Target analysis by integration of transcriptome and ChIP-seq data with BETA View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-12

AUTHORS

Su Wang, Hanfei Sun, Jian Ma, Chongzhi Zang, Chenfei Wang, Juan Wang, Qianzi Tang, Clifford A Meyer, Yong Zhang, X Shirley Liu

ABSTRACT

The combination of ChIP-seq and transcriptome analysis is a compelling approach to unravel the regulation of gene expression. Several recently published methods combine transcription factor (TF) binding and gene expression for target prediction, but few of them provide an efficient software package for the community. Binding and expression target analysis (BETA) is a software package that integrates ChIP-seq of TFs or chromatin regulators with differential gene expression data to infer direct target genes. BETA has three functions: (i) to predict whether the factor has activating or repressive function; (ii) to infer the factor's target genes; and (iii) to identify the motif of the factor and its collaborators, which might modulate the factor's activating or repressive function. Here we describe the implementation and features of BETA to demonstrate its application to several data sets. BETA requires ~1 GB of RAM, and the procedure takes 20 min to complete. BETA is available open source at http://cistrome.org/BETA/. More... »

PAGES

2502-2515

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nprot.2013.150

DOI

http://dx.doi.org/10.1038/nprot.2013.150

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1022717163

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24263090


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": "Chromatin Immunoprecipitation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Expression Profiling", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Software", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Transcriptome", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Tongji University", 
          "id": "https://www.grid.ac/institutes/grid.24516.34", 
          "name": [
            "Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Su", 
        "id": "sg:person.01360423170.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01360423170.85"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tongji University", 
          "id": "https://www.grid.ac/institutes/grid.24516.34", 
          "name": [
            "Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sun", 
        "givenName": "Hanfei", 
        "id": "sg:person.01255232436.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01255232436.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tongji University", 
          "id": "https://www.grid.ac/institutes/grid.24516.34", 
          "name": [
            "Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ma", 
        "givenName": "Jian", 
        "id": "sg:person.01142157322.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01142157322.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Harvard University", 
          "id": "https://www.grid.ac/institutes/grid.38142.3c", 
          "name": [
            "Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zang", 
        "givenName": "Chongzhi", 
        "id": "sg:person.0751245470.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0751245470.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tongji University", 
          "id": "https://www.grid.ac/institutes/grid.24516.34", 
          "name": [
            "Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Chenfei", 
        "id": "sg:person.013501755077.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013501755077.30"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tongji University", 
          "id": "https://www.grid.ac/institutes/grid.24516.34", 
          "name": [
            "Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Juan", 
        "id": "sg:person.0714473376.97", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0714473376.97"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tongji University", 
          "id": "https://www.grid.ac/institutes/grid.24516.34", 
          "name": [
            "Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tang", 
        "givenName": "Qianzi", 
        "id": "sg:person.01333215717.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01333215717.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Harvard University", 
          "id": "https://www.grid.ac/institutes/grid.38142.3c", 
          "name": [
            "Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Meyer", 
        "givenName": "Clifford A", 
        "id": "sg:person.010256757607.98", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010256757607.98"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tongji University", 
          "id": "https://www.grid.ac/institutes/grid.24516.34", 
          "name": [
            "Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Yong", 
        "id": "sg:person.011774204647.95", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011774204647.95"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Harvard University", 
          "id": "https://www.grid.ac/institutes/grid.38142.3c", 
          "name": [
            "Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "X Shirley", 
        "id": "sg:person.01007325065.71", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01007325065.71"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1093/nar/gkl1041", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002652611"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkm415", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003264848"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nprot.2012.101", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003624809", 
          "https://doi.org/10.1038/nprot.2012.101"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/emboj.2010.342", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004531791"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btp554", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006715941"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkq963", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007075476"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btr628", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008345624"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1901", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012239798", 
          "https://doi.org/10.1038/ng1901"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1901", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012239798", 
          "https://doi.org/10.1038/ng1901"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0601180103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013675034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1917", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015337823", 
          "https://doi.org/10.1038/ng1917"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1917", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015337823", 
          "https://doi.org/10.1038/ng1917"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/25.16.3318", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015811515"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.229102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022792016"
        ], 
        "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": "sg:pub.10.1186/gb-2009-10-3-r29", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024677703", 
          "https://doi.org/10.1186/gb-2009-10-3-r29"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2011-12-8-r83", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025406648", 
          "https://doi.org/10.1186/gb-2011-12-8-r83"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/b978-0-12-385075-1.00003-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025671827"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2008-9-9-r137", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027608848", 
          "https://doi.org/10.1186/gb-2008-9-9-r137"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-61779-292-2_16", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029355479", 
          "https://doi.org/10.1007/978-1-61779-292-2_16"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-61779-292-2_16", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029355479", 
          "https://doi.org/10.1007/978-1-61779-292-2_16"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bib/bbs017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029766327"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nprot.2012.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030124536", 
          "https://doi.org/10.1038/nprot.2012.016"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/0008-5472.can-08-2632", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031812516"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkr332", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034228291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btg347", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034593423"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0506580102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037705714"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0506580102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037705714"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-8-454", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040376636", 
          "https://doi.org/10.1186/1471-2105-8-454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gni179", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040526112"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0914285107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041195832"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btp340", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041238764"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/0008-5472.can-09-0395", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041337378"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btr552", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043563611"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature10066", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045516695", 
          "https://doi.org/10.1038/nature10066"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1677/jme.1.01478", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045865023"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.febslet.2004.07.055", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046471558"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/0008-5472.can-11-2091", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048819142"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.molcel.2007.05.041", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048974498"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2009-10-3-r25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049583368", 
          "https://doi.org/10.1186/gb-2009-10-3-r25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cell.2005.08.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049638413"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cell.2005.08.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049638413"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1000010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050133600"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkp1180", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052469829"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.6026/97320630002428", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1073593888"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2013-12", 
    "datePublishedReg": "2013-12-01", 
    "description": "The combination of ChIP-seq and transcriptome analysis is a compelling approach to unravel the regulation of gene expression. Several recently published methods combine transcription factor (TF) binding and gene expression for target prediction, but few of them provide an efficient software package for the community. Binding and expression target analysis (BETA) is a software package that integrates ChIP-seq of TFs or chromatin regulators with differential gene expression data to infer direct target genes. BETA has three functions: (i) to predict whether the factor has activating or repressive function; (ii) to infer the factor's target genes; and (iii) to identify the motif of the factor and its collaborators, which might modulate the factor's activating or repressive function. Here we describe the implementation and features of BETA to demonstrate its application to several data sets. BETA requires ~1 GB of RAM, and the procedure takes 20 min to complete. BETA is available open source at http://cistrome.org/BETA/.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/nprot.2013.150", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2697615", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2529282", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1037502", 
        "issn": [
          "1754-2189", 
          "1750-2799"
        ], 
        "name": "Nature Protocols", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "12", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "8"
      }
    ], 
    "name": "Target analysis by integration of transcriptome and ChIP-seq data with BETA", 
    "pagination": "2502-2515", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "6c3a2996e30e8b2c16d427de65d88396c58ef7e7b555cb10fd460d5ec0b967b9"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "24263090"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101284307"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/nprot.2013.150"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1022717163"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/nprot.2013.150", 
      "https://app.dimensions.ai/details/publication/pub.1022717163"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T16:47", 
    "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_8669_00000536.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://www.nature.com/articles/nprot.2013.150"
  }
]
 

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/nprot.2013.150'

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/nprot.2013.150'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/nprot.2013.150'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/nprot.2013.150'


 

