ArticleNucleic acids research2017
Discovery and validation of information theory-based transcription factor and cofactor binding site motifs.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 30 citations in OpenAlex.
- Article
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- Baf155 regulates skeletal muscle metabolism via HIF-1a signaling.PLoS biology · 2023Article
- BML: a versatile web server for bipartite motif discovery.Briefings in bioinformatics · 2022Article
- Biologically relevant transfer learning improves transcription factor binding prediction.Genome biology · 2021Article
- MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs.Bioinformatics (Oxford, England) · 2020Article
- TFregulomeR reveals transcription factors' context-specific features and functions.Nucleic acids research · 2020Article
- Expression Changes Confirm Genomic Variants Predicted to Result in Allele-Specific, Alternative mRNA Splicing.Frontiers in genetics · 2020Article
- A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections.F1000Research · 2020Article
- DIpartite: A tool for detecting bipartite motifs by considering base interdependencies.PloS one · 2019Article
- BRCA1 and BRCA2 5' noncoding region variants identified in breast cancer patients alter promoter activity and protein binding.Human mutation · 2018Article
- Classifying human promoters by occupancy patterns identifies recurring sequence elements, combinatorial binding, and spatial interactions.BMC biology · 2018Article
- Modular discovery of monomeric and dimeric transcription factor binding motifs for large data sets.Nucleic acids research · 2018Article
- Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations.F1000Research · 2018Article
- Prevalence and spectrum of germline rare variants in BRCA1/2 and PALB2 among breast cancer cases in Sarawak, Malaysia.Breast cancer research and treatment · 2017Article
- Deconvolving sequence features that discriminate between overlapping regulatory annotations.PLoS computational biology · 2017Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data from ChIP-seq experiments can derive the genome-wide binding specificities of transcription factors (TFs) and other regulatory proteins. We analyzed 765 ENCODE ChIP-seq peak datasets of 207 human TFs with a novel motif discovery pipeline based on recursive, thresholded entropy minimization. This approach, while obviating the need to compensate for skewed nucleotide composition, distinguishes true binding motifs from noise, quantifies the strengths of individual binding sites based on computed affinity and detects adjacent cofactor binding sites that coordinate with the targets of primary, immunoprecipitated TFs. We obtained contiguous and bipartite information theory-based position weight matrices (iPWMs) for 93 sequence-specific TFs, discovered 23 cofactor motifs for 127 TFs and revealed six high-confidence novel motifs. The reliability and accuracy of these iPWMs were determined via four independent validation methods, including the detection of experimentally proven binding sites, explanation of effects of characterized SNPs, comparison with previously published motifs and statistical analyses. We also predict previously unreported TF coregulatory interactions (e.g. TF complexes). These iPWMs constitute a powerful tool for predicting the effects of sequence variants in known binding sites, performing mutation analysis on regulatory SNPs and predicting previously unrecognized binding sites and target genes.
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