MycoVarP: Mycobacterium Variant and Drug Resistance Prediction Pipeline for Whole-Genome Sequence Data Analysis

Sandeep Swargam; Indu Kumari; Amit Kumar; Dibyabhaba Pradhan; Anwar Alam; Harpreet Singh; Anuja Jain; Kangjam Rekha Devi; Vishal Trivedi; Jogesh Sarma; Mahmud Hanif; Kanwar Narain; Nasreen Zafar Ehtesham; Seyed Ehtesham Hasnain; Shandar Ahmad

doi:10.3389/fbinf.2021.805338

MycoVarP: Mycobacterium Variant and Drug Resistance Prediction Pipeline for Whole-Genome Sequence Data Analysis

Front Bioinform. 2022 Jun 3:1:805338. doi: 10.3389/fbinf.2021.805338. eCollection 2021.

Authors

Sandeep Swargam^{1

2}, Indu Kumari³, Amit Kumar⁴, Dibyabhaba Pradhan⁴, Anwar Alam³, Harpreet Singh⁴, Anuja Jain⁵, Kangjam Rekha Devi⁶, Vishal Trivedi⁷, Jogesh Sarma⁸, Mahmud Hanif⁹, Kanwar Narain⁶, Nasreen Zafar Ehtesham³, Seyed Ehtesham Hasnain^{1

10}, Shandar Ahmad⁵

Affiliations

¹ Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology, Hauz Khas, New Delhi, India.
² Department of Molecular Medicine, School of Interdisciplinary Sciences, Jamia Hamdard, New Delhi, India.
³ Inflammation Biology and Cell Signalling Lab, Safdarjung Hospital Campus, ICMR National Institute of Pathology, New Delhi, India.
⁴ ICMR Computational Genomics Centre, Informatics Systems and Research Management (ISRM) Division, Indian Council of Medical Research (ICMR), New Delhi, India.
⁵ School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.
⁶ ICMR-Regional Medical Research Centre, Dibrugarh, India.
⁷ Department of Biosciences and Bioengineering, Indian Institute of Technology-Guwahati, Guwahati, India.
⁸ Department of Pulmonary Medicine, Guwahati, India.
⁹ New Delhi Tuberculosis Centre, New Delhi, India.
¹⁰ Department of Life Sciences, Sharda University, Greater NOIDA, India.

Abstract

Whole-genome sequencing (WGS) provides a comprehensive tool to analyze the bacterial genomes for genotype-phenotype correlations, diversity of single-nucleotide variant (SNV), and their evolution and transmission. Several online pipelines and standalone tools are available for WGS analysis of Mycobacterium tuberculosis (Mtb) complex (MTBC). While they facilitate the processing of WGS data with minimal user expertise, they are either too general, providing little insights into bacterium-specific issues such as gene variations, INDEL/synonymous/PE-PPE (IDP family), and drug resistance from sample data, or are limited to specific objectives, such as drug resistance. It is understood that drug resistance and lineage-specific issues require an elaborate prioritization of identified variants to choose the best target for subsequent therapeutic intervention. Mycobacterium variant pipeline (MycoVarP) addresses these specific issues with a flexible battery of user-defined and default filters. It provides an end-to-end solution for WGS analysis of Mtb variants from the raw reads and performs two quality checks, viz, before trimming and after alignments of reads to the reference genome. MycoVarP maps the annotated variants to the drug-susceptible (DS) database and removes the false-positive variants, provides lineage identification, and predicts potential drug resistance. We have re-analyzed the WGS data reported by Advani et al. (2019) using MycoVarP and identified some additional variants not reported so far. We conclude that MycoVarP will help in identifying nonsynonymous, true-positive, drug resistance-associated variants more effectively and comprehensively, including those within the IDP of the PE-PPE/PGRS family, than possible from the currently available pipelines.

Keywords: MDR-TB; Mycobacterium tuberculosis; PE-PPE/PGRS family; drug resistance; drug susceptible; lineage prediction; single-nucleotide variants.