OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Journal Article · 05 January 2022 · PLoS Computational Biology (Online)

DOI: https://doi.org/10.1371/journal.pcbi.1009731 · OSTI ID:1840808

Krishnakumar, Raga ; Ruffing, Anne M. ; Segata, ed., Nicola

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

• Sponsoring Organization: USDOE
• OSTI ID: 1840808
• Journal Information: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 1 Vol. 18; ISSN 1553-7358
• Publisher: Public Library of Science (PLoS)Copyright Statement
• Country of Publication: United States
• Language: English