Comparative Study

. 2014 Nov 20;515(7527):355-64.

doi: 10.1038/nature13992.

A comparative encyclopedia of DNA elements in the mouse genome

Feng Yue¹, Yong Cheng², Alessandra Breschi³, Jeff Vierstra⁴, Weisheng Wu⁵, Tyrone Ryba⁶, Richard Sandstrom⁴, Zhihai Ma², Carrie Davis⁷, Benjamin D Pope⁶, Yin Shen⁸, Dmitri D Pervouchine³, Sarah Djebali³, Robert E Thurman⁴, Rajinder Kaul⁴, Eric Rynes⁴, Anthony Kirilusha⁹, Georgi K Marinov⁹, Brian A Williams⁹, Diane Trout⁹, Henry Amrhein⁹, Katherine Fisher-Aylor⁹, Igor Antoshechkin⁹, Gilberto DeSalvo⁹, Lei-Hoon See⁷, Meagan Fastuca⁷, Jorg Drenkow⁷, Chris Zaleski⁷, Alex Dobin⁷, Pablo Prieto³, Julien Lagarde³, Giovanni Bussotti³, Andrea Tanzer¹⁰, Olgert Denas¹¹, Kanwei Li¹¹, M A Bender¹², Miaohua Zhang¹³, Rachel Byron¹³, Mark T Groudine¹⁴, David McCleary⁸, Long Pham⁸, Zhen Ye⁸, Samantha Kuan⁸, Lee Edsall⁸, Yi-Chieh Wu¹⁵, Matthew D Rasmussen¹⁵, Mukul S Bansal¹⁵, Manolis Kellis¹⁶, Cheryl A Keller⁵, Christapher S Morrissey⁵, Tejaswini Mishra⁵, Deepti Jain⁵, Nergiz Dogan⁵, Robert S Harris⁵, Philip Cayting², Trupti Kawli², Alan P Boyle², Ghia Euskirchen², Anshul Kundaje², Shin Lin², Yiing Lin², Camden Jansen¹⁷, Venkat S Malladi², Melissa S Cline¹⁸, Drew T Erickson², Vanessa M Kirkup¹⁸, Katrina Learned¹⁸, Cricket A Sloan², Kate R Rosenbloom¹⁸, Beatriz Lacerda de Sousa¹⁹, Kathryn Beal²⁰, Miguel Pignatelli²⁰, Paul Flicek²⁰, Jin Lian²¹, Tamer Kahveci²², Dongwon Lee²³, W James Kent¹⁸, Miguel Ramalho Santos¹⁹, Javier Herrero²⁴, Cedric Notredame³, Audra Johnson⁴, Shinny Vong⁴, Kristen Lee⁴, Daniel Bates⁴, Fidencio Neri⁴, Morgan Diegel⁴, Theresa Canfield⁴, Peter J Sabo⁴, Matthew S Wilken²⁵, Thomas A Reh²⁵, Erika Giste⁴, Anthony Shafer⁴, Tanya Kutyavin⁴, Eric Haugen⁴, Douglas Dunn⁴, Alex P Reynolds⁴, Shane Neph⁴, Richard Humbert⁴, R Scott Hansen⁴, Marella De Bruijn²⁶, Licia Selleri²⁷, Alexander Rudensky²⁸, Steven Josefowicz²⁸, Robert Samstein²⁸, Evan E Eichler⁴, Stuart H Orkin²⁹, Dana Levasseur³⁰, Thalia Papayannopoulou³¹, Kai-Hsin Chang³⁰, Arthur Skoultchi³², Srikanta Gosh³², Christine Disteche³³, Piper Treuting³⁴, Yanli Wang³⁵, Mitchell J Weiss³⁶, Gerd A Blobel³⁷, Xiaoyi Cao³⁸, Sheng Zhong³⁸, Ting Wang³⁹, Peter J Good⁴⁰, Rebecca F Lowdon⁴⁰, Leslie B Adams⁴⁰, Xiao-Qiao Zhou⁴⁰, Michael J Pazin⁴⁰, Elise A Feingold⁴⁰, Barbara Wold⁹, James Taylor¹¹, Ali Mortazavi¹⁷, Sherman M Weissman²¹, John A Stamatoyannopoulos⁴, Michael P Snyder², Roderic Guigo³, Thomas R Gingeras⁷, David M Gilbert⁶, Ross C Hardison⁵, Michael A Beer²³, Bing Ren⁸; Mouse ENCODE Consortium

