ArticleBMC genomics2014
dCaP: detecting differential binding events in multiple conditions and proteins.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A MAD-Bayes Algorithm for State-Space Inference and Clustering with Application to Querying Large Collections of ChIP-Seq Data Sets.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- InCoB2014: mining biological data from genomics for transforming industry and health.BMC genomics · 2014Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundCurrent ChIP-seq studies are interested in comparing multiple epigenetic profiles across several cell types and tissues simultaneously for studying constitutive and differential regulation. Simultaneous analysis of multiple epigenetic features in many samples can gain substantial power and specificity than analyzing individual features and/or samples separately. Yet there are currently few tools can perform joint inference of constitutive and differential regulation in multi-feature-multi-condition contexts with statistical testing. Existing tools either test regulatory variation for one factor in multiple samples at a time, or for multiple factors in one or two samples. Many of them only identify binary rather than quantitative variation, which are sensitive to threshold choices.
resultsWe propose a novel and powerful method called dCaP for simultaneously detecting constitutive and differential regulation of multiple epigenetic factors in multiple samples. Using simulation, we demonstrate the superior power of dCaP compared to existing methods. We then apply dCaP to two datasets from human and mouse ENCODE projects to demonstrate its utility. We show in the human dataset that the cell-type specific regulatory loci detected by dCaP are significantly enriched near genes with cell-type specific functions and disease relevance. We further show in the mouse dataset that dCaP captures genomic regions showing significant signal variations for TAL1 occupancy between two mouse erythroid cell lines. The novel TAL1 occupancy loci detected only by dCaP are highly enriched with GATA1 occupancy and differential gene expression, while those detected only by other methods are not.
conclusionsHere, we developed a novel approach to utilize the cooperative property of proteins to detect differential binding given multivariate ChIP-seq samples to provide better power, aiming for complementing existing approaches and providing new insights in the method development in this field.
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