CNV-guided multi-read allocation for ChIP-seq

Motivation: In chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) and other short-read sequencing experiments, a considerable fraction of the short reads align to multiple locations on the reference genome (multi-reads). Inferring the origin of multi-reads is critical for accurately mapping reads to repetitive regions. Current state-of-the-art multi-read allocation algorithms rely on the read counts in the local neighborhood of the alignment locations and ignore the variation in the copy numbers of these regions. Copy-number variation (CNV) can directly affect the read densities and, therefore, bias allocation of multi-reads. Results: We propose cnvCSEM (CNV-guided ChIP-Seq by expectation-maximization algorithm), a flexible framework that incorporates CNV in multi-read allocation. cnvCSEM eliminates the CNV bias in multi-read allocation by initializing the read allocation algorithm with CNV-aware initial values. Our data-driven simulations illustrate that cnvCSEM leads to higher read coverage with satisfactory accuracy and lower loss in read-depth recovery (estimation). We evaluate the biological relevance of the cnvCSEM-allocated reads and the resultant peaks with the analysis of several ENCODE ChIP-seq datasets.