A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data.

ABSTRACT:

Background

The advent of next generation sequencing technology has accelerated efforts to map and catalogue copy number variation (CNV) in genomes of important micro-organisms for public health. A typical analysis of the sequence data involves mapping reads onto a reference genome, calculating the respective coverage, and detecting regions with too-low or too-high coverage (deletions and amplifications, respectively). Current CNV detection methods rely on statistical assumptions (e.g., a Poisson model) that may not hold in general, or require fine-tuning the underlying algorithms to detect known hits. We propose a new CNV detection methodology based on two Poisson hierarchical models, the Poisson-Gamma and Poisson-Lognormal, with the advantage of being sufficiently flexible to desc

SUBMITTER: Sepulveda N 

PROVIDER: S-EPMC3679970 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature