a comparative encyclopedia of dna elements in the mouse genome

ARTICLE OPENdoi:10.1038/nature13992

A comparative encyclopedia of DNAelements in the mouse genomeA list of authors and their affiliations appears at the end of the paper

The laboratorymouse shares themajority of its protein-coding geneswithhumans,making it the premiermodel organismin biomedical research, yet the two mammals differ in significant ways. To gain greater insights into both shared andspecies-specific transcriptional and cellular regulatory programs in the mouse, the Mouse ENCODE Consortium hasmapped transcription, DNase I hypersensitivity, transcription factor binding, chromatin modifications and replicationdomains throughout the mouse genome in diverse cell and tissue types. By comparing with the human genome, we notonly confirm substantial conservation in the newly annotated potential functional sequences, but also find a large degreeof divergence of sequences involved in transcriptional regulation, chromatin state and higher order chromatin organi-zation. Our results illuminate the wide range of evolutionary forces acting on genes and their regulatory regions, andprovide a general resource for research into mammalian biology and mechanisms of human diseases.

Despite the widespread use of mouse models in biomedical research1,the genetic and genomic differences betweenmice and humans remainto be fully characterized. At the sequence level, the two species havediverged substantially: approximately onehalf of humangenomicDNAcan be aligned to mouse genomic DNA, and only a small fraction (38%) is estimated to be under purifying selection across mammals2. Atthe cellular level, a systematic comparison is still lacking. Recent studieshave revealed divergent DNA binding patterns for a limited number oftranscription factors across multiple related mammals38, suggestingpotentially wide-ranging differences in cellular functions and regula-tory mechanisms9,10. To fully understand how DNA sequences con-tribute to the uniquemolecular and cellular traits inmouse, it is crucialto have a comprehensive catalogue of the genes and non-coding func-tional sequences in the mouse genome.

Advances in DNA sequencing technologies have led to the develop-mentofRNA-seq (RNAsequencing),DNase-seq (DNase Ihypersensitivesites sequencing),ChIP-seq (chromatin immunoprecipitation followedbyDNAsequencing), andothermethods that allow rapid andgenome-wide analysis of transcription, replication, chromatin accessibility, chro-matin modifications and transcription factor binding in cells11. Usingthese large-scale approaches, the ENCODE consortium has produceda catalogue of potential functional elements in the human genome12.Notably, 62% of the human genome is transcribed in one or more celltypes13, and 20% of humanDNA is associated with biochemical signa-tures typical of functional elements, including transcription factorbind-ing, chromatin modification and DNase hypersensitivity. The resultssupport the notion that nucleotides outside themammalian-conservedgenomic regions could contribute to species-specific traits6,12,14.

We have applied the same high-throughput approaches to over 100mouse cell types and tissues15, producing a coordinated group of datasets for annotating the mouse genome. Integrative analyses of thesedata sets uncoveredwidespread transcriptional activities, dynamic geneexpressionandchromatinmodificationpatterns, abundant cis-regulatoryelements, and remarkably stable chromosome domains in the mousegenome.Thegenerationof thesedata sets also allowedanunprecedentedlevel of comparisonof genomic features ofmouseandhuman.Describedin the current manuscript and companion works, these comparisonsrevealed both conserved sequence features andwidespread divergencein transcription and regulation. Some of the key findings are:

. Although much conservation exists, the expression profiles of manymouse genes involved in distinct biological pathways show consider-able divergence from their human orthologues.

. A large portion of the cis-regulatory landscape has diverged betweenmouse andhuman, although themagnitudeof regulatoryDNAdiver-gence varies widely between different classes of elements active indifferent tissue contexts.

. Mouse and human transcription factor networks are substantiallymore conserved than cis-regulatory DNA.

. Species-specific candidate regulatory sequences are significantlyenriched for particular classes of repetitive DNA elements.

. Chromatin state landscape in a cell lineage is relatively stable in bothhuman and mouse.

. Chromatin domains, interrogated through genome-wide analysis ofDNA replication timing, are developmentally stable and evolution-arily conserved.

Overview of data production and initial processingTo annotate potential functional sequences in the mouse genome, weusedChIP-seq, RNA-seq andDNase-seq to profile transcription factorbinding, chromatinmodification, transcriptome and chromatin acces-sibility in a collection of 123mouse cell types and primary tissues (Fig. 1a,SupplementaryTables 13).Additionally, to interrogate large-scale chro-matin organization across different cell types, we also used amicroarray-based technique togenerate replication-timingprofiles in18mouse tissuesand cell types (SupplementaryTable 3)16. Altogether,weproduced over1,000 data sets. The list of the data sets and all the supporting materialfor thismanuscript are also available at website http://mouseencode.org.Belowwebriefly outline the experimental approach and initial datapro-cessing for each class of sequence features.

RNA transcriptomeTo comprehensively identify the genic regions that produce transcriptsin themouse genome, we performedRNA-seq experiments in 69 differ-entmouse tissues and cell typeswith twobiological replicates each (Sup-plementaryTable 3, Supplementary Information) anduncovered436,410contigs (Supplementary Table 4). Confirming previous reports13,17,18

and similar to the human genome, the mouse genome is pervasivelytranscribed (Fig. 1b), with 46% capable of producing polyadenylated

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messenger RNAs (mRNA). By comparison, 39% of the human gen-ome is devoted to making mRNAs. In both species, the vast majority(8793%) of exonic nucleotideswere detected as transcribed, confirm-ing the sensitivity of the approach. However, a higher percentage ofintronic sequences were detected as transcribed in themouse, and thismight be owing to a greater sequencing depth and broader spectrumofbiological samples analysed in mouse (Fig. 1b).

Candidate cis-regulatory sequencesTo identify potential cis-regulatory regions in the mouse genome, weused three complementary approaches that involvedmapping of chro-matin accessibility, specific transcription factor occupancy sites andhistonemodification patterns. All of these approaches have previouslybeen shown to uncover cis regulatory elements with high accuracy andsensitivity19,20.

BymappingDNase I hypersensitive sites (DHSs) in55mouse cell andtissue types21, we identified a combined total of ,1.5 million distinctDHSs at a false discovery rate (FDR) of 1% (Supplementary Table 5)22.Genomic footprinting analysis in a subset (25) of these cell types fur-ther delineated 8.9 million distinct transcription factor footprints.De novo derivation of a cis-regulatory lexicon from mouse transcrip-tion factor footprints revealed a recognition repertoire nearly identicalwith that of the human, including both known and novel recognitionmotifs25.

We used ChIP-seq to determine the binding sites for a total of 37transcription factors in various subsets of 33 cell/tissue types. Of these37 transcription factors, 24were also extensivelymapped in themurine

andhumanerythroid cellmodels (MELandK562) andB-lymphoid celllines (CH12 and GM12878)23. In total we defined 2,107,950 discreteChIP-seqpeaks, representing differential cell/tissue occupancy patternsof 280,396 distinct transcription factor binding sites (SupplementaryMethods and Supplementary Table 6).

We also performed ChIP-seq for as many as nine histone H3mod-ifications (H3K4me1, H3K4me2, H3K4me3, H3K9me3, H3K27ac,H3K27me3,H3K36me3,H3K79me2andH3K79me3) inup to23mousetissues and cell types permark.We applied a supervisedmachine learn-ing technique, random-forest based enhancerprediction fromchromatinstate (RFECS), to three histone modifications (H3K4me1, H3K4me3andH3K27ac)24, identifying a total of 82,853 candidate promoters and291,200 candidate enhancers in the mouse genome (SupplementaryTables 7 and 8). To functionally validate the predictions, we randomlyselected 76 candidate promoter elements (average size 1,000 bp, Sup-plementary Table 9) and 183 candidate enhancer elements (averagesize1,000 bp, SupplementaryTable10). For candidatepromoterelements,we cloned these previously unannotated sequences into reporter con-structs, and performed luciferase reporter assays via transient transfec-tion in pertinentmouse cell lines . For candidate enhancer elements,weperformed functional validation assayusing a high throughputmethod(see SupplementaryMethods). Overall, 66/76 (87%) candidate promo-ters and 129/183 (70.5%) candidate enhancers showed significant activityin these assays, compared to 2/30 randomly selected negative controls(Supplementary Fig. 1c).

Collectively, our studies assignedpotential regulatory function to12.6%of the mouse genome (Fig. 1c).

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Figure 1 | Overview of the mouse ENCODE data sets. a, A genome browsersnapshot shows the primary data and annotated sequence features in themouseCH12 cells (Methods). b, Chart shows that much of the human and mousegenomes is transcribed in one or more cell and tissue samples. c, A bar chartshows the percentages of the mouse genome annotated as various types ofcis-regulatory elements (Methods). DHS, DNase hypersensitive sites; TF,transcription factor. d, Pie charts show the fraction of the entire genome that iscovered by each of the seven states in the mouse embryonic stem cells (mESC)

and adult heart. e, Charts showing the number of replication timing (RT)boundaries in specific mouse and human cell types, and the total number ofboundaries from all cell types combined. ESC, embryonic stem cell; endomeso,endomesoderm; NPC, neural precursor; GM06990, B lymphocyte; HeLa-S3,cervical carcinoma; IMR90, fetal lung fibroblast; EPL, early primitiveectoderm-like cell; EBM6/EpiSC, epiblast stem cell; piPSC, partially inducedpluripotent stem cell; MEF, mouse embryonic fibroblast; MEL, murineerythroleukemia; CH12, B-cell lymphoma.

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Transcription factor networksWeexplored the transcription factor networks and combinatorial tran-scription factor bindingpatterns in themouse samples in twocompanionpapers, and compared these networks to regulatory circuitry modelsgenerated for the human genome23,25. Fromgenomic footprints, we con-structed transcription-factor-to-transcription-factor cross-regulatorynetwork in each of 25 cell/tissue types for a total of,500 transcriptionfactors with known recognition sequences. Analyses of these networksrevealed regulatory relationships between transcription factor genes thatare strongly preserved in human and mouse, in spite of the extensiveplasticity of the cis-regulatory landscape (detailed below).Whereas only22%of transcription factor footprints are conserved, nearly 50%of cross-regulatory connections between mouse transcription factors are con-served inhuman through the innovationofnovel binding sites.Moreover,analysis of networkmotifs shows that larger-scale architectural features ofmouse andhuman transcription factor networks are strikingly similar25.

Chromatin statesWe produced integrative maps of chromatin states in 15 mouse tissueand cell types and six human cell lines (SupplementaryTable 11), usinga hiddenMarkovmodel (chromHMM)26,27 that allowed us to segmentthe genome in each cell type into seven distinct combination of chro-matin modification marks (or chromatin states). One state is charac-terized by the absence of any chromatin marks, while every other statefeatures either predominantly onemodification or a combination of twomodifications (Extended Data Table 1, Supplementary Information).Theportion of the genome in each chromatin state variedwith cell type(Fig. 1d, Supplementary Fig. 2). Similar proportions of the genome arefound in the active states in each cell type, for both mouse and human.Interestingly, excluding the unmarked state, the fractionof eachgenomethat is in theH3K27me3-dominated, transcriptionally repressed state isthemost variable, suggestingaprofoundrole of transcriptional repressionin shaping the cis-regulatory landscape duringmammalian development.

Replication domainsReplication-timing, the temporal order inwhichmegabase-sized geno-mic regions replicate during S-phase, is linked to the spatial organizationof chromatin in the nucleus2831, serving as a useful proxy for trackingdifferences in genome architecture between cell types32,33. Since differenttypes of chromatin are assembled at different times during the S phase34,changes in replication timingduringdifferentiation could elicit changesin chromatin structure across large domains. We obtained 36 mouseand 31 human replication-timing profiles covering 11 and 9 distinctstagesofdevelopment, respectively (SupplementaryTable12).Wedefinedreplication boundaries as the sites where replication profiles changeslope fromsynchronously replicating segments (discussed later).A totalof 64,535 and 50,194 boundaries identified across allmouse and humandata sets, respectively, weremapped to 4,322 and 4,675 positions, witheach cell type displaying replication-timing transitions at 5080% ofthese positions (Fig. 1e).

Annotation of orthologous coding and non-coding genesTo facilitate a systematic comparison of the transcriptome, cis-regulatoryelements and chromatin landscape between the human and mousegenomes, we built a high-quality set of humanmouse orthologues ofprotein coding and non-coding genes35. The list of protein-coding orth-ologues, basedonphylogenetic reconstruction, contains a total of 15,736one-to-one and a smaller set of one-to-many andmany-to-many ortho-logue pairs (Supplementary Tables 1315). We also inferred ortholo-gous relationships among short non-codingRNAgenes using a similarphylogenetic approach.Weestablishedone-to-onehumanmouseorth-ologues for 151,257 internal exon pairs (Supplementary Table 16) and204,887 intronpairs (SupplementaryTable17), andpredicted2,717 (3,446)novel human (respectively, mouse) exons (Supplementary Table 18).Additionally, we mapped the 17,547 human long non-coding RNA(lncRNA) transcripts annotated inGencodev10onto themouse genome.

We found 2,327 (13.26%) human lncRNA transcripts (correspondingto 1,679, or 15.48%, of the lncRNAgenes) homologous to 5,067putativemouse transcripts (corresponding to 3,887putativegenes) (SupplementaryFig. 3, SupplementaryTable 19).Consistentwithprevious observations,only a small fractionof lncRNAsare constrainedat theprimary sequencelevel, with rapid evolutionary turnover36. Other comparisons of humanandmouse transcriptomes, covering areas including pre-mRNA splic-ing, antisense and intergenicRNA transcription, are detailed in an asso-ciated paper37.

Divergent and conserved gene expression patternsPrevious studies have revealed remarkable examples of species-specificgene expression patterns that underlie phenotypic changes duringevolution3842. In these cases changes inexpressionof a single genebetweenclosely related species led toadaptive changes.However, it is not clearhowextensive the changes in expression patterns are betweenmoredistantlyrelated species, suchasmouse andhuman,with some studies emphasiz-ing similarities in transcriptome patterns of orthologous tissues4345 andothers emphasizing substantial interspecies differences46. Our initialanalyses revealed that gene expression patterns tended to clustermoreby species rather than by tissue (Fig. 2a). To resolve the sets of genescontributing to different components in the clustering, we employedvariancedecomposition (seeMethods) to estimate, for eachorthologoushumanmouse gene pair, the proportion of the variance in expressionthat is contributedby tissue andby species (Fig. 2b).This analysis revealedthe setsof geneswhose expressionvariesmoreacross tissues thanbetweenspecies, and those whose expression varies more between species thanacross tissues.As expected, the clusteringof theRNA-seq samples is dom-inated either by species or tissues, depending on the gene set employed(Extended Data Fig. 1a, b). Furthermore, removal of the,4,800 genesthatdrive the species-specific clustering (see ref. 47, SupplementaryFig. 1d

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therein) or normalizationmethods that reduce the species effects revealtissue-specific patternsof expression in the same samples (ExtendedDataFig. 1c). Categorizing orthologous gene pairs into these groups shouldenablemore informative translation of research results betweenmouseand human. In particular, for gene pairs whose variance in expressionis largest between tissues (and less between species), mouse should be aparticularly informative model for human biology. In contrast, inter-pretationof studies involving geneswhose variance in expression is largerbetween species needs to take into account the species variation. Therelative contributions of species-specific and tissue-specific factors toeach genes expression are further explored in two associated papers37,47.

To further identify genes with conserved expression patterns andthose that have diverged between humans and mice, we developed anovel method, referred to as neighbourhood analysis of conserved co-expression (NACC), to compare the transcriptional programs of orth-ologous genes in away that did not require preciselymatched cell lines,tissues or developmental stages, as long as a sufficiently diverse panel ofsamples is used in each species (Supplementary Methods). Observingthat the orthologues of most sets of co-expressed genes in one speciesremained significantly correlated across samples in the other species,we use themean of these small correlated sets of orthologous genes as areference expressionpattern in theother species.We computeEuclideandistance to the reference pattern in the multi-dimensional tissue/geneexpression space as a relative measure of conservation of expression ofeach gene. Specifically, for each human gene (the test gene), we definedthemost similarly expressed set of genes (n5 20) across all the humansamples as that genes co-expressionneighbourhood.We thenquantifythe average distance between the transcript levels of the mouse ortho-logue of the test gene and the transcript levels of eachmouse orthologueof the neighbourhood genes across the mouse samples. We then invertthe analysis, and choose amouse test gene anddefine a similar gene co-expression neighbourhood in the mouse samples, and calculate theaverage distance between the expression of orthologues of the test geneand expression of neighbourhood genes across the human samples. Theaverage change in the human-to-mouse andmouse-to-human distances,referred herein as a NACC score, is a symmetricmeasure of the degreeof conservation of co-expression for each gene. The distribution of thisquantity for each gene is shown in Fig. 2c, showing that genes in onespecies show a strong tendency to be co-expressed with orthologues ofsimilarly expressed genes in the other species compared to randomgenes(also see Supplementary Information).Wequantify thedegree towhicha specific biological process diverges betweenhuman andmouse as theaverage NACC scores of genes in each gene ontology category by cal-culating a z-score using random sampling of equal size sets of genes.Figure 2d shows that genes coding for proteins in the nuclear and intra-cellularorganelle compartments, and involved inRNAprocessing,nucleicacid metabolic processes, chromatin organization and other intracel-lularmetabolic processes, tend to exhibitmore similar gene expressionpatterns betweenhuman andmouse.On the other hand, genes involvedin extracellularmatrix, cellular adhesion, signalling receptors, immuneresponses and other cell-membrane-related processes aremore diverged(for a complete list of all GO categories and conservation analysis, seeSupplementary Table 21). As a control, when we applied the NACCanalysis to two different replicates of RNA-seq data sets from the samespecies, nodifference inbiological processes canbedetected (Supplemen-tary Fig. 5).

Several lines of evidence indicate that NACC is a sensitive androbust method to detect conserved as well as diverged gene expressionpatterns from a panel of imperfectlymatched tissue samples. First, whenwe applied NACC to a set of simulated data sets, we found that NACCis robust for the diversity and conservationof themousehuman samplepanel (in Supplementary Fig. 6). Second, we randomly sampled sub-sets of the full panel of samples anddemonstrated that the categories ofhumanmouse divergence shown in Fig. 2d are robust to the particu-lar sets of samples we selected (Supplementary Fig. 7). Third, when werepeatedNACCon a limited collection ofmore closelymatched tissues

and primary cell types (see SupplementaryMethods), the biological pro-cesses detected as conserved and species-specific in the larger panel ofmismatchedhumanmouse samples are largely recapitulated, althoughsome pathways are detected with somewhat less significance, probablyowing to the smaller number of data sets used (Supplementary Fig. 8).In summary, the NACC results support and extend the principal com-ponent analysis, showing that while large differences between mouseand human transcriptome profiles can be observed (revealed in PC1),genes involved indistinct cellular pathways or functional groups exhibitdifferent degreesof conservationof expressionpatternsbetweenhumanandmouse,with some strongly preserved andothers changingmarkedly.

Prevalent species-specific regulatory sequences alongwith a core of conserved regulatory sequencesTo better understand how divergence of cis-regulatory sequences islinked to the range of conservation patterns detected in comparisonsof gene expressionprogramsbetween species,we examined evolutionarypatterns in our predicted regulatory sequences. Previous studies haveidentifiedawide rangeof evolutionarypatternsandrates for cis-regulatoryregions inmammals5,8, but there are still questions regarding the over-all degree of similarity and divergence between the cis-regulatory land-scapes in the mouse and human. The variety of assays and breadth oftissue and cell-type coverage in themouseENCODEdata thereforepro-vide an opportunity to address this problem more comprehensively.

We first determined sequencehomologyof the predicted cis-elementsin themouse and human genomes.We established one-to-one and one-to-manymapping of human andmouse bases derived from reciprocalchained blastz alignments48 and identified conserved cis-regulatorysequences49. This analysis showed that 79.3%of chromatin-based enhan-cer predictions, 79.6% of chromatin-based promoter predictions, 67.1%of the DHS, and 66.7% of the transcription factor binding sites in themouse genome have homologues in the human genome with at least10%overlappingnucleotides, while by randomchance one expects 51.2%,52.3%,44.3%and39.3%, respectively (Fig. 3a, Supplementary Informationfor details). With a more stringent cutoff that requires 50% alignmentof nucleotides, we found that 56.4%of the enhancer predictions, 62.4%of promoter predictions, 61.5% of DHS, and 53.3% of the transcrip-tion factor binding sites have homologues, comparedwith an expectedfrequency of 34%, 33.8%, 33.6% and 33.7% by random chance (Sup-plementary Fig. 9). The candidatemouse regulatory regionswith humanhomologues are listed in Supplementary Tables 2225. Thus, betweenhalf and two-thirds of candidate regulatory regions demonstrate a sig-nificant enrichment in sequence conservationbetweenhumanandmouse.Theremaininghalf toone-thirdhaveno identifiableorthologous sequence.

The candidate regulatory regions in mouse with no orthologue inhumancould arise either because theywere generatedby lineage-specificevents, such as transposition, orbecause the orthologue in the other spe-cieswas lost. Species-specific cis-regulatory sequences have been reportedbefore3,14, but the fractionof regulatory sequences in this category remainsdebatable andmay vary with different roles in regulation.We find that15% (12,387 out of 82,853) of candidate mouse promoters and 16.6%(48,245 out of 291,200) of candidate enhancers (both predicted by pat-terns of histonemodifications) haveno sequence orthologue in humans(SupplementaryTables 26, 28, for details please refer to SupplementaryMethods section). However, the question remains as to whether thesespecies-specific elements are truly functional elements or simply corre-spond to false-positive predictions due to measurement errors or bio-logical noise. Supporting the function of mouse-specific cis elements,18outof 20 randomly selected candidatemouse-specific promoters testedpositive using reporter assays inmouse embryonic stem cells, where theywere initially identified (Fig. 3b, Supplementary Table 27). Further, whenthese 18mouse-specific promoters were tested using reporter assays inthe human embryonic stem cells, all of them also exhibited significantpromoter activities (Extended Data Fig. 2a, Supplementary Table 27),indicating that themajority of candidatemouse-specific promoters areindeed functional sequences,which are either gained in themouse lineage

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or lost in thehuman lineage. Similarly, amajority of the candidatemouse-specific enhancers discovered in embryonic stem cells are also likely bonafide cis elements, as 70.2% (26 out of 37) candidate enhancers randomlyselected from this group were found to exhibit enhancer activities inreporter assays (Fig. 3b, Supplementary Table 29). Like the candidatemouse-specific promoters, 61.5% (16out of 26) of the candidatemouse-specific enhancers also show enhancer activities in human embryonicstem cells (Extended Data Fig. 2a).

We next testedwhether the rapidly diverged cis-regulatory elementswould correspond to the same cellular pathways shown to be less con-served by the NACC analysis of gene expression programs. Indeed,gene ontology analysis revealed that the mouse-specific regulatory ele-ments are significantly enriched near genes involved in immune func-tion (Fig. 3c), in agreement with the divergent transcription patternsfor these genes reported earlier and a previous report based on a smallernumber of primate-specific candidate regulatory regions50. This sug-gests that regulation of genes involved in immune function tends to bespecies-specific50, just as the protein-coding sequences coding for immu-nity, pheromones and other environmental genes are frequent targetsfor adaptive selection in each species2,51. The target genes for mouse-specific transcription factor binding sites (Supplementary Table 30) areenriched inmolecular functions such as histone acetyltransferase activityand high-density lipoprotein particle receptor activity, in addition toimmune function (IgG binding).

Wenext investigated themechanisms generatingmouse-specific cis-regulatory sequences: loss in human, gain inmouse, or both. 89% (42,947out of 48,245) of mouse-specific enhancers and 85% (10,535 out of12,387) of mouse-specific promoters overlap with at least one class ofrepeat elements (compared to 78% by random chance). Confirmingearlier reports5254, we found thatmouse-specific candidate promotersand enhancers are significantly enriched for repetitiveDNAsequences,

with several classes of repeat DNA highly represented (Fig. 3d and Ex-tendedData Fig. 2b). Furthermore,mouse-specific transcription factorbinding sites are highly enriched inmobile elements such as short inter-spersed elements (SINEs) and long terminal repeats (LTRs)55.

The 50% to 60% of candidate regulatory regions with sequencesconserved between mouse and human are a mixture of (1) sequenceswhose function has been preserved via strong constraint since thesespecies diverged, (2) sequences that have been co-opted (or exapted)to perform different functions in the other species, and (3) sequenceswhose orthologue in the other species no longer has a discernable func-tion, but divergence by evolutionary drift has not been sufficient to pre-vent sequence alignmentbetweenmouse andhuman. Several companionpapers delve deeply into these issues22,23,49. In particular, ref. 23 shows thatthe conservation of transcription factor binding at orthologous positions(falling in category (1)) is associated with pleiotropic roles of enhancers,as evidenced by activity in multiple tissues. References 22,49 describethe exaptation of conserved regulatory sequences for other functions.

We surveyed the conservation of function in the subset of mousecandidate cis elements that have sequence counterparts in the humangenome.Of the 51,661 chromatin-based promoter predictions that havehuman orthologues, 44% (22,655) of them are still predicted as promo-ters in human on the basis of the same analysis of histone modifications(Supplementary Table 31, see SupplementaryMethods for details). Ofthe 164,428 chromatin-based enhancer predictions that have humanorthologues, 40% (64,962) of them are predicted as an enhancer inhuman (Supplementary Table 32). The remaining 5660%of candidatemouse regulatory regions with a human orthologue fall into category(2) or (3) (see earlier), that is, the orthologous sequence in human eitherperforms a different function or does notmaintain a detectable function.

One caveat of the above observation is that the tissues or cell sam-ples used in the survey were not perfectly matched. To better examinethe conservation of biochemical activities among these predicted cis-regulatory elements with orthologues between mouse and human, weanalysed the chromatin modifications at the promoter or enhancerpredictions in a broad set of 23 mouse tissue and cell types with theneighbourhood co-expression association analysis (NACC) methoddescribed above. Instead of gene expression levels, we selected the his-tone modification H3K27ac as an indicator of promoter or enhanceractivity as previously reported56. As shown inFig. 4a, the promoter pre-dictions (blue) show a significantly higher correlation in the level ofH3K27ac in human and mouse than the random controls (red). Simi-larly,most chromatin-based enhancer predictions in themouse genomeexhibit conserved chromatinmodification patterns in the human, albeitto a lesser degree than thepromoters (Fig. 4b).NACCanalysis onDNase-seq signal resulted in very similar distributions of conserved chromatinaccessibility patterns at promoters (Fig. 4c) and enhancers (Fig. 4d). Thusmany sequence-conserved candidate cis-regulatory elements appearedto have conserved patterns of activities in mice and humans.

Taken together, these analyses show that themammalian cis-regulatorylandscapes in thehuman andmouse genomes are substantially different,driven primarily by gain or loss of sequence elements during evolution.These species-specific candidate regulatory elements are enriched neargenes involved in stress response, immunity and certainmetabolic pro-cesses, and contain elevated levels of repeated DNA elements. On theother hand, a core set of candidate regulatory sequences are conservedand display similar activity profiles in humans and mice.

Chromatinstate landscapereflects tissueandcell identitiesWe examined gene-centred chromatin state maps in the mouse andhuman cell types (see SupplementaryMethods) (Fig. 5a, SupplementaryFig. 10). In all cell types, the low-expressed genes were almost uniformlyin chromatin states with the repressiveH3K27me3mark or in the stateunmarked by these histonemodifications. In contrast, expressed genesshowed the canonical pattern ofH3K4me3 at the transcription start sitesurrounded byH3K4me1, followed byH3K36me3-dominated states inthe remainder of the transcription unit. A similar pattern was seen for

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Figure 3 | Comparative analysis of the cis-elements predicted in the humanand mouse genome. a, Chart shows the fractions of the predicted mousecis-regulatory elements with homologous sequences in the human genome(Methods). TFBS, transcription factor binding site. b, A bar chart shows thefraction of the DNA fragments tested positive in the reporter assays performedeither using mouse embryonic stem cells (mESCs) or mouse embryonicfibroblasts (MEF). c, A chart shows the gene ontology (GO) categories enrichednear the predicted mouse-specific enhancers. d, A bar chart shows thepercentage of the predicted mouse-specific enhancers containing varioussubclasses of LTR and SINE elements. As control, the predicted mouse ciselements with homologous sequences in the human genome or randomgenomic regions are included.

ARTICLE RESEARCH



all the active genes, regardless of the level of expression; the only excep-tion was a tendency for the H3K4me3 to spread further into the tran-scription unit for the most highly expressed genes. The same binaryrelationship between chromatin statemaps and expression levels of geneswas observed in mouse and human cell types (Supplementary Fig. 10).

For bothmouse and human cells, themajority of the genomewas inthe unmarked state in each cell type, consistent with previous obser-vations inDrosophila57 and human cell lines12 (Supplementary Fig. 2).About 55% of the mouse genome was in an unmarked state in all the15 cell types examined, while 65% is unmarked in all six human celltypes. For genes that were in the unmarked state inmouse, their ortho-logues in human also tended to be in the unmarked state, and vice versa,leading to a positive correlation for the amount of geneneighbourhoodsin unmarked states (Supplementary Fig. 11). Strong correlations werealso observed in profiles of other chromatin marks averaged over celllines and tissues37. The genes in the unmarked zones were depleted oftranscribed nucleotides relative to the number expected based on frac-tion of the genome included, and the levels of the transcripts mapped

there were lower than those seen in the active chromatin states (Sup-plementary Fig. 12).

Previous studies revealed limited changes of the chromatin statesin lineage-restricted cells as they undergo large-scale changes in geneexpression during maturation5860. The chromatin state maps recapi-tulated this result, showing very similar patterns of chromatin modi-fication in a cell line model for proliferating erythroid progenitor cells(G1E) and inmaturing erythroblasts (G1E-ER4 cells treated with oes-tradiol) across geneswhose expression level changed significantlyduringmaturation (Fig. 5b, SupplementaryFig. 10b). This limited change raisedthe possibility that the chromatin landscape, once established duringlineage commitment, dictates a permissive (or restrictive) environmentfor the gene regulatory programs in each cell lineage60, and that thechromatin states may differ between cell lineages. We tested this byexamining the chromatin state maps for genes that were differentiallyexpressed between haematopoietic cell lineages (erythroblasts versusmegakaryocytes), and we found marked differences between the twocell types (Fig. 5c and Supplementary Fig. 10b). Genes expressed at ahigher level in megakaryocytes than in erythroblasts were all in activechromatin states in megakaryocytes, but many were in inactive chro-matin states in erythroblasts (Fig. 5c). In the converse situation, genesexpressedat ahigher level in erythroblasts than inmegakaryocytes showedmore inactive states in the cells inwhich theywere repressed (Supplemen-tary Fig. 10b). These greater differences in chromatin states correlatingwith differential expression of genes between, but not within, cell line-ages support themodel that chromatin states are establishedduring theprocess of lineage commitment. The clustering of cell types together bylineage based on chromatin state maps (Supplementary Fig. 10c) alsosupports the model that the landscape of active and repressed chro-matin is established no later than lineage commitment, and that thislandscape is a defining feature of each cell type. Greater differences inchromatin states correlating with differences in gene expression werealso observed when comparing average chromatin profiles in humanand mouse37.

Mouse chromatin states inform interpretation of humandisease-associated sequence variantsTo investigate whether the mouse chromatin states were informativeon sequence variants linked to human diseases by genome-wide asso-ciation studies (GWAS), we combined the chromatin state segmenta-tions of the fifteen mouse samples into a refined segmentation, whichwe used to train a self-organizingmap (SOM)61 on four histonemodi-fication ChIP-seq data sets (H3K4me3, H3K4me1, H3K36me3 andH3K27me3) for each mouse sample. We mapped 4,265 single nucleo-tide polymorphisms (SNPs) from the human GWAS studies uniquelyonto the mouse genome and scored these SNPs onto the trained SOMtodeterminewhether SNP subsetswere enriched in specific areas of the

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shown by white dots. TSS, transcription start site. b, c, Distribution ofchromatin states in humanmouse one-to-one orthologues that aredifferentially expressed genes between erythroid progenitor and erythroblastsmodels (b) and between erythroblast and megakaryocyte (c).

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RESEARCH ARTICLE



map. As shown in Fig. 6a, the highest enriched H3K4me1 unit in thekidney contains five GWAS hits (P value, 3.953 10214) on differentchromosomes related to blood characteristics such as platelet counts(Fig. 6a, ExtendedData Table 2a). Similarly, the second highest enrichedunit in liver H3K36me3 contained six GWAS hits (P value, 7.54310231) related to cholesterol and alcohol dependence out of twelve inthat unit (Fig. 6b, ExtendedData Table 2b). In contrast, one of the highestunits in brainH3K27me3has fiveGWAShits (P value, 4.933 10233)on different chromosomes associated with brain disorders/responseto addictive substances (Fig. 6c, Extended Data Table 2c). This unit isdifferent from the other examples in that it is enriched for H3K27me3signal in multiple tissues, with brain being the highest. 801 out of the1,350 units of the map showed statistical enrichment of SNPs of 0.05after HolmBonferroni correction for multiple hypothesis testing,55% of which (accounting for 1,750 GWAS hits) had signal for at leastone histone mark that ranked within the top 100 units on the map(Fig. 6d). The best histone marks for enriched GWAS units were pri-marilyH3K4me1 (23%),H3K36me3 (18%) andH3K27me3 (12%),withH3K4me3 accounting for less than 2% of the remainder. Together theseresults suggest that the chromatin state maps can be used to identifypotential sites for functional characterization inmouse for humanGWAShits. Indeed, ref. 23 shows that conserved DNA segments bound byorthologous transcription factors in human and mouse are enrichedfor trait-associated SNPs mapped by GWAS.

Large-scale chromatin domains are developmentallystable and evolutionarily conservedWe mapped the positions of early and late replication timing bound-aries in each of 36mouse and 31 human profiles (Fig. 7a). Significantlyclustered boundary positions (above the 95th percentile of re-sampledpositions) were identified and peaks in boundary density were alignedbetween cell types using a commonheuristic (ExtendedData Fig. 3a, b,Supplementary Fig. 13). After alignment, consensus boundaries werefurther classified by orientation and amount of replication timing sepa-ration, resulting in amore stringent filtering of boundaries (Supplemen-taryFigs 14, 15).Overall,we found that88%ofboundarypositions (versus20% expected for random alignment; Fisher exact test P, 23 10216)alignedposition and orientation between twoormore cell types in bothmouse andhuman (that is, 12%werecell-type-specific, Fig. 7b, ExtendedData Fig. 3). Pair-wise comparisons of boundarieswere consistentwith

developmental similarity between cell types (Supplementary Fig. 16).The earliest and latest replicating boundaries weremost well preservedbetween cell types, while those of mid-S replicating boundaries werehighly variable (Extended Data Fig. 3e, f).

Interestingly, the greatest number of boundaries was detected inembryonic stemcells inboth species,with significant reduction inbound-ary numbers during differentiation (Supplementary Fig. 16), consistentwith consolidation of domains and by proxy large-scale chromatin orga-nization into larger constant timing regions during differentiation62.Given that over half of the mouse and human genomes exhibit signifi-cant replication timing changes during development16,63, these obser-vations support the model that developmental plasticity in replicationtiming is derived from differential regulation of replication timingwithin constant timing regions whose boundaries are preserved duringdevelopment.

Although conservation of replication timing between mouse andhuman has been reported29,30, the conservation of replicating timingboundaries has not been examined. We converted boundary coordi-nates6 100 kb across boundary positions between species, revealingsignificant overlap (Fig. 7c, d; P, 2.23 10216 by Fishers exact testrelative to a randomized boundary list). The level of conservation ofthe positions of boundaries improved from a median of 27% for cell-type-specific boundaries to 70% for boundaries preserved in nine ormore cell types (Fig. 7c), demonstrating that boundaries most highlypreserved during development were the most conserved across spe-cies. This was consistent with results for transcription (Fig. 2), as wellas the previous observation that suggests that an increased plasticity ofreplication timing during development is associatedwith increased plas-ticity of replication timing during evolution64. Together, these findingsidentify evolutionarily labile versus constrained domains of the mam-malian genome at the megabase scale.

Given the linkbetween replication and chromatin assembly,we com-pared replication timing and levels of other chromatin properties in

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Figure 6 | Human GWAS hits when mapped onto mouse genome areassociated with specific chromatin states. a, A self-organization map ofhistone modification H3K4me1 shows association between kidney H3K4me1state and specific GWAS hits associated with urate levels (Methods). b, Liver-specific H3K36me3 unit shows enrichment in GWAS hits related tocholesterol, alcohol dependence and triglyceride levels. c, Brain-specificH3K27me3 high unit shows enrichment in GWAS SNPs associated withneurological disorders. d, Characterization of every unit with statisticallysignificant GWAS enrichments in terms of highest histone modification signalin at least one sample. Units with no signal in top 100 map units for everyhistone modification are listed as none. RPKM, reads per kilobase per millionreads mapped.

