Improving analysis of transcription factor binding sites within ChIP-Seq data based on topological motif enrichment View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12

AUTHORS

Rebecca Worsley Hunt, Anthony Mathelier, Luis del Peso, Wyeth W Wasserman

ABSTRACT

BACKGROUND: Chromatin immunoprecipitation (ChIP) coupled to high-throughput sequencing (ChIP-Seq) techniques can reveal DNA regions bound by transcription factors (TF). Analysis of the ChIP-Seq regions is now a central component in gene regulation studies. The need remains strong for methods to improve the interpretation of ChIP-Seq data and the study of specific TF binding sites (TFBS). RESULTS: We introduce a set of methods to improve the interpretation of ChIP-Seq data, including the inference of mediating TFs based on TFBS motif over-representation analysis and the subsequent study of spatial distribution of TFBSs. TFBS over-representation analysis applied to ChIP-Seq data is used to detect which TFBSs arise more frequently than expected by chance. Visualization of over-representation analysis results with new composition-bias plots reveals systematic bias in over-representation scores. We introduce the BiasAway background generating software to resolve the problem. A heuristic procedure based on topological motif enrichment relative to the ChIP-Seq peaks' local maximums highlights peaks likely to be directly bound by a TF of interest. The results suggest that on average two-thirds of a ChIP-Seq dataset's peaks are bound by the ChIP'd TF; the origin of the remaining peaks remaining undetermined. Additional visualization methods allow for the study of both inter-TFBS spatial relationships and motif-flanking sequence properties, as demonstrated in case studies for TBP and ZNF143/THAP11. CONCLUSIONS: Topological properties of TFBS within ChIP-Seq datasets can be harnessed to better interpret regulatory sequences. Using GC content corrected TFBS over-representation analysis, combined with visualization techniques and analysis of the topological distribution of TFBS, we can distinguish peaks likely to be directly bound by a TF. The new methods will empower researchers for exploration of gene regulation and TF binding. More... »

