ArticleBioinformatics (Oxford, England)2012
An effective statistical evaluation of ChIPseq dataset similarity.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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42 citing papers in PubMed, 63 citations in OpenAlex.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
motivationChIPseq is rapidly becoming a common technique for investigating protein-DNA interactions. However, results from individual experiments provide a limited understanding of chromatin structure, as various chromatin factors cooperate in complex ways to orchestrate transcription. In order to quantify chromtain interactions, it is thus necessary to devise a robust similarity metric applicable to ChIPseq data. Unfortunately, moving past simple overlap calculations to give statistically rigorous comparisons of ChIPseq datasets often involves arbitrary choices of distance metrics, with significance being estimated by computationally intensive permutation tests whose statistical power may be sensitive to non-biological experimental and post-processing variation.
resultsWe show that it is in fact possible to compare ChIPseq datasets through the efficient computation of exact P-values for proximity. Our method is insensitive to non-biological variation in datasets such as peak width, and can rigorously model peak location biases by evaluating similarity conditioned on a restricted set of genomic regions (such as mappable genome or promoter regions). Applying our method to the well-studied dataset of Chen et al. (2008), we elucidate novel interactions which conform well with our biological understanding. By comparing ChIPseq data in an asymmetric way, we are able to observe clear interaction differences between cofactors such as p300 and factors that bind DNA directly. AVAILABILITY: Source code is available for download at http://sonorus.princeton.edu/IntervalStats/IntervalStats.tar.gz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.