AMAISE: a machine learning approach to index-free sequence enrichment.

ABSTRACT: Metagenomics holds potential to improve clinical diagnostics of infectious diseases, but DNA from clinical specimens is often dominated by host-derived sequences. To address this, researchers employ host-depletion methods. Laboratory-based host-depletion methods, however, are costly in terms of time and effort, while computational host-depletion methods rely on memory-intensive reference index databases and struggle to accurately classify noisy sequence data. To solve these challenges, we propose an index-free tool, AMAISE (A Machine Learning Approach to Index-Free Sequence Enrichment). Applied to the task of separating host from microbial reads, AMAISE achieves over 98% accuracy. Applied prior to metagenomic classification, AMAISE results in a 14-18% decrease in memory usage compared to u

SUBMITTER: Krishnamoorthy M 

PROVIDER: S-EPMC9184628 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature