ArticleNucleic acids research2017
Mocap: large-scale inference of transcription factor binding sites from chromatin accessibility.
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 22 papers.
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Who cites it
22 citing papers in PubMed.
- Modeling strategies forBioinformatics advances · 2026Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Predicting CTCF cell type active binding sites in human genome.Scientific reports · 2024Article
- A Unifying Hypothesis for the Genome Dynamics Proposed to Underlie Neuropsychiatric Phenotypes.Genes · 2024Review
- NetTIME: a multitask and base-pair resolution framework for improved transcription factor binding site prediction.Bioinformatics (Oxford, England) · 2022Article
- Elucidating the biology of transcription factor-DNA interaction for accurate identification of cis-regulatory elements.Current opinion in plant biology · 2022Review
- Alternative transcription start sites contribute to acute-stress-induced transcriptome response in human skeletal muscle.Human genomics · 2022Article
- Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome.Genome biology · 2022Article
- Automated generation of context-specific gene regulatory networks with a weighted approach inInterface focus · 2021Article
- DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks.BMC bioinformatics · 2021Article
- Evidence of widespread, independent sequence signature for transcription factor cobinding.Genome research · 2021Article
- An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced transcription factor binding.Genome biology · 2021Article
- Sequence and chromatin determinants of transcription factor binding and the establishment of cell type-specific binding patterns.Biochimica et biophysica acta. Gene regulatory mechanisms · 2020Review
- A comprehensive review of computational prediction of genome-wide features.Briefings in bioinformatics · 2020Article
- regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data.Frontiers in bioengineering and biotechnology · 2020Article
- IMPACT: Genomic Annotation of Cell-State-Specific Regulatory Elements Inferred from the Epigenome of Bound Transcription Factors.American journal of human genetics · 2019Article
- Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells.Genome research · 2019Article
- Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets using protocol-specific bias modeling.Genome biology · 2019Article
- Anchor: trans-cell type prediction of transcription factor binding sites.Genome research · 2019Article
- Accurate prediction of cell type-specific transcription factor binding.Genome biology · 2019Article
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5 authors.
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Abstract
Differential binding of transcription factors (TFs) at cis-regulatory loci drives the differentiation and function of diverse cellular lineages. Understanding the regulatory interactions that underlie cell fate decisions requires characterizing TF binding sites (TFBS) across multiple cell types and conditions. Techniques, e.g. ChIP-Seq can reveal genome-wide patterns of TF binding, but typically requires laborious and costly experiments for each TF-cell-type (TFCT) condition of interest. Chromosomal accessibility assays can connect accessible chromatin in one cell type to many TFs through sequence motif mapping. Such methods, however, rarely take into account that the genomic context preferred by each factor differs from TF to TF, and from cell type to cell type. To address the differences in TF behaviors, we developed Mocap, a method that integrates chromatin accessibility, motif scores, TF footprints, CpG/GC content, evolutionary conservation and other factors in an ensemble of TFCT-specific classifiers. We show that integration of genomic features, such as CpG islands improves TFBS prediction in some TFCT. Further, we describe a method for mapping new TFCT, for which no ChIP-seq data exists, onto our ensemble of classifiers and show that our cross-sample TFBS prediction method outperforms several previously described methods.
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