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

287 TRIPLES      21 PREDICATES      73 URIs      25 LITERALS      13 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/nprot.2013.150 schema:about N0c4567622173402abcb80600b6a14d79
2 N2ffc3463869644a5a0137cff383152c3
3 N8775067bc4d24c00b1ae54e1d6db7c22
4 Nb6ff1ff5161640d0b55b01f0ed857fdb
5 anzsrc-for:06
6 anzsrc-for:0604
7 schema:author N385f180aedc0487f9760775010b2e4b7
8 schema:citation sg:pub.10.1007/978-1-61779-292-2_16
9 sg:pub.10.1038/nature10066
10 sg:pub.10.1038/nbt.1630
11 sg:pub.10.1038/ng1901
12 sg:pub.10.1038/ng1917
13 sg:pub.10.1038/nprot.2012.016
14 sg:pub.10.1038/nprot.2012.101
15 sg:pub.10.1186/1471-2105-8-454
16 sg:pub.10.1186/gb-2008-9-9-r137
17 sg:pub.10.1186/gb-2009-10-3-r25
18 sg:pub.10.1186/gb-2009-10-3-r29
19 sg:pub.10.1186/gb-2011-12-8-r83
20 https://doi.org/10.1016/b978-0-12-385075-1.00003-2
21 https://doi.org/10.1016/j.cell.2005.08.020
22 https://doi.org/10.1016/j.febslet.2004.07.055
23 https://doi.org/10.1016/j.molcel.2007.05.041
24 https://doi.org/10.1038/emboj.2010.342
25 https://doi.org/10.1073/pnas.0506580102
26 https://doi.org/10.1073/pnas.0601180103
27 https://doi.org/10.1073/pnas.0914285107
28 https://doi.org/10.1093/bib/bbs017
29 https://doi.org/10.1093/bioinformatics/btg347
30 https://doi.org/10.1093/bioinformatics/btp340
31 https://doi.org/10.1093/bioinformatics/btp554
32 https://doi.org/10.1093/bioinformatics/btr552
33 https://doi.org/10.1093/bioinformatics/btr628
34 https://doi.org/10.1093/nar/25.16.3318
35 https://doi.org/10.1093/nar/gkl1041
36 https://doi.org/10.1093/nar/gkm415
37 https://doi.org/10.1093/nar/gkp1180
38 https://doi.org/10.1093/nar/gkq963
39 https://doi.org/10.1093/nar/gkr332
40 https://doi.org/10.1093/nar/gni179
41 https://doi.org/10.1101/gr.229102
42 https://doi.org/10.1158/0008-5472.can-08-2632
43 https://doi.org/10.1158/0008-5472.can-09-0395
44 https://doi.org/10.1158/0008-5472.can-11-2091
45 https://doi.org/10.1371/journal.pcbi.1000010
46 https://doi.org/10.1677/jme.1.01478
47 https://doi.org/10.6026/97320630002428
48 schema:datePublished 2013-12
49 schema:datePublishedReg 2013-12-01
50 schema:description The combination of ChIP-seq and transcriptome analysis is a compelling approach to unravel the regulation of gene expression. Several recently published methods combine transcription factor (TF) binding and gene expression for target prediction, but few of them provide an efficient software package for the community. Binding and expression target analysis (BETA) is a software package that integrates ChIP-seq of TFs or chromatin regulators with differential gene expression data to infer direct target genes. BETA has three functions: (i) to predict whether the factor has activating or repressive function; (ii) to infer the factor's target genes; and (iii) to identify the motif of the factor and its collaborators, which might modulate the factor's activating or repressive function. Here we describe the implementation and features of BETA to demonstrate its application to several data sets. BETA requires ~1 GB of RAM, and the procedure takes 20 min to complete. BETA is available open source at http://cistrome.org/BETA/.
51 schema:genre research_article
52 schema:inLanguage en
53 schema:isAccessibleForFree true
54 schema:isPartOf N670fa69ce6854073b14a06958cc60fda
55 Ncf138697587940b486456bf3e1d38fd0
56 sg:journal.1037502
57 schema:name Target analysis by integration of transcriptome and ChIP-seq data with BETA
58 schema:pagination 2502-2515
59 schema:productId N3f11a69477674bcfaa7a10f205a6f694
60 N5e90b53b13e1467ab31c07e19ffc250a
61 N63fed742d0164468b3795b394a3ca023
62 N7850459142df49649208542d2bc73079
63 Ndad9b7cce37e40caa7e3b8c1d9319530
64 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022717163
65 https://doi.org/10.1038/nprot.2013.150
66 schema:sdDatePublished 2019-04-10T16:47
67 schema:sdLicense https://scigraph.springernature.com/explorer/license/
68 schema:sdPublisher N413b39f70cdd4e7bbb564a8d164acde1
69 schema:url http://www.nature.com/articles/nprot.2013.150
70 sgo:license sg:explorer/license/
71 sgo:sdDataset articles