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Figure 7 | Replication timing boundaries preserved among tissues areconserved in mice and humans. a, Depiction of a timing transition region(TTR) between the early and late replication domains. Early and lateboundaries are defined as slope changes at either end of TTRs. b, Boundariesconserved between species for matched mouse and human cell types as afunction of preservation among mouse cell types. c, Percentage of boundariesconserved between species (bar graph) and overall conservation of boundariesbetween comparable mouse and human cell types (CH12 versus GM06990,mESC versus hESC, mouse epiblast stem cells (mEpiSC) versus hESC) as afunction of preservation amongmouse cell types. d, A Venn diagram comparesthe replication timing boundaries identified in the mouse and human genome.

ARTICLE RESEARCH



200-kb windows across the genome (Supplementary Fig. 17). Featuresassociatedwith active enhancers (H3K4me1,H3K27ac,DNase I sensi-tivity) weremore closely correlated to replication timing than featuresassociated with active transcription (RNA polymerase II, H3K4me3,H3K36me3, H3K79me2). By contrast, the correlation of replicationtiming to repressive features, such asH3K9me3,was poor and cell-type-specific, consistent with prior results. A more stringent comparison ofdifferences in chromatin to differences in replication timing betweencell types (ExtendedData Fig. 3c, g, Supplementary Fig. 17) again revealedthatmarks of enhancers, including p300,H3K4me1 andH3K27ac, andDNase I sensitivity weremore strongly correlated to replication timingthan marks of active transcription.

ConclusionBy comparing the transcriptional activities, chromatin accessibilities,transcription factor binding, chromatin landscapes and replication tim-ing throughout themouse genome in awide spectrumof tissues and celltypes, we have made significant progress towards a comprehensivecatalogue of potential functional elements in the mouse genome. Thecatalogue described in the current study should provide a valuable ref-erence to guide researchers to formulate new hypotheses and developnew mouse models, in the same way as the recent human ENCODEstudies have impacted the research community12.

We providemultiple lines of evidence that gene expression and theirunderlying regulatoryprogramshave substantially diverged between thehuman and mouse lineages although a subset of core regulatory pro-grams are largely conserved. The divergence of regulatory programsbetweenmouse and human ismanifested not only in the gain or loss ofcis-regulatory sequences in the mouse genome, but also in the lack ofconservation in regulatory activities across different tissues and cell types.This finding is in line with previous observations of rapidly evolvingtranscription factor binding inmammals, flies and yeasts, and highlightsthe dynamic nature of gene regulatory programs in different species3,4,7,65.Furthermore, by comprehensivelydelineating thepotential cis-regulatoryelementswedemonstrated that specific groups of genes and regulatoryelements have undergone more rapid evolution than others. Of parti-cular interest is the finding that cis-regulatory sequencesnext to immune-system-related genes are more divergent. The finding of species-specificcis-elements near genes involved in immune function suggests rapidevolution of regulatory mechanisms related to the immune system.Indeed, previous studies have uncovered extensive differences in theimmune systems among differentmouse strains and between humansand mice66, ranging from relative makeup of the innate immune andadaptive immune cells66, to gene expressionpatterns in various immunecell types67, and transcriptional responses toacute inflammatory insults68,69.At least some of these differences may be attributed to distinct regu-latory mechanisms67, and our finding that many predicted mouse ciselements near geneswith immune function lack sequence conservationsupports the model that evolution of cis-regulatory sequences contri-butes to differences in the immune systems between humans andmice.More generally, our findings are consistent with the view that changesin transcriptional regulatory sequences are a source for phenotypic dif-ferences in species evolution.

Howcanspecies-specific gainsor lossof cis-regulatory elementsduringevolution be compatible with their putative regulatory function? Thefinding of different rates of divergence associated with regulatory pro-grams of distinct biological pathways suggests complex forces drivingthe evolution of the cis-regulatory landscape in mammals. We discov-ered that specific classes of endogenous retroviral elements are enrichedat the species-specific putative cis-regulatory elements, implicating trans-position of DNA as a potentialmechanism leading to divergence of generegulatory programs during evolution. Previous studies have shown thatendogenous retroviral elements can be transcribed in a tissue-specificmanner70,71, with a fraction of themderived from enhancers and neces-sary for transcriptionof genes involved inpluripotency72,73. Future studieswill be necessary to determine whether retroviral elements at or near

enhancers are generally involved in driving tissue-specific gene expres-sion programs in different mammalian species.

Despite thedivergence of the regulatory landscapebetweenmouse andhuman, thepatternof chromatin states (definedbyhistonemodifications)and the large-scale chromatin domains are highly similar between thetwo species. Half of the genome is well conserved in replication timing(and by proxy, chromatin interaction compartment) with the otherhalf highly plastic both between cell types and between species. It willbe interesting to investigate the significance of these conserved anddivergent classes ofDNA elements at different scales, bothwith regardto the forces driving evolution and for implications of the use of thelaboratory mouse as a model for human disease.

Online ContentMethods, along with any additional Extended Data display itemsandSourceData, are available in theonline versionof thepaper; referencesuniqueto these sections appear only in the online paper.

Received 3 February; accepted 24 October 2014.

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Supplementary Information is available in the online version of the paper.

Acknowledgements This work is funded by grants R01HG003991 (B.R.),1U54HG007004 (T.R.G.), 3RC2HG005602 (M.P.S.), GM083337 and GM085354(D.M.G.), F31CA165863 (B.D.P.), RC2HG005573andR01DK065806 (R.C.H.) from theNational Institutes ofHealth, andBIO2011-26205 from theSpanishPlanNacional andERC 294653 (to R.G.). J.V. is supported by a National Science Foundation GraduateResearch Fellowship under grant no. DGE-071824. K.B., M.P., J.H. and P.F.acknowledge the Wellcome Trust (grant number 095908), the NHGRI (grant numberU01HG004695) and the European Molecular Biology Laboratory. We thankG. Hon for helping the analysis of high-throughput enhancer validation. L.S. issupported by R01HD043997-09. S.L. was supported by grants F32HL110473and K99HL119617.

Author Contributions F.Y., Y.C., A.B., J.V., W.W., T.R., M.A.Beer, R.C.H., J.A.S., M.P.S., R.G.,T.R.G., D.M.G. and B.R. led the data analysis effort, R.Sandstrom, Z.M., C.D., B.D.P., Y.S.,R.C.H., J.A.S., M.P.S., R.G., T.R.G., D.M.G. and B.R. led the data production. F.Y., M.A.Beer,L.E., Y.C., P.C., A.B., A.K., S.L., Y.L., J.V., R.Sandstrom, R.E.T., E.R., E.H., A.P.R., S.N., R.H.,W.W., T.M., R.S.H., C.J., A.M., B.D.P., T.R., T.K., D.Lee, O.D., J.T., C.Z., A.D., D.D.P., S.D., P.P.,J.Lagarde, G.B., A.T., K.B., M.P., P.F. and J.H. analysed data. Y.S., D.M., L.P., Z.Y., S.K., Z.M.,T.K., G.E., J.Lian, S.M.W., R.K., M.A.Bender, S.L., Y.L., M.Z., R.B., M.T.G., A.J., S.V., K.L., D.B.,F.N., M.D., T.C., R.S.H., P.J.S., M.S.W., T.A.R., E.G., A.S., T.K., E.H., D.D., M.D.B., L.S., A.R., S.J.,R.Samstein, E.E.E., S.H.O., D.Levasseur, T.P., K.-H.C., A.S., C.D., P.T., W.W., C.A.K., C.S.M.,T.M., D.J., N.D., B.D.P., T.R., C.D., L.-H.S., M.F., J.D. produced data. F.Y., Y.C., W.W., T.R.,B.D.P., S.L., Y.L., C.J., C.D., A.D., A.B., D.D.P., S.D., C.N., A.M., J.A.S., M.P.S., R.G., T.R.G.,D.M.G., R.C.H., M.A.Beer., B.R. wrote the manuscript. The role of the NHGRI ProjectManagement Group (P.J.G., R.F.L., L.B.A., X.-Q.Z., M.J.P., E.A.F.) in the preparation ofthis paper was limited to coordination and scientific management of the MouseENCODE consortium.

