MTBCtools: a non-exhaustive list of software tools/resources for bioinformatics analyses of Mycobacterium tuberculosis complex, the causative agent of tuberculosis

Aims and objectives: Bioinformatics and computational tools are increasingly used in a wide variety of scientific studies. Genotyping, genomics and whole genome sequencing (WGS) methods allowed the generation of high amounts of data that are difficult to analyze manually. The purpose of this review is to establish a potential list of available databases and bioinformatics tools/resources related to Mycobacterium tuberculosis complex (MTBC), the causative agent of tuberculosis (TB). Methods: At present, a total of 36 tools with freely-accessible links were recovered from publications indexed in PubMed or Google Scholar. Following keywords were searched: “Mycobacterium tuberculosis AND database”, “Mycobacterium tuberculosis AND web tool”, “Mycobacterium tuberculosis AND tool”, “Mycobacterium tuberculosis AND bioinformatics”. Resources found were classified into several thematic sections/categories such as biological databases, drug resistance, or prediction/classification tools. Among recently available resources (published in 2019), one can mention COMBAT-TB-NeoDB ( https://neodb.sanbi.ac.za/browser/ ), CPLP-TB ( http://cplp-tb.ff.ulisboa.pt/ ), DeepAMR ( http://www.robots.ox.ac.uk/~davidc/code.php ), SecProMTB ( http://server.malab.cn/SecProMTB/index.jsp ), SITVIT2 ( http://www.pasteur-guadeloupe.fr:8081/SITVIT2 ), SNP-IT ( https://github.com/samlipworth/snpit ), StackTB ( https://stacktb.cs.sfu.ca/ ), and TBProfiler ( http://tbdr.lshtm.ac.uk/ ). Results: Although several tools/resources have been developed over time, many are either not regularly updated, or not made available permanently. We argue in favor of sustainable development of resources in order to assist the scientific community and policy makers to take timely concerted action for TB control. At “Institut Pasteur de la Guadeloupe”, we intend to build a newer generation of SITVIT databases including classical genotyping, epidemiologic and demographic data, information on drug resistance, as well as WGS data. Several thousands of complete genome sequences are today available for MTBC strains on public sequence repositories (such as GenBank, DDBJ, or ENA). Thanks to recently developed tools, data extracted from available complete sequences would be juxtaposed and stored for comparative and molecular epidemiological studies. Conclusion: Although, the present non-exhaustive list is certainly not “revolutionary”, it would nonetheless foster the construction of integrated pipelines/tools related to specific biological questions concerning MTBC. This list will be made available soon through Wikipedia; and in the meantime it is already available for consultation via link: http://www.pasteur-guadeloupe.fr:8081/SpolSimilaritySearch/info.jsp .