ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest.

ABSTRACT: Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-pos

SUBMITTER: Li J 

PROVIDER: S-EPMC6938691 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature