cardiovascularКардиоваскулярная терапия и профилактикаCardiovascular Therapy and Prevention1728-88002619-0125«SILICEA-POLIGRAF» LLC10.15829/1728-8800-2024-4195IRNCAQcardiovascular-4195Research ArticleОБЗОРЫ ЛИТЕРАТУРЫREVIEW ARTICLESБиоинформатический подход к обработке данных высокопроизводительного секвенирования молекул малых РНКBioinformatics approach to processing data from high-throughput sequencing of small RNA moleculeshttps://orcid.org/0000-0003-0723-0493ЖариковаА. А.ZharikovaA. A.

Анастасия Александровна Жарикова — к.б.н., в.н.с. Института персонализированной терапии и профилактики, лаборатория молекулярной генетики, старший преподаватель факультета биоинженерии и биоинформатики.

Москва

Moscow

azharikova89@gmail.comhttps://orcid.org/0000-0002-9056-8796ВяткинЮ. В.VyatkinYu. V.

Юрий Викторович Вяткин — программист Института персонализированной терапии и профилактики, с.н.с. лаборатории геномной и медицинской биоинформатики Института перспективных исследований проблем искусственного интеллекта и интеллектуальных систем.

Москва

Moscow

vyatkin@gmail.comhttps://orcid.org/0000-0003-4765-8021КиселеваА. В.KiselevaA. V.

Анна Витальевна Киселева — к.б.н., в.н.с. Института персонализированной терапии и профилактики, руководитель лаборатории молекулярной генетики.

Москва

Moscow

sanyutabe@gmail.comhttps://orcid.org/0000-0001-5989-6233МешковА. Н.MeshkovA. N.

Алексей Николаевич Мешков — д.м.н., руководитель Института персонализированной терапии и профилактики.

Москва

Moscow

meshkov@lipidclinic.ruФГБУ "Национальный медицинский исследовательский центр терапии и профилактической медицины" Минздрава России; ФГБОУ ВО "Московский государственный университет им. М.В. Ломоносова"National Medical Research Center for Therapy and Preventive Medicine; Lomonosov Moscow State UniversityФГБУ "Национальный медицинский исследовательский центр терапии и профилактической медицины" Минздрава РоссииNational Medical Research Center for Therapy and Preventive Medicine2024301220242311Биобанкирование41954195Copyright © Жарикова А.А., Вяткин Ю.В., Киселева А.В., Мешков А.Н., 20252025Жарикова А.А., Вяткин Ю.В., Киселева А.В., Мешков А.Н.Zharikova A.A., Vyatkin Y.V., Kiseleva A.V., Meshkov A.N.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://cardiovascular.elpub.ru/jour/article/view/4195

Высокопроизводительное секвенирование молекул малых РНК (рибонуклеиновых кислот) широко применяют для поиска маркеров, характерных для различных заболеваний, а также при изучении регуляции экспрессии генов. Протокол обработки данных состоит из множества этапов, включающих стадии анализа качества исходных данных и результатов секвенирования, картирования и исследования экспрессионного профиля детектируемых молекул малых РНК. Для реализации каждого шага исследования уже разработан целый арсенал программ и специфических пакетов. Инструментальная композиция итогового биоинформатического протокола критически важна для корректной обработки данных и возможности воспроизвести исследование. В настоящем обзоре описан наиболее универсальный протокол обработки результатов высокопроизводительного секвенирования молекул малых РНК, включающий все основные этапы и наиболее широко используемые программы.

High-throughput sequencing of small ribonucleic acid (RNA) molecules is widely used to search for markers of various diseases, as well as to study the regulation of gene expression. The data processing protocol consists of many stages, including the stages of analyzing the initial data quality and sequencing results, mapping and studying the expression profile of the detected small RNA molecules. A whole arsenal of programs and specific packages has already been developed to implement each study step. The instrumental composition of the final bioinformatics protocol is critically important for the correct data processing and study reproduction. This review describes the most universal protocol for processing the results of high-throughput sequencing of small RNA molecules, including all the main stages and the most widely used programs.

