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Updated: Sep 26, 2025

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae
Published on: April 25, 2015
Diel gene expression improves software prediction of cyanobacterial operons
1Department of Computer Science, San Jose State University, San Jose, CA, United States.
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.
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