Publication Date
4-11-2022
Document Type
Article
Publication Title
PeerJ
Volume
10
DOI
10.7717/peerj.13259
Abstract
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.
Keywords
Crocosphaera, Cyanobacteria, Diel, Operon
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Computer Science
Recommended Citation
Philip Heller. "Diel gene expression improves software prediction of cyanobacterial operons" PeerJ (2022). https://doi.org/10.7717/peerj.13259
