Partnership with TBResist

TBResist welcomes new partners with the ability to expand the database and scientific program we are collaborating in order to create.

Partnership is offered for members who can either expand the TBResist Database or with the ability to contribute to data analysis or both. The activities of joining partners are protected under the data polices detailed in the data protection section of this website.

Confirmed partners become members of the TBResist consortium, providing partners with rights to vote on scientific proposals regarding the database.

Benefits to Members

By signing the data sharing agreement, members have access to the following benefits/ privileges:

  • Access to consortium data
  • Ability to propose future research initiatives
  • Analytical services provided by consortium members
  • Data cleaning services
  • Authorship either as active contributing author or as member of TBResist.

Guiding Principle: Rapid Data Sharing Among Members and Public

  • All raw sequence data, incl RNA-Seq data submitted as rapidly as possibe to apropriate public databases
  • Clinical and other metadata: each participant must have a data sharing and release plan at for membership. Desire to release as soon as possible but can be delayed for up to 9mos or upon publication

Cohort Recruitment Process

Any institute/program or individual may submit a patient cohort for inclusion in TBresist.

All eligible cohorts will be prioritized. Then specific patients in each cohort will be selected to ensure sampling priority for resistant strains of interest. These criteria will be clearly defined in the early phases of Tbresist.

Cohorts that contain demographic data, clinical data, laboratory drug sensitivity testing results, and DNA or DNA sequenceing data will be a priority for consideration for inclusion in Tbresist.  However, once a critical mass of these ‘complete’ datasets have been included in TBResist, datasets that include only clinical data or only genomic data may be considered to further develop the predictive model.