Computational Techniques for Human Genome Resequencing Using Mated Gapped Reads

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Unchained base reads on self-assembling DNA nanoarrays have recently emerged as a promising approach to low-cost, high-quality resequencing of human genomes. Because of unique characteristics of these mated pair reads, existing computational methods for resequencing assembly, such as those based on map-consensus calling, are not adequate for accurate variant calling. We describe novel computational methods developed for accurate calling of SNPs and short substitutions and indels (a posteriori probability given the observed reads. For each candidate sequence, this probability is computed using Bayesian statistics with a simple read generation model and simplifying assumptions that make the problem computationally tractable. The optimization process iteratively applies one-base substitutions, insertions, and deletions until convergence is achieved to an optimum diploid sequence. A local de novo assembly procedure that generalizes approaches based on De Bruijn graphs is used to seed the optimization process in order to reduce the chance of converging to local optima. Finally, a correlation-based filter is applied to reduce the false positive rate caused by the presence of repetitive regions in the reference genome.

This publication has 22 references indexed in Scilit: