Sustainable Development in Bioinformatics to Support Public Health

To tackle antimicrobial resistance (AMR), genomics surveillance plays a very vital role. However, genomics surveillance would be overwhelming for LMIC such as Nepal. Nevertheless, whole genome data is available to open access at ENA and several facilities in Nepal are at least equipped to perform phenotypic analysis. Therefore, in this study, we aim to develop a correlation model/platform that can correlate the phenotypic data to genotypic information, with significant accuracy using machine learning. In absence of such model, whole genome sequencing of all isolates would be necessary to investigate the genotypic information of that organism. Therefore, the correlation model with be developed for Salmonella Typhi and Para typhi A, which will eventually be upgraded to include diverse organisms of interest. This correlation model will be open sourced and can be easily accessed by the interested health institutions. Additionally, our health system lacks the network, workflow, and standardization in reporting formats to communicate the AMR information between health research/diagnostic institutions.

Study Duration: 06/2022 – Present

Objectives:
In this study, we aim to develop standardized reporting formats for the communication of AMR information

Outcomes:
This study is still underway. The harmonized reports generated will be easier to share and compatible across AMR predicting platform. The model developed from our study will be able to correlate the phenotypic data to genotypic information. This correlation model will be open sourced and can be easily accessed by the interested health institutions.

Team Members:

Principal Investigator: Prof. Dr. Rajeev Shrestha

Co-Investigator(s): Nishan Katuwal, Navin Adhikari, Prabin Shakya

Molecular Biologist/Bioinformatician: Nishan Katuwal

Supported by: Public Health Alliance for Genomic Epidemiology (PHA4GE)

Collaborating Partners: Public Health Alliance for Genomic Epidemiology, South Africa