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

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 indivi...

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Vydáno v:	PLoS computational biology Ročník 15; číslo 12; s. e1007556
Hlavní autoři:	Li, Jiajin, Jew, Brandon, Zhan, Lingyu, Hwang, Sungoo, Coppola, Giovanni, Freimer, Nelson B., Sul, Jae Hoon
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 01.12.2019 Public Library of Science (PLoS)
Témata:	Algorithms Biology and Life Sciences Classification Computational Biology Computer and Information Sciences Control methods Databases, Genetic - statistics & numerical data Datasets Disease Filtration Geffen, David Gene sequencing Genetic diversity Genetic variance Genetic Variation Genomes Genotype & phenotype Genotyping High-Throughput Nucleotide Sequencing - standards High-Throughput Nucleotide Sequencing - statistics & numerical data Humans Learning algorithms Machine Learning Next-generation sequencing Physical Sciences Polymorphism, Single Nucleotide Psychiatry Quality Control Research and Analysis Methods Software Whole genome sequencing Whole Genome Sequencing - standards Whole Genome Sequencing - statistics & numerical data Los Angeles California United States > US California
ISSN:	1553-7358, 1553-734X, 1553-7358
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Popis
Shrnutí:	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-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data.
Bibliografie:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1007556