Affiliations

¹ 1] Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA. [2] Department of Biochemistry and Molecular Biology, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania 17033, USA.
² Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA.
³ Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain.
⁴ Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.
⁵ Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
⁶ Department of Biological Science, 319 Stadium Drive, Florida State University, Tallahassee, Florida 32306-4295, USA.
⁷ Functional Genomics, Cold Spring Harbor Laboratory, Bungtown Road, Cold Spring Harbor, New York 11724, USA.
⁸ Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA.
⁹ Division of Biology, California Institute of Technology, Pasadena, California 91125, USA.
¹⁰ 1] Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain. [2] Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Waehringerstrasse 17/3/303, A-1090 Vienna, Austria.
¹¹ Departments of Biology and Mathematics and Computer Science, Emory University, O. Wayne Rollins Research Center, 1510 Clifton Road NE, Atlanta, Georgia 30322, USA.
¹² 1] Department of Pediatrics, University of Washington, Seattle, Washington 98195, USA. [2] Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.
¹³ Basic Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.
¹⁴ 1] Basic Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA. [2] Department of Radiation Oncology, University of Washington, Seattle, Washington 98195, USA.
¹⁵ Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA.
¹⁶ 1] Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA. [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.
¹⁷ Department of Developmental and Cell Biology, University of California, Irvine, Irvine, California 92697, USA.
¹⁸ Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz (UCSC), Santa Cruz, California 95064, USA.
¹⁹ Departments of Obstetrics/Gynecology and Pathology, and Center for Reproductive Sciences, University of California San Francisco, San Francisco, California 94143, USA.
²⁰ European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
²¹ Yale University, Department of Genetics, PO Box 208005, 333 Cedar Street, New Haven, Connecticut 06520-8005, USA.
²² Computer &Information Sciences &Engineering, University of Florida, Gainesville, Florida 32611, USA.
²³ McKusick-Nathans Institute of Genetic Medicine and Department of Biomedical Engineering, Johns Hopkins University, 733 N. Broadway, BRB 573 Baltimore, Maryland 21205, USA.
²⁴ 1] European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. [2] Bill Lyons Informatics Centre, UCL Cancer Institute, University College London, London WC1E 6DD, UK.
²⁵ Department of Biological Structure, University of Washington, HSB I-516, 1959 NE Pacific Street, Seattle, Washington 98195, USA.
²⁶ MRC Molecular Haemotology Unit, University of Oxford, Oxford OX3 9DS, UK.
²⁷ Department of Cell and Developmental Biology, Weill Cornell Medical College, New York, New York 10065, USA.
²⁸ HHMI and Ludwig Center at Memorial Sloan Kettering Cancer Center, Immunology Program, Memorial Sloan Kettering Cancer Canter, New York, New York 10065, USA.
²⁹ Dana Farber Cancer Institute, Harvard Medical School, Cambridge, Massachusetts 02138, USA.
³⁰ University of Iowa Carver College of Medicine, Department of Internal Medicine, Iowa City, Iowa 52242, USA.
³¹ Division of Hematology, Department of Medicine, University of Washington, Seattle, Washington 98195, USA.
³² Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York 10461, USA.
³³ Department of Pathology, University of Washington, Seattle, Washington 98195, USA.
³⁴ Department of Comparative Medicine, University of Washington, Seattle, Washington 98195, USA.
³⁵ Bioinformatics and Genomics program, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
³⁶ Department of Hematology, St Jude Children's Research Hospital, Memphis, Tennessee 38105, USA.
³⁷ 1] Division of Hematology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA. [2] Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
³⁸ Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA.
³⁹ Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63108, USA.
⁴⁰ NHGRI, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA.

PMID: 25409824
PMCID: PMC4266106
DOI: 10.1038/nature13992

Comparative Study

A comparative encyclopedia of DNA elements in the mouse genome

Feng Yue et al. Nature. 2014.

. 2014 Nov 20;515(7527):355-64.

doi: 10.1038/nature13992.

