Identification of active transcriptional regulatory elements from GRO-seq data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-05

AUTHORS

Charles G Danko, Stephanie L Hyland, Leighton J Core, Andre L Martins, Colin T Waters, Hyung Won Lee, Vivian G Cheung, W Lee Kraus, John T Lis, Adam Siepel

ABSTRACT

Modifications to the global run-on and sequencing (GRO-seq) protocol that enrich for 5'-capped RNAs can be used to reveal active transcriptional regulatory elements (TREs) with high accuracy. Here, we introduce discriminative regulatory-element detection from GRO-seq (dREG), a sensitive machine learning method that uses support vector regression to identify active TREs from GRO-seq data without requiring cap-based enrichment (https://github.com/Danko-Lab/dREG/). This approach allows TREs to be assayed together with gene expression levels and other transcriptional features in a single experiment. Predicted TREs are more enriched for several marks of transcriptional activation—including expression quantitative trait loci, disease-associated polymorphisms, acetylated histone 3 lysine 27 (H3K27ac) and transcription factor binding—than those identified by alternative functional assays. Using dREG, we surveyed TREs in eight human cell types and provide new insights into global patterns of TRE function. More... »

PAGES

433-438

References to SciGraph publications

  • 2009-07. Cohesins form chromosomal cis-interactions at the developmentally regulated IFNG locus in NATURE
  • 2012-09. An integrated encyclopedia of DNA elements in the human genome in NATURE
  • 2014-02. Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape in NATURE BIOTECHNOLOGY
  • 2011-11. Discovery of active enhancers through bidirectional expression of short transcripts in GENOME BIOLOGY
  • 2014-12. Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers in NATURE GENETICS
  • 2012-09. An expansive human regulatory lexicon encoded in transcription factor footprints in NATURE
  • 2013-09. Transcriptome and genome sequencing uncovers functional variation in humans in NATURE
  • 2011-05-05. Mapping and analysis of chromatin state dynamics in nine human cell types in NATURE
  • 2007-03. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome in NATURE GENETICS
  • 2011-03. Chromatin accessibility pre-determines glucocorticoid receptor binding patterns in NATURE GENETICS
  • 2014-12. Dynamic reorganization of the AC16 cardiomyocyte transcriptome in response to TNFα signaling revealed by integrated genomic analyses in BMC GENOMICS
  • 2014-03. An atlas of active enhancers across human cell types and tissues in NATURE
  • 2013-12. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position in NATURE METHODS
  • 2010-05. Widespread transcription at neuronal activity-regulated enhancers in NATURE
  • 2010-08. Discovery and characterization of chromatin states for systematic annotation of the human genome in NATURE BIOTECHNOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nmeth.3329

