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Evaluating the performance of tools used to call minority variants from whole genome short-read data
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 tr...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6234735/ https://www.ncbi.nlm.nih.gov/pubmed/30483597 http://dx.doi.org/10.12688/wellcomeopenres.13538.2 |
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author | Said Mohammed, Khadija Kibinge, Nelson Prins, Pjotr Agoti, Charles N. Cotten, Matthew Nokes, D.J. Brand, Samuel Githinji, George |
author_facet | Said Mohammed, Khadija Kibinge, Nelson Prins, Pjotr Agoti, Charles N. Cotten, Matthew Nokes, D.J. Brand, Samuel Githinji, George |
author_sort | Said Mohammed, Khadija |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6234735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-62347352018-11-26 Evaluating the performance of tools used to call minority variants from whole genome short-read data Said Mohammed, Khadija Kibinge, Nelson Prins, Pjotr Agoti, Charles N. Cotten, Matthew Nokes, D.J. Brand, Samuel Githinji, George Wellcome Open Res Research Article 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. F1000 Research Limited 2018-09-13 /pmc/articles/PMC6234735/ /pubmed/30483597 http://dx.doi.org/10.12688/wellcomeopenres.13538.2 Text en Copyright: © 2018 Said Mohammed K et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Said Mohammed, Khadija Kibinge, Nelson Prins, Pjotr Agoti, Charles N. Cotten, Matthew Nokes, D.J. Brand, Samuel Githinji, George Evaluating the performance of tools used to call minority variants from whole genome short-read data |
title | Evaluating the performance of tools used to call minority variants from whole genome short-read data |
title_full | Evaluating the performance of tools used to call minority variants from whole genome short-read data |
title_fullStr | Evaluating the performance of tools used to call minority variants from whole genome short-read data |
title_full_unstemmed | Evaluating the performance of tools used to call minority variants from whole genome short-read data |
title_short | Evaluating the performance of tools used to call minority variants from whole genome short-read data |
title_sort | evaluating the performance of tools used to call minority variants from whole genome short-read data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6234735/ https://www.ncbi.nlm.nih.gov/pubmed/30483597 http://dx.doi.org/10.12688/wellcomeopenres.13538.2 |
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