The goal of this Core is to provide a central system for managing information generated about functions of genes in M. tuberculosis (Mtb) and presenting it to the public. The overall Program Project is anticipated to generate a large amount of data related to identification of functions for genes in the Mtb genome. In particular, major types of genome-scale data to be generated by each of the component projects include: data from sequencing transposon-insertion libraries in knock-down mutants;statistical estimates of changes in essentiality of other genes or regions of the genome for genetic interaction mapping, whole-genome sequencing of phenotypic and suppressor mutants, and gene expression analysis (RNA-Seq) in bacterial mutants. A web site will be developed to provide access to the raw data generated, as well as visual display of derived information (heat maps, genome browser, plots of transposon insertion density, etc.). In addition, we will focus on providing statistical analysis of the data, such as rigorous identification of statistically significant changes in gene expression and gene essentiality. All this data will be combined to make inferences of gene function, for which we will use Gene Ontology (GO) terms. The website will provides links to all sources of evidence supporting each inference, as well as tools for grouping/counting/searching genes by GO term.

Public Health Relevance

M. tuberculosis the most prevalent human pathogen worldwide, and a better understanding of the biology of the organism through the functions of genes in the genome is needed for development of new therapies. The goal of this Core is to compile the data generated by this Program Project and make it available in an accessible, interpreted, and browsable form that will shed light on unknown functions of Mtb proteins.

National Institute of Health (NIH)
Research Program--Cooperative Agreements (U19)
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Special Emphasis Panel (ZAI1)
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Harvard University
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Kery, Mary Beth; Feldman, Monica; Livny, Jonathan et al. (2014) TargetRNA2: identifying targets of small regulatory RNAs in bacteria. Nucleic Acids Res 42:W124-9
DeJesus, Michael A; Ioerger, Thomas R (2013) A Hidden Markov Model for identifying essential and growth-defect regions in bacterial genomes from transposon insertion sequencing data. BMC Bioinformatics 14:303