ArticleBioinformatics (Oxford, England)2010
JAMIE: joint analysis of multiple ChIP-chip experiments.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Sandcastle: software for revealing latent information in multiple experimental ChIP-chip datasets via a novel normalisation procedure.Scientific reports · 2015Article
- Identifying differential transcription factor binding in ChIP-seq.Frontiers in genetics · 2015Article
- jMOSAiCS: joint analysis of multiple ChIP-seq datasets.Genome biology · 2013Article
- Differential principal component analysis of ChIP-seq.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
- Exploring the cooccurrence patterns of multiple sets of genomic intervals.BioMed research international · 2013Article
- CpG array analysis of histone H3 lysine 4 trimethylation by chromatin immunoprecipitation linked to microarrays analysis in peripheral blood mononuclear cells of IgA nephropathy patients.Yonsei medical journal · 2012Article
- A new approach for the joint analysis of multiple ChIP-seq libraries with application to histone modification.Statistical applications in genetics and molecular biology · 2012Article
- MM-ChIP enables integrative analysis of cross-platform and between-laboratory ChIP-chip or ChIP-seq data.Genome biology · 2011Article
- ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments.Bioinformatics (Oxford, England) · 2010Article
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Authors and funding
2 authors.
Funding
Abstract
motivationChromatin immunoprecipitation followed by genome tiling array hybridization (ChIP-chip) is a powerful approach to identify transcription factor binding sites (TFBSs) in target genomes. When multiple related ChIP-chip datasets are available, analyzing them jointly allows one to borrow information across datasets to improve peak detection. This is particularly useful for analyzing noisy datasets.
resultsWe propose a hierarchical mixture model and develop an R package JAMIE to perform the joint analysis. The genome is assumed to consist of background and potential binding regions (PBRs). PBRs have context-dependent probabilities to become bona fide binding sites in individual datasets. This model captures the correlation among datasets, which provides basis for sharing information across experiments. Real data tests illustrate the advantage of JAMIE over a strategy that analyzes individual datasets separately. AVAILABILITY: JAMIE is freely available from http://www.biostat.jhsph.edu/~hji/jamie
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