Markov Methods For Identifying Chip-Seq Peaks

Abstract

Used to analyze protein interactions with DNA, Chromatin Immunoprecipitation sequencing (ChIP-seq) uses high throughput sequencing technologies to map millions of short DNA "reads" to a reference genome. As the majority of reads map to a protein binding region for a specific protein of interest, a large read count at any given position indicates the presence of a binding region, so that scientists seek "peaks," areas of high counts along the genome. This thesis presents several methods to identify binding regions, utilizing hidden Markov model methods. Unlike existing methods, the final model, HiDe-Peak, accounts for both several major covariates, including mappability and GC content, as well as the dependence between counts present in the dataset. On real data, HiDe-Peak performs in line with existing methods, and in simulations, outperforms its competitors.