Abstract
Bacterial small non-coding RNAs (sRNAs) are ubiquitous regulatory RNAs involved in controlling several cellular processes by targeting multiple mRNAs. The large diversity of sRNAs in terms of their length, sequence, and function poses a challenge for computational sRNA prediction. There are several bacterial sRNA prediction tools. Most of them use sequence-derived features or rely on phylogenetic conservation. Recently, a new sRNA predictor (sRNARanking) showed that using genomic context features outperformed methods based on sequence-derived features. Here we comparatively assessed the effect of using sequence-derived features together with genomic context features for computational sRNA prediction and generated a new model sRNARanking v2 with increased predictive performance in terms of the area under the precision-recall curve (AUPRC). sRNARanking v2 is available at:
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Acknowledgments
This research was partially supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) to L.P.-C. (Grant number RGPIN: 2019-05247). This research was enabled in part by support provided by ACENET (www.ace-net.ca/) and Compute Canada (www.computecanada.ca).
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Sorkhian, M., Nagari, M., Elsisy, M., Peña-Castillo, L. (2022). Improving Bacterial sRNA Identification By Combining Genomic Context and Sequence-Derived Features. In: Chicco, D., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2021. Lecture Notes in Computer Science(), vol 13483. Springer, Cham. https://doi.org/10.1007/978-3-031-20837-9_6
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