    DOI

    http://dx.doi.org/10.1038/nmeth.3329

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1031924307

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/25799441


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Genetics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Artificial Intelligence", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Cell Line", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gene Expression Regulation", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genome-Wide Association Study", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Histones", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "K562 Cells", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Polymorphism, Single Nucleotide", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Quantitative Trait Loci", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Regulatory Elements, Transcriptional", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Software", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Cornell University", 
              "id": "https://www.grid.ac/institutes/grid.5386.8", 
              "name": [
                "Baker Institute for Animal Health, Cornell University, Ithaca, New York, USA.", 
                "Department of Biomedical Sciences, Cornell University, Ithaca, New York, USA.", 
                "Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Danko", 
            "givenName": "Charles G", 
            "id": "sg:person.0773424232.23", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0773424232.23"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hyland", 
            "givenName": "Stephanie L", 
            "id": "sg:person.01123377105.25", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01123377105.25"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cornell University", 
              "id": "https://www.grid.ac/institutes/grid.5386.8", 
              "name": [
                "Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Core", 
            "givenName": "Leighton J", 
            "id": "sg:person.0734467773.03", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734467773.03"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cornell University", 
              "id": "https://www.grid.ac/institutes/grid.5386.8", 
              "name": [
                "Graduate Field in Computational Biology, Cornell University, Ithaca, New York, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Martins", 
            "givenName": "Andre L", 
            "id": "sg:person.0703430345.02", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0703430345.02"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cornell University", 
              "id": "https://www.grid.ac/institutes/grid.5386.8", 
              "name": [
                "Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Waters", 
            "givenName": "Colin T", 
            "id": "sg:person.0725151130.67", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0725151130.67"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cornell University", 
              "id": "https://www.grid.ac/institutes/grid.5386.8", 
              "name": [
                "Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lee", 
            "givenName": "Hyung Won", 
            "id": "sg:person.01354054105.37", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354054105.37"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Howard Hughes Medical Institute", 
              "id": "https://www.grid.ac/institutes/grid.413575.1", 
              "name": [
                "Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, USA.", 
                "Howard Hughes Medical Institute, Chevy Chase, Maryland, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cheung", 
            "givenName": "Vivian G", 
            "id": "sg:person.01247117763.17", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247117763.17"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "The University of Texas Southwestern Medical Center", 
              "id": "https://www.grid.ac/institutes/grid.267313.2", 
              "name": [
                "Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA.", 
                "Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, Texas, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kraus", 
            "givenName": "W Lee", 
            "id": "sg:person.0762253654.71", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0762253654.71"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cornell University", 
              "id": "https://www.grid.ac/institutes/grid.5386.8", 
              "name": [
                "Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lis", 
            "givenName": "John T", 
            "id": "sg:person.0647743124.75", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0647743124.75"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cornell University", 
              "id": "https://www.grid.ac/institutes/grid.5386.8", 
              "name": [
                "Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Siepel", 
            "givenName": "Adam", 
            "id": "sg:person.0734340315.34", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734340315.34"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.cell.2007.12.014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000099864"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1164096", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001930208"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1164096", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001930208"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1138341", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002326113"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.2798", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005420455", 
              "https://doi.org/10.1038/nbt.2798"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.136127.111", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007044518"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pgen.1002610", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007203358"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1229386", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008911240"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng1966", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010017851", 
              "https://doi.org/10.1038/ng1966"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1961189.1961199", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013637525"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1162228", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013963804"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1162228", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013963804"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature08079", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014979003", 
              "https://doi.org/10.1038/nature08079"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature08079", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014979003", 
              "https://doi.org/10.1038/nature08079"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1232542", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016321428"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gks1284", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016676991"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature09033", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016906145", 
              "https://doi.org/10.1038/nature09033"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature09033", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016906145", 
              "https://doi.org/10.1038/nature09033"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pgen.1001114", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019134957"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.2688", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020615304", 
              "https://doi.org/10.1038/nmeth.2688"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.2688", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020615304", 
              "https://doi.org/10.1038/nmeth.2688"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pgen.1004226", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020920881"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature12787", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022158752", 
              "https://doi.org/10.1038/nature12787"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.112623.110", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025271009"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.molcel.2013.01.038", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027313497"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.molcel.2013.02.015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027424538"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cell.2012.12.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027494799"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/bts277", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027920536"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11247", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029065430", 
              "https://doi.org/10.1038/nature11247"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.3142", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029090833", 
              "https://doi.org/10.1038/ng.3142"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btq033", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036892131"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.celrep.2013.01.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037763179"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11212", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039250372", 
              "https://doi.org/10.1038/nature11212"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.152306.112", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040001375"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1162253", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040407890"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1162253", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040407890"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature09906", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043304663", 
              "https://doi.org/10.1038/nature09906"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cell.2011.03.042", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044799507"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ymeth.2009.03.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045350517"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ymeth.2009.03.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045350517"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.1016071107", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047051560"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-15-155", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047925956", 
              "https://doi.org/10.1186/1471-2164-15-155"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.1662", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050061238", 
              "https://doi.org/10.1038/nbt.1662"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2011-12-11-r113", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050928883", 
              "https://doi.org/10.1186/gb-2011-12-11-r113"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pgen.1004610", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051146799"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.759", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052022882", 
              "https://doi.org/10.1038/ng.759"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.759", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052022882", 
              "https://doi.org/10.1038/ng.759"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.759", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052022882", 
              "https://doi.org/10.1038/ng.759"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.759", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052022882", 
              "https://doi.org/10.1038/ng.759"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature12531", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052616209", 
              "https://doi.org/10.1038/nature12531"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.7554/elife.00808", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052849395"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.celrep.2014.01.037", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053153401"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2015-05", 
        "datePublishedReg": "2015-05-01", 
        "description": "Modifications to the global run-on and sequencing (GRO-seq) protocol that enrich for 5'-capped RNAs can be used to reveal active transcriptional regulatory elements (TREs) with high accuracy. Here, we introduce discriminative regulatory-element detection from GRO-seq (dREG), a sensitive machine learning method that uses support vector regression to identify active TREs from GRO-seq data without requiring cap-based enrichment (https://github.com/Danko-Lab/dREG/). This approach allows TREs to be assayed together with gene expression levels and other transcriptional features in a single experiment. Predicted TREs are more enriched for several marks of transcriptional activation\u2014including expression quantitative trait loci, disease-associated polymorphisms, acetylated histone 3 lysine 27 (H3K27ac) and transcription factor binding\u2014than those identified by alternative functional assays. Using dREG, we surveyed TREs in eight human cell types and provide new insights into global patterns of TRE function.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1038/nmeth.3329", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.2509219", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2438968", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2497011", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2684473", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2529485", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1033763", 
            "issn": [
              "1548-7091", 
              "1548-7105"
            ], 
            "name": "Nature Methods", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "5", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "12"
          }
        ], 
        "name": "Identification of active transcriptional regulatory elements from GRO-seq data", 
        "pagination": "433-438", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "9814b4f7dffc8b68732a3063dc12f61277390fbae4e8fe517b0c6c8e1ae50f68"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "25799441"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "101215604"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/nmeth.3329"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1031924307"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/nmeth.3329", 
          "https://app.dimensions.ai/details/publication/pub.1031924307"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T23:12", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8693_00000425.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://www.nature.com/articles/nmeth.3329"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/nmeth.3329'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/nmeth.3329'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/nmeth.3329'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/nmeth.3329'


