Knowledge Integration for Analyzing ChIP-seq Data

摘要

To capture the genomic profiles for histone modification, chromatin immunoprecipitation (ChIP) is combined with next generation sequencing, which is called ChIP-seq. However, enriched regions generated from the ChlP-seq data are only evaluated on the limited knowledge acquired from manually examining the relevant biological literature. This paper proposes a novel framework. which integrates multiple knowledge sources such as biological literature, Gene Ontology, and microarray data. In order to precisely analyze ChlP-seq data for histone modification, knowledge integration is based on a unified probabilistic model. The model is employed to re-rank 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 ChlP-seq data analysis.