The site of the Media Sphera Publishers contains materials intended solely for healthcare professionals.
By closing this message, you confirm that you are a certified medical professional or a student of a medical educational institution.

Login
EN
+7 (495) 482-4118
+7 (495) 482-4329
+8 800 250-18-12
  • (toll-free number for subscriptions)
    Mon-Fri from 10 a.m. to 6 p.m.

  • © 1998-2026
+7 (495) 482-43-29
EN
All journals
Journal
Year
Year
Search result: 0

Patrushev M.V.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Genomic Center

Prokopenko A.V.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Genomic Center

Izotova A.O.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Genomic Center

Agumava A.A.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Complex of Medical Primatology

Pertova D.S.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Genomic Center

Matyushkin I.V.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Complex of Medical Primatology

Chzhu O.P.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Complex of Medical Primatology

Sharko F.S.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Genomic Center

Toshchakov S.V.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’

Integration of genetic information into the digital ecosystems of modern primate biobanks: opportunities and challenges

Authors:

Patrushev M.V., Prokopenko A.V., Izotova A.O., Agumava A.A., Pertova D.S., Matyushkin I.V., Chzhu O.P., Sharko F.S., Toshchakov S.V.

More about the authors

Journal: Molecular Genetics, Microbiology and Virology. 2025;43(4‑2): 42‑54

DOI: 10.17116/molgen20254304242

Read: 435 times

Buy for 300₽
Contact the author
Table of contents

INTEGRATION OF GENETIC INFORMATION INTO THE DIGITAL ECOSYSTEMS OF MODERN PRIMATE BIOBANKS: OPPORTUNITIES AND CHALLENGES
INTEGRATION OF GENETIC INFORMATION INTO THE DIGITAL ECOSYSTEMS OF MODERN PRIMATE BIOBANKS: OPPORTUNITIES AND CHALLENGES

To cite this article:

Patrushev MV, Prokopenko AV, Izotova AO, et al. Integration of genetic information into the digital ecosystems of modern primate biobanks: opportunities and challenges. Molecular Genetics, Microbiology and Virology. 2025;43(4‑2):42‑54. (In Russ.)
https://doi.org/10.17116/molgen20254304242

Подписка 2026 Молекулярная генетика, микробиология и вирусология (Molecular Genetics, Microbiology and Virology)
МСФ на ЯМ
Использование инфографики авторами научных работ
Recommended articles:
Modern unde­rstanding of epigenetic inhe­ritance mechanisms and their role in the evolutionary process. Mole­cular Gene­tics, Microbiology and Viro­logy. 2025;(4-2):6-20
FLM Working Group «Quality control based on patient outcomes in real time (PBRTQC)»: the first step. Labo­ratory Service. 2025;(1):48-49
Real-time Patient Sample Quality Control (PBRTQC): Overview and reco­mmendations. Labo­ratory Service. 2025;(1):50-58
Quality control of immu­nohistochemical studies: compulsory duty or urgent need?. Russian Journal of Archive of Pathology. 2025;(2):43-52
Digi­talization in medi­cine and healthcare in Russia. Rege­nerative Biotechnologies, Preventive, Digi­tal and Predictive Medi­cine. 2025;(2):5-9
Approaches to the digi­tization of clinical guidelines. Medi­cal Technologies. Asse­ssment and Choice. 2025;(2):39-47
Quality control of HLA-typing in labo­ratories in Russia. Labo­ratory Service. 2025;(2):24-28
Orga­nization of the HLA-typing labo­ratories. Labo­ratory Service. 2025;(3):26-32
Prevalence of clinically significant PIK3CA muta­tions: resu­lts of tumor sample analysis from 1452 patients in a Russian cohort. P.A. Herzen Journal of Onco­logy. 2025;(6):32-38
Abstract:

THE PURPOSE

Of this review is to summarize current state of primate biobanks and their role in international scientific networks, as well as specialized genomic resources for research on hominids and other models. Modern methods of sample authentication and genetic passportization, such as microsatellite and SNP analysis, are presented, ensuring high-precision individual identification and quality control of biological materials. Special attention is given to the importance of standardization processes and international collaboration to enhance research efficiency and preserve genetic diversity.

CONCLUSION

The authors decided that the integration of genetic information into digital ecosystems of primate biobanks leads to a transition from traditional collections to intelligent platforms utilizing advanced analysis and modeling technologies, opening new prospects for research and personalized medicine.

Keywords:

biobanking
primates
passportization
quality control
digitalization
NGS

Authors:

Patrushev M.V.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Genomic Center

Prokopenko A.V.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Genomic Center

Izotova A.O.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Genomic Center

Agumava A.A.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Complex of Medical Primatology

Pertova D.S.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Genomic Center

Matyushkin I.V.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Complex of Medical Primatology

Chzhu O.P.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Complex of Medical Primatology

Sharko F.S.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’ — Kurchatov Genomic Center

Toshchakov S.V.

Federal State Budget Scientific Institution National Research Centre ‘Kurchatov Institute’

Received:

14.10.2025

Accepted:

28.10.2025

Close metadata

References:

  1. Bukreeva AS, Malsagova KA, Petrovskiy DV, Butkova TV, Nakhod VI, Rudnev VR, et al. Biobank Digitalization: From Data Acquisition to Efficient Use. Biology (Basel). 2024;13(12):957.  https://doi.org/10.3390/biology13120957
  2. Harding JD. Genomic Tools for the Use of Nonhuman Primates in Translational Research. ILAR J. 2017;58(1):59-68.  https://doi.org/10.1093/ilar/ilw042
  3. Messaoudi I, Estep R, Robinson B, Wong SW. Nonhuman primate models of human immunology. Antioxid Redox Signal. 2011;14(2):261-273.  https://doi.org/10.1089/ars.2010.3241
  4. Rogers J, Gibbs RA. Comparative primate genomics: emerging patterns of genome content and dynamics. Nat Rev Genet. 2014;15(5):347-359.  https://doi.org/10.1038/nrg3707
  5. Vallender EJ, Miller GM. Nonhuman primate models in the genomic era: a paradigm shift. ILAR J. 2013;54(2):154-165.  https://doi.org/10.1093/ilar/ilt044
  6. Alkhatib R, Gaede KI. Data Management in Biobanking: Strategies, Challenges, and Future Directions. BioTech (Basel). 2024;13(3):34.  https://doi.org/10.3390/biotech13030034
  7. Vallender EJ, Hotchkiss CE, Lewis AD, Rogers J, Stern JA, Peterson SM, et al. Nonhuman primate genetic models for the study of rare diseases. Orphanet J Rare Dis. 2023;18(1):20.  https://doi.org/10.1186/s13023-023-02619-3
  8. Müller H, Malservet N, Quinlan P, Reihs R, Penicaud M, Chami A, et al. From the evaluation of existing solutions to an all-inclusive package for biobanks. Health Technol (Berl). 2017;7(1):89-95.  https://doi.org/10.1007/s12553-016-0175-x
  9. Reynolds T, Johnson EC, Huggett SB, Bubier JA, Palmer RHC, Agrawal A, et al. Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration. Neuropsychopharmacology. 2021;46(1):86-97.  https://doi.org/10.1038/s41386-020-00795-5
  10. Medina-Martínez JS, Arango-Ossa JE, Levine MF, Zhou Y, Gundem G, Kung AL, et al. Isabl Platform, a digital biobank for processing multimodal patient data. BMC Bioinformatics. 2020;21(1):549.  https://doi.org/10.1186/s12859-020-03879-7
  11. Calvino G, Peconi C, Strafella C, Trastulli G, Megalizzi D, Andreucci S, et al. Federated Learning: Breaking Down Barriers in Global Genomic Research. Genes (Basel). 2024;15(12):1650. https://doi.org/10.3390/genes15121650
  12. Procyk E, Meunier M. BioSimia, France CNRS network for nonhuman primate biomedical research in infectiology, immunology, and neuroscience. Curr Res Neurobiol. 2022;3:100051. https://doi.org/10.1016/j.crneur.2022.100051
  13. Capitanio JP. Knowledge of Biobehavioral Organization Can Facilitate Better Science: A Review of the BioBehavioral Assessment Program at the California National Primate Research Center. Animals (Basel). 2021;11(8):2445. https://doi.org/10.3390/ani11082445
  14. Wu DD, Qi XG, Yu L, Li M, Liu ZJ, Yoder AD, et al. Initiation of the Primate Genome Project. Zool Res. 2022;43(2):147-149.  https://doi.org/10.24272/j.issn.2095-8137.2022.001
  15. Corrales C, Luciano S, Astrin J. Biodiversity biobanks: a landscape analysi. Published online 2023. https://doi.org/10.3897/arphapreprints.e103105
