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

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. Author summary: The genome of Mycobacterium tuberculosis (Mtb), the bacterium that causes tuberculosis, provides important information on drug resistance and can predict with which drug a patient can be best treated for tuberculosis. The Mtb genome also gives information on how Mtb spreads in communities. If the Mtb genomes from two patients suffering from tuberculosis are very similar, then these two patients are likely linked in terms of transmission. Characterizing the genome of Mtb is thus important for controlling the disease in the patient and in the population. We developed the MAGMA pipeline, a software that can accurately identify genomic variants in Mtb, even when the amounts of Mtb bacteria in the patient sample are low and contaminating bacteria are present. The genomic variants identified by the software are then analyzed using the WHO reference to identify the drug resistance profile of a patient's Mtb strain. The genetic distances between genomes are also calculated to identify closely related genomes. Using clinical Mtb samples from 79 patients, we show the strengths and advantages of the MAGMA pipeline. We conclude that MAGMA can rapidly and accurately obtain important genomic information for Mtb in clinical settings and communicates the results in a clinically relevant way. [ABSTRACT FROM AUTHOR]