ArticleBioinformatics (Oxford, England)2011
TIP: a probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Systematic target function annotation of human transcription factors.BMC biology · 2018Article
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- Large-Scale trans-eQTLs Affect Hundreds of Transcripts and Mediate Patterns of Transcriptional Co-regulation.American journal of human genetics · 2017Article
- Inferring condition-specific targets of human TF-TF complexes using ChIP-seq data.BMC genomics · 2017Article
- Contextual Refinement of Regulatory Targets Reveals Effects on Breast Cancer Prognosis of the Regulome.PLoS computational biology · 2017Article
- Integrated network analysis reveals distinct regulatory roles of transcription factors and microRNAs.RNA (New York, N.Y.) · 2016Article
- iTAR: a web server for identifying target genes of transcription factors using ChIP-seq or ChIP-chip data.BMC genomics · 2016Article
- ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles.Nucleic acids research · 2016Article
- Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic Value.Molecular cancer research : MCR · 2016Article
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3 authors.
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Abstract
motivationChIP-seq and ChIP-chip experiments have been widely used to identify transcription factor (TF) binding sites and target genes. Conventionally, a fairly 'simple' approach is employed for target gene identification e.g. finding genes with binding sites within 2 kb of a transcription start site (TSS). However, this does not take into account the number of sites upstream of the TSS, their exact positioning or the fact that different TFs appear to act at different characteristic distances from the TSS.
resultsHere we propose a probabilistic model called target identification from profiles (TIP) that quantitatively measures the regulatory relationships between TFs and target genes. For each TF, our model builds a characteristic, averaged profile of binding around the TSS and then uses this to weight the sites associated with a given gene, providing a continuous-valued 'regulatory' score relating each TF and potential target. Moreover, the score can readily be turned into a ranked list of target genes and an estimate of significance, which is useful for case-dependent downstream analysis.
conclusionWe show the advantages of TIP by comparing it to the 'simple' approach on several representative datasets, using motif occurrence and relationship to knock-out experiments as metrics of validation. Moreover, we show that the probabilistic model is not as sensitive to various experimental parameters (including sequencing depth and peak-calling method) as the simple approach; in fact, the lesser dependence on sequencing depth potentially utilizes the result of a ChIP-seq experiment in a more 'cost-effective' manner. CONTACT: mark.gerstein@yale.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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