Bayesian localization of CNV candidates in WGS data within minutes.

ABSTRACT:

Background

Full Bayesian inference for detecting copy number variants (CNV) from whole-genome sequencing (WGS) data is still largely infeasible due to computational demands. A recently introduced approach to perform Forward-Backward Gibbs sampling using dynamic Haar wavelet compression has alleviated issues of convergence and, to some extent, speed. Yet, the problem remains challenging in practice.

Results

In this paper, we propose an improved algorithmic framework for this approach. We provide new space-efficient data structures to query sufficient statistics in logarithmic time, based on a linear-time, in-place transform of the data, which also improves on the compression ratio. We also propose a new approach to efficiently store and update marginal state counts obtained f

SUBMITTER: Wiedenhoeft J 

PROVIDER: S-EPMC6757390 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature