uzspbgmuУчёные записки Первого Санкт-Петербургского государственного медицинского университета имени академика И. П. ПавловаThe Scientific Notes of the Pavlov University1607-41812541-8807Academician I.P. Pavlov First St. Petersburg State Medical University10.24884/1607-4181-2025-32-1-11-20uzspbgmu-1092Research ArticleОБЗОРЫ И ЛЕКЦИИREVIEWS AND LECTURESОбзор программ для оценки патогенности генетических вариантовReview of programs for assessing the pathogenicity of genetic variantshttps://orcid.org/0000-0002-5849-1311БугД. С.BugD. S.

Буг Дмитрий Сергеевич, младший научный сотрудник, НИЦ биоинформатики НОИ биомедицины

197022, Санкт-Петербург, ул. Льва Толстого, д. 6-8

Bug Dmitrii S., Junior Research Fellow, Bioinformatics Research Center of the Research Institute of Biomedicine

6-8, L’va Tolstogo str., Saint Petersburg, 197022

bug.dmitrii@yandex.ruhttps://orcid.org/0000-0002-1489-5058НаркевичА. Н.NarkevichA. N.

Наркевич Артем Николаевич, доктор медицинских наук, доцент, профессор кафедры общественного здоровья и здравоохранения

454092, г. Челябинск, ул. Воровского, д. 64

Narkevich Artem N., Dr. of Sci. (Med.), Associate Professor, Professor of the Department of Public Health

64, Vorovskogo str., Chelyabinsk, 454092

narkevichart@gmail.comhttps://orcid.org/0000-0001-6397-824XПетуховаН. В.PetukhovaN. V.

Петухова Наталья Витальевна, кандидат медицинских наук, руководитель центра НИЦ биоинформатики НОИ биомедицины

197022, Санкт-Петербург, ул. Льва Толстого, д. 6-8

Petukhova Natalia V., Cand. of Sci. (Med.), Head of the Bioinformatics Research Center of the Research Institute of Biomedicine

6-8, L’va Tolstogo str., Saint Petersburg, 197022

nvp.bioinfo@gmail.comПервый Санкт-Петербургский государственный медицинский университет им. акад. И.П. ПавловаРоссияPavlov UniversityRussian FederationЮжно-Уральский государственный медицинский университетРоссияSouth-Ural State Medical UniversityRussian Federation2025070620253211120Copyright © Буг Д.С., Наркевич А.Н., Петухова Н.В., 20252025Буг Д.С., Наркевич А.Н., Петухова Н.В.Bug D.S., Narkevich A.N., Petukhova N.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.sci-notes.ru/jour/article/view/1092

В настоящее время молекулярно-генетические методы играют важную роль в диагностике ряда патологий. Внедрение массового параллельного секвенирования значительно увеличило объем данных, описывающих варианты ДНК пациентов с различными заболеваниями, но клиническое значение многих из этих результатов остается неизвестным. Для оценки эффекта генетических вариантов широко используется автоматическое определение клинической значимости вариантов при помощи программ-предикторов. Отечественные и международные руководства по интерпретации данных, полученных методами массового параллельного секвенирования, рекомендуют использовать программы-предикторы для определения клинического значения генетических вариантов. Однако, принципы работы и характеристики этих программ в научной литературе описаны недостаточно. В данном обзоре на примере наиболее популярных программ-предикторов представлены основные принципы их работы, которые используются для оценки патогенности вариантов.

Currently, molecular genetic methods play an essential role in the diagnostic process for diverse pathologies. The introduction of mass parallel sequencing has significantly increased the amount of data on DNA variants in patients with various diseases, but the clinical significance of many of these findings remains unknown. Widely used methods of variant effect evaluation include the automatic determination of the pathogenicity of variants using specialized predictors. Domestic and international guidelines for the interpretation of data obtained through mass parallel sequencing recommend the use of predictive programs to determine the clinical significance of genetic variants. However, there is a lack of detailed information about the principles and characteristics of these programs in the scientific literature. In this review, we present the basic principles that are used to evaluate the pathogenicity of variations using the example of some of the most widely used predictive programs.

оценка патогенностивариантмутацияопределение клинического эффектаpathogenicity assessmentvariantmutationclinical effect evaluationReferencesCook C. E., Bergman M. T., Finn R. D. et al. The European Bioinformatics Institute in 2016: Data growth and integration // Nucleic Acids Res. – 2015. – Vol. 44, № D1. – P. D20–D26. https://doi.org/10.1093%2Fnar%2Fgkv1352.Cook C. E., Bergman M. T., Finn R. D. et al. The European Bioinformatics Institute in 2016: Data growth and integration // Nucleic Acids Res. 2015;44(D1):D20–D26. https://doi.org/10.1093%2Fnar%2Fgkv1352.Gagan J., Van Allen E. M. Next-generation sequencing to guide cancer therapy // Genome Med. – 2015. – Vol. 7, № 1. – P. 80. https://doi.org/10.1186/s13073-015-0203-x.Gagan J., Van Allen E. M. Next-generation sequencing to guide cancer therapy // Genome Med. 2015;7(1):80. https://doi.org/10.1186/s13073-015-0203-x.Рыжкова О., Кардымон О., Прохорчук Е. и др. Руководство по интерпретации данных последовательности ДНК человека, полученных методами массового параллельного секвенирования (MPS) (редакция 2018, версия 2) // Медицинская генетика. – 2020. – Т. 18, № 2. – C. 3–23. https://doi.org/10.25557/2073-7998.2019.02.3-23.Ryzhkova O. P., Kardymon O. L., Prohorchuk E. B. et al. Guidelines for the interpretation of massive parallel sequencing variants (update 2018, v2) // Medical genetics. 2019;18(2):3–23. (In Russ.).Спектор М., Ясько Л., Друй А. Интерпретация соматических генетических вариантов, выявленных методом высокопроизводительного секвенирования опухолевой ДНК, на примере онкологических заболеваний детского возраста // Медицинская генетика – 2021. – Т. 20, № 3. – C. 3–25. https://doi.org/10.25557/2073-7998.2021.03.3-25.Spektor M. A., Yasko L. A., Druy A. E. The interpretation of somatic genetic variants identified with high-throughput sequencing of DNA from paediatric solid tumors // Medical Genetics. 2021;20(3):3–25. (In Russ.).Cooper G. M., Stone E. A., Asimenos G. et al. Distribution and intensity of constraint in mammalian genomic sequence // Genome Res. – 2005. – Vol. 15, № 7. – P. 901–913. https://doi.org/10.1101/gr.3577405.Cooper G. M., Stone E. A., Asimenos G. et al. Distribution and intensity of constraint in mammalian genomic sequence // Genome Res. 2005;15(7):901–913. https://doi.org/10.1101/gr.3577405.Garber M., Guttman M., Clamp M. et al. Identifying novel constrained elements by exploiting biased substitution patterns // Bioinformatics. – 2009. – Vol. 25, № 12. – P. i54–i62. https://doi.org/10.1093%2Fbioinformatics%2Fbtp190.Garber M., Guttman M., Clamp M. et al. Identifying novel constrained elements by exploiting biased substitution patterns // Bioinformatics. 2009;25(12):i54–i62. https://doi.org/10.1093%2Fbioinformatics%2Fbtp190.Pollard K. S., Hubisz M. J., Rosenbloom K. R. et al. Detection of nonneutral substitution rates on mammalian phylogenies // Genome Res. – 2009. – Vol. 20, № 1. – P. 110–121. https://doi.org/10.1101/gr.097857.109.Pollard K. S., Hubisz M. J., Rosenbloom K. R. et al. Detection of nonneutral substitution rates on mammalian phylogenies // Genome Res. 2009;20(1):110–121. https://doi.org/10.1101/gr.097857.109.Siepel A., Bejerano G., Pedersen J. S. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes // Genome Res. – 2005. – Vol. 15, № 8. – P. 1034–1050. https://doi.org/10.1101/gr.3715005.Siepel A., Bejerano G., Pedersen J. S. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes // Genome Res. 2005;15(8):1034–1050. https://doi.org/10.1101/gr.3715005.Adzhubei I. A., Schmidt S., Peshkin L. et al. A method and server for predicting damaging missense mutations // Nat Methods. – 2010. – Vol. 7, № 4. – P. 248–249. https://doi.org/10.1038/nmeth0410-248.Adzhubei I. A., Schmidt S., Peshkin L. et al. A method and server for predicting damaging missense mutations // Nat Methods. 2010;7(4):248–249. https://doi.org/10.1038/nmeth0410-248.Ashkenazy H., Abadi S., Martz E. et al. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules // Nucleic Acids Res. – 2016. – Vol. 44, № W1. – P. W344–W350. https://doi.org/10.1093/nar/gkw408.Ashkenazy H., Abadi S., Martz E. et al. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules // Nucleic Acids Res. 2016;44(W1):W344–W350. https://doi.org/10.1093/nar/gkw408.Calabrese R., Capriotti E., Fariselli P. et al. Functional annotations improve the predictive score of human disease-related mutations in proteins // Hum. Mutat. – 2009. – Vol. 30, № 8. – P. 1237–1244. https://doi.org/10.1002/humu.21047.Calabrese R., Capriotti E., Fariselli P. et al. Functional annotations improve the predictive score of human disease-related mutations in proteins // Hum. Mutat. 2009;30(8):1237–1244. https://doi.org/10.1002/humu.21047.Choi Y., Sims G. E., Murphy S. et al. Predicting the Functional Effect of Amino Acid Substitutions and Indels // PLoS ONE. – 2012. – Vol. 7, № 10. – P. e46688. https://doi.org/10.1371/journal.pone.0046688.Choi Y., Sims G. E., Murphy S. et al. Predicting the Functional Effect of Amino Acid Substitutions and Indels // PLoS ONE. 2012;7(10):e46688. https://doi.org/10.1371/journal.pone.0046688.Frazer J., Notin P., Dias M. et al. Disease variant prediction with deep generative models of evolutionary data // Nature. – 2021. – Vol. 599, № 7883. – P. 91–95. https://doi.org/10.1038/s41586-021-04043-8.Frazer J., Notin P., Dias M. et al. Disease variant prediction with deep generative models of evolutionary data // Nature. 2021;599(7883):91–95. https://doi.org/10.1038/s41586-021-04043-8.Reva B., Antipin Y., Sander C. Predicting the functional impact of protein mutations: application to cancer genomics // Nucleic Acids Research. – 2011. – Vol. 39, № 17. – P. e118–e118. https://doi.org/10.1093/nar/gkr407.Reva B., Antipin Y., Sander C. Predicting the functional impact of protein mutations: application to cancer genomics // Nucleic Acids Research. 2011;39(17):e118–e118. https://doi.org/10.1093/nar/gkr407.Sim N., Kumar P., Hu J. et al. SIFT web server: predicting effects of amino acid substitutions on proteins // Nucleic Acids Research. – 2012. – Vol. 40, № W1. – P. W452–W457. https://doi.org/10.1093/nar/gks539.Sim N., Kumar P., Hu J. et al. SIFT web server: predicting effects of amino acid substitutions on proteins // Nucleic Acids Research. 2012;40(W1):W452–W457. https://doi.org/10.1093/nar/gks539.Capriotti E., Fariselli P. Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants // Hum Genet. – 2022. – Vol. 141, № 10. – P. 1649–1658. https://doi.org/10.1007/s00439-021-02419-4.Capriotti E., Fariselli P. Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants // Hum Genet. 2022;141(10):1649–1658. https://doi.org/10.1007/s00439-021-02419-4.Adebali O., Reznik A. O., Ory D. S. et al. Establishing the precise evolutionary history of a gene improves prediction of disease-causing missense mutations // Genetics in Medicine. – 2016. – Vol. 18, № 10. – P. 1029–1036. https://doi.org/10.1038/gim.2015.208.Adebali O., Reznik A. O., Ory D. S. et al. Establishing the precise evolutionary history of a gene improves prediction of disease-causing missense mutations // Genetics in Medicine. 2016;18(10):1029–1036. https://doi.org/10.1038/gim.2015.208.Li W., Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences // Bioinformatics. – 2006. – Vol. 22, № 13. – P. 1658–1659. https://doi.org/10.1093/bioinformatics/btl158.Li W., Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences // Bioinformatics. 2006;22(13):1658–1659. https://doi.org/10.1093/bioinformatics/btl158.Ng P. C. SIFT: predicting amino acid changes that affect protein function // Nucleic Acids Research. – 2003. – Vol. 31, № 13. – P. 3812–3814. https://doi.org/10.1093/nar/gkg509.Ng P. C. SIFT: predicting amino acid changes that affect protein function // Nucleic Acids Research. 2003;31(13):3812–3814. https://doi.org/10.1093/nar/gkg509.Thompson J. D., Higgins D. G., Gibson T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice // Nucl Acids Res. – 2007. – Vol. 22, № 22. – P. 4673–4680. https://doi.org/10.1093/nar/22.22.4673.Thompson J. D., Higgins D. G., Gibson T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice // Nucl Acids Res. 2007;22(22):4673–4680. https://doi.org/10.1093/nar/22.22.4673.Sievers F., Wilm A., Dineen D. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega // Molecular Systems Biology. – 2011. – Vol. 7, № 1. – P. 539. https://doi.org/10.1038/msb.2011.75.Sievers F., Wilm A., Dineen D. et al. Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega // Molecular Systems Biology. 2011;7(1):539. https://doi.org/10.1038/msb.2011.75.Katoh K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform // Nucleic Acids Research. – 2002. – Vol. 30, № 14. – P. 3059–3066. https://doi.org/10.1093/nar/gkf436.Katoh K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform // Nucleic Acids Research. 2002;30(14):3059–3066. https://doi.org/10.1093/nar/gkf436.Mount D.W. Using Iterative Methods for Global Multiple Sequence Alignment // Cold Spring Harb Protoc. – 2009. – Vol. 2009, № 7. – P. pdb.top44. https://doi.org/10.1101/pdb.top44.Mount D. W. Using Iterative Methods for Global Multiple Sequence Alignment // Cold Spring Harb Protoc. 2009;2009(7):pdb.top44. https://doi.org/10.1101/pdb.top44.Edgar R.C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity // BMC Bioinformatics. – 2004. – Vol. 5, № 1. – P. 113. https://doi.org/10.1186/1471-2105-5-113.Edgar R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity // BMC Bioinformatics. 2004;5(1):113. https://doi.org/10.1186/1471-2105-5-113.Morgenstern B. DIALIGN 2: improvement of the segment-to-segment approach to multiple sequence alignment // Bioinformatics. – 2002. – Vol. 15, № 3. – P. 211-218. https://doi.org/10.1093/bioinformatics/15.3.211.Morgenstern B. DIALIGN 2: improvement of the segment-to-segment approach to multiple sequence alignment // Bioinformatics. 2002;15(3):211–218. https://doi.org/10.1093/bioinformatics/15.3.211.Notredame C. SAGA: sequence alignment by genetic algorithm // Nucleic Acids Research. – 2002. – Vol. 24, № 8. – P. 1515–1524. https://doi.org/10.1093%2Fnar%2F24.8.1515.Notredame C. SAGA: sequence alignment by genetic algorithm. Nucleic Acids Research. 2002;24(8):1515–1524. https://doi.org/10.1093%2Fnar%2F24.8.1515.Sievers F., Higgins D.G. QuanTest2: benchmarking multiple sequence alignments using secondary structure prediction // Bioinformatics. – 2019. – Vol. 36, № 1. – P. 90–95. https://doi.org/10.1093/bioinformatics/btz552.Sievers F., Higgins D. G. QuanTest2: benchmarking multiple sequence alignments using secondary structure prediction // Bioinformatics. 2019;36(1):90–95. https://doi.org/10.1093/bioinformatics/btz552.Ng P.C., Henikoff S. Predicting Deleterious Amino Acid Substitutions // Genome Res. – 2002. – Vol. 11, № 5. – P. 863–874. https://doi.org/10.1101/gr.176601.Ng P. C., Henikoff S. Predicting Deleterious Amino Acid Substitutions // Genome Res. 2002;11(5):863–874. https://doi.org/10.1101/gr.176601.Shihab H.A., Gough J., Cooper D.N. et al. Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models // Human Mutation. – 2012. – Vol. 34, № 1. – P. 57–65. https://doi.org/10.1002/humu.22225.Shihab H. A., Gough J., Cooper D. N. et al. Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models // Human Mutation. 2012;34(1):57–65. https://doi.org/10.1002/humu.22225.Tang H., Thomas P. D. PANTHER-PSEP: predicting disease-causing genetic variants using position-specific evolutionary preservation // Bioinformatics. – 2016. – Vol. 32, № 14. – P. 2230–2232. https://doi.org/10.1093/bioinformatics/btw222.Tang H., Thomas P. D. PANTHER-PSEP: predicting disease-causing genetic variants using position-specific evolutionary preservation // Bioinformatics. 