     

    This table displays all metadata directly associated to this object as RDF triples.

    341 TRIPLES      21 PREDICATES      82 URIs      32 LITERALS      20 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/nmeth.3329 schema:about N27dfe0e5ac1149a787f8ecbc81595593
    2 N29022d3119ff47eb950ecfe571714963
    3 N6130086bfd5c4c98b9d23008b7447939
    4 N66dece90ad374e8f97c893a66d06c6d5
    5 N98e6ce143c4247128b06cb2fa0fb722d
    6 Na2c4fc24005046d2a7d5b3e0014d3671
    7 Na7ec400b64a24321b2c1c9cfe01eec2b
    8 Nd5edf76fa122415b8c8fe4dafdbb1d3d
    9 Ne5d4a9a6234b460e9fd1ce2aa1b1b073
    10 Nf6ec13e215e0452c8e5ded631f8775c2
    11 Nfc7baf8c380e4772b87e22ba7a8b69e2
    12 anzsrc-for:06
    13 anzsrc-for:0604
    14 schema:author N4711863394404457bbe143465a010fef
    15 schema:citation sg:pub.10.1038/nature08079
    16 sg:pub.10.1038/nature09033
    17 sg:pub.10.1038/nature09906
    18 sg:pub.10.1038/nature11212
    19 sg:pub.10.1038/nature11247
    20 sg:pub.10.1038/nature12531
    21 sg:pub.10.1038/nature12787
    22 sg:pub.10.1038/nbt.1662
    23 sg:pub.10.1038/nbt.2798
    24 sg:pub.10.1038/ng.3142
    25 sg:pub.10.1038/ng.759
    26 sg:pub.10.1038/ng1966
    27 sg:pub.10.1038/nmeth.2688
    28 sg:pub.10.1186/1471-2164-15-155
    29 sg:pub.10.1186/gb-2011-12-11-r113
    30 https://doi.org/10.1016/j.cell.2007.12.014
    31 https://doi.org/10.1016/j.cell.2011.03.042
    32 https://doi.org/10.1016/j.cell.2012.12.009
    33 https://doi.org/10.1016/j.celrep.2013.01.004
    34 https://doi.org/10.1016/j.celrep.2014.01.037
    35 https://doi.org/10.1016/j.molcel.2013.01.038
    36 https://doi.org/10.1016/j.molcel.2013.02.015
    37 https://doi.org/10.1016/j.ymeth.2009.03.003
    38 https://doi.org/10.1073/pnas.1016071107
    39 https://doi.org/10.1093/bioinformatics/btq033
    40 https://doi.org/10.1093/bioinformatics/bts277
    41 https://doi.org/10.1093/nar/gks1284
    42 https://doi.org/10.1101/gr.112623.110
    43 https://doi.org/10.1101/gr.136127.111
    44 https://doi.org/10.1101/gr.152306.112
    45 https://doi.org/10.1126/science.1138341
    46 https://doi.org/10.1126/science.1162228
    47 https://doi.org/10.1126/science.1162253
    48 https://doi.org/10.1126/science.1164096
    49 https://doi.org/10.1126/science.1229386
    50 https://doi.org/10.1126/science.1232542
    51 https://doi.org/10.1145/1961189.1961199
    52 https://doi.org/10.1371/journal.pgen.1001114
    53 https://doi.org/10.1371/journal.pgen.1002610
    54 https://doi.org/10.1371/journal.pgen.1004226
    55 https://doi.org/10.1371/journal.pgen.1004610
    56 https://doi.org/10.7554/elife.00808
    57 schema:datePublished 2015-05
    58 schema:datePublishedReg 2015-05-01
    59 schema:description Modifications to the global run-on and sequencing (GRO-seq) protocol that enrich for 5'-capped RNAs can be used to reveal active transcriptional regulatory elements (TREs) with high accuracy. Here, we introduce discriminative regulatory-element detection from GRO-seq (dREG), a sensitive machine learning method that uses support vector regression to identify active TREs from GRO-seq data without requiring cap-based enrichment (https://github.com/Danko-Lab/dREG/). This approach allows TREs to be assayed together with gene expression levels and other transcriptional features in a single experiment. Predicted TREs are more enriched for several marks of transcriptional activation—including expression quantitative trait loci, disease-associated polymorphisms, acetylated histone 3 lysine 27 (H3K27ac) and transcription factor binding—than those identified by alternative functional assays. Using dREG, we surveyed TREs in eight human cell types and provide new insights into global patterns of TRE function.
    60 schema:genre research_article
    61 schema:inLanguage en
    62 schema:isAccessibleForFree true
    63 schema:isPartOf Nce9cc501f45b4ab7a609e40df2bfa1d4
    64 Nd1f22564ebbd40cab4c11285f33a5554
    65 sg:journal.1033763
    66 schema:name Identification of active transcriptional regulatory elements from GRO-seq data
    67 schema:pagination 433-438
