ArticlePLoS computational biology2019
ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- A CC-NB-ARC-LRR Gene Regulates Bract Morphology in Cotton.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Generative haplotype prediction outperforms statistical methods for small variant detection in next-generation sequencing data.Bioinformatics (Oxford, England) · 2024Article
- [Artificial intelligence and acute kidney injury].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2024Review
- From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism.Journal of personalized medicine · 2023Article
- Glucocorticoid Receptor Gene (Genes · 2023Article
- Genome-Wide SNP and Indel Discovery in Abaca (Current issues in molecular biology · 2023Article
- Discordant calls across genotype discovery approaches elucidate variants with systematic errors.Genome research · 2023Article
- Validation of genetic variants from NGS data using deep convolutional neural networks.BMC bioinformatics · 2023Article
- FVC as an adaptive and accurate method for filtering variants from popular NGS analysis pipelines.Communications biology · 2022Article
- What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics.Human genetics · 2022Review
- A quality control portal for sequencing data deposited at the European genome-phenome archive.Briefings in bioinformatics · 2022Article
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
- Machine learning random forest for predicting oncosomatic variant NGS analysis.Scientific reports · 2021Article
- Efficient collection of a large number of mutations by mutagenesis of DNA damage response defective animals.Scientific reports · 2021Article
- seqQscorer: automated quality control of next-generation sequencing data using machine learning.Genome biology · 2021Article
- Uncovering missed indels by leveraging unmapped reads.Scientific reports · 2019Article
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Authors and funding
7 authors.
Funding
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-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.
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