BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization.
Identifieur interne : 000847 ( PubMed/Curation ); précédent : 000846; suivant : 000848BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization.
Auteurs : Carl G. De Boer [États-Unis] ; Aviv Regev [États-Unis]Source :
- BMC bioinformatics [ 1471-2105 ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
- métabolisme : Facteurs de transcription.
- Humains, Sites de fixation, Épigénomique.
English descriptors
- KwdEn :
- MESH :
- chemical , metabolism : Transcription Factors.
- methods : Epigenomics.
- Binding Sites, Humans.
Abstract
Variation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark.
DOI: 10.1186/s12859-018-2255-6
PubMed: 29970004
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De Boer</name><affiliation wicri:level="1"><nlm:affiliation>Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142</wicri:regionArea></affiliation></author><author><name sortKey="Regev, Aviv" sort="Regev, Aviv" uniqKey="Regev A" first="Aviv" last="Regev">Aviv Regev</name><affiliation wicri:level="1"><nlm:affiliation>Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. carlgdeboer@gmail.com.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2018">2018</date><idno type="RBID">pubmed:29970004</idno><idno type="pmid">29970004</idno><idno type="doi">10.1186/s12859-018-2255-6</idno><idno type="wicri:Area/PubMed/Corpus">000847</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000847</idno><idno type="wicri:Area/PubMed/Curation">000847</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000847</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization.</title><author><name sortKey="De Boer, Carl G" sort="De Boer, Carl G" uniqKey="De Boer C" first="Carl G" last="De Boer">Carl G. De Boer</name><affiliation wicri:level="1"><nlm:affiliation>Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142</wicri:regionArea></affiliation></author><author><name sortKey="Regev, Aviv" sort="Regev, Aviv" uniqKey="Regev A" first="Aviv" last="Regev">Aviv Regev</name><affiliation wicri:level="1"><nlm:affiliation>Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. carlgdeboer@gmail.com.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Binding Sites</term><term>Epigenomics (methods)</term><term>Humans</term><term>Transcription Factors (metabolism)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Facteurs de transcription (métabolisme)</term><term>Humains</term><term>Sites de fixation</term><term>Épigénomique ()</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Transcription Factors</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Epigenomics</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Facteurs de transcription</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Binding Sites</term><term>Humans</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Humains</term><term>Sites de fixation</term><term>Épigénomique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Variation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">29970004</PMID><DateCompleted><Year>2019</Year><Month>02</Month><Day>13</Day></DateCompleted><DateRevised><Year>2019</Year><Month>02</Month><Day>15</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><Issue>1</Issue><PubDate><Year>2018</Year><Month>07</Month><Day>03</Day></PubDate></JournalIssue><Title>BMC bioinformatics</Title><ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization.</ArticleTitle><Pagination><MedlinePgn>253</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12859-018-2255-6</ELocationID><Abstract><AbstractText Label="BACKGROUND">Variation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark.</AbstractText><AbstractText Label="RESULTS">BROCKMAN represents each sample as a vector of epigenomic-mark-associated DNA word frequencies, and decomposes the resulting matrix to find hidden structure in the data, followed by unsupervised grouping of samples and identification of the TFs that distinguish groups. Applied to single cell ATAC-seq, BROCKMAN readily distinguished cell types, treatments, batch effects, experimental artifacts, and cycling cells. We show that each variable component in the k-mer landscape reflects a set of co-varying TFs, which are often known to physically interact. For example, in K562 cells, AP-1 TFs were central determinant of variability in chromatin accessibility through their variable expression levels and diverse interactions with other TFs. We provide a theoretical basis for why cooperative TF binding - and any associated epigenomic mark - is inherently more variable than non-cooperative binding.</AbstractText><AbstractText Label="CONCLUSIONS">BROCKMAN and related approaches will help gain a mechanistic understanding of the trans determinants of chromatin variability between cells, treatments, and individuals.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>de Boer</LastName><ForeName>Carl G</ForeName><Initials>CG</Initials><Identifier Source="ORCID">0000-0001-8935-5921</Identifier><AffiliationInfo><Affiliation>Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Regev</LastName><ForeName>Aviv</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. carlgdeboer@gmail.com.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Biology, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02140, USA. carlgdeboer@gmail.com.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Howard Hughes Medical Institute, Chevy Chase, MD, 20815, USA. carlgdeboer@gmail.com.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>RM1 HG006193</GrantID><Acronym>HG</Acronym><Agency>NHGRI NIH HHS</Agency><Country>United States</Country></Grant><Grant><Agency>CIHR</Agency><Country>Canada</Country></Grant><Grant><Acronym>HHMI</Acronym><Agency>Howard Hughes Medical Institute</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>07</Month><Day>03</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Bioinformatics</MedlineTA><NlmUniqueID>100965194</NlmUniqueID><ISSNLinking>1471-2105</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D014157">Transcription Factors</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D001665" MajorTopicYN="N">Binding Sites</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D057890" MajorTopicYN="N">Epigenomics</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014157" MajorTopicYN="N">Transcription Factors</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Chromatin</Keyword><Keyword MajorTopicYN="Y">Clustering</Keyword><Keyword MajorTopicYN="Y">Decomposition</Keyword><Keyword MajorTopicYN="Y">Epigenome</Keyword><Keyword MajorTopicYN="Y">Factorization</Keyword><Keyword MajorTopicYN="Y">K-mer</Keyword><Keyword MajorTopicYN="Y">N-gram</Keyword><Keyword MajorTopicYN="Y">Single-cell</Keyword><Keyword MajorTopicYN="Y">Transcription factor</Keyword><Keyword MajorTopicYN="Y">scATAC-seq</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2017</Year><Month>09</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2018</Year><Month>06</Month><Day>20</Day></PubMedPubDate><PubMedPubDate 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