Open AccessDiel gene expression improves software prediction of cyanobacterial operons
Author(s) -
Philip Heller
Publication year - 2022
Publication title -
peerj
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2167-8359
DOI - 10.7717/peerj.13259
Subject(s) - operon , computational biology , biology , genome , software , cyanobacteria , computer science , gene , genetics , bacteria , escherichia coli , programming language
Cyanobacteria are important participants in global biogeochemical process, but their metabolic processes and genomic functions are incompletely understood. In particular, operon structure, which can provide valuable metabolic and genomic insight, is difficult to determine experimentally, and algorithmic operon predictions probably underestimate actual operon extent. A software method is presented for enhancing current operon predictions by incorporating information from whole-genome time-series expression studies, using a Machine Learning classifier. Results are presented for the marine cyanobacterium Crocosphaera watsonii . A total of 15 operon enhancements are proposed. The source code is publicly available.