Robust and rapid algorithms facilitate large-scale whole genome sequencing downstream analysis in an integrative framework.

ABSTRACT: Whole genome sequencing (WGS) is a promising strategy to unravel variants or genes responsible for human diseases and traits. However, there is a lack of robust platforms for a comprehensive downstream analysis. In the present study, we first proposed three novel algorithms, sequence gap-filled gene feature annotation, bit-block encoded genotypes and sectional fast access to text lines to address three fundamental problems. The three algorithms then formed the infrastructure of a robust parallel computing framework, KGGSeq, for integrating downstream analysis functions for whole genome sequencing data. KGGSeq has been equipped with a comprehensive set of analysis functions for quality control, filtration, annotation, pathogenic prediction and statistical tests. In the tests with whole geno

SUBMITTER: Li M 

PROVIDER: S-EPMC5435951 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature