Новая эра биоинформатики
- Авторы: Аксенова А.Ю.1, Жук А.С.1,2,3, Степченкова Е.И.1,3, Семенихин В.А.4, Ланговой М.А.5
-
Учреждения:
- Санкт-Петербургский государственный университет
- Университет ИТМО
- Институт общей генетики им. Н.И. Вавилова Российской академии наук, Санкт-Петербургский филиал
- Матеомика, Инновационный центр Сколково
- Центр искусственного интеллекта СПбГУ
- Выпуск: Том 23, № 2 (2025)
- Страницы: 211-219
- Раздел: Проблемы генетического образования
- URL: https://journals.rcsi.science/ecolgenet/article/view/317610
- DOI: https://doi.org/10.17816/ecogen637074
- EDN: https://elibrary.ru/JFNADD
- ID: 317610
Цитировать
Аннотация
Биоинформатика — это быстро развивающаяся дисциплина на стыке биологии, информатики и математики. Научно-технический прогресс в области биологических и биомедицинских наук за последние годы привел к стремительному росту объемов данных. Для анализа и интерпретации больших данных нужны мощные вычислительные инструменты и специалисты с глубокими знаниями в различных областях, включая молекулярную биологию, генетику, программирование и математику. В настоящее время происходит стремительная интеграция методов машинного и глубокого машинного обучения в различные области биологии и медицины, что в существенной степени меняет формат биоинформатических решений и позволяет говорить о наступлении новой эры в биоинформатике. Разработка новых алгоритмов и способов эффективного анализа данных с использованием искусственного интеллекта является основой для будущего развития этой области. В этой связи спрос на специалистов, способных преодолеть разрыв между биологическими и математическими дисциплинами, продолжает расти, что требует соответствующей адаптации учебных программ. В статье рассматриваются последние тенденции в биоинформатике, такие как развитие мультиомиксных подходов и использование искусственного интеллекта, а также подчеркивается важность многопрофильного образования с углубленным обучением в области математики и статистики для подготовки нового поколения ученых, способных стимулировать инновации в этой динамичной области науки.
Ключевые слова
Полный текст
Открыть статью на сайте журналаОб авторах
Анна Юрьевна Аксенова
Санкт-Петербургский государственный университет
Автор, ответственный за переписку.
Email: a.aksenova@spbu.ru
ORCID iD: 0000-0002-1601-1615
SPIN-код: 4914-7675
кандидат биол. наук
Россия, Санкт-ПетербургАнна Сергеевна Жук
Санкт-Петербургский государственный университет; Университет ИТМО; Институт общей генетики им. Н.И. Вавилова Российской академии наук, Санкт-Петербургский филиал
Email: ania.zhuk@gmail.com
ORCID iD: 0000-0001-8683-9533
SPIN-код: 2223-5306
кандидат биол. наук, доцент
Россия, Санкт-Петербург; Санкт-Петербург; Санкт-ПетербургЕлена Игоревна Степченкова
Санкт-Петербургский государственный университет; Институт общей генетики им. Н.И. Вавилова Российской академии наук, Санкт-Петербургский филиал
Email: stepchenkova@gmail.com
ORCID iD: 0000-0002-5854-8701
SPIN-код: 9121-7483
кандидат биол. наук
Россия, Санкт-Петербург; Санкт-ПетербургВячеслав Алексеевич Семенихин
Матеомика, Инновационный центр Сколково
Email: vasemenikhin@hse.ru
ORCID iD: 0000-0001-6923-0363
SPIN-код: 2251-5652
Россия, Москва
Михаил Анатольевич Ланговой
Центр искусственного интеллекта СПбГУ
Email: mikhail@langovoy.com
ORCID iD: 0000-0002-7593-0830
SPIN-код: 6905-9451
Dr. rer. nat.
