Moscow, Moscow, Russian Federation
Institute of Cytology and Genetics SB RAS
Novosibirsk, Novosibirsk, Russian Federation
Moscow, Moscow, Russian Federation
Moscow, Moscow, Russian Federation
from 01.01.2019 until now
Institute of Cytology and Genetics SB RAS
Novosibirsk State University
Peoples’ Friendship University of Russia (Agrarno-Tehnologicheskiy Institut, professor)
from 01.01.2020 to 01.01.1921
Moscow, Moscow, Russian Federation
The pursuit of microRNA target genes necessitates the creation of novel software and web services. MicroRNAs, abbreviated as short non-coding RNA molecules, hold a pivotal role in metabolic regulation, plant responses to environmental stress, and gene expression. Gaining insights into microRNA functions and investigating their target genes can advance drug development and address biotechnological challenges. However, the study and identification of microRNA targets within the genome present technical obstacles. MicroRNA molecules may not exhibit complete complementarity with their mRNA targets. These molecules either contribute to mRNA degradation or inhibit translation, and this process can transpire without full target complementarity. Consequently, the delineation of targets solely based on the principle of complementarity lacks unequivocal clarity. Moreover, a single microRNA molecule can correspond to multiple target genes simultaneously. The solution entails harnessing substantial datasets, employing machine learning techniques, and leveraging neural networks. In bioinformatics, neural networks serve a variety of functions, encompassing the analysis of biomedical data, diagnostics, prediction, classification, and nucleotide sequence segmentation. The pursuit and anticipation of microRNA targets through machine learning methods are currently undergoing vigorous development. A comparative assessment of contemporary neural networks for this task has been executed. A neural network-driven web service for microRNA prediction has been created. The server aspect of the service was developed using the Python programming language and the Flask library. The Mitar neural network, founded on deep learning, was employed. This network demonstrates heightened precision in predicting microRNA targets. We deliberate on the applications of miRNA prediction in gene expression analysis. Sustained research efforts are imperative to enhance the efficiency and broaden the capabilities of the developed computer system.
bioinformatics, microRNA, recognition, neural networks, medical informatics, web-service
1. Chao H., Zhang S., Hu Y., Ni Q., Xin S., Zhao L., Ivanisenko V.A., Orlov Y.L., Chen M. Integrating omics databases for enhanced crop breeding. J. Integr Bioinform., 2023, p. 20230012, doi:https://doi.org/10.1515/jib-2023-0012.
2. Quillet A., Saad C., Ferry G., Anouar Y., Vergne N., Lecroq T., Dubessy C. Improving Bioinformatics Prediction of microRNA Targets by Ranks Aggregation. Front. Genet., 2020, vol. 10, p. 1330, doi:https://doi.org/10.3389/fgene.2019.01330.
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4. Orlov Y.L., Babenko V.N., Dergilev A.V., Galieva A.G., Dobrovolskaya O.B., Chen M. Databases and computer resources on plant mirna to study its role in abiotic stress response. In book: Plant Genetics, Genomics, Bioinformatics, and Biotechnology (PlantGen2019) Abstracts. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019, p. 150, doi:https://doi.org/10.18699/PlantGen2019-132.
5. Orlov Y.L., Dobrovolskaya O., Yuan C.H., Afonnikov D.A., Zhu Y., Chen M. Integrative computer analysis of antisense transcripts and miRNA targets in plant genomes. Journal of Stress Physiology & Biochemistry, 2012, vol. 8, no. 3, p. S7.
6. Orlov Yu.L., Tsukanov A.V., Bogomolov A.G., Dobrovolskaya O.B. Bioinformatics methods for searching for non-coding RNAs associated with plant drought resistance. Collection of abstracts of the republican scientific conference modern problems of genetics, genomics and biotechnology. Academy of Sciences of the Republic of Uzbekistan, Center for Genomics and Bioinformatics. May 18, 2018. Tashkent, 2018, pp. 139-142 (In Russ.).
7. Wang J., Meng X., Dobrovolskaya O.B., Orlov Y.L., Chen M. Non-coding RNAs and Their Roles in Stress Response in Plants. Genomics Proteomics Bioinformatics, 2017, vol. 15, no. 5, pp. 301-312, doi:https://doi.org/10.1016/j.gpb.2017.01.007.
8. Voropaeva E.N., Pospelova T.I., Orlov Y.L., Churkina M.I., Berezina O.V., Gurazheva A.A., Ageeva T.A., Seregina O.B., Maksimov V.N. The Methylation of the p53 Targets the Genes MIR-203, MIR-129-2, MIR-34A and MIR-34B/C in the Tumor Tissue of Diffuse Large B-Cell Lymphoma. Genes, 2022, vol. 13, no. 8, pp. 1401, doi:https://doi.org/10.3390/genes13081401.
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10. Klimontov V.V., Koshechkin K.A., Orlova N.G., Sekacheva M.I., Orlov Y.L. Medical Genetics, Genomics and Bioinformatics-2022. International Journal of Molecular Sciences, 2023, vol. 24, no. 10, p. 8968, doi:https://doi.org/10.3390/ijms24108968.
11. Matsuyama H., Suzuki H.I. Systems and Synthetic microRNA Biology: From Biogenesis to Disease Pathogenesis. International Journal of Molecular Sciences, 2019, vol. 21, pp. 132, doi:https://doi.org/10.3390/ijms21010132.
12. Anashkina A.A., Leberfarb E.Y., Orlov Y.L. Recent Trends in Cancer Genomics and Bioinformatics Tools Development. Int. J. Mol. Sci., 2021, 22, p. 12146, doi:https://doi.org/10.3390/ijms222212146.
13. Riffo-Campos L., Riquelme I., Brebi-Mieville P. Tools for Sequence- Based miRNA Target Prediction: What to Choose? International Journal of Molecular Sciences, 2016, vol. 17, no. 12, p. 1987, doi:https://doi.org/10.3390/ijms17121987.
14. Putta P., Orlov Y.L., Podkolodnyy N.L., Mitra C.K. Relatively conserved common short sequences in transcription factor binding sites and miRNA. Vavilov Journal of Genetics and Breeding, 2011, vol. 15, no. 4, pp. 750-756.
15. Schmidhuber J. Deep Learning. Scholarpedia, 2015, vol. 10, no. 11, p. 32832, doi:https://doi.org/10.4249/scholarpedia.32832.
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17. Vasu S., Kumano K., Darden C.M., Rahman I., Lawrence M.C., Naziruddin B. MicroRNA Signatures as Future Biomarkers for Diagnosis of Diabetes States. Cells, 2019, vol. 8, no. 12, p. 1533, doi:https://doi.org/10.3390/cells8121533.
18. Yuan Y., Weidhaas J.B. Functional microRNA binding site variants. Molecular Oncology, 2019, vol. 13, no. 1, pp. 4-8, doi:https://doi.org/10.1002/1878-0261.12421.
19. Orlov Y.L., Potapov V.N. Complexity: an internet resource for analysis of DNA sequence complexity. Nucleic Acids Res., 2004, iss. 32, pp. 628-33, doi:https://doi.org/10.1093/nar/gkh466.
20. Vityaev E.E., Orlov Yu.L., Vishnevsky O.V., Belenok A.S., Kolchanov N.A. Computer system “GENE DISCOVERY” for searching for patterns of organization of regulatory sequences of eukaryotes. Molecular Biology, 2001, vol. 35, no. 6, pp. 952-960 (In Russ.).
21. Vityaev E.E., Orlov Y.L., Vishnevsky O.V., Pozdnyakov M.A., Kolchanov N.A. Computer system "Gene Discovery" for promoter structure analysis. In Silico Biol., 2002, vol. 2, pp. 257-62.
22. Orlov Y.L., Filippov V.P., Potapov V.N., Kolchanov N.A. Construction of stochastic context trees for genetic texts. In Silico Biol., 2002, vol. 2, no. 3, pp. 233-247.
23. Orlov Y.L., Boekhorst R., Abnizova I.I. Statistical measures of the structure of genomic sequences: entropy, complexity, and position information. J Bioinform Comput Biol., 2006, vol. 4, pp. 523-536, doi:https://doi.org/10.1142/s0219720006001801.
24. Mitina A.V., Orlov Yu.L. Assessment of linguistic complexity of genetic sequences of SARS-CoV-2 strains. Collection of scientific papers of the VII Congress of Biophysicists of Russia: in 2 volumes, vol. 1 - Krasnodar: Printing house of FGBOU VO “KubGTU”, 2023, p. 330 (In Russ.).
25. Dergilev A.I., Orlova N.G., Mitina A.V., Orlov Yu.L. Application of methods for assessing text complexity to the analysis of genomic clusters of transcription factor binding sites. Collection of scientific papers of the VII Congress of Biophysicists of Russia: in 2 volumes, vol.1 - Krasnodar: Printing house of FGBOU VO “KubGTU”, 2023, pp. 335-336 (In Russ.).
26. Gorbenko I.V., Petrushin I.S., Shcherban A.B., Orlov Y.L., Konstantinov Y.M. Short Interrupted Repeat Cassette (SIRC)-Novel Type of Repetitive DNA Element Found in Arabidopsis thaliana. Int J Mol Sci., 2023, vol. 24, no. 13, p. 11116, doi:https://doi.org/10.3390/ijms241311116.
27. Voropaeva E.N., Pospelova T.I., Voevoda M.I., Maksimov V.N., Orlov Y.L., Seregina O.B. Clinical aspects of TP53 gene inactivation in diffuse large B-cell lymphoma. BMC Med Genomics, 2019, vol. 12, suppl. 2, p. 35, doi:https://doi.org/10.1186/s12920-019-0484-9.
28. Babenko V.N., Bragin A.O., Spitsina A.M., Chadaeva I.V., Galieva E.R., Orlova G.V., Medvedeva I.V., Orlov Y.L. Analysis of differential gene expression by RNA-seq data in brain areas of laboratory animals. J Integr Bioinform., 2016, vol.13, no. 4, p. 292, doi:https://doi.org/10.2390/biecoll-jib-2016-292.
29. Orlov Y.L., Baranova A.V. Editorial: Bioinformatics of Genome Regulation and Systems Biology. Front Genet., 2020, vol. 11, pp. 625, doi:https://doi.org/10.3389/fgene.2020.00625.
30. Orlov Y., Dobrovolskaya O., Chen M., Hofestaedt R. Bioinformatics of genome regulation and structure – 2020 papers collection. Journal of Integrative Bioinformatics, 2020 vol. 17, no. 4, p. 20200038, doi:https://doi.org/10.1515/jib-2020-0038.
31. Spitsina A.M., Orlov Y.L., Podkolodnaya N.N. et al. Supercomputer analysis of genomics and transcriptomics data revealed by high-throughput DNA sequencing. Program systems: theory and applications, 2015, vol. 6, no. 1(24), pp. 157-174 (In Russ.).
32. Orlov Y.L., Bragin A.O., Babenko R.O., Dresvyannikova A.E., Kovalev S.S., Shaderkin I.A., Orlova N.G., Naumenko F.M. Integrated Computer Analysis of Genomic Sequencing Data Based on ICGenomics Tool. In: Advances in Intelligent Systems, Computer Science and Digital Economics. CSDEIS 2019, AISC 1127. International Journal of Intelligent Systems and Applications (IJISA), 2020, pp. 154-164, doi:https://doi.org/10.1007/978-3-030-39216-1_15.
33. Orlov Y.L., Bragin A.O. et al. ICGenomics: a program complex for analysis of symbol sequences in genomics. Vavilov Journal of Genetics and Breeding, 2012, vol. 16, no. (4/1), pp. 732-741 (In Russ.).
34. Veljkovic A.N., Orlov Y.L., Mitic N.S. BioGraph: Data Model for Linking and Querying Diverse Biological Metadata. International Journal of Molecular Sciences, 2023, vol. 24, no. 8, pp. 6954, doi:https://doi.org/10.3390/ijms24086954.
