On the Project of an Effective Software Platform for Working with Genetic Data of Respiratory Viruses

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

Progress in sequencing technologies, i.e. reading the nucleotide sequences of living organisms, has led to a rapid growth of the amount of genetic data. The largest global projects that accumulate this information and provide online access to it are Genbank and GISAID. Also they provide basic capabilities for analyzing this data online, but they are quite limited. This significantly limits our abilities to effectively solve a number of scientific problems and tasks, so we decided to develop our own domestic (Russian) web platform with capabilities which we need. The main goal of this project is to provide a team of researchers with the opportunity to effectively solve problems in bioinformatics, virology and epidemiology, based on modern, effective, reasonably selected software solutions operating with high performance and providing many useful functionalities which can be extended by adding new necessary programs for analyzing and modeling. The web platform we are implementing will allow to download, store, search and analyze genomic sequences of viruses, such as influenza, SARS-CoV-2 and, in perspective, other viral pathogens. In addition, the project will develop and advance through efforts of IT part of our team taking into account actual needs of bioinformaticians and virologists. We plan to make it available to researchers around the world and periodically update both the software and the data (from open sources) to improve the convenience and efficiency for scientists working in the relevant areas.

About the authors

Alexander V. Mordvinov

A.P. Ershov Institute of Informatics Systems, Siberian Branch of the Russian Academy of Sciences

Author for correspondence.
Email: a.mordvinov@g.nsu.ru

Ph.D. student, assistant at the Department of Informatics Systems, Faculty of Information Technologies

Russian Federation, Novosibirsk

Arseny V. Stuchinsky

A.P. Ershov Institute of Informatics Systems, Siberian Branch of the Russian Academy of Sciences; Novosibirsk State University

Email: a.stuchinskii@g.nsu.ru

software engineer, postgraduate student of the Department of Programming, the Faculty of Mechanics and Mathematics

Russian Federation, Novosibirsk; Novosibirsk

Anton P. Devyaterikov

A.P. Ershov Institute of Informatics Systems, Siberian Branch of the Russian Academy of Sciences

Email: a.devyaterikov@g.nsu.ru

Ph.D. student, software engineer

Russian Federation, Novosibirsk

Sergey S. Khayrulin

A.P. Ershov Institute of Informatics Systems, Siberian Branch of the Russian Academy of Sciences

Email: s.khayrulin@gmail.com

junior researcher

Russian Federation, Novosibirsk

Natalia V. Palyanova

Research Institute of Virology, Federal Research Center for Fundamental and Translational Medicine, Siberian Branch of the Russian Academy of Sciences

Email: natalia.palyanova@gmail.com

junior researcher

Russian Federation, Novosibirsk

Andrey Yu. Palyanov

A.P. Ershov Institute of Informatics Systems, Siberian Branch of the Russian Academy of Sciences; Novosibirsk State University; Research Institute of Virology, Federal Research Center for Fundamental and Translational Medicine, Siberian Branch of the Russian Academy of Sciences

Email: palyanov@iis.nsk.su

Doctor of physics and mathematics, director; researcher; senior lecturer at the Department of Programming, Faculty of Mechanics and Mathematics

Russian Federation, Novosibirsk; Novosibirsk; Novosibirsk

References

  1. Bogner P., Capua I., Lipman D.J., Cox N.J. A global initiative on sharing avian flu data // Nature. 2006. № 442(7106), С. 981. https://doi.org/10.1038/442981a
  2. Farley M.M. 2009 H1N1 influenza: a twenty-first century pandemic with roots in the early twentieth century // Am. J. Med. Sci. 2010. № 340(3), С. 202-208. https://doi.org/10.1097/MAJ.0b013e3181e937b0
  3. Tanner W.D., Toth D.J., Gundlapalli A.V. The pandemic potential of avian influenza A(H7N9) virus: a review // Epidemiol. Infect. 2015. № 143(16), С. 3359-3374. https://doi.org/10.1017/S0950268815001570.
  4. Martellucci C.A., Flacco M.E., Cappadona R., Bravi F., Mantovani L., Manzoli L. SARS-CoV-2 pandemic: An overview // Advances in Biological Regulation. 2020. № 77, С. 100736. https://doi.org/10.1016/j.jbior.2020.100736
  5. Si Y., Wu W., Xue X., Sun X., Qin Y., Li Y., Qiu C., Li Y., Zhuo Z., Mi Y., Zheng P. The evolution of SARS-CoV-2 and the COVID-19 pandemic // PeerJ. 2023. № 11, С. e15990. https://doi.org/10.7717/peerj.15990.
  6. Lenharo M. GISAID in crisis: can the controversial COVID genome database survive? // Nature. 2023. № 617(7961), С. 455-457. https://doi.org/10.1038/d41586-023-01517-9
  7. https://microbius.ru/news/gisaid-v-krizise-smozhet-li-vyzhit-vyzyvayuschaya-spory-baza-dannyh-genomov-covid
  8. Sayers E.W., Cavanaugh M., Clark K., Pruitt K.D., Schoch C.L., Sherry S.T., et al. GenBank // Nucleic Acids Res. 2022. №50(D1), С. D161-D164. https://doi.org/10.1093/nar/gkab1135.
  9. Sayers E.W., Cavanaugh M., Clark K., Pruitt K.D., et al. GenBank 2024 Update // Nucleic Acids Research. 2024. № 52(D1), С. D134–D137. https://doi.org/10.1093/nar/gkad903.
  10. Wu F., Zhao S., Yu B., Chen Y.M., Wang W., et al. A new coronavirus associated with human respiratory disease in China // Nature. 2020. № 579(7798), С. 265-269. https://doi.org/10.1038/s41586-020-2008-3.
  11. http://courseware.cutm.ac.in/wp-content/uploads/2020/10/ Gen-Bank.pdf.
  12. Pertsemlidis A., Fondon J.W. Having a BLAST with bioinformatics (and avoiding BLASTphemy) // Genome Biology. 2001. № 2(10), С. reviews2002.1. https://doi.org/10.1186/gb-2001-2-10-reviews2002
  13. Aksamentov I., Roemer C. et al. Nextclade: clade assignment, mutation calling and quality control for viral genomes // J. Open Source Software. 2021. № 6(67), С. 3773. https://doi.org/10.21105/joss.03773.
  14. Ahdritz G., Bouatta N., Floristean C., Kadyan S., et al. OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization // Nature Methods. 2024. №21(8), C. 1514-1524. https://doi.org/10.1038/s41592-024-02272-z.
  15. Devyaterikov A.P., Palyanov A.Yu. Acceleration of recombinant viral sequences search by 3SEQ algorithm via adding support of multi-threaded calculations and considering sample collection dates // Mathematical Biology and Bioinformatics. 2024. № 19(2), С. 338-353. https://doi: 10.17537/2024.19.338. https://github.com/NotNa19/RecombinantDetector.
  16. Lam H.M., Ratmann O. and Boni M.F. Improved algorithmic complexity for the 3SEQ recombination detection algorithm // Mol. Biol. Evol. 2018. №35, С. 247–251. https://doi.org/10.1093/molbev/msx263.

Supplementary files

Supplementary Files
Action
1. JATS XML


Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).