TB database: an integrated platform for tuberculosis research. Nucleic Acids Res 37(Database issue):D499-D508

Department of Biochemistry, Stanford University, CA 94305, USA.
Nucleic Acids Research (Impact Factor: 9.11). 11/2008; 37(Database issue):D499-508. DOI: 10.1093/nar/gkn652
Source: PubMed


The effective control of tuberculosis (TB) has been thwarted by the need for prolonged, complex and potentially toxic drug regimens, by reliance on an inefficient vaccine and by the absence of biomarkers of clinical status. The promise of the genomics era for TB control is substantial, but has been hindered by the lack of a central repository that collects and integrates genomic and experimental data about this organism in a way that can be readily accessed and analyzed. The Tuberculosis Database (TBDB) is an integrated database providing access to TB genomic data and resources, relevant to the discovery and development of TB drugs, vaccines and biomarkers. The current release of TBDB houses genome sequence data and annotations for 28 different Mycobacterium tuberculosis strains and related bacteria. TBDB stores pre- and post-publication gene-expression data from M. tuberculosis and its close relatives. TBDB currently hosts data for nearly 1500 public tuberculosis microarrays and 260 arrays for Streptomyces. In addition, TBDB provides access to a suite of comparative genomics and microarray analysis software. By bringing together M. tuberculosis genome annotation and gene-expression data with a suite of analysis tools, TBDB ( provides a unique discovery platform for TB research.

Download full-text


Available from: Gavin Sherlock,
26 Reads
  • Source
    • "The process of dataset curation was previously described by us and for updating purposes we performed searches for recent papers describing molecules and known targets in Mtb. We manually curated molecules and data combined with URL links to literature and TBDB [41],[42] and these were deposited in the CDD database [31]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Background We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible. Results We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets. Conclusions TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool.
    Journal of Cheminformatics 08/2014; 6(1):38. DOI:10.1186/s13321-014-0038-2 · 4.55 Impact Factor
  • Source
    • "The 19 mycobacterial strain sequences used in this study were all complete and either published [24,78,81-90] or directly submitted to GenBank [91] (Additional file 2: Table S1). The following sites were utilized for analysis of the genomes (Additional file 2: Table S2): The comparative genomic profile for the enzymes of interest were initiated by homology searches of known M. tuberculosis H37Rv genes at TubercuList [92], GenoList [93] or TBDB [94]. Where necessary for further analysis direct BLAST analysis was performed at NCBI [95], utilising protein sequence for BLASTp or DNA sequence for BLASTn particularly for the analysis of Mycobacterium sp. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Mycobacteria comprise diverse species including non-pathogenic, environmental organisms, animal disease agents and human pathogens, notably Mycobacterium tuberculosis. Considering that the mycobacterial cell wall constitutes a significant barrier to drug penetration, the aim of this study was to conduct a comparative genomics analysis of the repertoire of enzymes involved in peptidoglycan (PG) remodelling to determine the potential of exploiting this area of bacterial metabolism for the discovery of new drug targets. We conducted an in silico analysis of 19 mycobacterial species/clinical strains for the presence of genes encoding resuscitation promoting factors (Rpfs), penicillin binding proteins, endopeptidases, L,D-transpeptidases and N-acetylmuramoyl-L-alanine amidases. Our analysis reveals extensive genetic multiplicity, allowing for classification of mycobacterial species into three main categories, primarily based on their rpf gene complement. These include the M. tuberculosis Complex (MTBC), other pathogenic mycobacteria and environmental species. The complement of these genes within the MTBC and other mycobacterial pathogens is highly conserved. In contrast, environmental strains display significant genetic expansion in most of these gene families. Mycobacterium leprae retains more than one functional gene from each enzyme family, underscoring the importance of genetic multiplicity for PG remodelling. Notably, the highest degree of conservation is observed for N-acetylmuramoyl-L-alanine amidases suggesting that these enzymes are essential for growth and survival. PG remodelling enzymes in a range of mycobacterial species are associated with extensive genetic multiplicity, suggesting functional diversification within these families of enzymes to allow organisms to adapt.
    BMC Microbiology 03/2014; 14(1):75. DOI:10.1186/1471-2180-14-75 · 2.73 Impact Factor
  • Source
    • "Bioinformatics analysis of the mtp gene and corresponding coded amino acid sequence The complete genome sequences of some Mycobacterial spp. and other respiratory organisms are available on the NCBI website [35] (, the TB database [36] (www.tbdb. org), the Broad Institute website [37] "
    [Show abstract] [Hide abstract]
    ABSTRACT: Novel biomarkers are urgently needed for point of care TB diagnostics. In this study, we investigated the potential of the pilin subunit protein encoded by the mtp gene as a diagnostic biomarker. BLAST analysis of the mtp gene on published genome databases, and amplicon sequencing were performed in Mycobacterium tuberculosis Complex (MTBC) strains and other organisms. The protein secondary structure of the amino acid sequences of non-tuberculous Mycobacteria that partially aligned with the mtp sequence was analysed with PredictProtein software. The mtp gene and corresponding amino acid sequence of MTBC were 100% homologous with H37Rv, in contrast to the partial alignment of the non-tuberculous Mycobacteria. The mtp gene was present in all 91 clinical isolates of MTBC. Except for 2 strains with point mutations, the sequence was 100% conserved among the clinical strains. The mtp gene could not be amplified in all non-tuberculous Mycobacteria and respiratory organisms. The predicted MTP protein structure of Mycobacterium avium, Mycobacterium ulcerans and Mycobacterium abscessus differed significantly from that of the M. tuberculosis, which was similar to Mycobacterium marinum. The absence of the mtp gene in non-tuberculous Mycobacteria and other respiratory bacteria suggests that its encoded product, the pilin subunit protein of M. tuberculosis may be a suitable marker for a point of care TB test.
    Tuberculosis 03/2014; 94(3):338-345. DOI:10.1016/ · 2.71 Impact Factor
Show more