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. 2014 Apr 25:15:308.
doi: 10.1186/1471-2164-15-308.

Genome-wide Mycobacterium tuberculosis variation (GMTV) database: a new tool for integrating sequence variations and epidemiology

Ekaterina N Chernyaeva  1 , Marina V Shulgina, Mikhail S Rotkevich, Pavel V Dobrynin, Serguei A Simonov, Egor A Shitikov, Dmitry S Ischenko, Irina Y Karpova, Elena S Kostryukova, Elena N Ilina, Vadim M Govorun, Vyacheslav Y Zhuravlev, Olga A Manicheva, Peter K Yablonsky, Yulia D Isaeva, Elena Y Nosova, Igor V Mokrousov, Anna A Vyazovaya, Olga V Narvskaya, Alla L Lapidus, Stephen J O'Brien
Affiliations

Genome-wide Mycobacterium tuberculosis variation (GMTV) database: a new tool for integrating sequence variations and epidemiology

Ekaterina N Chernyaeva et al. BMC Genomics. .

Abstract

Background: Tuberculosis (TB) poses a worldwide threat due to advancing multidrug-resistant strains and deadly co-infections with Human immunodeficiency virus. Today large amounts of Mycobacterium tuberculosis whole genome sequencing data are being assessed broadly and yet there exists no comprehensive online resource that connects M. tuberculosis genome variants with geographic origin, with drug resistance or with clinical outcome.

Description: Here we describe a broadly inclusive unifying Genome-wide Mycobacterium tuberculosis Variation (GMTV) database, (http://mtb.dobzhanskycenter.org) that catalogues genome variations of M. tuberculosis strains collected across Russia. GMTV contains a broad spectrum of data derived from different sources and related to M. tuberculosis molecular biology, epidemiology, TB clinical outcome, year and place of isolation, drug resistance profiles and displays the variants across the genome using a dedicated genome browser. GMTV database, which includes 1084 genomes and over 69,000 SNP or Indel variants, can be queried about M. tuberculosis genome variation and putative associations with drug resistance, geographical origin, and clinical stages and outcomes.

Conclusions: Implementation of GMTV tracks the pattern of changes of M. tuberculosis strains in different geographical areas, facilitates disease gene discoveries associated with drug resistance or different clinical sequelae, and automates comparative genomic analyses among M. tuberculosis strains.

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Figures

Figure 1
Figure 1
Regions of the Russian Federation, from which M. tuberculosis isolates were obtained. Source regions of M. tuberculosis strains genomes included into the database are highlighted with pink color.
Figure 2
Figure 2
Genotypes of M. tuberculosis isolates included into GMTV database. Spoligotypes were identified using conventional spacer oligonucleotide typing technique and genetic clades were identified using SNP analysis.
Figure 3
Figure 3
Size distribution of Indels in M. tuberculosis genome. (A) Indels distribution without Quality threshold, (B) Indels distribution with Quality threshold 30. One- and nine-nucleotide size Indels are the most common among M. tuberculosis isolates in GMTV database.
Figure 4
Figure 4
An output of a query (SNP). Query was based on SNPs in M. tuberculosis isolates with streptomycin and isoniazid resistance in coding regions with Quality threshold 30.
See this image and copyright information in PMC

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