  16. Coorens THH, Guillaumet-Adkins A, Kovner R, Linn RL, Roberts VHJ, Sule A, et al. The human and non-human primate developmental GTEx projects. Nature. 2025;637(8046):557-564.  https://doi.org/10.1038/s41586-024-08244-9
  17. Shen Y, Shao M, Hao ZZ, Huang M, Xu N, Liu S. Multimodal Nature of the Single-cell Primate Brain Atlas: Morphology, Transcriptome, Electrophysiology, and Connectivity. Neurosci Bull. 2024;40(4):517-532.  https://doi.org/10.1007/s12264-023-01160-4
  18. Liu X, Gao T, Lu T, Bao Y, Schumann G, Lu L. China Brain Project: from bench to bedside. Sci Bull (Beijing). 2023;68(5):444-447.  https://doi.org/10.1016/j.scib.2023.02.023
  19. Bimber BN, Raboin MJ, Letaw J, Nevonen KA, Spindel JE, McCouch SR, et al. Whole-genome characterization in pedigreed non-human primates using genotyping-by-sequencing (GBS) and imputation. BMC Genomics. 2016;17(1):676.  https://doi.org/10.1186/s12864-016-2966-x
  20. Mao Y, Harvey WT, Porubsky D, Munson KM, Hoekzema K, Lewis AP, et al. Structurally divergent and recurrently mutated regions of primate genomes. Cell. 2024;187(6):1547-1562.e13.  https://doi.org/10.1016/j.cell.2024.01.052
  21. Backman JD, Li AH, Marcketta A, Sun D, Mbatchou J, Kessler MD, et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature. 2021;599(7886):628-634.  https://doi.org/10.1038/s41586-021-04103-z
  22. von Thaden A, Cocchiararo B, Jarausch A, Jüngling H, Karamanlidis AA, Tiesmeyer A, et al. Assessing SNP genotyping of noninvasively collected wildlife samples using microfluidic arrays. Sci Rep. 2017;7(1):10768. https://doi.org/10.1038/s41598-017-10647-w
  23. Pasaniuc B, Rohland N, McLaren PJ, Garimella K, Zaitlen N, Li H, et al. Extremely low-coverage sequencing and imputation increases power for genome-wide association studies. Nat Genet. 2012;44(6):631-635.  https://doi.org/10.1038/ng.2283
  24. Rubinacci S, Hofmeister RJ, Sousa da Mota B, Delaneau O. Imputation of low-coverage sequencing data from 150,119 UK Biobank genomes. Nat Genet. 2023;55(7):1088-1090. https://doi.org/10.1038/s41588-023-01438-3
  25. Jasinska AJ. Resources for functional genomic studies of health and development in nonhuman primates. Am J Phys Anthropol. 2020;171 Suppl 70:174-194.  https://doi.org/10.1002/ajpa.24051
  26. Blutt SE, Coarfa C, Neu J, Pammi M. Multiomic Investigations into Lung Health and Disease. Microorganisms. 2023;11(8):2116. https://doi.org/10.3390/microorganisms11082116
  27. Tung J, Zhou X, Alberts SC, Stephens M, Gilad Y. The genetic architecture of gene expression levels in wild baboons. Elife. 2015;4:e04729. https://doi.org/10.7554/eLife.04729
  28. Wang A, Chiou J, Poirion OB, Buchanan J, Valdez MJ, Verheyden JM, et al. Single-cell multiomic profiling of human lungs reveals cell-type-specific and age-dynamic control of SARS-CoV2 host genes. Elife. 2020;9:e62522. https://doi.org/10.7554/eLife.62522
  29. Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, et al. Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol. 2018;14(6):e8124. https://doi.org/10.15252/msb.20178124
  30. Zhuang XL, Zhang JJ, Shao Y, Ye Y, Chen CY, Zhou L, et al. Integrative Omics Reveals Rapidly Evolving Regulatory Sequences Driving Primate Brain Evolution. Mol Biol Evol. 2023;40(8):msad173. https://doi.org/10.1093/molbev/msad173
  31. Juan D, Santpere G, Kelley JL, Cornejo OE, Marques-Bonet T. Current advances in primate genomics: novel approaches for understanding evolution and disease. Nat Rev Genet. 2023;24(5):314-331.  https://doi.org/10.1038/s41576-022-00554-w
  32. Housman G, Gilad Y. Prime time for primate functional genomics. Curr Opin Genet Dev. 2020;62:1-7.  https://doi.org/10.1016/j.gde.2020.04.007
  33. L Rocha J, Lou RN, Sudmant PH. Structural variation in humans and our primate kin in the era of telomere-to-telomere genomes and pangenomics. Curr Opin Genet Dev. 2024;87:102233. https://doi.org/10.1016/j.gde.2024.102233
  34. Borisov N, Buzdin A. Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect. Biomedicines. 2022;10(9):2318. https://doi.org/10.3390/biomedicines10092318
