We are proposing a large and complex program to discover gene funcfion in Mycobacterium tuberculosis. Core A is dedicated to managing this complexity to ensure that we are able to achieve our goals and to disseminate our findings and the reagents we produce. To accomplish this we will need to manage our internal resources, particularly to provide appropriate access to the technical resources available in the Cores, and permit proper oversight by the NIH. We plan to fulfill these goals in two ways. First, we will facilitate management of the program. Access to resources will be agreed upon by a management committee consisfing of representatives of the projects and cores. We will be aided by the use of a web-based management software suite, customized together with Core E, that will allow us to track the progress with individual genes and to make efficient use of Core technologies. Second, we will provide platforms to exchange ideas and experiences among the investigators and sponsors. We will have regular meetings of the investigators, both physical and virtual, to exchange information. Moreover, program leaders will meet annually with program officers at the NIH. All management will be overseen by the Core Leader with help from an experienced program manager.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Program--Cooperative Agreements (U19)
Project #
1U19AI107774-01
Application #
8597704
Study Section
Special Emphasis Panel (ZAI1-FDS-M (M1))
Project Start
2013-07-02
Project End
2018-06-30
Budget Start
2013-07-02
Budget End
2014-06-30
Support Year
1
Fiscal Year
2013
Total Cost
$112,654
Indirect Cost
$15,555
Name
Harvard University
Department
Type
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
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