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Evaluating the performance of tools used to call minority variants from whole genome short-read data
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Mohammed, Khadija Said, Kibinge, Nelson, Prins, Pjotr, Agoti, Charles N., Cotten, Matthew, Nokes, D. James, Brand, Samuel and Githinji, George (2018) Evaluating the performance of tools used to call minority variants from whole genome short-read data. Wellcome Open Research, 3 . 21. doi:10.12688/wellcomeopenres.13538.2
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WRAP-evaluating-performance-tools-used-call-minority-variants-whole-genome-short-read-data-Nokes-2018.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2313Kb) | Preview |
Official URL: http://dx.doi.org/10.12688/wellcomeopenres.13538.2
Abstract
Background
High-throughput whole genome sequencing facilitates investigation of minority virus sub-populations from virus positive samples. Minority variants are useful in understanding within and between host diversity, population dynamics and can potentially assist in elucidating person-person transmission pathways. Several minority variant callers have been developed to describe low frequency sub-populations from whole genome sequence data. These callers differ based on bioinformatics and statistical methods used to discriminate sequencing errors from low-frequency variants.
Methods
We evaluated the diagnostic performance and concordance between published minority variant callers used in identifying minority variants from whole-genome sequence data from virus samples. We used the ART-Illumina read simulation tool to generate three artificial short-read datasets of varying coverage and error profiles from an RSV reference genome. The datasets were spiked with nucleotide variants at predetermined positions and frequencies. Variants were called using FreeBayes, LoFreq, Vardict, and VarScan2. The variant callers’ agreement in identifying known variants was quantified using two measures; concordance accuracy and the inter-caller concordance.
Results
The variant callers reported differences in identifying minority variants from the datasets. Concordance accuracy and inter-caller concordance were positively correlated with sample coverage. FreeBayes identified the majority of variants although it was characterised by variable sensitivity and precision in addition to a high false positive rate relative to the other minority variant callers and which varied with sample coverage. LoFreq was the most conservative caller.
Conclusions
We conducted a performance and concordance evaluation of four minority variant calling tools used to identify and quantify low frequency variants. Inconsistency in the quality of sequenced samples impacts on sensitivity and accuracy of minority variant callers. Our study suggests that combining at least three tools when identifying minority variants is useful in filtering errors when calling low frequency variants.
| Item Type: | Journal Article | ||||||||||||||||||
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| Subjects: | Q Science > QH Natural history Q Science > QR Microbiology |
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| Divisions: | Faculty of Science > Life Sciences (2010- ) | ||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Genomics, Viruses -- Africa, Bioinformatics | ||||||||||||||||||
| Journal or Publication Title: | Wellcome Open Research | ||||||||||||||||||
| Publisher: | F1000Research | ||||||||||||||||||
| ISSN: | 2398-502X | ||||||||||||||||||
| Official Date: | 5 March 2018 | ||||||||||||||||||
| Dates: |
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| Date of first compliant deposit: | 9 October 2018 | ||||||||||||||||||
| Volume: | 3 | ||||||||||||||||||
| Article Number: | 21 | ||||||||||||||||||
| DOI: | 10.12688/wellcomeopenres.13538.2 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Access rights to Published version: | Open Access | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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