Funding News Edition: May 16, 2018 See more articles in this edition
Multidisciplinary studies of multidrug resistant and extensively drug resistant tuberculosis (MDR/XDR-TB) are critical, as numerous clinical and biological factors influence drug resistance, treatment outcomes, and disease risk. Limited access to patient data and the appropriate analytical tools makes analyzing such studies a challenge.
TB Portals is a unique resource in this field; it is a web-based, open-access platform that contains data from TB patients in high-burden countries, especially those with MDR/XDR-TB. Patient records are de-identified and include sociobehavioral, clinical, and imaging data, as well as genomic information for the strain of Mycobacterium tuberculosis (M.tb) infecting the patient. You can visualize over 1,100 cases through the site’s central Data Portal.
Beyond simple search and visualization, TB Portals also contains tools that enable advanced exploration and analysis of patient data. The Data Exploration Portal (DEPOT) performs meta-domain analyses, statistically comparing user-selected patient case groups to identify differentiating factors across all types of data and their descriptors. DEPOT also enables search and selection of patient cases through 155 data descriptors and through image and genomic similarity.
The newly developed Genomic Analysis Portal (G-AP) allows users to explore over 600 M.tb genomes that are linked to patient cases in TB Portals. G-AP uses open-source genomic analysis tools to enable features such as TB drug resistance prediction, genome-wide association studies, and genome browsing.
Later this year, the Radiomics Analysis Portal (R-AP) will apply image analysis to CT scans to explore important changes in the lungs, which should also have implications for TB diagnosis and prognosis.
Get started by completing email registration for TB Portals, which will allow you to save searches and perform custom analyses. Learn more by exploring the website or by sending questions to TBPortalsInfo@niaid.nih.gov.