Computational Pan-genome Mapping and pairwise SNP-distance improve Detection of Mycobacterium tuberculosis Transmission Clusters

Abstract

Next-generation sequencing based base-by-base distance measures have become an integral complement to epidemiological investigation of infectious disease outbreaks. This study introduces PANPASCO, a computational pan-genome mapping based, pairwise distance method that is highly sensitive to differences between cases, even when located in regions of lineage specific reference genomes. We show that our approach is superior to previously published methods in several datasets and across different Mycobacterium tuberculosis lineages, as its characteristics allow the comparison of a high number of diverse samples in one analysis -a scenario that becomes more and more likely with the increased usage of whole-genome sequencing in transmission surveillance. Author summaryTuberculosis still is a threat to global health. It is essential to detect and interrupt transmissions to stop the spread of this infectious disease. With the rising use of next-generation sequencing methods, its application in the surveillance of Mycobacterium tuberculosis has become increasingly important in the last years. The main goal of molecular surveillance is the identification of patient-patient transmission and cluster detection. The mutation rate of M. tuberculosis is very low and stable. Therefore, many existing methods for comparative analysis of isolates provide inadequate results since their resolution is too limited. There is a need for a method that takes every detectable difference into account. We developed PANPASCO, a novel approach for comparing pairs of isolates using all genomic information available for each pair. We combine improved SNP-distance calculation with the use of a pan-genome incorporating more than 100 M. tuberculosis reference genomes for read mapping prior to variant detection. We thereby enable the collective analysis and comparison of similar and diverse isolates associated with different M. tuberculosis strains.

Improving tuberculosis surveillance by detecting international transmission using publicly available whole-genome sequencing data

“…Raw reads were subjected to quality control with Trimmomatic [12] and Flash [13]. The trimmed and filtered reads were mapped to two different reference genomes: the M. tuberculosis H37Rv strain and a pan-genome reference built from 146 M. tuberculosis genomes [14, 15] with bwa mem [16]. Duplicated reads were marked and reads with mapping quality less than 10 were excluded.…”

“…We used PANPASCO [15] to calculate relative pairwise SNP distance between all isolates classified as MDR-TB or XDR-TB in the public and German dataset. This method builds on two parts to enable distance calculation for large, diverse datasets: mapping all reads to a computational pan-genome including 146 M. tuberculosis genomes and distance calculation for each individual pair of samples.…”

Mycobacterium tuberculosis complex lineage 5 exhibits high levels of within-lineage genomic diversity and differing gene content compared to the type strain H37Rv

“…Other authors have also reported that, because of the genetic variability between strains, using a single strain genome as reference genome lacks accuracy [95][96][97]. This MTBC-wide pangenome approach has been suggested before, including for other organisms [38,47,[95][96][97][98][99][100]. However, such an approach also has its own drawbacks, including difficulty in mapping reads that bridge the boundaries between accessory genes and the rest of the genome, comparing strains of different lineages including phylogenetic analyses and retention of gene names and codes in clinical use, where H37Rv is deeply embedded [89].…”