Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data. Issue 1 (27th December 2021)

• Record Type: Journal Article
• Authors: Goossens, Sander N; Heupink, Tim H; De Vos, Elise; Dippenaar, Anzaan; De Vos, Margaretha; Warren, Rob; Van Rie, Annelies
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 1(2022)
• Publication Date: 2021-12-27
• Subjects: M. tuberculosis -- minor variant calling -- low-frequency variant calling -- LoFreq -- whole genome sequencing -- benchmarking; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab541
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: 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.