Antimicrobial resistance (AMR) remains a major threat to global health. Profiling the collective AMR genes within a metagenome (the "resistome") facilitates greater understanding of AMR gene diversity and dynamics. In turn, this can allow for gene surveillance, individualised treatment of bacterial infections and more sustainable use of antimicrobials. However, resistome profiling can be complicated by high similarity between reference genes, as well as the sheer volume of sequencing data and the complexity of existing analysis workflows.Here at the Hartree Centre we are researching how we can apply novel algorithms to solve genomics problems such as these through our Innovation Return on Research (IROR) programme.
The latest study, led by Will Rowe, has resulted in a new method to rapidly identify AMR genes in big data (metagenomes) and has shown it to be faster and more accurate than other current tools.
The method has been implemented as a software package (GROOT: Graphing Resistance Out Of meTagenomes) and is available here (MIT license).
Read the paper in full here and find out more about Innovation Return on Research (IROR), a collaborative programme with IBM Research that aims to solve industrial problems and create economic and societal impact programme.
“Indexed variation graphs for efficient and accurate resistome profiling.”, W. P M Rowe, Martyn D Winn, Bioinformatics