72 rdf:type schema:ScholarlyArticle
73 N08adc030fc6946cdae1c85e0e98343ce rdf:first sg:person.0751245470.33
74 rdf:rest N1f454be6a9934405aaaf98eaf23a10c0
75 N0c4567622173402abcb80600b6a14d79 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
76 schema:name Chromatin Immunoprecipitation
77 rdf:type schema:DefinedTerm
78 N1f454be6a9934405aaaf98eaf23a10c0 rdf:first sg:person.013501755077.30
79 rdf:rest N2212923d890340de8c0332890753bf42
80 N2212923d890340de8c0332890753bf42 rdf:first sg:person.0714473376.97
81 rdf:rest N3a2bbba47d6e4782a3d6445438080b0a
82 N2ffc3463869644a5a0137cff383152c3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
83 schema:name Gene Expression Profiling
84 rdf:type schema:DefinedTerm
85 N385f180aedc0487f9760775010b2e4b7 rdf:first sg:person.01360423170.85
86 rdf:rest N51ffa3a3a6bf45b5b43a3bf006cef6fa
87 N3a2bbba47d6e4782a3d6445438080b0a rdf:first sg:person.01333215717.49
88 rdf:rest Ne91e3b5fa3b54bb8bd9e9ff473e0fecf
89 N3f11a69477674bcfaa7a10f205a6f694 schema:name readcube_id
90 schema:value 6c3a2996e30e8b2c16d427de65d88396c58ef7e7b555cb10fd460d5ec0b967b9
91 rdf:type schema:PropertyValue
92 N413b39f70cdd4e7bbb564a8d164acde1 schema:name Springer Nature - SN SciGraph project
93 rdf:type schema:Organization
94 N512e968e7b724365b4ede2a0b5942bc6 rdf:first sg:person.01007325065.71
95 rdf:rest rdf:nil
96 N51ffa3a3a6bf45b5b43a3bf006cef6fa rdf:first sg:person.01255232436.33
97 rdf:rest Nb7011880dfbb49fbbfb3a9f3c8e78d71
98 N5e90b53b13e1467ab31c07e19ffc250a schema:name doi
99 schema:value 10.1038/nprot.2013.150
100 rdf:type schema:PropertyValue
101 N63fed742d0164468b3795b394a3ca023 schema:name pubmed_id
102 schema:value 24263090
103 rdf:type schema:PropertyValue
104 N670fa69ce6854073b14a06958cc60fda schema:volumeNumber 8
105 rdf:type schema:PublicationVolume
106 N7850459142df49649208542d2bc73079 schema:name dimensions_id
107 schema:value pub.1022717163
108 rdf:type schema:PropertyValue
109 N8775067bc4d24c00b1ae54e1d6db7c22 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
110 schema:name Transcriptome
111 rdf:type schema:DefinedTerm
112 Nb6ff1ff5161640d0b55b01f0ed857fdb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
113 schema:name Software
114 rdf:type schema:DefinedTerm
115 Nb7011880dfbb49fbbfb3a9f3c8e78d71 rdf:first sg:person.01142157322.39
116 rdf:rest N08adc030fc6946cdae1c85e0e98343ce
117 Ncf138697587940b486456bf3e1d38fd0 schema:issueNumber 12
118 rdf:type schema:PublicationIssue
119 Ndad9b7cce37e40caa7e3b8c1d9319530 schema:name nlm_unique_id
120 schema:value 101284307
121 rdf:type schema:PropertyValue
122 Ne91e3b5fa3b54bb8bd9e9ff473e0fecf rdf:first sg:person.010256757607.98
123 rdf:rest Nee1643896d80406fa1df34dd6f87b954
124 Nee1643896d80406fa1df34dd6f87b954 rdf:first sg:person.011774204647.95
125 rdf:rest N512e968e7b724365b4ede2a0b5942bc6
126 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
127 schema:name Biological Sciences
128 rdf:type schema:DefinedTerm
129 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
130 schema:name Genetics
131 rdf:type schema:DefinedTerm
132 sg:grant.2529282 http://pending.schema.org/fundedItem sg:pub.10.1038/nprot.2013.150
133 rdf:type schema:MonetaryGrant
134 sg:grant.2697615 http://pending.schema.org/fundedItem sg:pub.10.1038/nprot.2013.150
135 rdf:type schema:MonetaryGrant
136 sg:journal.1037502 schema:issn 1750-2799
137 1754-2189
138 schema:name Nature Protocols
139 rdf:type schema:Periodical
140 sg:person.01007325065.71 schema:affiliation https://www.grid.ac/institutes/grid.38142.3c
141 schema:familyName Liu
142 schema:givenName X Shirley
143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01007325065.71
144 rdf:type schema:Person
145 sg:person.010256757607.98 schema:affiliation https://www.grid.ac/institutes/grid.38142.3c
146 schema:familyName Meyer
147 schema:givenName Clifford A
148 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010256757607.98
149 rdf:type schema:Person
150 sg:person.01142157322.39 schema:affiliation https://www.grid.ac/institutes/grid.24516.34
151 schema:familyName Ma
152 schema:givenName Jian
153 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01142157322.39
