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Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data

The study of genetic minority variants is fundamental to the understanding of complex processes such as evolution, fitness, transmission, virulence, heteroresistance and drug tolerance in Mycobacterium tuberculosis (Mtb). We evaluated the performance of the variant calling tool LoFreq to detect de n...

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Autores principales: Goossens, Sander N, Heupink, Tim H, De Vos, Elise, Dippenaar, Anzaan, De Vos, Margaretha, Warren, Rob, Van Rie, Annelies
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769888/
https://www.ncbi.nlm.nih.gov/pubmed/34962257
http://dx.doi.org/10.1093/bib/bbab541
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author Goossens, Sander N
Heupink, Tim H
De Vos, Elise
Dippenaar, Anzaan
De Vos, Margaretha
Warren, Rob
Van Rie, Annelies
author_facet Goossens, Sander N
Heupink, Tim H
De Vos, Elise
Dippenaar, Anzaan
De Vos, Margaretha
Warren, Rob
Van Rie, Annelies
author_sort Goossens, Sander N
collection PubMed
description The study of genetic minority variants is fundamental to the understanding of complex processes such as evolution, fitness, transmission, virulence, heteroresistance and drug tolerance in Mycobacterium tuberculosis (Mtb). We evaluated the performance of the variant calling tool LoFreq to detect de novo as well as drug resistance conferring minor variants in both in silico and clinical Mtb next generation sequencing (NGS) data. The in silico simulations demonstrated that LoFreq is a conservative variant caller with very high precision (≥96.7%) over the entire range of depth of coverage tested (30x to1000x), independent of the type and frequency of the minor variant. Sensitivity increased with increasing depth of coverage and increasing frequency of the variant, and was higher for calling insertion and deletion (indel) variants than for single nucleotide polymorphisms (SNP). The variant frequency limit of detection was 0.5% and 3% for indel and SNP minor variants, respectively. For serial isolates from a patient with DR-TB; LoFreq successfully identified all minor Mtb variants in the Rv0678 gene (allele frequency as low as 3.22% according to targeted deep sequencing) in whole genome sequencing data (median coverage of 62X). In conclusion, LoFreq can successfully detect minor variant populations in Mtb NGS data, thus limiting the need for filtering of possible false positive variants due to sequencing error. The observed performance statistics can be used to determine the limit of detection in existing whole genome sequencing Mtb data and guide the required depth of future studies that aim to investigate the presence of minor variants.
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spelling pubmed-87698882022-01-20 Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data Goossens, Sander N Heupink, Tim H De Vos, Elise Dippenaar, Anzaan De Vos, Margaretha Warren, Rob Van Rie, Annelies Brief Bioinform Case Study The study of genetic minority variants is fundamental to the understanding of complex processes such as evolution, fitness, transmission, virulence, heteroresistance and drug tolerance in Mycobacterium tuberculosis (Mtb). We evaluated the performance of the variant calling tool LoFreq to detect de novo as well as drug resistance conferring minor variants in both in silico and clinical Mtb next generation sequencing (NGS) data. The in silico simulations demonstrated that LoFreq is a conservative variant caller with very high precision (≥96.7%) over the entire range of depth of coverage tested (30x to1000x), independent of the type and frequency of the minor variant. Sensitivity increased with increasing depth of coverage and increasing frequency of the variant, and was higher for calling insertion and deletion (indel) variants than for single nucleotide polymorphisms (SNP). The variant frequency limit of detection was 0.5% and 3% for indel and SNP minor variants, respectively. For serial isolates from a patient with DR-TB; LoFreq successfully identified all minor Mtb variants in the Rv0678 gene (allele frequency as low as 3.22% according to targeted deep sequencing) in whole genome sequencing data (median coverage of 62X). In conclusion, LoFreq can successfully detect minor variant populations in Mtb NGS data, thus limiting the need for filtering of possible false positive variants due to sequencing error. The observed performance statistics can be used to determine the limit of detection in existing whole genome sequencing Mtb data and guide the required depth of future studies that aim to investigate the presence of minor variants. Oxford University Press 2021-12-27 /pmc/articles/PMC8769888/ /pubmed/34962257 http://dx.doi.org/10.1093/bib/bbab541 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Case Study
Goossens, Sander N
Heupink, Tim H
De Vos, Elise
Dippenaar, Anzaan
De Vos, Margaretha
Warren, Rob
Van Rie, Annelies
Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data
title Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data
title_full Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data
title_fullStr Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data
title_full_unstemmed Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data
title_short Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data
title_sort detection of minor variants in mycobacterium tuberculosis whole genome sequencing data
topic Case Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769888/
https://www.ncbi.nlm.nih.gov/pubmed/34962257
http://dx.doi.org/10.1093/bib/bbab541
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