ArticleNucleic acids research2012
Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 39 citations in OpenAlex.
- A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome.BMC genomics · 2022Article
- PCRMS: a database of predicted cis-regulatory modules and constituent transcription factor binding sites in genomes.Database : the journal of biological databases and curation · 2022Article
- Accurate prediction ofNAR genomics and bioinformatics · 2021Article
- Removing Background Co-occurrences of Transcription Factor Binding Sites Greatly Improves the Prediction of Specific Transcription Factor Cooperations.Frontiers in genetics · 2018Article
- A novel method for predicting activity of cis-regulatory modules, based on a diverse training set.Bioinformatics (Oxford, England) · 2017Article
- A New Algorithm for Identifying Cis-Regulatory Modules Based on Hidden Markov Model.BioMed research international · 2017Article
- Maps of context-dependent putative regulatory regions and genomic signal interactions.Nucleic acids research · 2016Article
- Analysis of Genomic Sequence Motifs for Deciphering Transcription Factor Binding and Transcriptional Regulation in Eukaryotic Cells.Frontiers in genetics · 2016Review
- PC-TraFF: identification of potentially collaborating transcription factors using pointwise mutual information.BMC bioinformatics · 2015Article
- De novo prediction of cis-regulatory elements and modules through integrative analysis of a large number of ChIP datasets.BMC genomics · 2014Article
- SIOMICS: a novel approach for systematic identification of motifs in ChIP-seq data.Nucleic acids research · 2014Article
- Discover context-specific combinatorial transcription factor interactions by integrating diverse ChIP-Seq data sets.Nucleic acids research · 2014Article
- CisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.PloS one · 2014Article
- Systematic identification of transcriptional regulatory modules from protein-protein interaction networks.Nucleic acids research · 2014Article
- COPS: detecting co-occurrence and spatial arrangement of transcription factor binding motifs in genome-wide datasets.PloS one · 2012Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the binding sites of the assayed transcription factor (TF) should be located, but also allows restricting the valid CRMs to those that contain the assayed TF (here referred to as applying CRM detection in a query-based mode). In this study, we show that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor. To be able to handle the large datasets, the query-based setting and other specificities proper to CRM detection on ChIP-Seq based data, we developed a novel powerful CRM detection method 'CPModule'. By applying it on a well-studied ChIP-Seq data set involved in self-renewal of mouse embryonic stem cells, we demonstrate how our tool can recover combinatorial regulation of five known TFs that are key in the self-renewal of mouse embryonic stem cells. Additionally, we make a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.