PAGES

472

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2164-15-472

DOI

http://dx.doi.org/10.1186/1471-2164-15-472

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051872966

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24927817


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": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Animals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Base Composition", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Binding Sites", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Chromatin Immunoprecipitation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computational Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genome", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "High-Throughput Nucleotide Sequencing", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Nucleotide Motifs", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Transcription Factors", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of British Columbia", 
          "id": "https://www.grid.ac/institutes/grid.17091.3e", 
          "name": [
            "Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada", 
            "Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Worsley Hunt", 
        "givenName": "Rebecca", 
        "id": "sg:person.012352046644.38", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012352046644.38"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of British Columbia", 
          "id": "https://www.grid.ac/institutes/grid.17091.3e", 
          "name": [
            "Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mathelier", 
        "givenName": "Anthony", 
        "id": "sg:person.0650436334.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0650436334.03"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Autonomous University of Madrid", 
          "id": "https://www.grid.ac/institutes/grid.5515.4", 
          "name": [
            "Universidad Aut\u00f3noma de Madrid, Biochemistry, 28029, Madrid, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "del Peso", 
        "givenName": "Luis", 
        "id": "sg:person.0576502353.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0576502353.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of British Columbia", 
          "id": "https://www.grid.ac/institutes/grid.17091.3e", 
          "name": [
            "Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada", 
            "Centre for Molecular Medicine and Therapeutics, 950 W.28th Avenue, V5Z 4H4, Vancouver, BC, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wasserman", 
        "givenName": "Wyeth W", 
        "id": "sg:person.01164162122.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01164162122.26"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1093/emboj/16.1.173", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000225347"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature08514", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001394004", 
          "https://doi.org/10.1038/nature08514"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature08514", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001394004", 
          "https://doi.org/10.1038/nature08514"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2010-11-2-r19", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001873253", 
          "https://doi.org/10.1186/gb-2010-11-2-r19"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/g3.112.003202", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002118368"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/g3.112.003202", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002118368"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gks1172", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003865873"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0001623", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006044882"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btr189", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008669948"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btp163", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011232850"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cell.2008.04.043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011546580"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.molcel.2010.05.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012935976"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.104356.109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013376793"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btq488", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013620280"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.094144.109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019092889"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gks433", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019163615"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btn305", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022099792"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt.1630", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023421025", 
          "https://doi.org/10.1038/nbt.1630"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cell.2011.11.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026652255"
        ], 
        "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/10.1371/journal.pcbi.1002638", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028007921"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.104471.109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028308185"
        ], 
        "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": "https://doi.org/10.1152/ajpcell.00386.2006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030062242"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0011425", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030321502"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1186176", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030603437"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng.1036", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031633581", 
          "https://doi.org/10.1038/ng.1036"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/sj.onc.1200839", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032614069", 
          "https://doi.org/10.1038/sj.onc.1200839"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/sj.onc.1200839", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032614069", 
          "https://doi.org/10.1038/sj.onc.1200839"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1316064110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034946777"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkr1104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037277442"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkp950", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037453195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.gene.2004.07.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038294073"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gks1089", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039358249"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkr341", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039806696"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkh103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040312195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3109/10715762.2010.507670", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042568523"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkr377", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043523064"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0083506", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044310769"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkt088", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044820193"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btg1021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045072621"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/18.8.1135", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046379219"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.molcel.2010.01.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048072203"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.m508138200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048389585"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0905443106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048603640"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng0501-77", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049836241", 
          "https://doi.org/10.1038/ng0501-77"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng0501-77", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049836241", 
          "https://doi.org/10.1038/ng0501-77"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0011471", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049857332"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/genetics.112.138685", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050322133"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/genetics.112.138685", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050322133"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkq710", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050917766"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bib/bbs038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052618674"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1128/mcb.05504-11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053452549"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-12", 
    "datePublishedReg": "2014-12-01", 
    "description": "BACKGROUND: Chromatin immunoprecipitation (ChIP) coupled to high-throughput sequencing (ChIP-Seq) techniques can reveal DNA regions bound by transcription factors (TF). Analysis of the ChIP-Seq regions is now a central component in gene regulation studies. The need remains strong for methods to improve the interpretation of ChIP-Seq data and the study of specific TF binding sites (TFBS).\nRESULTS: We introduce a set of methods to improve the interpretation of ChIP-Seq data, including the inference of mediating TFs based on TFBS motif over-representation analysis and the subsequent study of spatial distribution of TFBSs. TFBS over-representation analysis applied to ChIP-Seq data is used to detect which TFBSs arise more frequently than expected by chance. Visualization of over-representation analysis results with new composition-bias plots reveals systematic bias in over-representation scores. We introduce the BiasAway background generating software to resolve the problem. A heuristic procedure based on topological motif enrichment relative to the ChIP-Seq peaks' local maximums highlights peaks likely to be directly bound by a TF of interest. The results suggest that on average two-thirds of a ChIP-Seq dataset's peaks are bound by the ChIP'd TF; the origin of the remaining peaks remaining undetermined. Additional visualization methods allow for the study of both inter-TFBS spatial relationships and motif-flanking sequence properties, as demonstrated in case studies for TBP and ZNF143/THAP11.\nCONCLUSIONS: Topological properties of TFBS within ChIP-Seq datasets can be harnessed to better interpret regulatory sequences. Using GC content corrected TFBS over-representation analysis, combined with visualization techniques and analysis of the topological distribution of TFBS, we can distinguish peaks likely to be directly bound by a TF. The new methods will empower researchers for exploration of gene regulation and TF binding.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/1471-2164-15-472", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2520061", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1023790", 
        "issn": [
          "1471-2164"
        ], 
        "name": "BMC Genomics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "15"
      }
    ], 
    "name": "Improving analysis of transcription factor binding sites within ChIP-Seq data based on topological motif enrichment", 
    "pagination": "472", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "f717d5f344b82c09fce200cb20f8557c1db9603df980fff772f27a32f68006fb"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "24927817"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100965258"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2164-15-472"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1051872966"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2164-15-472", 
      "https://app.dimensions.ai/details/publication/pub.1051872966"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T20:46", 
    "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_8684_00000508.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186%2F1471-2164-15-472"
  }
]
 

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.10.1186/1471-2164-15-472'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2164-15-472'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2164-15-472'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2164-15-472'


 

