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International Work-Conference on Bioinformatics and Biomedical Engineering

IWBBIO 2022: Bioinformatics and Biomedical Engineering pp 429–441Cite as

Collecting SARS-CoV-2 Encoded miRNAs via Text Mining

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13346))

Abstract

Established text mining approaches can be used to identify miRNAs mentioned in published papers and preprints. Here, we apply such a targeted approach to the LitCovid literature collection in order to find viral miRNAs published in connection to SARS-CoV-2. As LitCovid aims at being a comprehensive collection of literature on new findings on SARS-CoV-2 and the COVID-19 pandemic, it is perfectly suited for our goal of finding all reported SARS-CoV-2 miRNAs. The identified miRNAs provide an up-to-date and quite comprehensive collection of potential viral miRNAs, which is a useful resource for further research to fight the current pandemic.

We identified 564 putative SARS-CoV-2 miRNAs together with the respective evidences, i.e. the original publications, and collect them for critical review. The text mining method and the corresponding synonym list are optimized for finding viral miRNAs and the results are manually curated. Since not all miRNAs were experimentally verified, the collection might contain false positives, but it is highly sensitive. Moreover, the text mining approach and resulting collection of miRNA candidates can be useful resources for further SARS-CoV-2 research and for experimental validation.

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Acknowledgements

This work as been supported by Deutsche Forschungsgemeinschaft (DFG), CRC1123, project Z02 (MJ+RZ).

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Authors and Affiliations

  1. Department of Informatics, Ludwig-Maximilians-Universität München, Amalienstr. 17, 80333, Munich, Germany

    Alexandra Schubö, Armin Hadziahmetovic, Markus Joppich & Ralf Zimmer

Authors
  1. Alexandra Schubö
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  2. Armin Hadziahmetovic
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  3. Markus Joppich
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  4. Ralf Zimmer
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Corresponding author

Correspondence to Armin Hadziahmetovic .

Editor information

Editors and Affiliations

  1. Marcelina Siebold Guest Relations Dept., University of Granada, Granada, Spain

    Ignacio Rojas

  2. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  3. ETSIIT. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. ETSIIT, University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Schubö, A., Hadziahmetovic, A., Joppich, M., Zimmer, R. (2022). Collecting SARS-CoV-2 Encoded miRNAs via Text Mining. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13346. Springer, Cham. https://doi.org/10.1007/978-3-031-07704-3_35

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  • DOI: https://doi.org/10.1007/978-3-031-07704-3_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07703-6

  • Online ISBN: 978-3-031-07704-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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