A bioinformatics pipeline for Mycobacterium tuberculosis sequencing that cleans contaminant reads from sputum samples

• Published in: PloS one 2021-10, Vol.16 (10), p.e0258774-e0258774
• Main Authors: Cuevas-Córdoba, Betzaida, Fresno, Cristóbal, Haase-Hernández, Joshua I, Barbosa-Amezcua, Martín, Mata-Rocha, Minerva, Muñoz-Torrico, Marcela, Salazar-Lezama, Miguel A, Martínez-Orozco, José A, Narváez-Díaz, Luis A, Salas-Hernández, Jorge, González-Covarrubias, Vanessa, Soberón, Xavier
• Format: Article
• Language: English
• Subjects: Analysis; Bacteria; Bioinformatics; Biology and Life Sciences; Computational biology; Computer programs; Contaminants; Deoxyribonucleic acid; DNA; DNA sequencing; DNA viruses; Drug resistance; Gene mapping; Genetic aspects; Genetic diversity; Genetic variance; Genetically engineered microorganisms; Genomes; Genomics; Hybridization; Mapping; Medicine and Health Sciences; Microorganisms; Mycobacterium; Mycobacterium tuberculosis; Next-generation sequencing; Nucleotide sequencing; Parameter identification; Pipe cleaning; Pipeline design; Quality control; Realignment; Reliability; Research and Analysis Methods; Software; Sputum; Taxonomy; Tuberculosis; Viruses
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0258774

Next-Generation Sequencing (NGS) is widely used to investigate genomic variation. In several studies, the genetic variation of Mycobacterium tuberculosis has been analyzed in sputum samples without previous culture, using target enrichment methodologies for NGS. Alignments obtained by different programs generally map the sequences under default parameters, and from these results, it is assumed that only Mycobacterium reads will be obtained. However, variants of interest microorganism in clinical samples can be confused with a vast collection of reads from other bacteria, viruses, and human DNA. Currently, there are no standardized pipelines, and the cleaning success is never verified since there is a lack of rigorous controls to identify and remove reads from other sputum-microorganisms genetically similar to M. tuberculosis. Therefore, we designed a bioinformatic pipeline to process NGS data from sputum samples, including several filters and quality control points to identify and eliminate non-M. tuberculosis reads to obtain a reliable genetic variant report. Our proposal uses the SURPI software as a taxonomic classifier to filter input sequences and perform a mapping that provides the highest percentage of Mycobacterium reads, minimizing the reads from other microorganisms. We then use the filtered sequences to perform variant calling with the GATK software, ensuring the mapping quality, realignment, recalibration, hard-filtering, and post-filter to increase the reliability of the reported variants. Using default mapping parameters, we identified reads of contaminant bacteria, such as Streptococcus, Rhotia, Actinomyces, and Veillonella. Our final mapping strategy allowed a sequence identity of 97.8% between the input reads and the whole M. tuberculosis reference genome H37Rv using a genomic edit distance of three, thus removing 98.8% of the off-target sequences with a Mycobacterium reads loss of 1.7%. Finally, more than 200 unreliable genetic variants were removed during the variant calling, increasing the report's reliability.