TY - GEN
T1 - Collecting SARS-CoV-2 Encoded miRNAs via Text Mining
AU - Schubö, Alexandra
AU - Hadziahmetovic, Armin
AU - Joppich, Markus
AU - Zimmer, Ralf
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - COVID-19
KW - Literature search
KW - miRNAs
KW - SARS-CoV-2
KW - svRNAs
KW - Text-mining
KW - Viral miRNAs
UR - https://www.scopus.com/pages/publications/85132964125
U2 - 10.1007/978-3-031-07704-3_35
DO - 10.1007/978-3-031-07704-3_35
M3 - Conference contribution
AN - SCOPUS:85132964125
SN - 9783031077036
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 429
EP - 441
BT - Bioinformatics and Biomedical Engineering - 9th International Work-Conference, IWBBIO 2022, Proceedings
A2 - Rojas, Ignacio
A2 - Valenzuela, Olga
A2 - Rojas, Fernando
A2 - Herrera, Luis Javier
A2 - Ortuño, Francisco
PB - Springer
T2 - 9th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2022
Y2 - 27 June 2022 through 30 June 2022
ER -