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

291 TRIPLES      21 PREDICATES      88 URIs      32 LITERALS      20 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2164-15-472 schema:about N15190388190545c88693fe90c180c705
2 N2af9d00b12a64d40a28a312ff25a22ac
3 N541efeebb0dd47afa86c7d3d8c50bedd
4 N5ddda91bb9a14826bf4e2d439180c80e
5 N6dbec4b168b9420b9c633dc5db2bcbc5
6 N7bba1828e41846f4aea6d98b27c86936
7 N87d3882c79ab41f3bf48ea1b4d997bb2
8 Na3cc81fb2fdc469c824ba4e5f35d5025
9 Nd51ba1cc5c5e4795841fcb3134ac29dd
10 Nd736a287e8aa4ffd95bfa0e664cfe2de
11 Ndf943c6cdd0a46f1b70770381db9715c
12 anzsrc-for:06
13 anzsrc-for:0604
14 schema:author N4fbee427910547ffb6f82ae1fc42bf54
15 schema:citation sg:pub.10.1038/nature08514
16 sg:pub.10.1038/nature11247
17 sg:pub.10.1038/nbt.1630
18 sg:pub.10.1038/ng.1036
19 sg:pub.10.1038/ng0501-77
20 sg:pub.10.1038/sj.onc.1200839
21 sg:pub.10.1186/gb-2010-11-2-r19
22 https://doi.org/10.1016/j.cell.2008.04.043
23 https://doi.org/10.1016/j.cell.2011.11.013
24 https://doi.org/10.1016/j.cell.2012.12.009
25 https://doi.org/10.1016/j.gene.2004.07.010
26 https://doi.org/10.1016/j.molcel.2010.01.016
27 https://doi.org/10.1016/j.molcel.2010.05.004
28 https://doi.org/10.1073/pnas.0905443106
29 https://doi.org/10.1073/pnas.1316064110
30 https://doi.org/10.1074/jbc.m508138200
31 https://doi.org/10.1093/bib/bbs038
32 https://doi.org/10.1093/bioinformatics/18.8.1135
33 https://doi.org/10.1093/bioinformatics/btg1021
34 https://doi.org/10.1093/bioinformatics/btn305
35 https://doi.org/10.1093/bioinformatics/btp163
36 https://doi.org/10.1093/bioinformatics/btq488
37 https://doi.org/10.1093/bioinformatics/btr189
38 https://doi.org/10.1093/emboj/16.1.173
39 https://doi.org/10.1093/nar/gkh103
40 https://doi.org/10.1093/nar/gkp950
41 https://doi.org/10.1093/nar/gkq710
42 https://doi.org/10.1093/nar/gkr1104
43 https://doi.org/10.1093/nar/gkr341
44 https://doi.org/10.1093/nar/gkr377
45 https://doi.org/10.1093/nar/gks1089
46 https://doi.org/10.1093/nar/gks1172
47 https://doi.org/10.1093/nar/gks433
48 https://doi.org/10.1093/nar/gkt088
49 https://doi.org/10.1101/gr.094144.109
50 https://doi.org/10.1101/gr.104356.109
51 https://doi.org/10.1101/gr.104471.109
52 https://doi.org/10.1126/science.1186176
53 https://doi.org/10.1128/mcb.05504-11
54 https://doi.org/10.1152/ajpcell.00386.2006
55 https://doi.org/10.1371/journal.pcbi.1002638
56 https://doi.org/10.1371/journal.pone.0001623
57 https://doi.org/10.1371/journal.pone.0011425
58 https://doi.org/10.1371/journal.pone.0011471
59 https://doi.org/10.1371/journal.pone.0083506
60 https://doi.org/10.1534/g3.112.003202
61 https://doi.org/10.1534/genetics.112.138685
62 https://doi.org/10.3109/10715762.2010.507670
63 schema:datePublished 2014-12
64 schema:datePublishedReg 2014-12-01
65 schema:description BACKGROUND: Chromatin immunoprecipitation (ChIP) coupled to high-throughput sequencing (ChIP-Seq) techniques can reveal DNA regions bound by transcription factors (TF). Analysis of the ChIP-Seq regions is now a central component in gene regulation studies. The need remains strong for methods to improve the interpretation of ChIP-Seq data and the study of specific TF binding sites (TFBS). RESULTS: We introduce a set of methods to improve the interpretation of ChIP-Seq data, including the inference of mediating TFs based on TFBS motif over-representation analysis and the subsequent study of spatial distribution of TFBSs. TFBS over-representation analysis applied to ChIP-Seq data is used to detect which TFBSs arise more frequently than expected by chance. Visualization of over-representation analysis results with new composition-bias plots reveals systematic bias in over-representation scores. We introduce the BiasAway background generating software to resolve the problem. A heuristic procedure based on topological motif enrichment relative to the ChIP-Seq peaks' local maximums highlights peaks likely to be directly bound by a TF of interest. The results suggest that on average two-thirds of a ChIP-Seq dataset's peaks are bound by the ChIP'd TF; the origin of the remaining peaks remaining undetermined. Additional visualization methods allow for the study of both inter-TFBS spatial relationships and motif-flanking sequence properties, as demonstrated in case studies for TBP and ZNF143/THAP11. CONCLUSIONS: Topological properties of TFBS within ChIP-Seq datasets can be harnessed to better interpret regulatory sequences. Using GC content corrected TFBS over-representation analysis, combined with visualization techniques and analysis of the topological distribution of TFBS, we can distinguish peaks likely to be directly bound by a TF. The new methods will empower researchers for exploration of gene regulation and TF binding.
66 schema:genre research_article
67 schema:inLanguage en
68 schema:isAccessibleForFree true
69 schema:isPartOf N1b577bde08144b619f9c76dc01093d1f
70 Nef541488b1964b25b0f8ffe0ab59ac73
71 sg:journal.1023790
72 schema:name Improving analysis of transcription factor binding sites within ChIP-Seq data based on topological motif enrichment
73 schema:pagination 472
74 schema:productId N0cda2463df38474dbe7ed12ea8ac0e5e
75 N3c5e4165720c409b81088c9fa10eddeb
76 N69968f678465470e928a6ba6365646a9
77 N7de5155384b041ff9b33ab61de79868d
78 Nbd47b58b07cf4f2da3e0655f95655572
79 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051872966
80 https://doi.org/10.1186/1471-2164-15-472
81 schema:sdDatePublished 2019-04-10T20:46
82 schema:sdLicense https://scigraph.springernature.com/explorer/license/
83 schema:sdPublisher N992d10ae64f44eff9beeea0cb97dbb3c
84 schema:url http://link.springer.com/10.1186%2F1471-2164-15-472
85 sgo:license sg:explorer/license/
86 sgo:sdDataset articles
87 rdf:type schema:ScholarlyArticle
88 N0cda2463df38474dbe7ed12ea8ac0e5e schema:name pubmed_id
89 schema:value 24927817
90 rdf:type schema:PropertyValue
91 N15190388190545c88693fe90c180c705 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
92 schema:name Base Composition
93 rdf:type schema:DefinedTerm
94 N1b577bde08144b619f9c76dc01093d1f schema:issueNumber 1
95 rdf:type schema:PublicationIssue
96 N2af9d00b12a64d40a28a312ff25a22ac schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
97 schema:name Computational Biology
98 rdf:type schema:DefinedTerm
99 N3c5e4165720c409b81088c9fa10eddeb schema:name doi
100 schema:value 10.1186/1471-2164-15-472
101 rdf:type schema:PropertyValue
102 N4ca25b46ca2e457b9a5cab26725bc2ab rdf:first sg:person.01164162122.26
103 rdf:rest rdf:nil
104 N4fbee427910547ffb6f82ae1fc42bf54 rdf:first sg:person.012352046644.38
105 rdf:rest Ndb54c7167d2843d9802cabac7b8fb439
106 N541efeebb0dd47afa86c7d3d8c50bedd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
107 schema:name Binding Sites
108 rdf:type schema:DefinedTerm
109 N5ddda91bb9a14826bf4e2d439180c80e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
110 schema:name Transcription Factors
111 rdf:type schema:DefinedTerm
112 N69968f678465470e928a6ba6365646a9 schema:name readcube_id
113 schema:value f717d5f344b82c09fce200cb20f8557c1db9603df980fff772f27a32f68006fb
114 rdf:type schema:PropertyValue
115 N6dbec4b168b9420b9c633dc5db2bcbc5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
116 schema:name Reproducibility of Results
117 rdf:type schema:DefinedTerm
118 N7bba1828e41846f4aea6d98b27c86936 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
119 schema:name Chromatin Immunoprecipitation
120 rdf:type schema:DefinedTerm
121 N7de5155384b041ff9b33ab61de79868d schema:name dimensions_id
122 schema:value pub.1051872966
123 rdf:type schema:PropertyValue
124 N87d3882c79ab41f3bf48ea1b4d997bb2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
125 schema:name Animals
126 rdf:type schema:DefinedTerm
127 N992d10ae64f44eff9beeea0cb97dbb3c schema:name Springer Nature - SN SciGraph project
128 rdf:type schema:Organization
129 Na3cc81fb2fdc469c824ba4e5f35d5025 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
130 schema:name Genome
131 rdf:type schema:DefinedTerm
132 Nbd47b58b07cf4f2da3e0655f95655572 schema:name nlm_unique_id
133 schema:value 100965258
134 rdf:type schema:PropertyValue
135 Nd51ba1cc5c5e4795841fcb3134ac29dd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
136 schema:name Algorithms
137 rdf:type schema:DefinedTerm
138 Nd736a287e8aa4ffd95bfa0e664cfe2de schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
139 schema:name High-Throughput Nucleotide Sequencing
140 rdf:type schema:DefinedTerm
141 Ndb54c7167d2843d9802cabac7b8fb439 rdf:first sg:person.0650436334.03
142 rdf:rest Nf40e3e7be9834aa6873587e8540d05a8
143 Ndf943c6cdd0a46f1b70770381db9715c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
144 schema:name Nucleotide Motifs
145 rdf:type schema:DefinedTerm
146 Nef541488b1964b25b0f8ffe0ab59ac73 schema:volumeNumber 15
147 rdf:type schema:PublicationVolume
148 Nf40e3e7be9834aa6873587e8540d05a8 rdf:first sg:person.0576502353.28
149 rdf:rest N4ca25b46ca2e457b9a5cab26725bc2ab