Россия, Санкт-ПетербургСписок литературы
- Alser M, Lindegger J, Firtina C, et al. From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures. Comput Struct Biotechnol J. 2022;20:4579–4599.doi: 10.1016/j.csbj.2022.08.019
- Tan YC, Kumar AU, Wong YP, Ling APK. Bioinformatics approaches and applications in plant biotechnology. J Genet Eng Biotechnol. 2022;20(1):1–13. doi: 10.1186/S43141-022-00394-5/TABLES/2
- Naqvi RZ, Mahmood MA, Mansoor S, et al. Omics-driven exploration and mining of key functional genes for the improvement of food and fiber crops. Front Plant Sci. 2023;14:1273859. doi: 10.3389/FPLS.2023.1273859/PDF
- Srivastava R. Applications of artificial intelligence multiomics in precision oncology. J Cancer Res Clin Oncol. 2023;149:503–510.doi: 10.1007/S00432-022-04161-4/METRICS
- Pezoulas VC, Hazapis O, Lagopati N, et al. Machine learning approaches on high throughput ngs data to unveil mechanisms of function in biology and disease. Cancer Genom Proteom. 2021;18(5):605–626.doi: 10.21873/CGP.20284
- Sadee W, Wang D, Hartmann K, Toland AE. Pharmacogenomics: Driving personalized medicine. Pharmacol Rev. 2023;75(4):789–814.doi: 10.1124/PHARMREV.122.000810
- Uesaka K, Oka H, Kato R, et al. Bioinformatics in bioscience and bioengineering: recent advances, applications, and perspectives. J Biosci Bioeng. 2022;134(5):363–373. doi: 10.1016/J.JBIOSC.2022.08.004
- Jamialahmadi H, Khalili-Tanha G, Nazari E, Rezaei-Tavirani M. Artificial intelligence and bioinformatics: A journey from traditional techniques to smart approaches. Gastroenterol Hepatol Bed Bench. 2024;17(3):241–252. doi: 10.22037/GHFBB.V17I3.2977
- Riess O, Sturm M, Menden B, et al. Genomes in clinical Care.NPJ Genomic Med. 2024;9:20. doi: 10.1038/s41525-024-00402-2
- Mosele F, Remon J, Mateo J, et al. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: A report from the ESMO Precision Medicine Working Group. Ann Oncol. 2020;31(11):1491–1505. doi: 10.1016/j.annonc.2020.07.014
- Morganti S, Tarantino P, Ferraro E, et al. Next generation sequencing (NGS): A revolutionary technology in pharmacogenomics and personalized medicine in cancer. In: Ruiz-Garcia E, Astudillo-de la Vega H, editors. Translational research and onco-omics applications in the era of cancer personal genomics. Advances in experimental medicine and biology. Vol. 1168. Springer,Cham; 2019. P. 9–30. doi: 10.1007/978-3-030-24100-1_2
- Edsjö A, Gisselsson D, Staaf J, et al. Current and emerging sequencing-based tools for precision cancer medicine. Mol Aspects Med. 2024;96:101250. doi: 10.1016/J.MAM.2024.101250
- Abdellaoui A, Yengo L, Verweij KJH, Visscher PM. 15 years of GWAS discovery: Realizing the promise. Am J Hum Genet. 2023;110(2):179–194. doi: 10.1016/j.ajhg.2022.12.011
- Defo J, Awany D, Ramesar R. From SNP to pathway-based GWAS meta-analysis: Do current meta-analysis approaches resolve power and replication in genetic association studies? Brief Bioinform. 2023;24(1):bbac600. doi: 10.1093/bib/bbac600
- Yadav D, Patil-Takbhate B, Khandagale A, et al. Next-generation sequencing transforming clinical practice and precision medicine. Clin Chim Acta. 2023;551:117568. doi: 10.1016/J.CCA.2023.117568
- Roberto TM, Jorge MA, Francisco GV, et al. Strategies for improving detection of circulating tumor DNA using next generation sequencing. Cancer Treat Rev. 2023;119:102595. doi: 10.1016/J.CTRV.2023.102595
- Shegekar T, Vodithala S, Juganavar A. The emerging role of liquid biopsies in revolutionising cancer diagnosis and therapy. Cureus. 2023;15(8): e43650. doi: 10.7759/CUREUS.43650
- Jenkins M, Seasely AR, Subramaniam A. Prenatal genetic testing 2: Diagnostic tests. Curr Opin Pediatr. 2022;34(6):553–558.doi: 10.1097/MOP.0000000000001174
- Schäfer RA, Guo Q, Yang R. ScanNeo2: A comprehensive workflow for neoantigen detection and immunogenicity prediction from diverse genomic and transcriptomic alterations. Bioinformatics. 2023;39(11): btad659. doi: 10.1093/bioinformatics/btad659
