Improving candidate Biosynthetic Gene Clusters in fungi through reinforcement learning.

ABSTRACT:

Motivation

Precise identification of Biosynthetic Gene Clusters (BGCs) is a challenging task. Performance of BGC discovery tools is limited by their capacity to accurately predict components belonging to candidate BGCs, often overestimating cluster boundaries. To support optimizing the composition and boundaries of candidate BGCs, we propose reinforcement learning approach relying on protein domains and functional annotations from expert curated BGCs.

Results

The proposed reinforcement learning method aims to improve candidate BGCs obtained with state-of-the-art tools. It was evaluated on candidate BGCs obtained for two fungal genomes, Aspergillus niger and Aspergillus nidulans. The results highlight an improvement of the gene precision by above 15% for TOUCAN, fungiSMASH an

SUBMITTER: Almeida H 

PROVIDER: S-EPMC9364373 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature