biostudies-literatureArrieta-Ortiz MLHHS | National Institutes of HealthSimons FoundationNIDDK NIH HHSNIAID NIH HHSZegar Family FoundationNIGMS NIH HHSe00057-20https://www.ebi.ac.uk/biostudies/studies/S-EPMC8534726biostudies-literatureUnknown5(3)Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the <i>Inferelator</i> This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimentalmSystemsInference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks.PMC8534726R01-DK103358-01T32 AI100853R01 DK103358R01 AI128215R01 GM112192R01-GM112192-01R01 AI1419535T32AI100853U19 AI106761Hafemeister CShuster BBaliga NSArrieta-Ortiz MLBonneau REichenberger PfalseInference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks.Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the <i>Inferelator</i> This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental2020-01-01T00:00:00Z2020 Jun2026-06-17T06:24:22.789Z2025-02-19T01:26:15.396ZS-EPMC85347263248773910.1128/mSystems.00057-20