ArticleBioinformatics (Oxford, England)2010
Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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48 citing papers in PubMed, 72 citations in OpenAlex.
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- Deep learning-assisted genome-wide characterization of massively parallel reporter assays.Nucleic acids research · 2022Article
- Oncogenic enhancers drive esophageal squamous cell carcinogenesis and metastasis.Nature communications · 2021Article
- Genomic Mechanisms Governing Mineral Homeostasis and the Regulation and Maintenance of Vitamin D Metabolism.JBMR plus · 2021Article
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- A comprehensive review of computational prediction of genome-wide features.Briefings in bioinformatics · 2020Article
- Pervasive H3K27 Acetylation Leads to ERV Expression and a Therapeutic Vulnerability in H3K27M Gliomas.Cancer cell · 2019Article
- Mutant NPM1 Maintains the Leukemic State through HOX Expression.Cancer cell · 2018Article
- A novel method for improved accuracy of transcription factor binding site prediction.Nucleic acids research · 2018Article
- FUN-LDA: A Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: Methods and Applications.American journal of human genetics · 2018Article
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- Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia.Integrative biology : quantitative biosciences from nano to macro · 2016Article
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- Computational Prediction of the Global Functional Genomic Landscape: Applications, Methods, and Challenges.Human heredity · 2016Review
- Identifying Cell Type-Specific Transcription Factors by Integrating ChIP-seq and eQTL Data-Application to Monocyte Gene Regulation.Gene regulation and systems biology · 2016Article
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13 authors at 1 institution in 1 country.
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Abstract
motivationHistone acetylation (HAc) is associated with open chromatin, and HAc has been shown to facilitate transcription factor (TF) binding in mammalian cells. In the innate immune system context, epigenetic studies strongly implicate HAc in the transcriptional response of activated macrophages. We hypothesized that using data from large-scale sequencing of a HAc chromatin immunoprecipitation assay (ChIP-Seq) would improve the performance of computational prediction of binding locations of TFs mediating the response to a signaling event, namely, macrophage activation.
resultsWe tested this hypothesis using a multi-evidence approach for predicting binding sites. As a training/test dataset, we used ChIP-Seq-derived TF binding site locations for five TFs in activated murine macrophages. Our model combined TF binding site motif scanning with evidence from sequence-based sources and from HAc ChIP-Seq data, using a weighted sum of thresholded scores. We find that using HAc data significantly improves the performance of motif-based TF binding site prediction. Furthermore, we find that within regions of high HAc, local minima of the HAc ChIP-Seq signal are particularly strongly correlated with TF binding locations. Our model, using motif scanning and HAc local minima, improves the sensitivity for TF binding site prediction by approximately 50% over a model based on motif scanning alone, at a false positive rate cutoff of 0.01. AVAILABILITY: The data and software source code for model training and validation are freely available online at http://magnet.systemsbiology.net/hac.
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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 OpenQuestion graph.