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Prioritizing bona fide bacterial small RNAs with machine learning classifiers

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Prioritizing bona fide bacterial small RNAs with machine learning classifiers https://t.co/Qs3OqjWmaZ
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Prioritizing bona fide bacterial small RNAs with machine learning classifiers https://t.co/82o3xs1UYX
Prioritizing bona fide bacterial small RNAs with machine learning classifiers https://t.co/Oh6ialAVgb Bacterial small non-coding RNAs (sRNAs) are involved in the control of several cellular processes. Hundreds of putative sRNAs have been identified in many bacterial sp…
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Supplemental Information

Positive and negative sRNA instances of five bacterial species

Feature vectors as obtained by sRNACharP for positive (bona fide sRNA) and negative (random genomic sequences) instances of five bacterial species. These datasets were used to train and validate the machine learning models discussed in the manuscript.

DOI: 10.7287/peerj.preprints.26974v1/supp-1

Additional Information

Competing Interests

The authors declare that they have no competing interests. Lourdes Peña-Castillo is an Academic Editor for PeerJ.

Author Contributions

Erik JJ Eppenhof performed the experiments, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Lourdes Peña-Castillo conceived and designed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Data Deposition

The following information was supplied regarding data availability:

Code is available at:

https://github.com/BioinformaticsLabAtMUN/sRNACharP

https://github.com/BioinformaticsLabAtMUN/sRNARanking

Data is included as additional file.

Funding

This work was supported by a Discovery Grant (No. 402087-2011) of the Natural Sciences and Engineering Research Council of Canada (NSERC) to LPC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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