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A Probabilistic Method for identifying Transcription Factor Target Genes from ChIP-Seq Binding Profiles.
Bioinformatics. 2011 Oct 29;
Authors: Cheng C, Min R, Gerstein M
Abstract
MOTIVATION: ChIP-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 2kb 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. RESULTS: Here we propose a probabilistic model called TIP (Target Identification from Profiles) 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.Conclusion: We show the advantages of TIP by comparing it to the "simple" approach on several representative data sets, 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: [email protected].
PMID: 22039215 [PubMed - as supplied by publisher]
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A Probabilistic Method for identifying Transcription Factor Target Genes from ChIP-Seq Binding Profiles.
Bioinformatics. 2011 Oct 29;
Authors: Cheng C, Min R, Gerstein M
Abstract
MOTIVATION: ChIP-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 2kb 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. RESULTS: Here we propose a probabilistic model called TIP (Target Identification from Profiles) 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.Conclusion: We show the advantages of TIP by comparing it to the "simple" approach on several representative data sets, 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: [email protected].
PMID: 22039215 [PubMed - as supplied by publisher]
More...