Author Information Reprints and permissions information is available atwww.nature.com/reprints. The authors declare no competing financial interests.Readers are welcome to comment on the online version of the paper. Correspondenceand requests for materials should be addressed to B.R. ([email protected]); M.A.Beer([email protected]); R.C.H. ([email protected]); D.M.G. ([email protected]); T.R.G.([email protected]); R.G. ([email protected]); M.P.S. ([email protected])or J.A.S. ([email protected]).

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Feng Yue1,2{*, Yong Cheng3*, Alessandra Breschi4*, Jeff Vierstra5*, WeishengWu6{*,Tyrone Ryba7{*, Richard Sandstrom5*, Zhihai Ma3*, Carrie Davis8*, Benjamin D.Pope7*, Yin Shen1*, Dmitri D. Pervouchine4, Sarah Djebali4, Robert E. Thurman5,Rajinder Kaul5, EricRynes5, AnthonyKirilusha9, Georgi K.Marinov9, BrianA.Williams9,Diane Trout9, Henry Amrhein9, Katherine Fisher-Aylor9, Igor Antoshechkin9, GilbertoDeSalvo9, Lei-Hoon See8, Meagan Fastuca8 Jorg Drenkow8, Chris Zaleski8, AlexDobin8, Pablo Prieto4, Julien Lagarde4, Giovanni Bussotti4, Andrea Tanzer4,10, OlgertDenas11, Kanwei Li11, M. A. Bender12,13, Miaohua Zhang14, Rachel Byron14, Mark T.Groudine14,15, DavidMcCleary1, LongPham1, ZhenYe1, SamanthaKuan1, LeeEdsall1,Yi-Chieh Wu16, Matthew D. Rasmussen16, Mukul S. Bansal16, Manolis Kellis16,17,Cheryl A. Keller6, Christapher S. Morrissey6, Tejaswini Mishra6, Deepti Jain6, NergizDogan6, Robert S. Harris6, Philip Cayting3, Trupti Kawli3, Alan P. Boyle3{, GhiaEuskirchen3, Anshul Kundaje3, Shin Lin3, Yiing Lin3, Camden Jansen18, Venkat S.Malladi3, Melissa S. Cline19, Drew T. Erickson3, Vanessa M. Kirkup19, KatrinaLearned19, Cricket A. Sloan3, Kate R. Rosenbloom19, Beatriz Lacerda de Sousa20,Kathryn Beal21, Miguel Pignatelli21, Paul Flicek21, Jin Lian22, Tamer Kahveci23,Dongwon Lee24, W. James Kent19, Miguel Ramalho Santos20, Javier Herrero21,25,Cedric Notredame4, Audra Johnson5, Shinny Vong5, Kristen Lee5, Daniel Bates5,Fidencio Neri5, Morgan Diegel5, Theresa Canfield5, Peter J. Sabo5, Matthew S.Wilken26, Thomas A. Reh26, Erika Giste5, Anthony Shafer5, Tanya Kutyavin5, EricHaugen5,DouglasDunn5, AlexP. Reynolds5, ShaneNeph5, RichardHumbert5, R. ScottHansen5, Marella De Bruijn27, Licia Selleri28, Alexander Rudensky29, StevenJosefowicz29, Robert Samstein29, Evan E. Eichler5, Stuart H. Orkin30, DanaLevasseur31, Thalia Papayannopoulou32, Kai-Hsin Chang32, Arthur Skoultchi33,

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Srikanta Gosh33, Christine Disteche34, Piper Treuting35, Yanli Wang36, Mitchell J.Weiss37, Gerd A. Blobel38,39, Xiaoyi Cao40, Sheng Zhong40, Ting Wang41, Peter J.Good42, Rebecca F. Lowdon42{, Leslie B. Adams42{, Xiao-Qiao Zhou42, Michael J.Pazin42, Elise A. Feingold42, BarbaraWold9, James Taylor11, Ali Mortazavi18, ShermanM. Weissman22, John A. Stamatoyannopoulos5, Michael P. Snyder3, Roderic Guigo4,Thomas R. Gingeras8, David M. Gilbert7, Ross C. Hardison6, Michael A. Beer24*, BingRen1 & The Mouse ENCODE Consortium{

1Ludwig Institute for Cancer Research and University of California, San Diego School ofMedicine, 9500 Gilman Drive, La Jolla, California 92093, USA. 2Department ofBiochemistry and Molecular Biology, College of Medicine, The Pennsylvania StateUniversity, Hershey, Pennsylvania 17033, USA. 3Department of Genetics, StanfordUniversity, 300PasteurDrive,MC-5477Stanford, California94305,USA. 4Bioinformaticsand Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88,08003 Barcelona, Catalonia, Spain. 5Department of Genome Sciences, University ofWashington, Seattle, Washington 98195, USA. 6Center for Comparative Genomics andBioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University,University Park, Pennsylvania 16802, USA. 7Department of Biological Science, 319Stadium Drive, Florida State University, Tallahassee, Florida 32306-4295, USA.8Functional Genomics, Cold Spring Harbor Laboratory, Bungtown Road, Cold SpringHarbor, New York 11724, USA. 9Division of Biology, California Institute of Technology,Pasadena, California 91125, USA. 10Department of Theoretical Chemistry, Faculty ofChemistry, University of Vienna, Waehringerstrasse 17/3/303, A-1090 Vienna, Austria.11Departments of Biology andMathematics and Computer Science, Emory University, O.Wayne Rollins Research Center, 1510 Clifton Road NE, Atlanta, Georgia 30322, USA.12Department of Pediatrics, University of Washington, Seattle, Washington 98195, USA.13Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle,Washington 98109, USA. 14Basic Science Division, Fred Hutchinson Cancer ResearchCenter, Seattle,Washington98109, USA. 15DepartmentofRadiationOncology, Universityof Washington, Seattle, Washington 98195, USA. 16Computer Science and ArtificialIntelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge,Massachusetts 02139, USA. 17Broad Institute of MIT and Harvard, Cambridge,Massachusetts 02142, USA. 18Department of Developmental andCell Biology, Universityof California, Irvine, Irvine, California 92697, USA. 19Center for Biomolecular Science andEngineering, School of Engineering, University of California Santa Cruz (UCSC), SantaCruz, California 95064, USA. 20Departments of Obstetrics/Gynecology and Pathology,and Center for Reproductive Sciences, University of California San Francisco, SanFrancisco, California 94143, USA. 21European Molecular Biology Laboratory, EuropeanBioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. 22Yale University, Department of Genetics, PO Box 208005, 333 Cedar Street,

