ArticleNucleic acids research2017
Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.
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 59 papers.
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Who cites it
59 citing papers in PubMed.
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- Predicting CTCF cell type active binding sites in human genome.Scientific reports · 2024Article
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- Epigenome-wide impact of MAT2A sustains the androgen-indifferent state and confers synthetic vulnerability in ERG fusion-positive prostate cancer.Nature communications · 2024Article
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- Analysis of REST binding sites with canonical and non-canonical motifs in human cell lines.BMC medical genomics · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Multiomics analysis identifies novel facilitators of human dopaminergic neuron differentiation.EMBO reports · 2024Article
- Methylome-wide analysis in systemic microbial-induced experimental periodontal disease in mice with different susceptibility.Frontiers in cellular and infection microbiology · 2024Article
- MiREx: mRNA levels prediction from gene sequence and miRNA target knowledge.BMC bioinformatics · 2023Article
- Alterations in the hepatocyte epigenetic landscape in steatosis.Epigenetics & chromatin · 2023Article
- The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data.Bioinformatics (Oxford, England) · 2023Article
- xcore: an R package for inference of gene expression regulators.BMC bioinformatics · 2023Article
- TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors.GigaScience · 2022Article
- Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes.Human molecular genetics · 2022Review
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Authors and funding
26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.
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