- Xie N, Shen G, Gao W, et al. Neoantigens: Promising targets for cancer therapy. Signal Transduct Target Ther. 2023;8:9.doi: 10.1038/s41392-022-01270-x
- Kiyotani K, Chan HT, Nakamura Y. Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens. Cancer Sci. 2018;109(3):542–549. doi: 10.1111/CAS.13498
- See P, Lum J, Chen J, Ginhoux F. A single-cell sequencing guide for immunologists. Front Immunol. 2018;9:415498.doi: 10.3389/FIMMU.2018.02425/BIBTEX
- Choi H, Kim H, Chung H, et al. Application of computational algorithms for single-cell RNA-Seq and ATAC-Seq in neurodegenerative diseases. Brief Funct Genom. 2025;24: elae44. doi: 10.1093/BFGP/ELAE044
- Lee J-W, Cho J-Y. Comparative epigenetics of domestic animals: Focusing on DNA accessibility and its impact on gene regulation and traits. J Vet Sci. 2025;26(1):24259. doi: 10.4142/JVS.24259
- Cox OH, Seifuddin F, Guo J, et al. Implementation of the Methyl-Seq platform to identify tissue- and sex-specific DNA methylation differences in the rat epigenome. Epigenetics. 2024;19:2393945.doi: 10.1080/15592294.2024.2393945
- Li S-J, Gao X, Wang Z-H, et al. Cell-free DNA methylation patterns in aging and their association with inflamm-aging. Epigenomics. 2024;16(10):715–731.doi: 10.1080/17501911.2024.2340958
- Hubert J-N, Iannuccelli N, Cabau C, et al. Detection of DNA methylation signatures through the lens of genomic imprinting. Sci Rep. 2024;14:1694. doi: 10.1038/s41598-024-52114-3
- Lee H, Martinez-Agosto JA, Rexach J, Fogel BL. Next generation sequencing in clinical diagnosis. Lancet Neurol. 2019;18(5):426.doi: 10.1016/S1474-4422(19)30110-3
- Gibbs SN, Peneva D, Cuyun Carter G, et al. Comprehensive review on the clinical impact of next-generation sequencing tests for the management of advanced cancer. JCO Precis Oncol. 2023;7:715. doi: 10.1200/PO.22.00715
- Nurk S, Koren S, Rhie A, et al. The complete sequence of a human genome. Science. 2022;376(6588):44–53. doi: 10.1126/SCIENCE.ABJ6987
- Hoyt SJ, Storer JM, Hartley GA, et al. From telomere to telomere: the transcriptional and epigenetic state of human repeat elements. Science. 2022;376(6588):eabk3112. doi: 10.1126/science.abk3112
- Stephens ZD, Lee SY, Faghri F, et al. Big Data: Astronomical or genomical? PLOS Biol. 2015;13:e1002195. doi: 10.1371/JOURNAL.PBIO.1002195
- Katz K, Shutov O, Lapoint R, et al. The sequence read archive: A decade more of explosive growth. Nucleic Acids Res. 2022;50(D1):D387–D390. doi: 10.1093/NAR/GKAB1053
- Danielewski M, Szalata M, Nowak JK, et al. History of biological databases, their importance, and existence in modern scientific and policy context. Genes. 2025;16(1):100. doi: 10.3390/GENES16010100/S1
- Fedorov II, Protasov SA, Tarasova IA, Gorshkov MV. Ultrafast proteomics. Biochem. 2024;89:1349–1361. doi: 10.1134/S0006297924080017/FIGURES/4
- Anderton CR, Uhrig RG. The promising role of proteomes and metabolomes in defining the single-cell landscapes of plants. New Phytol. 2025;245(3):945–948. doi: 10.1111/NPH.20303
- Godoy Sanches PH, Clemente De Melo N, Porcari AM, Miguel De Carvalho L. Integrating molecular perspectives: strategies for comprehensive multi-omics integrative data analysis and machine learning applications in transcriptomics, proteomics, and metabolomics. Biology. 2024;13(11):848. doi: 10.3390/BIOLOGY13110848
- Wu S, Zhang S, Liu CM, et al. Recent advances in mass spectrometry-based protein interactome studies. Mol Cell Proteom. 2025;24(1):100887. doi: 10.1016/j.mcpro.2024.100887
- Dang V, Voigt B, Marcotte EM. Progress toward a comprehensive brain protein interactome. Biochem Soc Trans. 2025;53(1):303–314.doi: 10.1042/BST20241135
- Rahmati S, Emili A. Proximity labeling: precise proteomics technology for mapping receptor protein neighborhoods at the cancer cell surface.Cancers. 2025;17(2):179. doi: 10.3390/cancers17020179
- Edwards AN, Hsu KL. Emerging opportunities for intact and native protein analysis using chemical proteomics. Anal Chim Acta. 2025;1338:343551. doi: 10.1016/J.ACA.2024.343551
- Goel RK, Bithi N, Emili A. Trends in co-fractionation mass spectrometry: a new gold-standard in global protein interaction network discovery.Curr Opin Struct Biol. 2024;88:102880. doi: 10.1016/J.SBI.2024.102880
- Kim SG, Hwang JS, George NP, et al. Integrative metabolome and proteome analysis of cerebrospinal fluid in Parkinson’s disease. Int J Mol Sci. 2024;25(21):11406. doi: 10.3390/IJMS252111406/S1
- Wu D, Zhang L, Ding F. Current status and future directions of application of urine proteomics in neonatology. Front Pediatr. 2024;12:1509468. doi: 10.3389/FPED.2024.1509468/BIBTEX
- Kliuchnikova AA, Ilgisonis EV, Archakov AI, et al. Proteomic markers of aging and longevity: A systematic review. Int J Mol Sci. 2024;25(23):12634. doi: 10.3390/IJMS252312634/S1
- Nalla LV, Kanukolanu A, Yeduvaka M, Gajula SNR. Advancements in single-cell proteomics and mass spectrometry-based techniques for unmasking cellular diversity in triple negative breast cancer. Proteomics — Clin Appl. 2025;19(1):e202400101. doi: 10.1002/PRCA.202400101
- Pomella S, Melaiu O, Cifaldi L, et al. biomarkers identification in the microenvironment of oral squamous cell carcinoma: A systematic review of proteomic studies. Int J Mol Sci. 2024;25(16):8929.doi: 10.3390/IJMS25168929/S1
- Zhang Z, Huang J, Zhang Z, et al. Application of omics in the diagnosis, prognosis, and treatment of acute myeloid leukemia. Biomark Res. 2024;12:60. doi: 10.1186/s40364-024-00600-1
- ar do Perez G, Barber GP, Benet-Pages A, et al. The UCSC genome browser database: 2025 update. Nucleic Acids Res. 2025;53(D1):D1243–D1249. doi: 10.1093/NAR/GKAE974
- Dyer SC, Austine-Orimoloye O, Azov AG, et al. Ensembl 2025. Nucleic Acids Res. 2025;53(D1):D948–D957. doi: 10.1093/NAR/GKAE1071
- Rodriguez-Tomé P, Stoehr PJ, Cameron GN, Flores TP. The European Bioinformatics Institute (EBI) databases. Nucleic Acids Res. 1996;24(1):6–12. doi: 10.1093/NAR/24.1.6
- Consortium TU, Bateman A, Martin M-J, et al. UniProt: The universal protein knowledgebase in 2025. Nucleic Acids Res. 2025;53(D1):D609–D617. doi: 10.1093/NAR/GKAE1010
- Zardecki C, Dutta S, Goodsell DS, et al. PDB-101: Educational resources supporting molecular explorations through biology and medicine. Protein Sci. 2022;31(1S):129–140. doi: 10.1002/PRO.4200
- Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. doi: 10.1093/NAR/28.1.27
- Chang A, Jeske L, Ulbrich S, et al. BRENDA, the ELIXIR core data resource in 2021: New developments and updates. Nucleic Acids Res. 2021;49(D1):D498–D508. doi: 10.1093/NAR/GKAA1025
- Mondello A, Dal Bo M, Toffoli G, Polano M. Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges. Front Pharmacol. 2024;14:1260276. doi: 10.3389/fphar.2023.1260276
- Erfanian N, Heydari AA, Feriz AM, et al. Deep learning applications in single-cell genomics and transcriptomics data analysis. Biomed Pharmacother. 2023;165:115077. doi: 10.1016/J.BIOPHA.2023.115077
- Athaya T, Ripan RC, Li X, Hu H. Multimodal deep learning approaches for single-cell multi-omics data integration. Brief Bioinform. 2023;24(5): bbad313. doi: 10.1093/BIB/BBAD313
- Gulati GS, D’Silva JP, Liu Y, et al. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol. 2024;26:11–31. doi: 10.1038/s41580-024-00768-2
- Rivero-Garcia I, Torres M, Sánchez-Cabo F. Deep generative models in single-cell omics. Comput Biol Med. 2024;176:108561.doi: 10.1016/J.COMPBIOMED.2024.108561
- Kang M, Ko E, Mersha TB. A roadmap for multi-omics data integration using deep learning. Brief Bioinform. 2022;23(1):bbab454. doi: 10.1093/BIB/BBAB454
- Pun FW, Ozerov IV, Zhavoronkov A. AI-powered therapeutic target discovery. Trends Pharmacol Sci. 2023;44(9):561–572.doi: 10.1016/j.tips.2023.06.010
- Mann M, Kumar C, Zeng W-F, Strauss MT. Artificial intelligence for proteomics and biomarker discovery. Cell Syst. 2021;12(8):759–770. doi: 10.1016/j.cels.2021.06.006
- Wang L, Wen Z, Liu S-W, et al. Overview of AlphaFold2 and breakthroughs in overcoming its limitations. Comput Biol Med. 2024;176:108620. doi: 10.1016/j.compbiomed.2024.108620
- Zhang H, Lan J, Wang H, et al. AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease. Front Mol Biosci. 2024;11:1414916. doi: 10.3389/FMOLB.2024.1414916
- Varga JK, Schueler-Furman O. Who binds better? Let Alphafold2 decide! Angew Chemie. Int Ed. 2023;62(28):e202303526.doi: 10.1002/anie.202303526
- Bertoline LMF, Lima AN, Krieger JE, Teixeira SK. Before and after AlphaFold2: An overview of protein structure prediction. Front Bioinform. 2023;3:1120370. doi: 10.3389/FBINF.2023.1120370
- Borkakoti N, Thornton JM. AlphaFold2 protein structure prediction: Implications for drug discovery. Curr Opin Struct Biol. 2023;78:102526. doi: 10.1016/J.SBI.2022.102526
- Leman JK, Weitzner BD, Lewis SM, et al. Macromolecular modeling and design in rosetta: Recent methods and frameworks. Nat Methods. 2020;17:665–680. doi: 10.1038/S41592-020-0848-2
- Baek M, DiMaio F, Anishchenko I, et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science. 2021;373(6557):871–876. doi: 10.1126/science.abj8754
- Zhang G, Luo Y, Dai X, Dai Z. Benchmarking deep learning methods for predicting CRISPR/Cas9 SgRNA on- and off-target activities. Brief Bioinform. 2023;24(6):bbad333. doi: 10.1093/BIB/BBAD333
- Sherkatghanad Z, Abdar M, Charlier J, Makarenkov V. Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: A review. Brief Bioinform. 2023;24(3):bbad131. doi: 10.1093/BIB/BBAD131
- Lee M. Deep learning in CRISPR-cas systems: A review of recent studies. Front Bioeng Biotechnol. 2023;11:1226182. doi: 10.3389/fbioe.2023.1226182
- Sun D, Chen W, He J, et al. A novel method for screening malignant hematological diseases by constructing an optimal machine learning model based on blood cell parameters. BMC Med Inform Decis Mak. 2025;25:72. doi: 10.1186/s12911-025-02892-1
- Shan R, Li X, Chen J, et al. Interpretable machine learning to predict the malignancy risk of follicular thyroid neoplasms in extremely unbalanced data: retrospective cohort study and literature review. JMIR cancer.2025;11:e66269–e66269. doi: 10.2196/66269
- Ayhan B, Ayan E, Atsü S. Detection of dental caries under fixed dental prostheses by analyzing digital panoramic radiographs with artificial intelligence algorithms based on deep learning methods. BMC Oral Health. 2025;25:216. doi: 10.1186/s12903-025-05577-3
- Kovács KA, Kerepesi C, Rapcsák D, et al. Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences. Sci Rep. 2025;15:4868. doi: 10.1038/s41598-025-89339-9
- Guo L, Wang W, Xie X, et al. Machine learning-based models for genomic predicting neoadjuvant chemotherapeutic sensitivity in cervical cancer. Biomed Pharmacother. 2023;159:114256.doi: 10.1016/J.BIOPHA.2023.114256
- Zhao Y, Fu Z, Barnett EJ, et al. Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder. Transl Psychiatry. 2025;15:46.doi: 10.1038/s41398-025-03250-5
- Ivakhnenko AG, Lapa VG. Cybernetic predictive devices. Kyiv: Naukova Dumka; 1965. 214 p. URL: https://gwern.net/doc/ai/1966-ivakhnenko.pdf
- Ivakhnenko AG. Polynomial theory of complex systems. In: IEEE Trans. Syst. Man Cybern. 1971. Vol. 1. P. 364–378. doi: 10.1109/TSMC.1971.4308320
- Vapnik VN, Chervonenkis AJ. On one class of learning algorithms for pattern recognition. Automation and Remote Control. 1964;25:937–945. (In Russ.)
- Boltyansky VG, Gamkrelidze RV, Pontryagin LS. To the theory of optimal processes. Reports of the USSR Academy of Sciences. 1956;110:7–10. (In Russ.)
- Galushkin AI. Synthesis of multilayer systems of pattern recognition. Moscow: Energia; 1974. (In Russ.)
Дополнительные файлы
