A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data.
Identifieur interne : 001A64 ( PubMed/Curation ); précédent : 001A63; suivant : 001A65A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data.
Auteurs : Yaron Orenstein [Israël] ; Ron ShamirSource :
- Nucleic acids research [ 1362-4962 ] ; 2014.
Descripteurs français
- KwdFr :
- Analyse de séquence d'ADN, Analyse par réseau de protéines (), Animaux, Facteurs de transcription (métabolisme), Humains, Immunoprécipitation de la chromatine, Modèles biologiques, Oligonucléotides, Sites de fixation, Souris, Séquençage nucléotidique à haut débit, Technique SELEX (), Éléments de régulation transcriptionnelle.
- MESH :
- métabolisme : Facteurs de transcription.
- Analyse de séquence d'ADN, Analyse par réseau de protéines, Animaux, Humains, Immunoprécipitation de la chromatine, Modèles biologiques, Oligonucléotides, Sites de fixation, Souris, Séquençage nucléotidique à haut débit, Technique SELEX, Éléments de régulation transcriptionnelle.
English descriptors
- KwdEn :
- Animals, Binding Sites, Chromatin Immunoprecipitation, High-Throughput Nucleotide Sequencing, Humans, Mice, Models, Biological, Oligonucleotides, Protein Array Analysis (methods), Regulatory Elements, Transcriptional, SELEX Aptamer Technique (methods), Sequence Analysis, DNA, Transcription Factors (metabolism).
- MESH :
- chemical , metabolism : Transcription Factors.
- chemical : Oligonucleotides.
- methods : Protein Array Analysis, SELEX Aptamer Technique.
- Animals, Binding Sites, Chromatin Immunoprecipitation, High-Throughput Nucleotide Sequencing, Humans, Mice, Models, Biological, Regulatory Elements, Transcriptional, Sequence Analysis, DNA.
Abstract
Understanding gene regulation is a key challenge in today's biology. The new technologies of protein-binding microarrays (PBMs) and high-throughput SELEX (HT-SELEX) allow measurement of the binding intensities of one transcription factor (TF) to numerous synthetic double-stranded DNA sequences in a single experiment. Recently, Jolma et al. reported the results of 547 HT-SELEX experiments covering human and mouse TFs. Because 162 of these TFs were also covered by PBM technology, for the first time, a large-scale comparison between implementations of these two in vitro technologies is possible. Here we assessed the similarities and differences between binding models, represented as position weight matrices, inferred from PBM and HT-SELEX, and also measured how well these models predict in vivo binding. Our results show that HT-SELEX- and PBM-derived models agree for most TFs. For some TFs, the HT-SELEX-derived models are longer versions of the PBM-derived models, whereas for other TFs, the HT-SELEX models match the secondary PBM-derived models. Remarkably, PBM-based 8-mer ranking is more accurate than that of HT-SELEX, but models derived from HT-SELEX predict in vivo binding better. In addition, we reveal several biases in HT-SELEX data including nucleotide frequency bias, enrichment of C-rich k-mers and oligos and underrepresentation of palindromes.
DOI: 10.1093/nar/gku117
PubMed: 24500199
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The new technologies of protein-binding microarrays (PBMs) and high-throughput SELEX (HT-SELEX) allow measurement of the binding intensities of one transcription factor (TF) to numerous synthetic double-stranded DNA sequences in a single experiment. Recently, Jolma et al. reported the results of 547 HT-SELEX experiments covering human and mouse TFs. Because 162 of these TFs were also covered by PBM technology, for the first time, a large-scale comparison between implementations of these two in vitro technologies is possible. Here we assessed the similarities and differences between binding models, represented as position weight matrices, inferred from PBM and HT-SELEX, and also measured how well these models predict in vivo binding. Our results show that HT-SELEX- and PBM-derived models agree for most TFs. For some TFs, the HT-SELEX-derived models are longer versions of the PBM-derived models, whereas for other TFs, the HT-SELEX models match the secondary PBM-derived models. Remarkably, PBM-based 8-mer ranking is more accurate than that of HT-SELEX, but models derived from HT-SELEX predict in vivo binding better. In addition, we reveal several biases in HT-SELEX data including nucleotide frequency bias, enrichment of C-rich k-mers and oligos and underrepresentation of palindromes. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24500199</PMID><DateCompleted><Year>2014</Year><Month>07</Month><Day>22</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1362-4962</ISSN><JournalIssue CitedMedium="Internet"><Volume>42</Volume><Issue>8</Issue><PubDate><Year>2014</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Nucleic acids research</Title><ISOAbbreviation>Nucleic Acids Res.</ISOAbbreviation></Journal><ArticleTitle>A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data.</ArticleTitle><Pagination><MedlinePgn>e63</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/nar/gku117</ELocationID><Abstract><AbstractText>Understanding gene regulation is a key challenge in today's biology. The new technologies of protein-binding microarrays (PBMs) and high-throughput SELEX (HT-SELEX) allow measurement of the binding intensities of one transcription factor (TF) to numerous synthetic double-stranded DNA sequences in a single experiment. Recently, Jolma et al. reported the results of 547 HT-SELEX experiments covering human and mouse TFs. Because 162 of these TFs were also covered by PBM technology, for the first time, a large-scale comparison between implementations of these two in vitro technologies is possible. Here we assessed the similarities and differences between binding models, represented as position weight matrices, inferred from PBM and HT-SELEX, and also measured how well these models predict in vivo binding. Our results show that HT-SELEX- and PBM-derived models agree for most TFs. For some TFs, the HT-SELEX-derived models are longer versions of the PBM-derived models, whereas for other TFs, the HT-SELEX models match the secondary PBM-derived models. Remarkably, PBM-based 8-mer ranking is more accurate than that of HT-SELEX, but models derived from HT-SELEX predict in vivo binding better. In addition, we reveal several biases in HT-SELEX data including nucleotide frequency bias, enrichment of C-rich k-mers and oligos and underrepresentation of palindromes. </AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Orenstein</LastName><ForeName>Yaron</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv 69978, Israel.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shamir</LastName><ForeName>Ron</ForeName><Initials>R</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D003160">Comparative Study</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate 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