  35. Foltz SM, Greene CS, Taroni JN. Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously. Commun Biol. 2023;6(1):222.  https://doi.org/10.1038/s42003-023-04588-6
  36. Chorlton SD. Ten common issues with reference sequence databases and how to mitigate them. Front Bioinform. 2024;4:1278228. https://doi.org/10.3389/fbinf.2024.1278228
  37. Ambalavanan R, Snead RS, Marczika J, Towett G, Malioukis A, Mbogori-Kairichi M. Challenges and strategies in building a foundational digital health data integration ecosystem: a systematic review and thematic synthesis. Front Health Serv. 2025;5:1600689. https://doi.org/10.3389/frhs.2025.1600689
  38. Gangwal A, Lavecchia A. Artificial intelligence in preclinical research: enhancing digital twins and organ-on-chip to reduce animal testing. Drug Discov Today. 2025;30(5):104360. https://doi.org/10.1016/j.drudis.2025.104360
  39. Bartolomucci A, Kane AE, Gaydosh L, Razzoli M, McCoy BM, Ehninger D, et al. Animal Models Relevant for Geroscience: Current Trends and Future Perspectives in Biomarkers, and Measures of Biological Aging. J Gerontol A Biol Sci Med Sci. 2024;79(9):glae135. https://doi.org/10.1093/gerona/glae135
  40. Montesinos-López OA, Montesinos-López A, Mosqueda-González BA, Delgado-Enciso I, Chavira-Flores M, Crossa J, et al. Genomic prediction powered by multi-omics data. Front Genet. 2025;16:1636438. https://doi.org/10.3389/fgene.2025.1636438
  41. Wu J, Koelzer VH. Towards generative digital twins in biomedical research. Comput Struct Biotechnol J. 2024;23:3481-3488. https://doi.org/10.1016/j.csbj.2024.09.030
  42. Loewa A, Feng JJ, Hedtrich S. Human disease models in drug development. Nat Rev Bioeng. Published online May 11, 2023:1-15.  https://doi.org/10.1038/s44222-023-00063-3
  43. Nakamura T, Fujiwara K, Saitou M, Tsukiyama T. Non-human primates as a model for human development. Stem Cell Reports. 2021;16(5):1093-1103. https://doi.org/10.1016/j.stemcr.2021.03.021
  44. Marzi SJ, Schilder BM, Nott A, Frigerio CS, Willaime-Morawek S, Bucholc M, et al. Artificial intelligence for neurodegenerative experimental models. Alzheimers Dement. 2023;19(12):5970-5987. https://doi.org/10.1002/alz.13479
  45. Chafai N, Bonizzi L, Botti S, Badaoui B. Emerging applications of machine learning in genomic medicine and healthcare. Crit Rev Clin Lab Sci. 2024;61(2):140-163.  https://doi.org/10.1080/10408363.2023.2259466
  46. Cheng J, Lawrence C, Niepert M. VEGN: Variant Effect Prediction with Graph Neural Networks. Published online June 25, 2021. https://doi.org/10.48550/arXiv.2106.13642
  47. Gao H, Hamp T, Ede J, Schraiber JG, McRae J, Singer-Berk M, et al. The landscape of tolerated genetic variation in humans and primates. Science. 2023;380(6648):eabn8153. https://doi.org/10.1126/science.abn8197
  48. Pollen AA, Kilik U, Lowe CB, Camp JG. Human-specific genetics: new tools to explore the molecular and cellular basis of human evolution. Nat Rev Genet. 2023;24(10):687-711.  https://doi.org/10.1038/s41576-022-00568-4
  49. Yu H, He G, Wang W, Qin S, Wang Y, Bai M, et al. A graph neural network approach for accurate prediction of pathogenicity in multi-type variants. Brief Bioinform. 2025;26(2):bbaf151. https://doi.org/10.1093/bib/bbaf151
Contact the author
Email
Message
The publishing house "Media Sphere" does not provide treatment services, does not give recommendations on contacting medical institutions and specific specialists, on the prescription of medicines and dietary supplements.

Email Confirmation

An email was sent to test@gmail.com with a confirmation link. Follow the link from the letter to complete the registration on the site.

Email Confirmation

Submit Application

You can become an author of one of our magazines, send a request and we will consider it in during the day.

Name
Phone
E-mail
Message
Login
Registration
E-mail
Password
Forgot Password?

Log in to the site using your account in one of the services

Password recovery
E-mail
Login
Register

We use cооkies to improve the performance of the site. By staying on our site, you agree to the terms of use of cооkies. To view our Privacy and Cookie Policy, please. click here.