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Infect Genet Evol. 2016 Nov;45:359-368. doi: 10.1016/j.meegid.2016.09.013. Epub 2016 Sep 13.

Bioinformatics tools and databases for whole genome sequence analysis of Mycobacterium tuberculosis.

Author information

1
Department of Microbiology Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand; Research and Diagnostic Center for Emerging Infectious Diseases (RCEID), Khon Kaen University, Khon Kaen, Thailand. Electronic address: kiatichai@kku.ac.th.
2
Saw Swee Hock School of Public Health, National University of Singapore, Singapore. Electronic address: junhao.t@gmail.com.
3
Department of Microbiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand. Electronic address: angkana.cha@mahidol.ac.th.
4
Saw Swee Hock School of Public Health, National University of Singapore, Singapore; NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore; Department of Statistics and Applied Probability, National University of Singapore, Singapore; Life Sciences Institute, National University of Singapore, Singapore; Genome Institute of Singapore, Singapore. Electronic address: statyy@nus.edu.sg.
5
Saw Swee Hock School of Public Health, National University of Singapore, Singapore. Electronic address: twee_hee_ong@nuhs.edu.sg.

Abstract

Tuberculosis (TB) is an infectious disease of global public health importance caused by Mycobacterium tuberculosis complex (MTC) in which M. tuberculosis (Mtb) is the major causative agent. Recent advancements in genomic technologies such as next generation sequencing have enabled high throughput cost-effective generation of whole genome sequence information from Mtb clinical isolates, providing new insights into the evolution, genomic diversity and transmission of the Mtb bacteria, including molecular mechanisms of antibiotic resistance. The large volume of sequencing data generated however necessitated effective and efficient management, storage, analysis and visualization of the data and results through development of novel and customized bioinformatics software tools and databases. In this review, we aim to provide a comprehensive survey of the current freely available bioinformatics software tools and publicly accessible databases for genomic analysis of Mtb for identifying disease transmission in molecular epidemiology and in rapid determination of the antibiotic profiles of clinical isolates for prompt and optimal patient treatment.

KEYWORDS:

Analysis tools; Bioinformatics; Databases; Next generation sequencing; Tuberculosis; Whole genome sequencing

PMID:
27637931
DOI:
10.1016/j.meegid.2016.09.013
[Indexed for MEDLINE]

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