Poster: A Hidden Markov Model for Copy Number Variant prediction from Whole genome resequencing data

Copy number variants (CNVs) are important genetic factors for studying human diseases. While high-throughput whole genome re-sequencing provides multiple lines of evidence for detecting CNVs, computational algorithms need to be tailored for different type or size of CNVs under different experimental designs. To achieve optimal power and resolution of detecting CNVs at low-depth coverage, a Hidden Markov Model that integrates both depth of coverage and mate-pair relationship was implemented. The novelty of the algorithm is the inference of the likelihood of carrying a deletion jointly from multiple mate pairs in a region without the requirement of a single mate pairs being obvious outliers. By integrating all useful information in a comprehensive model, the method is able to detect medium-size deletions (100-2000 bp) at low depth-coverage (<;10× per sample). The method is applied to simulated data and demonstrates the power of detecting medium size deletions is close to theoretical values.