Development of a tool for pathogen detection using integrated multi omics approach

Abstract

Infections are caused by harmful microorganisms called pathogens such as bacteria, fungi, or virus. Clinical diagnostics play an important role in infectious disease management and there are various diagnostic methods to identify these pathogens. Traditional culture-based methods are limited, labour intensive and has low data throughput while molecular based methods such as PCR requires prior knowledge of newlinepathogens and are target specific. These limitations can result in delayed diagnosis, and in some cases the infections are undiagnosable as well. Next-Generation Sequencing newline(NGS) is a high throughput sequencing technology and has recently gained popularity in newlinethe field of clinical microbiology. NGS methods can detect pathogens without prior newlineknowledge of the sequence information and are becoming increasingly cost effective. It newlinealso offers comprehensive insights into genes and mutations in relation to mechanisms newlinesuch as development of Anti-Microbial Resistance (AMR) and pathogenicity. However, newlinea full-scale clinical implementation of NGS as a diagnostic tool comes with a few newlinechallenges. The current study aims to bridge some of the bioinformatic challenges of implementing NGS in infectious disease diagnostics. Firstly, pathogen identification and development of AMR are studied using two NGS methods WGS and mNGS. Secondly, user-friendly frameworks are developed using Snakemake that can be utilized by clinicians or microbiologists without extensive programming experience. newline