Authors

Affiliations

¹ 1] Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA. [2] Department of Biochemistry and Molecular Biology, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania 17033, USA.
² Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA.
³ Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain.
⁴ Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.
⁵ Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
⁶ Department of Biological Science, 319 Stadium Drive, Florida State University, Tallahassee, Florida 32306-4295, USA.
⁷ Functional Genomics, Cold Spring Harbor Laboratory, Bungtown Road, Cold Spring Harbor, New York 11724, USA.
⁸ Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA.
⁹ Division of Biology, California Institute of Technology, Pasadena, California 91125, USA.
¹⁰ 1] Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain. [2] Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Waehringerstrasse 17/3/303, A-1090 Vienna, Austria.
¹¹ Departments of Biology and Mathematics and Computer Science, Emory University, O. Wayne Rollins Research Center, 1510 Clifton Road NE, Atlanta, Georgia 30322, USA.
¹² 1] Department of Pediatrics, University of Washington, Seattle, Washington 98195, USA. [2] Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.
¹³ Basic Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.
¹⁴ 1] Basic Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA. [2] Department of Radiation Oncology, University of Washington, Seattle, Washington 98195, USA.
¹⁵ Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA.
¹⁶ 1] Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA. [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.
¹⁷ Department of Developmental and Cell Biology, University of California, Irvine, Irvine, California 92697, USA.
¹⁸ Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz (UCSC), Santa Cruz, California 95064, USA.
¹⁹ Departments of Obstetrics/Gynecology and Pathology, and Center for Reproductive Sciences, University of California San Francisco, San Francisco, California 94143, USA.
²⁰ European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
²¹ Yale University, Department of Genetics, PO Box 208005, 333 Cedar Street, New Haven, Connecticut 06520-8005, USA.
²² Computer &Information Sciences &Engineering, University of Florida, Gainesville, Florida 32611, USA.
²³ McKusick-Nathans Institute of Genetic Medicine and Department of Biomedical Engineering, Johns Hopkins University, 733 N. Broadway, BRB 573 Baltimore, Maryland 21205, USA.
²⁴ 1] European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. [2] Bill Lyons Informatics Centre, UCL Cancer Institute, University College London, London WC1E 6DD, UK.
²⁵ Department of Biological Structure, University of Washington, HSB I-516, 1959 NE Pacific Street, Seattle, Washington 98195, USA.
²⁶ MRC Molecular Haemotology Unit, University of Oxford, Oxford OX3 9DS, UK.
²⁷ Department of Cell and Developmental Biology, Weill Cornell Medical College, New York, New York 10065, USA.
²⁸ HHMI and Ludwig Center at Memorial Sloan Kettering Cancer Center, Immunology Program, Memorial Sloan Kettering Cancer Canter, New York, New York 10065, USA.
²⁹ Dana Farber Cancer Institute, Harvard Medical School, Cambridge, Massachusetts 02138, USA.
³⁰ University of Iowa Carver College of Medicine, Department of Internal Medicine, Iowa City, Iowa 52242, USA.
³¹ Division of Hematology, Department of Medicine, University of Washington, Seattle, Washington 98195, USA.
³² Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York 10461, USA.
³³ Department of Pathology, University of Washington, Seattle, Washington 98195, USA.
³⁴ Department of Comparative Medicine, University of Washington, Seattle, Washington 98195, USA.
³⁵ Bioinformatics and Genomics program, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
³⁶ Department of Hematology, St Jude Children's Research Hospital, Memphis, Tennessee 38105, USA.
³⁷ 1] Division of Hematology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA. [2] Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
³⁸ Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA.
³⁹ Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63108, USA.
⁴⁰ NHGRI, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA.

PMID: 25409824
PMCID: PMC4266106
DOI: 10.1038/nature13992

Abstract

The laboratory mouse shares the majority of its protein-coding genes with humans, making it the premier model organism in biomedical research, yet the two mammals differ in significant ways. To gain greater insights into both shared and species-specific transcriptional and cellular regulatory programs in the mouse, the Mouse ENCODE Consortium has mapped transcription, DNase I hypersensitivity, transcription factor binding, chromatin modifications and replication domains throughout the mouse genome in diverse cell and tissue types. By comparing with the human genome, we not only confirm substantial conservation in the newly annotated potential functional sequences, but also find a large degree of divergence of sequences involved in transcriptional regulation, chromatin state and higher order chromatin organization. Our results illuminate the wide range of evolutionary forces acting on genes and their regulatory regions, and provide a general resource for research into mammalian biology and mechanisms of human diseases.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing financial interests.

Figures

**Figure 1. Overview of the mouse ENCODE data sets.**
a, A genome browser snapshot shows the primary data and annotated sequence features in the mouse CH12 cells (Methods). b, Chart shows that much of the human and mouse genomes is transcribed in one or more cell and tissue samples. c, A bar chart shows the percentages of the mouse genome annotated as various types of *cis*-regulatory elements (Methods). DHS, DNase hypersensitive sites; TF, transcription factor. d, Pie charts show the fraction of the entire genome that is covered 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 of boundaries 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 primitive ectoderm-like cell; EBM6/EpiSC, epiblast stem cell; piPSC, partially induced pluripotent stem cell; MEF, mouse embryonic fibroblast; MEL, murine erythroleukemia; CH12, B-cell lymphoma. PowerPoint slide

**Figure 2. Comparative analysis of the gene expression programs in human and mouse samples.**
a, Principal component analysis (PCA) was performed for RNA-seq data for 10 human and mouse matching tissues. The expression values are normalized across the entire data set. Solid squares denote human tissues. Open squares denote mouse tissues. Each category of tissue is represented by a different colour. b, Gene expression variance decomposition (see Methods) estimates the relative contribution of tissue and species to the observed variance in gene expression for each orthologous human–mouse gene pair. Green dots indicate genes with higher between-tissue contribution and red dots genes with higher between-species contributions. c, Neighbourhood analysis of conserved co-expression (NACC) in human and mouse samples. The distribution of NACC scores for each gene is shown. d, A scatter plot shows the average of NACC score over the set of genes in each functional gene ontology category. Highlighted are those biological processes that tend to be more conserved between human and mouse and those processes that have been less conserved (see Supplementary Table 21 for list of genes). PowerPoint slide

