Comprehensive and accurate genetic variant identification from contaminated and low-coverage Mycobacterium tuberculosis whole genome sequencing data

Abstract

Improved understanding of the genomic variants that allow Mycobacterium tuberculosis (Mtb) to acquire drug resistance, or tolerance, and increase its virulence are important factors in controlling the current tuberculosis epidemic. Current approaches to Mtb sequencing, however, cannot reveal Mtb’s full genomic diversity due to the strict requirements of low contamination levels, high Mtb sequence coverage and elimination of complex regions. We have developed the XBS (compleX Bacterial Samples) bioinformatics pipeline, which implements joint calling and machine-learning-based variant filtering tools to specifically improve variant detection in the important Mtb samples that do not meet these criteria, such as those from unbiased sputum samples. Using novel simulated datasets, which permit exact accuracy verification, XBS was compared to the UVP and MTBseq pipelines. Accuracy statistics showed that all three pipelines performed equally well for sequence data that resemble those obtained from culture isolates of high depth of coverage and low-level contamination. In the complex genomic regions, however, XBS accurately identified 9.0 % more SNPs and 8.1 % more single nucleotide insertions and deletions than the WHO-endorsed unified analysis variant pipeline. XBS also had superior accuracy for sequence data that resemble those obtained directly from sputum samples, where depth of coverage is typically very low and contamination levels are high. XBS was the only pipeline not affected by low depth of coverage (5–10×), type of contamination and excessive contamination levels (>50 %). Simulation results were confirmed using whole genome sequencing (WGS) data from clinical samples, confirming the superior performance of XBS with a higher sensitivity (98.8%) when analysing culture isolates and identification of 13.9 % more variable sites in WGS data from sputum samples as compared to MTBseq, without evidence for false positive variants when rRNA regions were excluded. The XBS pipeline facilitates sequencing of less-than-perfect Mtb samples. These advances will benefit future clinical applications of Mtb sequencing, especially WGS directly from clinical specimens, thereby avoiding in vitro biases and making many more samples available for drug resistance and other genomic analyses. The additional genetic resolution and increased sample success rate will improve genome-wide association studies and sequence-based transmission studies.

Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data

“…Prior to LoFreq variant calling, in silico generated and clinical WGS fastq files were processed using the CompleX Bacterial Samples (XBS) pipeline to generate bam files [ 26 ]. Briefly, Fastq sequence data were mapped to the reference genome (H37Rv NC_000962.3) using BWA mem, after which the XBS pipeline performed identified read deduplication in the bam files using GATKMarkDuplicates (Picard) (Reference paper accepted, still to be added).…”

Transmission, distribution and drug resistance-conferring mutations of extensively drug-resistant tuberculosis in the Western Cape Province, South Africa

“…In 2008, the first-line GenoType MTBDR plus line probe assay (LPA; Hain LifeSciences) was introduced. Since 2013, the initial diagnostic assay in the Western Cape Province is Xpert MTB/RIF (Cepheid) [ 24 ], with further investigation by first-line LPA (INH and RIF) of RIF-resistant isolates. Second-line DST was done using culture-based methods.…”

Variants in Bedaquiline-Candidate-Resistance Genes: Prevalence in Bedaquiline-Naive Patients, Effect on MIC, and Association with Mycobacterium tuberculosis Lineage

“…Sequencing data were analysed using the XBS pipeline (19). All FASTQ sequence data were mapped to the H37Rv reference genome using the Burrows-Wheeler alignment tool BWA-MEM (20).…”