150 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
151 schema:name Biological Sciences
152 rdf:type schema:DefinedTerm
153 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
154 schema:name Genetics
155 rdf:type schema:DefinedTerm
156 sg:grant.2520061 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2164-15-472
157 rdf:type schema:MonetaryGrant
158 sg:journal.1023790 schema:issn 1471-2164
159 schema:name BMC Genomics
160 rdf:type schema:Periodical
161 sg:person.01164162122.26 schema:affiliation https://www.grid.ac/institutes/grid.17091.3e
162 schema:familyName Wasserman
163 schema:givenName Wyeth W
164 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01164162122.26
165 rdf:type schema:Person
166 sg:person.012352046644.38 schema:affiliation https://www.grid.ac/institutes/grid.17091.3e
167 schema:familyName Worsley Hunt
168 schema:givenName Rebecca
169 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012352046644.38
170 rdf:type schema:Person
171 sg:person.0576502353.28 schema:affiliation https://www.grid.ac/institutes/grid.5515.4
172 schema:familyName del Peso
173 schema:givenName Luis
174 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0576502353.28
175 rdf:type schema:Person
176 sg:person.0650436334.03 schema:affiliation https://www.grid.ac/institutes/grid.17091.3e
177 schema:familyName Mathelier
178 schema:givenName Anthony
179 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0650436334.03
180 rdf:type schema:Person
181 sg:pub.10.1038/nature08514 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001394004
182 https://doi.org/10.1038/nature08514
183 rdf:type schema:CreativeWork
184 sg:pub.10.1038/nature11247 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029065430
185 https://doi.org/10.1038/nature11247
186 rdf:type schema:CreativeWork
187 sg:pub.10.1038/nbt.1630 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023421025
188 https://doi.org/10.1038/nbt.1630
189 rdf:type schema:CreativeWork
190 sg:pub.10.1038/ng.1036 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031633581
191 https://doi.org/10.1038/ng.1036
192 rdf:type schema:CreativeWork
193 sg:pub.10.1038/ng0501-77 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049836241
194 https://doi.org/10.1038/ng0501-77
195 rdf:type schema:CreativeWork
196 sg:pub.10.1038/sj.onc.1200839 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032614069
197 https://doi.org/10.1038/sj.onc.1200839
198 rdf:type schema:CreativeWork
199 sg:pub.10.1186/gb-2010-11-2-r19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001873253
200 https://doi.org/10.1186/gb-2010-11-2-r19
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1016/j.cell.2008.04.043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011546580
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1016/j.cell.2011.11.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026652255
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1016/j.cell.2012.12.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027494799
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1016/j.gene.2004.07.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038294073
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1016/j.molcel.2010.01.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048072203
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1016/j.molcel.2010.05.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012935976
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1073/pnas.0905443106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048603640
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1073/pnas.1316064110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034946777
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1074/jbc.m508138200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048389585
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1093/bib/bbs038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052618674
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1093/bioinformatics/18.8.1135 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046379219
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1093/bioinformatics/btg1021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045072621
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1093/bioinformatics/btn305 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022099792
227 rdf:type schema:CreativeWork
228 https://doi.org/10.1093/bioinformatics/btp163 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011232850