    68 schema:productId N4f654f6d2eb041fe8ee55e4b5b0012d0
    69 N7b3e302be6d9414887e40e798ba2dc46
    70 N7e8503d8020145f8b31eaffec3734735
    71 N89eb7c6fa3074ef181fe80b3fc346891
    72 Nbcb2011853f64c659c8bc332009b7a0e
    73 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031924307
    74 https://doi.org/10.1038/nmeth.3329
    75 schema:sdDatePublished 2019-04-10T23:12
    76 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    77 schema:sdPublisher N6c2e56b4f35041e9babfd8577cfe1f7c
    78 schema:url http://www.nature.com/articles/nmeth.3329
    79 sgo:license sg:explorer/license/
    80 sgo:sdDataset articles
    81 rdf:type schema:ScholarlyArticle
    82 N0b4b983503994237a83b85f95133337c rdf:first sg:person.0734467773.03
    83 rdf:rest N13191ef62ee142c1b8c3df986c125779
    84 N12342a17c0204493b1a132d065631e12 rdf:first sg:person.0762253654.71
    85 rdf:rest Nc1b2aa6cb781465fb00049d8c489355b
    86 N13191ef62ee142c1b8c3df986c125779 rdf:first sg:person.0703430345.02
    87 rdf:rest Nde41ce6e2856459faff42f7cd02dd0a6
    88 N27dfe0e5ac1149a787f8ecbc81595593 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    89 schema:name Gene Expression Regulation
    90 rdf:type schema:DefinedTerm
    91 N29022d3119ff47eb950ecfe571714963 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    92 schema:name Polymorphism, Single Nucleotide
    93 rdf:type schema:DefinedTerm
    94 N4711863394404457bbe143465a010fef rdf:first sg:person.0773424232.23
    95 rdf:rest Naa7334947cfe4a0c83d20294a2f9ccf9
    96 N4f654f6d2eb041fe8ee55e4b5b0012d0 schema:name nlm_unique_id
    97 schema:value 101215604
    98 rdf:type schema:PropertyValue
    99 N6130086bfd5c4c98b9d23008b7447939 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    100 schema:name Regulatory Elements, Transcriptional
    101 rdf:type schema:DefinedTerm
    102 N66dece90ad374e8f97c893a66d06c6d5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    103 schema:name Artificial Intelligence
    104 rdf:type schema:DefinedTerm
    105 N6c2e56b4f35041e9babfd8577cfe1f7c schema:name Springer Nature - SN SciGraph project
    106 rdf:type schema:Organization
    107 N7783b39a69ae489aa8b3790b987b83da rdf:first sg:person.0734340315.34
    108 rdf:rest rdf:nil
    109 N7b3e302be6d9414887e40e798ba2dc46 schema:name readcube_id
    110 schema:value 9814b4f7dffc8b68732a3063dc12f61277390fbae4e8fe517b0c6c8e1ae50f68
    111 rdf:type schema:PropertyValue
    112 N7e8503d8020145f8b31eaffec3734735 schema:name pubmed_id
    113 schema:value 25799441
    114 rdf:type schema:PropertyValue
    115 N89eb7c6fa3074ef181fe80b3fc346891 schema:name doi
    116 schema:value 10.1038/nmeth.3329
    117 rdf:type schema:PropertyValue
    118 N95fc5b44aad44c1aada5cd1159360550 schema:name Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, USA.
    119 rdf:type schema:Organization
    120 N98e6ce143c4247128b06cb2fa0fb722d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    121 schema:name Histones
    122 rdf:type schema:DefinedTerm
    123 Na2c4fc24005046d2a7d5b3e0014d3671 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    124 schema:name Genome-Wide Association Study
    125 rdf:type schema:DefinedTerm
    126 Na7ec400b64a24321b2c1c9cfe01eec2b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    127 schema:name Quantitative Trait Loci
    128 rdf:type schema:DefinedTerm
    129 Naa7334947cfe4a0c83d20294a2f9ccf9 rdf:first sg:person.01123377105.25
    130 rdf:rest N0b4b983503994237a83b85f95133337c
    131 Nbcb2011853f64c659c8bc332009b7a0e schema:name dimensions_id
    132 schema:value pub.1031924307
    133 rdf:type schema:PropertyValue
    134 Nc1b2aa6cb781465fb00049d8c489355b rdf:first sg:person.0647743124.75
    135 rdf:rest N7783b39a69ae489aa8b3790b987b83da
    136 Ncdbcd5b764d24d809858a47130b399ca rdf:first sg:person.01354054105.37
    137 rdf:rest Ne3eee79e87564f69a4f881c3d7827428
    138 Nce9cc501f45b4ab7a609e40df2bfa1d4 schema:volumeNumber 12
    139 rdf:type schema:PublicationVolume
    140 Nd1f22564ebbd40cab4c11285f33a5554 schema:issueNumber 5
    141 rdf:type schema:PublicationIssue
