ArticleStatistical applications in genetics and molecular biology2012
A new approach for the joint analysis of multiple ChIP-seq libraries with application to histone modification.
Article in Statistical applications in genetics and molecular biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 20 citations in OpenAlex.
- Beyond the Standard GWAS-A Guide for Plant Biologists.Plant & cell physiology · 2025Review
- CLIMB: High-dimensional association detection in large scale genomic data.Nature communications · 2022Article
- 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
- An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.PLoS computational biology · 2014Article
- Joint analysis of expression profiles from multiple cancers improves the identification of microRNA-gene interactions.Bioinformatics (Oxford, England) · 2013Article
- A statistical framework for joint eQTL analysis in multiple tissues.PLoS genetics · 2013Article
- jMOSAiCS: joint analysis of multiple ChIP-seq datasets.Genome biology · 2013Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Most approaches for analyzing ChIP-Seq data are focused on inferring exact protein binding sites from a single library. However, frequently multiple ChIP-Seq libraries derived from differing cell lines or tissue types from the same individual may be available. In such a situation, a separate analysis for each tissue or cell line may be inefficient. Here, we describe a novel method to analyze such data that intelligently uses the joint information from multiple related ChIP-Seq libraries. We present our method as a two-stage procedure. First, separate single cell line analysis is performed for each cell line. Here, we use a novel mixture regression approach to infer the subset of genes that are most likely to be involved in protein binding in each cell line. In the second step, we combine the separate single cell line analyses using an Empirical Bayes algorithm that implicitly incorporates inter-cell line correlation. We demonstrate the usefulness of our method using both simulated data, as well as real H3K4me3 and H3K27me3 histone methylation libraries.
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Registered trials
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.