The objective of this Core is to provide sequencing services to support the other projects in this Program Project in their efforts to determine the functions of proteins in the M. tuberculosis (Mtb) genome. We will take advantage of next-generation sequencing technology (aka "deep-sequencing"), which enables novel ways of probing gene functions. Three primary activities are envisioned to support the main projects. First, deep-sequencing will be used to analyze changes in gene essentiality in knockout and knock-down mutants via transposon mutagenesis. Second, deep-sequencing will be used to efficiently evaluate changes in gene expression in mutants (also known as RNA-Seq). Finally, deep-sequencing will be used for characterization of mutations in phenotypic and suppressor mutants as a way to associate genes with known processes and pathways. These sequencing services will be provided to support the three scientific projects, which will select and prioritize the strains of interest for sequencing. The resulting data will be supplied back to the other projects through a genome browser and other tools, and will be made available to the public as a resource to augment and enrich our understanding of the functions of ORFs in the M. tuberculosis genome.

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 exploit next-generation DNA sequencing in several ways to provide insight on the functions of genes in the Mtb genome whose functions are currently unknown.

Agency
National Institute of Health (NIH)
Type
Research Program--Cooperative Agreements (U19)
Project #
5U19AI107774-02
Application #
8847837
Study Section
Special Emphasis Panel (ZAI1)
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Harvard University
Department
Type
DUNS #
City
Boston
State
MA
Country
United States
Zip Code
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