2016;32(14):2230–2232. https://doi.org/10.1093/bioinformatics/btw222.Glaser F., Pupko T., Paz I. et al. ConSurf: Identification of Functional Regions in Proteinsby Surface-Mapping of Phylogenetic Information // Bioinformatics. – 2002. – Vol. 19, № 1. – P. 163–164. https://doi.org/10.1093/bioinformatics/19.1.163.Glaser F., Pupko T., Paz I. et al. ConSurf: Identification of Functional Regions in Proteinsby Surface-Mapping of Phylogenetic Information // Bioinformatics. 2002;19(1):163–164. https://doi.org/10.1093/bioinformatics/19.1.163.Chun S., Fay J. C. Identification of deleterious mutations within three human genomes // Genome Res. – 2009. – Vol. 19, № 9. – P. 1553–1561. https://doi.org/10.1101/gr.092619.109.Chun S., Fay J. C. Identification of deleterious mutations within three human genomes // Genome Res. 2009;19(9):1553–1561. https://doi.org/10.1101/gr.092619.109.Stone E. A., Sidow A. Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity // Genome Res. – 2005. – Vol. 15, № 7. – P. 978–986. https://doi.org/10.1101/gr.3804205.Stone E. A., Sidow A. Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity // Genome Res. 2005;15(7):978–986. https://doi.org/10.1101/gr.3804205.Tavtigian S. V. Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral // Journal of Medical Genetics. – 2005. – Vol. 43, № 4. – P. 295–305. https://doi.org/10.1136/jmg.2005.033878.Tavtigian S. V. Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral // Journal of Medical Genetics. 2005;43(4):295–305. https://doi.org/10.1136/jmg.2005.033878.Frazer J., Notin P., Dias M. et al. Disease variant prediction with deep generative models of evolutionary data // Nature. – 2021. – Vol. 599, № 7883. – P. 91–95. https://doi.org/10.1038/s41586-021-04043-8.Frazer J., Notin P., Dias M. et al. Disease variant prediction with deep generative models of evolutionary data // Nature. 2021;599(7883):91–95. https://doi.org/10.1038/s41586-021-04043-8.Moles-Fernández A., Duran-Lozano L., Montalban G. et al. Computational Tools for Splicing Defect Prediction in Breast/Ovarian Cancer Genes: How Efficient Are They at Predicting RNA Alterations? // Front. Genet. – 2018. – Vol. 9. – P. 366. https://doi.org/10.3389/fgene.2018.00366.Moles-Fernández A., Duran-Lozano L., Montalban G. et al. Computational Tools for Splicing Defect Prediction in Breast/Ovarian Cancer Genes: How Efficient Are They at Predicting RNA Alterations? // Front. Genet. 2018;9:366. https://doi.org/10.3389/fgene.2018.00366.Mahmood K., Jung C., Philip G. et al. Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics // Hum Genomics. – 2017. – Vol. 11, № 1. – P. 10. https://doi.org/10.1186/s40246-017-0104-8.Mahmood K., Jung C., Philip G. et al. Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics // Hum Genomics. 2017;11(1):10. https://doi.org/10.1186/s40246-017-0104-8.Pollard K. S., Salama S. R., Lambert N. et al. An RNA gene expressed during cortical development evolved rapidly in humans // Nature. – 2006. – Vol. 443, № 7108. – P. 167–172. https://doi.org/10.1038/nature05113.Pollard K. S., Salama S. R., Lambert N. et al. An RNA gene expressed during cortical development evolved rapidly in humans // Nature. 2006;443(7108):167–172. https://doi.org/10.1038/nature05113.Rao C.R. Score Test: Historical Review and Recent Developments / Eds. N. Balakrishnan, H. N. Nagaraja, N. Kannan. – Boston, MA: Birkhäuser, 2005. – P. 3–20. http://dx.doi.org/10.1007/0-8176-4422-9_1.Rao C. R. Score Test: Historical Review and Recent Developments / Eds. Balakrishnan N., Nagaraja H. N., Kannan N. Advances in Ranking and Selection, Multiple Comparisons, and Reliability. – Boston, MA: Birkhäuser; 2005. – P. 3–20. https://doi.org/http://dx.doi.org/10.1007/0-8176-4422-9_1.Siepel A., Pollard K. S., Haussler D. New Methods for Detecting Lineage-Specific Selection / Eds. A. Apostolico et al. – Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. – P. 190–205. https://doi.org/10.1007/11732990_17.Siepel A., Pollard K. S., Haussler D. New Methods for Detecting Lineage-Specific Selection / Eds. Apostolico A. et al. Research in Computational Molecular Biology. –Berlin, Heidelberg: Springer Berlin Heidelberg; 2006. – P. 190–205. https://doi.org/10.1007/11732990_17.Kircher M., Witten D. M., Jain P. et al. A general framework for estimating the relative pathogenicity of human genetic variants // Nat Genet. – 2014. – Vol. 46, № 3. – P. 310–315. https://doi.org/10.1038/ng.2892.Kircher M., Witten D. M., Jain P. et al. A general framework for estimating the relative pathogenicity of human genetic variants // Nat Genet. 