An optimized genomic VCF workflow for precise identification of Mycobacterium tuberculosis cluster from cross-platform whole genome sequencing data

Areeya Disratthakit; Licht Toyo-Oka; Penpitcha Thawong; Pundharika Paiboonsiri; Nuanjun Wichukjinda; Pravech Ajawatanawong; Natthakan Thipkrua; Krairerk Suthum; Prasit Palittapongarnpim; Katsushi Tokunaga; Surakameth Mahasirimongkol

doi:10.1016/j.meegid.2019.104152

An optimized genomic VCF workflow for precise identification of Mycobacterium tuberculosis cluster from cross-platform whole genome sequencing data

Infect Genet Evol. 2020 Apr:79:104152. doi: 10.1016/j.meegid.2019.104152. Epub 2019 Dec 24.

Affiliations

¹ Department of Medical Sciences, Ministry of Public Health, Nonthaburi 11000, Thailand. Electronic address: areeya.d@dmsc.mail.go.th.
² Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan.
³ Department of Medical Sciences, Ministry of Public Health, Nonthaburi 11000, Thailand.
⁴ Department of Microbiology, Faculty of Science, Mahidol University, Bangkok 10400, Thailand.
⁵ The Office of Diseases Prevention and Control 5, Department of Diseases Control, Ministry of Public Health, Ratchaburi 70000, Thailand.
⁶ Department of Microbiology, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Ministry of Science and Technology, Pathum Thani 12120, Thailand.
⁷ Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan; Genome Medical Science Project (Toyama), National Center for Global Health and Medicine, Tokyo 162-8655, Japan.

PMID: 31881359
DOI: 10.1016/j.meegid.2019.104152

Abstract

Whole-genome sequencing (WGS) data allow for an inference of Mycobacterium tuberculosis (Mtb) clusters by using a pairwise genetic distance of ≤12 single nucleotide polymorphisms (SNPs) as a threshold. However, a problem of discrepancies in numbers of SNPs and genetic distance measurement is a great concern when combining WGS data from different next generation sequencing (NGS) platforms. We performed SNP variant calling on WGS data of 9 multidrug-resistant (MDR-TB), 3 extensively drug-resistant tuberculosis (XDR-TB) and a standard M. tuberculosis strain H37Rv from an Illumina/NextSeq500 and an Ion Torrent PGM. Variant calls were obtained using four different common variant calling tools, including Genome Analysis Toolkit (GATK) HaplotypeCaller (GATK-VCF workflow), GATK HaplotypeCaller and GenotypeGVCFs (GATK-GVCF workflow), SAMtools, and VarScan 2. Cross-platform pairwise SNP differences, minimum spanning networks and average nucleotide identity (ANI) were analysed to measure performance of the variant calling tools. Minimum pairwise SNP differences ranged from 2 to 14 SNPs when using GVCF workflow while maximum pairwise SNP differences ranged from 7 to 158 SNPs when using VarScan 2. ANI comparison between SNPs data from NextSeq500 and PGM of MDR-TB and XDR-TB showed maximum ANI of 99.7% and 99.0%, respectively, with GVCF workflow while the other SNP calling results showed lower ANI in a range of 98.6% to 95.1%. In this study, we suggest that the GVCF workflow showed the best performing variant caller to avoid cross-platform pairwise SNP differences.

Keywords: Cluster analysis; Mycobacterium tuberculosis; Next-generation sequencing; Outbreak investigation; Tuberculosis; Whole-genome sequencing.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Drug Resistance, Multiple, Bacterial
Extensively Drug-Resistant Tuberculosis / microbiology
Genome, Bacterial
High-Throughput Nucleotide Sequencing
Humans
Mycobacterium tuberculosis / classification*
Mycobacterium tuberculosis / genetics
Polymorphism, Single Nucleotide*
Tuberculosis / classification*
Tuberculosis / microbiology
Tuberculosis, Multidrug-Resistant / microbiology
Whole Genome Sequencing / methods*
Workflow