154 rdf:type schema:Person
155 sg:person.011774204647.95 schema:affiliation https://www.grid.ac/institutes/grid.24516.34
156 schema:familyName Zhang
157 schema:givenName Yong
158 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011774204647.95
159 rdf:type schema:Person
160 sg:person.01255232436.33 schema:affiliation https://www.grid.ac/institutes/grid.24516.34
161 schema:familyName Sun
162 schema:givenName Hanfei
163 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01255232436.33
164 rdf:type schema:Person
165 sg:person.01333215717.49 schema:affiliation https://www.grid.ac/institutes/grid.24516.34
166 schema:familyName Tang
167 schema:givenName Qianzi
168 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01333215717.49
169 rdf:type schema:Person
170 sg:person.013501755077.30 schema:affiliation https://www.grid.ac/institutes/grid.24516.34
171 schema:familyName Wang
172 schema:givenName Chenfei
173 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013501755077.30
174 rdf:type schema:Person
175 sg:person.01360423170.85 schema:affiliation https://www.grid.ac/institutes/grid.24516.34
176 schema:familyName Wang
177 schema:givenName Su
178 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01360423170.85
179 rdf:type schema:Person
180 sg:person.0714473376.97 schema:affiliation https://www.grid.ac/institutes/grid.24516.34
181 schema:familyName Wang
182 schema:givenName Juan
183 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0714473376.97
184 rdf:type schema:Person
185 sg:person.0751245470.33 schema:affiliation https://www.grid.ac/institutes/grid.38142.3c
186 schema:familyName Zang
187 schema:givenName Chongzhi
188 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0751245470.33
189 rdf:type schema:Person
190 sg:pub.10.1007/978-1-61779-292-2_16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029355479
191 https://doi.org/10.1007/978-1-61779-292-2_16
192 rdf:type schema:CreativeWork
193 sg:pub.10.1038/nature10066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045516695
194 https://doi.org/10.1038/nature10066
195 rdf:type schema:CreativeWork
196 sg:pub.10.1038/nbt.1630 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023421025
197 https://doi.org/10.1038/nbt.1630
198 rdf:type schema:CreativeWork
199 sg:pub.10.1038/ng1901 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012239798
200 https://doi.org/10.1038/ng1901
201 rdf:type schema:CreativeWork
202 sg:pub.10.1038/ng1917 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015337823
203 https://doi.org/10.1038/ng1917
204 rdf:type schema:CreativeWork
205 sg:pub.10.1038/nprot.2012.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030124536
206 https://doi.org/10.1038/nprot.2012.016
207 rdf:type schema:CreativeWork
208 sg:pub.10.1038/nprot.2012.101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003624809
209 https://doi.org/10.1038/nprot.2012.101
210 rdf:type schema:CreativeWork
211 sg:pub.10.1186/1471-2105-8-454 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040376636
212 https://doi.org/10.1186/1471-2105-8-454
213 rdf:type schema:CreativeWork
214 sg:pub.10.1186/gb-2008-9-9-r137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027608848
215 https://doi.org/10.1186/gb-2008-9-9-r137
216 rdf:type schema:CreativeWork
217 sg:pub.10.1186/gb-2009-10-3-r25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049583368
218 https://doi.org/10.1186/gb-2009-10-3-r25
219 rdf:type schema:CreativeWork
220 sg:pub.10.1186/gb-2009-10-3-r29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024677703
221 https://doi.org/10.1186/gb-2009-10-3-r29
222 rdf:type schema:CreativeWork
223 sg:pub.10.1186/gb-2011-12-8-r83 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025406648
224 https://doi.org/10.1186/gb-2011-12-8-r83
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1016/b978-0-12-385075-1.00003-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025671827
227 rdf:type schema:CreativeWork
228 https://doi.org/10.1016/j.cell.2005.08.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049638413
229 rdf:type schema:CreativeWork
