A new filtering method for improving the quality of variant discovery
Zhang, Chuanyi
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ZHANG-THESIS-2019.pdf
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https://hdl.handle.net/2142/106361
Description
Title
A new filtering method for improving the quality of variant discovery
Author(s)
Zhang, Chuanyi
Issue Date
2019-12-02
Director of Research (if dissertation) or Advisor (if thesis)
Ochoa, Idoia
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2020-03-02T22:15:03Z
Keyword(s)
Filtering
VCF file
Ensemble learning
Abstract
Variants identified by current genomic analysis pipelines contain many incorrectly called variants. These can be potentially eliminated by applying state-of-the-art filtering tools, such as Variant Quality Score Recalibration (VQSR) or Hard Filtering (HF). However, these methods are very user-dependent and fail to run in some cases. We propose Variant Ensemble Filter (VEF), a variant filtering tool based on decision tree ensemble methods that overcomes the main drawbacks of VQSR and HF. Contrary to these methods, we treat filtering as a supervised learning problem, using variant call data with known “true” variants, i.e., gold standard, for training. Once trained, VEF can be directly applied to filter the variants contained in a given VCF file (we consider training and testing VCF files generated with the same tools, as we assume they will share feature characteristics). For the analysis, we used Whole Genome Sequencing (WGS) human datasets for which the gold standards are available. We show on these data that the proposed filtering tool Variant Ensemble Filter (VEF) consistently outperforms VQSR and HF. In addition, we show that VEF generalizes well even when some features have missing values, when the training and testing datasets differ in coverage, and when sequencing pipelines other than GATK are used. Finally, since the training needs to be performed only once, there is a significant saving in running time when compared to VQSR (4 versus 50 minutes approximately for filtering the SNPs of a WGS Human sample).
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ZHANG-THESIS-2019.pdf
