Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Calculation of DNA Strand Breaks by Types of Electron Interaction with Monte Carlo Simulation Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

Collecting SARS-CoV-2 Encoded miRNAs via Text Mining

verfasst von : Alexandra Schubö, Armin Hadziahmetovic, Markus Joppich, Ralf Zimmer

Erschienen in: Bioinformatics and Biomedical Engineering

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Optimal Chair Location Through a Maximum Diversity Problem Genetic Algorithm Optimization
Nächstes Kapitel COVID-19 Severity Classification Using a Hierarchical Classification Deep Learning Model
Literatur
1.
Aydemir, M.N., Aydemir, H.B., Korkmaz, E.M., Budak, M., Cekin, N., Pinarbasi, E.: Computationally predicted SARS-CoV-2 encoded microRNAs target NFKB, JAK/STAT and TGFB signaling pathways. Gene Reports 22 (2021)
2.
Chen, Q., Allot, A., Lu, Z.: Keep up with the latest coronavirus research. Nature 579, 193–194 (2020)CrossRef
3.
Chen, Q., Allot, A., Lu, Z.: LitCovid: an open database of COVID-19 literature. Nucl. Acids Res. 49, D1534–D1540 (2021)
4.
Demirci, M.D.S., Adan, A.: Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection. PeerJ 8, e9369 (2020)
5.
Dong, E., Du, H., Gardner, L.: An interactive web-based dashboard to track COVID-19 in real time. Lancet. Infect. Dis 20, 533–534 (2020)CrossRef
6.
Fu, Z., et al.: A virus-derived microRNA-like small RNA serves as a serum biomarker to prioritize the COVID-19 patients at high risk of developing severe disease. Cell Discov. 7, 1–4 (2021)CrossRef
7.
Honnibal, M., et al.: explosion/spaCy: v2.1.7: improved evaluation, better language factories and bug fixes, August 2019
8.
Joppich, M.: Integrative bioinformatics applications for complex human disease contexts, November 2021
9.
Joppich, M., Weber, C., Zimmer, R.: Using context-sensitive text mining to identify miRNAs in different stages of atherosclerosis. Thromb. Haemost. 119, 1247–1264 (2019)CrossRef
10.
Kaiser, R., et al.: Self-sustaining IL-8 loops drive a prothrombotic neutrophil phenotype in severe COVID-19. JCI Insight 6 (2021)
11.
Karimi, E., Azari, H., Yari, M., Tahmasebi, A., Azad, M.H., Mousavi, P.: Interplay between SARS-CoV-2-derived miRNAs, immune system, vitamin D pathway and respiratory system. J. Cell Mol. Med. 25, 7825–7839 (2021)CrossRef
12.
Khan, M.A.A.K., Sany, M.R.U., Islam, M.S., Islam, A.B.M.M.K.: Epigenetic regulator miRNA pattern differences among SARS-CoV, SARS-CoV-2, and SARS-CoV-2 world-wide isolates delineated the mystery behind the epic pathogenicity and distinct clinical characteristics of pandemic COVID-19. Front. Genet. 11 (2020)
13.
Kozomara, A., Birgaoanu, M., Griffiths-Jones, S.: MiRBase: from microRNA sequences to function. Nucl. Acids Res. 47, D155–D162 (2019)
14.
Liao, M., et al.: Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. 26, 842–844 (2020)CrossRef
15.
Liu, Z., et al.: SARS-CoV-2 encoded microRNAs are involved in the process of virus infection and host immune response. J. Biomed. Res. 35, 216 (2021)CrossRef
16.
Meng, F., et al.: Viral MicroRNAs encoded by nucleocapsid gene of SARS-CoV-2 are detected during infection, and targeting metabolic pathways in host cells. Cells 10(7), 1762 (2021)CrossRef
17.
Merino, G.A., et al.: Novel SARS-CoV-2 encoded small RNAs in the passage to humans. Bioinformatics 36, 5571–5581 (2020)CrossRef
18.
Mishra, R., Kumar, A., Ingle, H., Kumar, H.: The interplay between viral-derived miRNAs and host immunity during infection. Front. Immunol. 10, 3079 (2020)CrossRef
19.
Morales, L., Oliveros, J.C., Fernandez-Delgado, R., tenOever, B.R., Enjuanes, L., Sola, I.: SARS-CoV-encoded small RNAs contribute to infection-associated lung pathology. Cell Host Microbe 21, 344–355 (2017)CrossRef
20.
Neumann, M., King, D., Beltagy, I., Ammar, W.: ScispaCy: fast and robust models for biomedical natural language processing. In: BioNLP 2019 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 18th BioNLP Workshop and Shared Task (2019)
21.
Nicolai, L., et al.: Vascular neutrophilic inflammation and immunothrombosis distinguish severe COVID-19 from influenza pneumonia. J. Thromb. Haemost. 19, 574–581 (2021)CrossRef
22.
Pawlica, P., et al.: SARS-CoV-2 expresses a microRNA-like small RNA able to selectively repress host genes. Proc. Natl. Acad. Sci. 118, e2116668118 (2021)
23.
Pekayvaz, K., et al.: Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection. Nat. Commun. 13, 1018 (2022)CrossRef
24.
Qureshi, A., Thakur, N., Monga, I., Thakur, A., Kumar, M.: VIRmiRNA: a comprehensive resource for experimentally validated viral miRNAs and their targets. Database J. Biol. Databases Curation 2014 (2014)
25.
Rahaman, M., Komanapalli, J., Mukherjee, M., Byram, P.K., Sahoo, S., Chakravorty, N.: Decrypting the role of predicted SARS-CoV-2 miRNAs in COVID-19 pathogenesis: a bioinformatics approach. Comput. Biol. Med. 136, 104669 (2021)
26.
Roy, S., et al.: Identification and host response interaction study of SARS-CoV-2 encoded miRNA-like sequences: an in silico approach. Comput. Biol. Med. 134, 104451 (2021)
27.
Saini, S., Saini, A., Thakur, C.J., Kumar, V., Gupta, R.D., Sharma, J.K.: Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity. Mol. Biol. Res. Commun. 9, 83 (2020)PubMedPubMedCentral
28.
Sarma, A., Phukan, H., Halder, N., Madanan, M.G.: An in-silico approach to study the possible interactions of miRNA between human and SARS-CoV-2. Comput. Biol. Chem. 88, 107352 (2020)
29.
Satyam, R., et al.: miRNAs in SARS-CoV 2: a spoke in the wheel of pathogenesis. Curr. Pharm. Des. 27, 1628–1641 (2020)CrossRef
30.
Singh, M., et al.: A virus-derived microRNA targets immune response genes during SARS-CoV-2 infection. EMBO Rep. 23, e54341 (2022)
31.
Verma, S., Dwivedy, A., Kumar, N., Biswal, B.K.: Computational prediction of SARS-CoV-2 encoded miRNAs and their 2 putative host targets. bioRxiv (2020)
32.
Wilk, A.J., et al.: A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. 26, 1070–1076 (2020)CrossRef
33.
Wyler, E., et al.: Transcriptomic profiling of SARS-CoV-2 infected human cell lines identifies HSP90 as target for COVID-19 therapy. iScience 24, 102151 (2021)
34.
Yu, T.Y., Chen, M., Wang, C.D.: Annotation of miRNAs in the COVID-19 Novel Coronavirus. J. Electron. Sci. Technol. 19, 100060 (2021)CrossRef
35.
Zhu, Y., et al.: SARS-CoV-2-encoded MiRNAs inhibit host type i interferon pathway and mediate allelic differential expression of susceptible gene. Front. Immunol. 12 (2021)
36.
Çetin, Z., Bayrak, T., Oğul, H., İlker Saygılı, E., Akkol, E.K.: Predicted SARS-CoV-2 miRNAs associated with epigenetic viral pathogenesis and the detection of new possible drugs for Covid-19. Curr. Drug Deliv. 18, 1595–1610 (2021)CrossRef
Metadaten
Titel
Collecting SARS-CoV-2 Encoded miRNAs via Text Mining
verfasst von
Alexandra Schubö
Armin Hadziahmetovic
Markus Joppich
Ralf Zimmer
Copyright-Jahr
2022
Buch
Bioinformatics and Biomedical Engineering

Print ISBN: 978-3-031-07703-6

Electronic ISBN: 978-3-031-07704-3

Copyright-Jahr: 2022

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

Premium Partner

    Bildnachweise