Abstract
Research over the past two decades has uncovered an unexpected complexity and intricacy of gene expression regulation in bacteria. Bacteria have (1) numerous small noncoding RNAs (sRNAs) which are ubiquitous regulators of gene expression, (2) a flexible and diverse promoter structure, and (3) transcription termination as another means of gene expression regulation.
To understand bacteria gene expression regulation, one needs to identify promoters, terminators, and sRNAs together with their targets. Here we describe the state of the art in computational methods to perform promoter recognition, sRNA identification, and sRNA target prediction. Additionally, we provide step-by-step instructions to use current approaches to perform these tasks.
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Funding provided by a Discovery Grant to L.P.C. from the Natural Sciences and Engineering Research Council (NSERC).
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Naskulwar, K., Chevez-Guardado, R., Peña-Castillo, L. (2021). Computational Methods for Elucidating Gene Expression Regulation in Bacteria. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 2190. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0826-5_4
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DOI: https://doi.org/10.1007/978-1-0716-0826-5_4
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