229 rdf:type schema:CreativeWork
230 https://doi.org/10.1093/bioinformatics/btq488 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013620280
231 rdf:type schema:CreativeWork
232 https://doi.org/10.1093/bioinformatics/btr189 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008669948
233 rdf:type schema:CreativeWork
234 https://doi.org/10.1093/emboj/16.1.173 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000225347
235 rdf:type schema:CreativeWork
236 https://doi.org/10.1093/nar/gkh103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040312195
237 rdf:type schema:CreativeWork
238 https://doi.org/10.1093/nar/gkp950 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037453195
239 rdf:type schema:CreativeWork
240 https://doi.org/10.1093/nar/gkq710 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050917766
241 rdf:type schema:CreativeWork
242 https://doi.org/10.1093/nar/gkr1104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037277442
243 rdf:type schema:CreativeWork
244 https://doi.org/10.1093/nar/gkr341 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039806696
245 rdf:type schema:CreativeWork
246 https://doi.org/10.1093/nar/gkr377 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043523064
247 rdf:type schema:CreativeWork
248 https://doi.org/10.1093/nar/gks1089 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039358249
249 rdf:type schema:CreativeWork
250 https://doi.org/10.1093/nar/gks1172 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003865873
251 rdf:type schema:CreativeWork
252 https://doi.org/10.1093/nar/gks433 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019163615
253 rdf:type schema:CreativeWork
254 https://doi.org/10.1093/nar/gkt088 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044820193
255 rdf:type schema:CreativeWork
256 https://doi.org/10.1101/gr.094144.109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019092889
257 rdf:type schema:CreativeWork
258 https://doi.org/10.1101/gr.104356.109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013376793
259 rdf:type schema:CreativeWork
260 https://doi.org/10.1101/gr.104471.109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028308185
261 rdf:type schema:CreativeWork
262 https://doi.org/10.1126/science.1186176 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030603437
263 rdf:type schema:CreativeWork
264 https://doi.org/10.1128/mcb.05504-11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053452549
265 rdf:type schema:CreativeWork
266 https://doi.org/10.1152/ajpcell.00386.2006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030062242
267 rdf:type schema:CreativeWork
268 https://doi.org/10.1371/journal.pcbi.1002638 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028007921
269 rdf:type schema:CreativeWork
270 https://doi.org/10.1371/journal.pone.0001623 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006044882
271 rdf:type schema:CreativeWork
272 https://doi.org/10.1371/journal.pone.0011425 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030321502
273 rdf:type schema:CreativeWork
274 https://doi.org/10.1371/journal.pone.0011471 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049857332
275 rdf:type schema:CreativeWork
276 https://doi.org/10.1371/journal.pone.0083506 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044310769
277 rdf:type schema:CreativeWork
278 https://doi.org/10.1534/g3.112.003202 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002118368
279 rdf:type schema:CreativeWork
280 https://doi.org/10.1534/genetics.112.138685 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050322133
281 rdf:type schema:CreativeWork
282 https://doi.org/10.3109/10715762.2010.507670 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042568523
283 rdf:type schema:CreativeWork
284 https://www.grid.ac/institutes/grid.17091.3e schema:alternateName University of British Columbia
285 schema:name Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
286 Centre for Molecular Medicine and Therapeutics, 950 W.28th Avenue, V5Z 4H4, Vancouver, BC, Canada
287 Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
288 rdf:type schema:Organization
289 https://www.grid.ac/institutes/grid.5515.4 schema:alternateName Autonomous University of Madrid
290 schema:name Universidad Autónoma de Madrid, Biochemistry, 28029, Madrid, Spain
291 rdf:type schema:Organization
 




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


...