**Figure 3. Comparative analysis of the *cis*-elements predicted in the human and mouse genome.**
a, Chart shows the fractions of the predicted mouse *cis*-regulatory elements with homologous sequences in the human genome (Methods). TFBS, transcription factor binding site. b, A bar chart shows the fraction of the DNA fragments tested positive in the reporter assays performed either using mouse embryonic stem cells (mESCs) or mouse embryonic fibroblasts (MEF). c, A chart shows the gene ontology (GO) categories enriched near the predicted mouse-specific enhancers. d, A bar chart shows the percentage of the predicted mouse-specific enhancers containing various subclasses of LTR and SINE elements. As control, the predicted mouse *cis* elements with homologous sequences in the human genome or random genomic regions are included. PowerPoint slide

**Figure 4. Analysis of conservation in biochemical activities at the predicted mouse *cis*-regulatory sequences with human orthologues.**
a, b, Histograms show the distribution of the NACC score for the chromatin modification H3K27ac signal at the predicted mouse promoters (a) or enhancers (b). c, d, Histograms show the distributions of NACC scores for DNase I signal at the promoter proximal (c) and distal (d) DNase I hypersensitive sites (DHS). PowerPoint slide

**Figure 5. Chromatin landscape is stable within individual cell lineages.**
a, Map displaying the distribution of chromatin states over the neighbourhoods of human–mouse one-to-one orthologue genes in CH12 cells. The gene neighbourhood intervals were sorted by the transcription level of each gene, shown by white dots. TSS, transcription start site. b, c, Distribution of chromatin states in human–mouse one-to-one orthologues that are differentially expressed genes between erythroid progenitor and erythroblasts models (b) and between erythroblast and megakaryocyte (c). PowerPoint slide

**Figure 6. Human GWAS hits when mapped onto mouse genome are associated with specific chromatin states.**
a, A self-organization map of histone modification H3K4me1 shows association between kidney H3K4me1 state and specific GWAS hits associated with urate levels (Methods). b, Liver-specific H3K36me3 unit shows enrichment in GWAS hits related to cholesterol, alcohol dependence and triglyceride levels. c, Brain-specific H3K27me3 high unit shows enrichment in GWAS SNPs associated with neurological disorders. d, Characterization of every unit with statistically significant GWAS enrichments in terms of highest histone modification signal in at least one sample. Units with no signal in top 100 map units for every histone modification are listed as none. RPKM, reads per kilobase per million reads mapped. PowerPoint slide

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

**Extended Data Figure 1. Clustering analysis of human and mouse tissue samples.**
a, RNA-seq data from Ilumina Body Map (adipose, adrenal, brain, colon, heart, kidney, liver, lung, ovary and testis) were analysed together with that from the matched mouse samples using clustering analysis. Genes with high variance across tissues were used, resulting in cell samples clustering by tissues, not by species. b, Clustering employing genes with high variance between species shows clustering by species instead of tissues. c, Principal Component Analysis (PCA) was performed for RNA-seq data for 10 human and mouse matching tissues. The expression values are normalized within each species and we observed the clustering of samples by tissue types.

**Extended Data Figure 2. Comparative analysis of sequence conservation in the *cis* elements predicted in the human and mouse genome.**
a, The predicted mouse-specific promoters and enhancers can function in human embryonic stem cells (hESCs). Percentages of predicted enhancers or promoters that test positive are shown in a bar chart. b, A bar chart shows the percentage of the predicted mouse-specific promoters containing various subclasses of LTR and SINE elements. As control, the predicted mouse *cis* elements with homologous sequences in the human genome or random genomic regions are included.

**Extended Data Figure 3. Replication timing boundaries preserved among tissues are conserved during evolution.**
a, Heat map of TTR overlap with positive (yellow) or negative (blue) slope. Replication timing (RT) boundaries were identified as clustered TTR endpoints (grey) above the 95th percentile (dashed line) of randomly resampled positions (black). b, Examples of constitutive 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 human cell types. e, f, Distribution of boundary replication timing in mouse (e) and human (f) as a function of preservation level between cell types. g, Comparison of changes in replication timing versus various histone marks across a segment of mouse chromosome 6.

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References

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A comparative encyclopedia of DNA elements in the mouse genome

Affiliations

A comparative encyclopedia of DNA elements in the mouse genome

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Molecular Biology Databases