ArticleNucleic acids research2009
High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites.
Article in Nucleic acids research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 69 citations in OpenAlex.
- Article
- A comprehensive review of computational prediction of genome-wide features.Briefings in bioinformatics · 2020Article
- Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites.Current genomics · 2017Review
- Imputation for transcription factor binding predictions based on deep learning.PLoS computational biology · 2017Article
- Integrating motif, DNA accessibility and gene expression data to build regulatory maps in an organism.Nucleic acids research · 2015Article
- Article
- Long non-coding RNA identification over mouse brain development by integrative modeling of chromatin and genomic features.Nucleic acids research · 2013Article
- Global Mapping of Transcription Factor Binding Sites by Sequencing Chromatin Surrogates: a Perspective on Experimental Design, Data Analysis, and Open Problems.Statistics in biosciences · 2013Article
- Deciphering the transcriptional regulation of microRNA genes in humans with ACTLocater.Nucleic acids research · 2013Article
- Genome-wide in silico prediction of gene expression.Bioinformatics (Oxford, England) · 2012Article
- Global identification of transcriptional regulators of pluripotency and differentiation in embryonic stem cells.Nucleic acids research · 2012Article
- Sequence and chromatin determinants of cell-type-specific transcription factor binding.Genome research · 2012Article
- Cell-type specificity of ChIP-predicted transcription factor binding sites.BMC genomics · 2012Article
- Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection.Nucleic acids research · 2012Article
- Identification of co-occurring transcription factor binding sites from DNA sequence using clustered position weight matrices.Nucleic acids research · 2012Article
- Epigenetic priors for identifying active transcription factor binding sites.Bioinformatics (Oxford, England) · 2012Article
- TIP: a probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles.Bioinformatics (Oxford, England) · 2011Article
- Identification of cis-regulatory sequence variations in individual genome sequences.Genome medicine · 2011Article
- Tissue-specific prediction of directly regulated genes.Bioinformatics (Oxford, England) · 2011Article
- Prediction of regulatory interactions from genome sequences using a biophysical model for the Arabidopsis LEAFY transcription factor.The Plant cell · 2011Article
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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
In silico prediction of transcription factor binding sites (TFBSs) is central to the task of gene regulatory network elucidation. Genomic DNA sequence information provides a basis for these predictions, due to the sequence specificity of TF-binding events. However, DNA sequence alone is an impoverished source of information for the task of TFBS prediction in eukaryotes, as additional factors, such as chromatin structure regulate binding events. We show that incorporating high-throughput chromatin modification estimates can greatly improve the accuracy of in silico prediction of in vivo binding for a wide range of TFs in human and mouse. This improvement is superior to the improvement gained by equivalent use of either transcription start site proximity or phylogenetic conservation information. Importantly, predictions made with the use of chromatin structure information are tissue specific. This result supports the biological hypothesis that chromatin modulates TF binding to produce tissue-specific binding profiles in higher eukaryotes, and suggests that the use of chromatin modification information can lead to accurate tissue-specific transcriptional regulatory network elucidation.
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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.