BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization
Identifieur interne : 000A55 ( Main/Exploration ); précédent : 000A54; suivant : 000A56BROCKMAN: 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
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
BROCKMAN and related approaches will help gain a mechanistic understanding of the
The online version of this article (10.1186/s12859-018-2255-6) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12859-018-2255-6
PubMed: 29970004
PubMed Central: 6029352
Affiliations:
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Le document en format XML
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">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>
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<author><name sortKey="Regev, Aviv" sort="Regev, Aviv" uniqKey="Regev A" first="Aviv" last="Regev">Aviv Regev</name>
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<series><title level="j">BMC Bioinformatics</title>
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<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>
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<keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Transcription Factors</term>
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<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Epigenomics</term>
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<keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Facteurs de transcription</term>
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<term>Humans</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p id="Par1">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 <italic>K</italic>
-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.</p>
</sec>
<sec><title>Results</title>
<p id="Par2">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 <italic>k</italic>
-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.</p>
</sec>
<sec><title>Conclusions</title>
<p id="Par3">BROCKMAN and related approaches will help gain a mechanistic understanding of the <italic>trans</italic>
determinants of chromatin variability between cells, treatments, and individuals.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (10.1186/s12859-018-2255-6) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
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