    142 Nd5edf76fa122415b8c8fe4dafdbb1d3d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    143 schema:name Humans
    144 rdf:type schema:DefinedTerm
    145 Nde41ce6e2856459faff42f7cd02dd0a6 rdf:first sg:person.0725151130.67
    146 rdf:rest Ncdbcd5b764d24d809858a47130b399ca
    147 Ne3eee79e87564f69a4f881c3d7827428 rdf:first sg:person.01247117763.17
    148 rdf:rest N12342a17c0204493b1a132d065631e12
    149 Ne5d4a9a6234b460e9fd1ce2aa1b1b073 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    150 schema:name K562 Cells
    151 rdf:type schema:DefinedTerm
    152 Nf6ec13e215e0452c8e5ded631f8775c2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    153 schema:name Cell Line
    154 rdf:type schema:DefinedTerm
    155 Nfc7baf8c380e4772b87e22ba7a8b69e2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    156 schema:name Software
    157 rdf:type schema:DefinedTerm
    158 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    159 schema:name Biological Sciences
    160 rdf:type schema:DefinedTerm
    161 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    162 schema:name Genetics
    163 rdf:type schema:DefinedTerm
    164 sg:grant.2438968 http://pending.schema.org/fundedItem sg:pub.10.1038/nmeth.3329
    165 rdf:type schema:MonetaryGrant
    166 sg:grant.2497011 http://pending.schema.org/fundedItem sg:pub.10.1038/nmeth.3329
    167 rdf:type schema:MonetaryGrant
    168 sg:grant.2509219 http://pending.schema.org/fundedItem sg:pub.10.1038/nmeth.3329
    169 rdf:type schema:MonetaryGrant
    170 sg:grant.2529485 http://pending.schema.org/fundedItem sg:pub.10.1038/nmeth.3329
    171 rdf:type schema:MonetaryGrant
    172 sg:grant.2684473 http://pending.schema.org/fundedItem sg:pub.10.1038/nmeth.3329
    173 rdf:type schema:MonetaryGrant
    174 sg:journal.1033763 schema:issn 1548-7091
    175 1548-7105
    176 schema:name Nature Methods
    177 rdf:type schema:Periodical
    178 sg:person.01123377105.25 schema:affiliation N95fc5b44aad44c1aada5cd1159360550
    179 schema:familyName Hyland
    180 schema:givenName Stephanie L
    181 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01123377105.25
    182 rdf:type schema:Person
    183 sg:person.01247117763.17 schema:affiliation https://www.grid.ac/institutes/grid.413575.1
    184 schema:familyName Cheung
    185 schema:givenName Vivian G
    186 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247117763.17
    187 rdf:type schema:Person
    188 sg:person.01354054105.37 schema:affiliation https://www.grid.ac/institutes/grid.5386.8
    189 schema:familyName Lee
    190 schema:givenName Hyung Won
    191 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354054105.37
    192 rdf:type schema:Person
    193 sg:person.0647743124.75 schema:affiliation https://www.grid.ac/institutes/grid.5386.8
    194 schema:familyName Lis
    195 schema:givenName John T
    196 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0647743124.75
    197 rdf:type schema:Person
    198 sg:person.0703430345.02 schema:affiliation https://www.grid.ac/institutes/grid.5386.8
    199 schema:familyName Martins
    200 schema:givenName Andre L
    201 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0703430345.02
    202 rdf:type schema:Person
    203 sg:person.0725151130.67 schema:affiliation https://www.grid.ac/institutes/grid.5386.8
    204 schema:familyName Waters
    205 schema:givenName Colin T
    206 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0725151130.67
    207 rdf:type schema:Person
    208 sg:person.0734340315.34 schema:affiliation https://www.grid.ac/institutes/grid.5386.8
    209 schema:familyName Siepel
    210 schema:givenName Adam
    211 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734340315.34
    212 rdf:type schema:Person
    213 sg:person.0734467773.03 schema:affiliation https://www.grid.ac/institutes/grid.5386.8
    214 schema:familyName Core
    215 schema:givenName Leighton J
    216 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734467773.03
    217 rdf:type schema:Person
    218 sg:person.0762253654.71 schema:affiliation https://www.grid.ac/institutes/grid.267313.2
    219 schema:familyName Kraus
    220 schema:givenName W Lee
    221 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0762253654.71
    222 rdf:type schema:Person
    223 sg:person.0773424232.23 schema:affiliation https://www.grid.ac/institutes/grid.5386.8
    224 schema:familyName Danko
    225 schema:givenName Charles G
    226 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0773424232.23
    227 rdf:type schema:Person
    228 sg:pub.10.1038/nature08079 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014979003
    229 https://doi.org/10.1038/nature08079
    230 rdf:type schema:CreativeWork
    231 sg:pub.10.1038/nature09033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016906145
    232 https://doi.org/10.1038/nature09033
    233 rdf:type schema:CreativeWork
    234 sg:pub.10.1038/nature09906 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043304663
    235 https://doi.org/10.1038/nature09906
    236 rdf:type schema:CreativeWork
    237 sg:pub.10.1038/nature11212 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039250372
    238 https://doi.org/10.1038/nature11212
    239 rdf:type schema:CreativeWork
    240 sg:pub.10.1038/nature11247 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029065430
    241 https://doi.org/10.1038/nature11247
    242 rdf:type schema:CreativeWork
    243 sg:pub.10.1038/nature12531 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052616209
    244 https://doi.org/10.1038/nature12531
    245 rdf:type schema:CreativeWork
    246 sg:pub.10.1038/nature12787 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022158752
    247 https://doi.org/10.1038/nature12787
    248 rdf:type schema:CreativeWork
    249 sg:pub.10.1038/nbt.1662 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050061238
    250 https://doi.org/10.1038/nbt.1662
    251 rdf:type schema:CreativeWork
    252 sg:pub.10.1038/nbt.2798 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005420455
    253 https://doi.org/10.1038/nbt.2798
    254 rdf:type schema:CreativeWork
    255 sg:pub.10.1038/ng.3142 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029090833
    256 https://doi.org/10.1038/ng.3142
    257 rdf:type schema:CreativeWork
    258 sg:pub.10.1038/ng.759 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052022882
    259 https://doi.org/10.1038/ng.759
    260 rdf:type schema:CreativeWork
    261 sg:pub.10.1038/ng1966 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010017851
    262 https://doi.org/10.1038/ng1966
    263 rdf:type schema:CreativeWork
    264 sg:pub.10.1038/nmeth.2688 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020615304
    265 https://doi.org/10.1038/nmeth.2688
    266 rdf:type schema:CreativeWork
    267 sg:pub.10.1186/1471-2164-15-155 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047925956
    268 https://doi.org/10.1186/1471-2164-15-155
    269 rdf:type schema:CreativeWork
    270 sg:pub.10.1186/gb-2011-12-11-r113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050928883
    271 https://doi.org/10.1186/gb-2011-12-11-r113
    272 rdf:type schema:CreativeWork
    273 https://doi.org/10.1016/j.cell.2007.12.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000099864
    274 rdf:type schema:CreativeWork
    275 https://doi.org/10.1016/j.cell.2011.03.042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044799507
    276 rdf:type schema:CreativeWork
    277 https://doi.org/10.1016/j.cell.2012.12.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027494799
    278 rdf:type schema:CreativeWork
    279 https://doi.org/10.1016/j.celrep.2013.01.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037763179
    280 rdf:type schema:CreativeWork
    281 https://doi.org/10.1016/j.celrep.2014.01.037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053153401
    282 rdf:type schema:CreativeWork
    283 https://doi.org/10.1016/j.molcel.2013.01.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027313497
    284 rdf:type schema:CreativeWork
    285 https://doi.org/10.1016/j.molcel.2013.02.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027424538
    286 rdf:type schema:CreativeWork
