-

SwitchGear

Genomics

Adams

Drive

Suite

Menlo

Park

California

USA

-LRB-

N.D.T.

S.F.A.

-RRB-

Nature

Publishing

Group

encode human evolution function transcripts genomic