New Haven, Connecticut 06520-8005, USA. 23Computer & Information Sciences &Engineering, University of Florida, Gainesville, Florida 32611, USA. 24McKusick-NathansInstitute of GeneticMedicine and Department of Biomedical Engineering, Johns HopkinsUniversity, 733 N. Broadway, BRB 573 Baltimore, Maryland 21205, USA. 25Bill LyonsInformatics Centre, UCL Cancer Institute, University College London, LondonWC1E 6DD,UK. 26Department of Biological Structure, University of Washington, HSB I-516, 1959NEPacific Street, Seattle, Washington 98195, USA. 27MRC Molecular Haemotology Unit,University of Oxford, Oxford OX3 9DS, UK. 28Department of Cell and DevelopmentalBiology, Weill Cornell Medical College, New York, New York 10065, USA. 29HHMI andLudwig Center at Memorial Sloan Kettering Cancer Center, Immunology Program,Memorial SloanKetteringCancerCanter,NewYork,NewYork10065,USA. 30DanaFarberCancer Institute, Harvard Medical School, Cambridge, Massachusetts 02138, USA.31University of Iowa Carver College of Medicine, Department of Internal Medicine, IowaCity, Iowa 52242, USA. 32Division of Hematology, Department of Medicine, University ofWashington, Seattle, Washington 98195, USA. 33Department of Cell Biology, AlbertEinstein College of Medicine, Bronx, New York 10461, USA. 34Department of Pathology,University of Washington, Seattle, Washington 98195, USA. 35Department ofComparative Medicine, University of Washington, Seattle, Washington 98195, USA.36Bioinformatics and Genomics program, The Pennsylvania State University, UniversityPark, Pennsylvania 16802, USA. 37Department of Hematology, St Jude ChildrensResearch Hospital, Memphis, Tennessee 38105, USA. 38Division of Hematology, TheChildrens Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA. 39PerelmanSchool of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania 19104,USA. 40Department of Bioengineering, University of California, San Diego, 9500 GilmanDrive, La Jolla, California 92093, USA. 41Department of Genetics, Center for GenomeSciences and Systems Biology, Washington University School of Medicine, St. Louis,Missouri 63108, USA. 42NHGRI, National Institutes of Health, 5635 Fishers Lane,Bethesda,Maryland20892-9307,USA.{Presentaddresses:DepartmentofBiochemistryand Molecular Biology, School of Medicine, The Pennsylvania State University, Hershey,Pennsylvania 17033, USA (F.Y.); BRCF Bioinformatics Core, University of Michigan, AnnArbor, Michigan 48105, USA (W.W.); Division of Natural Sciences, NewCollege of Florida,Sarasota, Florida 34243, USA (T.R.); Department of Computational Medicine andBioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA (A.P.B.);Washington University in St Louis, St Louis, Missouri 63108, USA (R.L.); University ofNorth CarolinaGillings School of Global Public Health, ChapelHill, NorthCarolina 27599,USA (L.B.A.).

*These authors contributed equally to this work.{Lists of participants and their affiliations appear in the Supplementary Information.

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Extended Data Figure 1 | Clustering analysis of human and mouse tissuesamples. a, RNA-seq data from Ilumina Body Map (adipose, adrenal, brain,colon, heart, kidney, liver, lung, ovary and testis) were analysed together withthat from the matched mouse samples using clustering analysis. Genes withhigh variance across tissues were used, resulting in cell samples clustering by

tissues, not by species. b, Clustering employing genes with high variancebetween species shows clustering by species instead of tissues. c, PrincipalComponent Analysis (PCA) was performed for RNA-seq data for 10 humanandmousematching tissues. The expression values are normalized within eachspecies and we observed the clustering of samples by tissue types.

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Extended Data Figure 2 | Comparative analysis of sequence conservationin the cis elements predicted in the human and mouse genome. a, Thepredicted mouse-specific promoters and enhancers can function in humanembryonic stem cells (hESCs). Percentages of predicted enhancers orpromoters that test positive are shown in a bar chart. b, A bar chart shows

the percentage of the predicted mouse-specific promoters containing varioussubclasses of LTR and SINE elements. As control, the predicted mouse ciselements with homologous sequences in the human genome or randomgenomic regions are included.

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Extended Data Figure 3 | Replication timing boundaries preserved amongtissues are conserved during evolution. a, Heat map of TTR overlap withpositive (yellow) or negative (blue) slope. Replication timing (RT) boundarieswere identified as clustered TTR endpoints (grey) above the 95th percentile(dashed line) of randomly resampled positions (black). b, Examples ofconstitutive boundaries (blue regions) and regulated boundaries (grey regions)highlighted. c, Spearman correlations between differences in chromatin feature

enrichment and differences in RT in non-overlapping 200-kb windows.d, Percentage of boundaries preserved between the indicated number of humancell types. e, f, Distribution of boundary replication timing in mouse (e) andhuman (f) as a function of preservation level between cell types. g, Comparisonof changes in replication timing versus various histone marks across a segmentof mouse chromosome 6.

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Extended Data Table 1 | A seven-state chromHMM model learned from four histone modifications in 15 mouse cell types or lines and sixhuman cell lines is shown

State Feature H3K27m3 H3K4m3 H3K4m1 H3K36m3 Average% Variaon1 K4m3 0.07 0.92 0.05 0.03 0.75 0.072 K4m1/3 0.17 0.85 0.88 0.05 0.55 0.103 K4m1 0.01 0.01 0.47 0.02 3.35 0.574 K4m1+K36m3 0.01 0.05 0.59 0.71 0.58 0.235 K36m3 0.00 0.00 0.01 0.42 6.31 1.546 Unmarked 0.01 0.00 0.00 0.00 85.45 9.207 K27m3 0.29 0.00 0.02 0.00 3.01 3.87

The numbers represent the emission probabilities of each histonemodification (column) in each chromatin state (row). The enriched histonemodifications in each state are summarized in the first column. Thefraction of genome assigned in each state was calculated (Supplementary Fig. 2). The average and variation of these fraction values across all included cell types/tissues are listed in the last two columns.

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Extended Data Table 2 | Self-organizing map of histone modifications shows enrichment of human GWAS SNPs when mapped onto mouse

a, Kidney-specific H3K4me1 that shows enrichment of specific GWAS hits associated with urate levels and metabolites. b, Liver-specific H3K36me3 unit shows enrichment in GWAS hits related to cholesterol,alcohol dependence and triglyceride levels. c, Brain-specific H3K27me3 signals show enrichment in GWAS SNPs associated with neurological disorders.

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TitleAuthorsAbstractOverview of data production and initial processingRNA transcriptomeCandidate cis-regulatory sequencesTranscription factor networksChromatin statesReplication domainsAnnotation of orthologous coding and non-coding genesDivergent and conserved gene expression patternsPrevalent species-specific regulatory sequences along with a core of conserved regulatory sequencesChromatin state landscape reflects tissue and cell identitiesMouse chromatin states inform interpretation of human disease-associated sequence variantsLarge-scale chromatin domains are developmentally stable and evolutionarily conservedConclusionReferencesFigure 1 Overview of the mouse ENCODE data sets.Figure 2 Comparative analysis of the gene expression programs in human and mouse samples.Figure 3 Comparative analysis of the cis-elements predicted in the human and mouse genome.Figure 4 Analysis of conservation in biochemical activities at the predicted mouse cis-regulatory sequences with human orthologues.Figure 5 Chromatin landscape is stable within individual cell lineages.Figure 6 Human GWAS hits when mapped onto mouse genome are associated with specific chromatin states.Figure 7 Replication timing boundaries preserved among tissues are conserved in mice and humans.Extended Data Figure 1 Clustering analysis of human and mouse tissue samples.Extended Data Figure 2 Comparative analysis of sequence conservation in the cis elements predicted in the human and mouse genome.Extended Data Figure 3 Replication timing boundaries preserved among tissues are conserved during evolution.Extended Data Table 1 A seven-state chromHMM model learned from four histone modifications in 15 mouse cell types or lines and six human cell lines is shownExtended Data Table 2 Self-organizing map of histone modifications shows enrichment of human GWAS SNPs when mapped onto mouse

a comparative encyclopedia of dna elements in the mouse genome

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mouse cell types

chromatin modifications

dna sequences

chromatin state

chromatin accessibility

transcription factor

dnase hypersensitivity

widespread use of mouse