Broad-Enrich: functional interpretation of large sets of broad genomic regions
Raymond G Cavalcante1, Chee Lee1, Ryan P Welch2
1Department of Computational Medicine and Bioinformatics, Department of Biostatistics and Center of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Motivation:
Functional enrichment testing facilitates the interpretation of Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) data in terms of pathways and other biological contexts. Previous methods developed and used to test for key gene sets affected in ChIP-seq experiments treat peaks as points, and are based on the number of peaks associated with a gene or a binary score for each gene. These approaches work well for transcription factors, but histone modifications often occur over broad domains, and across multiple genes.
Results:
To incorporate the unique properties of broad domains into functional enrichment testing, we developed Broad-Enrich, a method that uses the proportion of each gene's locus covered by a peak. We show that our method has a well-calibrated false-positive rate, performing well with ChIP-seq data having broad domains compared with alternative approaches. We illustrate Broad-Enrich with 55 ENCODE ChIP-seq datasets using different methods to define gene loci. Broad-Enrich can also be applied to other datasets consisting of broad genomic domains such as copy number variations.
Availability And Implementation:
http://broad-enrich.med.umich.edu for Web version and R package.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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