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2022 | OriginalPaper | Buchkapitel

Improving Bacterial sRNA Identification By Combining Genomic Context and Sequence-Derived Features

verfasst von : Mohammad Sorkhian, Megha Nagari, Moustafa Elsisy, Lourdes Peña-Castillo

Erschienen in: Computational Intelligence Methods for Bioinformatics and Biostatistics

Verlag: Springer International Publishing

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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:

https://github.com/BioinformaticsLabAtMUN/sRNARanking.

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Titel: Improving Bacterial sRNA Identification By Combining Genomic Context and Sequence-Derived Features
verfasst von: Mohammad Sorkhian
Megha Nagari
Moustafa Elsisy
Lourdes Peña-Castillo
Verlag: Springer International Publishing
Buch: Computational Intelligence Methods for Bioinformatics and Biostatistics
Print ISBN: 978-3-031-20836-2

Electronic ISBN: 978-3-031-20837-9

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-031-20837-9_6

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