MGDD: Mycobacterium tuberculosis Genome Divergence Database
- Equal contributors
1 Center for Computational Biology and Bioinformatics, School of Information Technology, Jawaharlal Nehru University, New Delhi 110067, India
2 Indian Statistical Institute, New Delhi 110016, India
3 School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India
BMC Genomics 2008, 9:373 doi:10.1186/1471-2164-9-373Published: 5 August 2008
Variation in genomes among different closely-related organisms can be linked to phenotypic differences. A number of mechanisms, such as replication error, repeat expansion and contraction, recombination and transposition can contribute to genomic differences. These processes lead to generation of SNPs, different types of repeat-based and transposons or IS-element-based polymorphisms, inversions and duplications and changes in synteny. A database of all the variations in a group of organisms is not only useful for understanding genotype-phenotype relationship but also in clinical applications. There is no database available at present that provides information about detailed genomic variations among different strains and species of Mycobacterium tuberculosis complex, organisms responsible for human diseases.
MGDD is a free web-based database that allows quick user friendly search to find different types of genomic variations among a group of fully sequenced organisms belonging to M. tuberculosis complex. The searches are based on data generated by pair wise comparison using a tool that has already been described. Different types of variations that can be searched are SNPs, indels, tandem repeats and divergent regions. The searches can be designed to find specific variations either in a given gene or any given location of the query genome with respect to any other genome currently available.
Web-based database MGDD can help to find all the possible differences that exists between two strains or species of M. tuberculosis complex. The search tool is very user-friendly and can be used by anyone not familiar with computational methods and will be useful to both clinicians and researchers working on tuberculosis and other Mycobacterial diseases.