    287 https://doi.org/10.1016/j.ymeth.2009.03.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045350517
    288 rdf:type schema:CreativeWork
    289 https://doi.org/10.1073/pnas.1016071107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047051560
    290 rdf:type schema:CreativeWork
    291 https://doi.org/10.1093/bioinformatics/btq033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036892131
    292 rdf:type schema:CreativeWork
    293 https://doi.org/10.1093/bioinformatics/bts277 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027920536
    294 rdf:type schema:CreativeWork
    295 https://doi.org/10.1093/nar/gks1284 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016676991
    296 rdf:type schema:CreativeWork
    297 https://doi.org/10.1101/gr.112623.110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025271009
    298 rdf:type schema:CreativeWork
    299 https://doi.org/10.1101/gr.136127.111 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007044518
    300 rdf:type schema:CreativeWork
    301 https://doi.org/10.1101/gr.152306.112 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040001375
    302 rdf:type schema:CreativeWork
    303 https://doi.org/10.1126/science.1138341 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002326113
    304 rdf:type schema:CreativeWork
    305 https://doi.org/10.1126/science.1162228 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013963804
    306 rdf:type schema:CreativeWork
    307 https://doi.org/10.1126/science.1162253 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040407890
    308 rdf:type schema:CreativeWork
    309 https://doi.org/10.1126/science.1164096 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001930208
    310 rdf:type schema:CreativeWork
    311 https://doi.org/10.1126/science.1229386 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008911240
    312 rdf:type schema:CreativeWork
    313 https://doi.org/10.1126/science.1232542 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016321428
    314 rdf:type schema:CreativeWork
    315 https://doi.org/10.1145/1961189.1961199 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013637525
    316 rdf:type schema:CreativeWork
    317 https://doi.org/10.1371/journal.pgen.1001114 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019134957
    318 rdf:type schema:CreativeWork
    319 https://doi.org/10.1371/journal.pgen.1002610 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007203358
    320 rdf:type schema:CreativeWork
    321 https://doi.org/10.1371/journal.pgen.1004226 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020920881
    322 rdf:type schema:CreativeWork
    323 https://doi.org/10.1371/journal.pgen.1004610 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051146799
    324 rdf:type schema:CreativeWork
    325 https://doi.org/10.7554/elife.00808 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052849395
    326 rdf:type schema:CreativeWork
    327 https://www.grid.ac/institutes/grid.267313.2 schema:alternateName The University of Texas Southwestern Medical Center
    328 schema:name Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
    329 Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
    330 rdf:type schema:Organization
    331 https://www.grid.ac/institutes/grid.413575.1 schema:alternateName Howard Hughes Medical Institute
    332 schema:name Howard Hughes Medical Institute, Chevy Chase, Maryland, USA.
    333 Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, USA.
    334 rdf:type schema:Organization
    335 https://www.grid.ac/institutes/grid.5386.8 schema:alternateName Cornell University
    336 schema:name Baker Institute for Animal Health, Cornell University, Ithaca, New York, USA.
    337 Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.
    338 Department of Biomedical Sciences, Cornell University, Ithaca, New York, USA.
    339 Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA.
    340 Graduate Field in Computational Biology, Cornell University, Ithaca, New York, USA.
    341 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...