The MAGMA pipeline for comprehensive genomic analyses of clinical Mycobacterium tuberculosis samples

Abstract

Background Whole genome sequencing (WGS) holds great potential for the management and control of tuberculosis. Accurate analysis of samples with low mycobacterial burden, which are characterized by low (<20x) coverage and high (>40%) levels of contamination, is challenging. We created the MAGMA (Maximum Accessible Genome for Mtb Analysis) bioinformatics pipeline for analysis of clinical Mtb samples. Methods and results High accuracy variant calling is achieved by using a long seedlength during read mapping to filter out contaminants, variant quality score recalibration with machine learning to identify genuine genomic variants, and joint variant calling for low Mtb coverage genomes. MAGMA automatically generates a standardized and comprehensive output of drug resistance information and resistance classification based on the WHO catalogue of Mtb mutations. MAGMA automatically generates phylogenetic trees with drug resistance annotations and trees that visualize the presence of clusters. Drug resistance and phylogeny outputs from sequencing data of 79 primary liquid cultures were compared between the MAGMA and MTBseq pipelines. The MTBseq pipeline reported only a proportion of the variants in candidate drug resistance genes that were reported by MAGMA. Notable differences were in structural variants, variants in highly conserved rrs and rrl genes, and variants in candidate resistance genes for bedaquiline, clofazmine, and delamanid. Phylogeny results were similar between pipelines but only MAGMA visualized clusters. Conclusion The MAGMA pipeline could facilitate the integration of WGS into clinical care as it generates clinically relevant data on drug resistance and phylogeny in an automated, standardized, and reproducible manner.

Characteristic SNPs defining the major multidrug-resistant Mycobacterium tuberculosis clusters identified by EuSeqMyTB to support routine surveillance, EU/EEA, 2017 to 2019

“…For snpCL28, the variant in position 1028437 c > T which defines snpCL28 is located in a transposase gene (Rv0922), and for snpCL29, the variant in position 3640351 c > T would be excluded in most SNP routine calling algorithms [ 12 ]. Other researchers have also observed that reliable calling of SNPs in these generally excluded regions is possible [ 13 , 14 ]. Importantly, nine of the 30 characteristic SNPs identified occur outside annotated reading frames and thus would not be captured using most core genome multi locus sequence typing (MLST) typing systems.…”

Droplet based whole genome amplification for sequencing minute amounts of purified Mycobacterium tuberculosis DNA

“…This is supported by the increasing number of unclassified reads in dMDA samples, particularly noticeable with very low input Mtb -DNA, as evidenced by the taxonomic classification tool, Kraken 2's, inability to assign a taxonomic origin to increasing proportions of reads. There is thus a compelling need for the use of Mtb WGS analysis pipelines, like MAGMA 19 , 20 , that are capable of handling contaminants, whether originating from genuine contamination or artificially introduced MDA artefacts. While MDA has not yet been evaluated for NGS of Mtb , MDA has been used successfully to increase the amount of genomic DNA from clinical smear-negative sputum specimens before processing the DNA with standard IS 6110 -specific PCR methods 30 .…”

“…The raw Illumina WGS reads (FASTQ) from the four dMDA and positive control sample were analysed using the MAGMA bioinformatics pipeline which aligns reads to the Mtb H37Rv (NC000962.3) reference genome for variant identification 19 , 20 . Variants called through the major variants workflow of MAGMA were used for subsequent analyses.…”

The relationship between zinc and epilepsy

“…(3) MAGMA Analysis MAGMA analysis is a gene-based analysis model that uses multiple linear principal components regression to analyze SNP function to obtain a gene and pathway analysis tool. The MAGMA analyses were performed on the software named magma ( https://ctg.cncr.nl/software/mactg.cncr.nl/software/ma ) [19] .…”