230 https://doi.org/10.1016/j.febslet.2004.07.055 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046471558
231 rdf:type schema:CreativeWork
232 https://doi.org/10.1016/j.molcel.2007.05.041 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048974498
233 rdf:type schema:CreativeWork
234 https://doi.org/10.1038/emboj.2010.342 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004531791
235 rdf:type schema:CreativeWork
236 https://doi.org/10.1073/pnas.0506580102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037705714
237 rdf:type schema:CreativeWork
238 https://doi.org/10.1073/pnas.0601180103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013675034
239 rdf:type schema:CreativeWork
240 https://doi.org/10.1073/pnas.0914285107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041195832
241 rdf:type schema:CreativeWork
242 https://doi.org/10.1093/bib/bbs017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029766327
243 rdf:type schema:CreativeWork
244 https://doi.org/10.1093/bioinformatics/btg347 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034593423
245 rdf:type schema:CreativeWork
246 https://doi.org/10.1093/bioinformatics/btp340 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041238764
247 rdf:type schema:CreativeWork
248 https://doi.org/10.1093/bioinformatics/btp554 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006715941
249 rdf:type schema:CreativeWork
250 https://doi.org/10.1093/bioinformatics/btr552 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043563611
251 rdf:type schema:CreativeWork
252 https://doi.org/10.1093/bioinformatics/btr628 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008345624
253 rdf:type schema:CreativeWork
254 https://doi.org/10.1093/nar/25.16.3318 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015811515
255 rdf:type schema:CreativeWork
256 https://doi.org/10.1093/nar/gkl1041 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002652611
257 rdf:type schema:CreativeWork
258 https://doi.org/10.1093/nar/gkm415 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003264848
259 rdf:type schema:CreativeWork
260 https://doi.org/10.1093/nar/gkp1180 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052469829
261 rdf:type schema:CreativeWork
262 https://doi.org/10.1093/nar/gkq963 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007075476
263 rdf:type schema:CreativeWork
264 https://doi.org/10.1093/nar/gkr332 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034228291
265 rdf:type schema:CreativeWork
266 https://doi.org/10.1093/nar/gni179 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040526112
267 rdf:type schema:CreativeWork
268 https://doi.org/10.1101/gr.229102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022792016
269 rdf:type schema:CreativeWork
270 https://doi.org/10.1158/0008-5472.can-08-2632 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031812516
271 rdf:type schema:CreativeWork
272 https://doi.org/10.1158/0008-5472.can-09-0395 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041337378
273 rdf:type schema:CreativeWork
274 https://doi.org/10.1158/0008-5472.can-11-2091 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048819142
275 rdf:type schema:CreativeWork
276 https://doi.org/10.1371/journal.pcbi.1000010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050133600
277 rdf:type schema:CreativeWork
278 https://doi.org/10.1677/jme.1.01478 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045865023
279 rdf:type schema:CreativeWork
280 https://doi.org/10.6026/97320630002428 schema:sameAs https://app.dimensions.ai/details/publication/pub.1073593888
281 rdf:type schema:CreativeWork
282 https://www.grid.ac/institutes/grid.24516.34 schema:alternateName Tongji University
283 schema:name Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China.
284 rdf:type schema:Organization
285 https://www.grid.ac/institutes/grid.38142.3c schema:alternateName Harvard University
286 schema:name Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, USA.
287 rdf:type schema:Organization
 




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


...