Analyzing ChIP-seq Data based on Multiple Knowledge Sources for Histone Modification

ChIP-seq is able to capture the genomic profiles for histone modification by combining chromatin immunoprecipitation (ChIP) with next generation sequencing. However, enriched regions generated from peak finding algorithms are evaluated only based on the limited knowledge acquired from manually examining the relevant biological literature. This paper proposes a novel framework of incorporating multiple knowledge sources, consisting of information extracted from biological literature, Gene Ontology, and microarray data, in order to precisely analyze ChIP-seq data for histone modification. The information is combined in a unified probabilistic model to rerank the enriched regions generated from peak finding algorithms. Through filtering the reranked enriched regions using some predefined threshold, more reliable and precise results could be generated. The combination of the multiple knowledge sources with the peaking finding algorithm produces a new paradigm for ChIP-seq data analysis.