2014;46(3):310–315. https://doi.org/10.1038/ng.2892.Quang D., Chen Y., Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants // Bioinformatics. – 2014. – Vol. 31, № 5. – P. 761–763. https://doi.org/10.1093/bioinformatics/btu703.Quang D., Chen Y., Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants // Bioinformatics. 2014;31(5):761–763. https://doi.org/10.1093/bioinformatics/btu703.Ionita-Laza I., McCallum K., Xu B. et al. A spectral approach integrating functional genomic annotations for coding and noncoding variants // Nat Genet. – 2016. – Vol. 48, № 2. – P. 214–220. https://doi.org/10.1038/ng.3477.Ionita-Laza I., McCallum K., Xu B. et al. A spectral approach integrating functional genomic annotations for coding and noncoding variants // Nat Genet. 2016;48(2):214–220. https://doi.org/10.1038/ng.3477.Shihab H.A., Rogers M.F., Gough J. et al. An integrative approach to predicting the functional effects of non-coding and coding sequence variation // Bioinformatics. – 2015. – Vol. 31, № 10. – P. 1536–1543. https://doi.org/10.1093/bioinformatics/btv009.Shihab H. A., Rogers M. F., Gough J. et al. An integrative approach to predicting the functional effects of non-coding and coding sequence variation // Bioinformatics. 2015;31(10):1536–1543. https://doi.org/10.1093/bioinformatics/btv009.Dong C., Wei P., Jian X. et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies // Human Molecular Genetics. – 2015. – Vol. 24, № 8. – P. 2125–2137. https://doi.org/10.1093/hmg/ddu733.Dong C., Wei P., Jian X. et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies // Human Molecular Genetics. 2015;24(8):2125–2137. https://doi.org/10.1093/hmg/ddu733.Jagadeesh K. A., Wenger A. M., Berger M. J. et al. M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity // Nat Genet. – 2016. – Vol. 48, № 12. – P. 1581–1586. https://doi.org/10.1038/ng.3703.Jagadeesh K. A., Wenger A. M., Berger M. J. et al. M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity // Nat Genet. 2016;48(12):1581–1586. https://doi.org/10.1038/ng.3703.Ioannidis N. M., Rothstein J. H., Pejaver V. et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants // The American Journal of Human Genetics. – 2016. – Vol. 99, № 4. – P. 877–885. https://doi.org/10.1016/j.ajhg.2016.08.016.Ioannidis N. M., Rothstein J. H., Pejaver V. et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants // The American Journal of Human Genetics. 2016;99(4):877–885. https://doi.org/10.1016/j.ajhg.2016.08.016.Koonin E. V. Orthologs, Paralogs, and Evolutionary Genomics // Annu. Rev. Genet. – 2005. – Vol. 39, № 1. – P. 309–338. https://doi.org/10.1146/annurev.genet.39.073003.114725.Koonin E. V. Orthologs, Paralogs, and Evolutionary Genomics // Annu. Rev. Genet. 2005;39(1):309–338. https://doi.org/10.1146/annurev.genet.39.073003.114725.Han J., Batey S., Nickson A. A. et al. The folding and evolution of multidomain proteins // Nat Rev Mol Cell Biol. – 2007. – Vol. 8, № 4. – P. 319–330. https://doi.org/10.1038/nrm2144.Han J., Batey S., Nickson A. A. et al. The folding and evolution of multidomain proteins // Nat Rev Mol Cell Biol. 2007;8(4):319–330. https://doi.org/10.1038/nrm2144.Lipton Z. C. The mythos of model interpretability // Queue. – 2020. – Vol. 16, № 3. – P. 31–57. https://doi.org/10.48550/arXiv.1606.03490.Lipton Z. C. The mythos of model interpretability // Queue.2020;16(3):31–57. https://doi.org/10.48550/arXiv.1606.03490.

The authors declare that there are no conflicts of interest present.