малые РНКнекодирующие РНКмикроРНКсеквенированиебиоинформатический протоколsmall RNAnon-coding RNAmicroRNAsequencingbioinformatics protocolReferencesShi J, Zhou T, Chen Q. Exploring the expanding universe of small RNAs. Nat Cell Biol. 2022;24:415-23. doi:10.1038/s41556-022-00880-5.Shi J, Zhou T, Chen Q. Exploring the expanding universe of small RNAs. Nat Cell Biol. 2022;24: 415–423. doi:10.1038/s41556-022-00880-5Kopp F, Mendell JT. Functional classification and experimental dissection of long noncoding RNAs. Cell. 2018;172:393-407. doi:10.1016/j.cell.2018.01.011.Kopp F, Mendell JT. Functional classification and experimental dissection of long noncoding RNAs. Cell. 2018;172: 393–407. doi:10.1016/j.cell.2018.01.011Frankish A, Carbonell-Sala S, Diekhans M, et al. GENCODE: reference annotation for the human and mouse genomes in 2023. Nucleic Acids Res. 2023;51:D942-9. doi:10.1093/nar/gkac1071.Frankish A, Carbonell-Sala S, Diekhans M, Jungreis I, Loveland JE, Mudge JM, et al. GENCODE: reference annotation for the human and mouse genomes in 2023. Nucleic Acids Res. 2023;51: D942–D949. doi:10.1093/nar/gkac1071Fazmin IT, Achercouk Z, Edling CE, et al. Circulating microRNA as a biomarker for coronary artery disease. Biomolecules. 2020;10: 1354. doi:10.3390/biom10101354.Fazmin IT, Achercouk Z, Edling CE, Said A, Jeevaratnam K. Circulating microRNA as a biomarker for coronary artery disease. Biomolecules. 2020;10: 1354. doi:10.3390/biom10101354Cui M, Wang H, Yao X, et al. Circulating MicroRNAs in cancer: Potential and challenge. Front Genet. 2019;10:626. doi:10.3389/fgene.2019.00626.Cui M, Wang H, Yao X, Zhang D, Xie Y, Cui R, et al. Circulating MicroRNAs in cancer: Potential and challenge. Front Genet. 2019;10: 626. doi:10.3389/fgene.2019.00626Grasso M, Piscopo P, Confaloni A, et al. Circulating miRNAs as biomarkers for neurodegenerative disorders. Molecules. 2014; 19:6891-910. doi:10.3390/molecules19056891.Grasso M, Piscopo P, Confaloni A, Denti MA. Circulating miRNAs as biomarkers for neurodegenerative disorders. Molecules. 2014;19: 6891–6910. doi:10.3390/molecules19056891Zharikova AA, Mironov AA. piRNAs: Biology and Bioinformatics. Mol Biol (Mosk). 2016;50:80-8. doi:10.7868/S0026898416010225.Zharikova AA, Mironov AA. piRNAs: Biology and Bioinformatics. Mol Biol (Mosk). 2016;50: 80–88. doi:10.7868/S0026898416010225Choudhuri S. Small noncoding RNAs: biogenesis, function, and emerging significance in toxicology. J Biochem Mol Toxicol. 2010;24:195-216. doi:10.1002/jbt.20325.Choudhuri S. Small noncoding RNAs: biogenesis, function, and emerging significance in toxicology. J Biochem Mol Toxicol. 2010;24: 195–216. doi:10.1002/jbt.20325Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993;75:843-54. doi:10.1016/0092-8674(93)90529-y.Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993;75: 843–854. doi:10.1016/0092-8674(93)90529-yBartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281-97. doi:10.1016/s0092-8674(04)00045-5.Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116: 281–297. doi:10.1016/s0092-8674(04)00045-5McAlexander MA, Phillips MJ, Witwer KW. Comparison of methods for miRNA extraction from plasma and quantitative recovery of RNA from cerebrospinal fluid. Front Genet. 2013;4:83. doi:10.3389/fgene.2013.00083.Hu T, Chitnis N, Monos D, Dinh A. Next-generation sequencing technologies: An overview. Hum Immunol. 2021;82: 801–811. doi:10.1016/j.humimm.2021.02.012Page K, Guttery DS, Zahra N, et al. Influence of plasma processing on recovery and analysis of circulating nucleic acids. PLoS One. 2013;8:e77963. doi:10.1371/journal.pone.0077963.Ewing B, Hillier L, Wendl MC, Green P. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 1998;8: 175–185. doi:10.1101/gr.8.3.175Wong RKY, MacMahon M, Woodside JV, et al. A comparison of RNA extraction and sequencing protocols for detection of small RNAs in plasma. BMC Genomics. 2019;20:446. doi:10.1186/s12864-019-5826-7.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30: 2114–2120. doi:10.1093/bioinformatics/btu170Sorefan K, Pais H, Hall AE, et al. Reducing ligation bias of small RNAs in libraries for next generation sequencing. Silence. 2012;3:4. doi:10.1186/1758-907X-3-4.Miga KH, Newton Y, Jain M, Altemose N, Willard HF, Kent WJ. Centromere reference models for human chromosomes X and Y satellite arrays. Genome Res. 2014;24: 697–707. doi:10.1101/gr.159624.113Hu T, Chitnis N, Monos D, Dinh A. Next-generation sequencing technologies: An overview. Hum Immunol. 2021;82:801-11. doi:10.1016/j.humimm.2021.02.012.Aganezov S, Yan SM, Soto DC, Kirsche M, Zarate S, Avdeyev P, et al. A complete reference genome improves analysis of human genetic variation. Science. 2022;376: eabl3533. doi:10.1126/science.abl3533Ewing B, Hillier L, Wendl MC, et al. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 1998;8:175-85. doi:10.1101/gr.8.3.175.Luu P-L, Ong P-T, Dinh T-P, Clark SJ. Benchmark study comparing liftover tools for genome conversion of epigenome sequencing data. NAR Genom Bioinform. 2020;2: lqaa054. doi:10.1093/nargab/lqaa054Smith T, Heger A, Sudbery I. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 2017;27:491-9. doi:10.1101/gr.209601.116.Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17: 13. doi:10.1186/s13059-016-0881-8Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114-20. doi:10.1093/bioinformatics/btu170.Harrison PW, Amode MR, Austine-Orimoloye O, Azov AG, Barba M, Barnes I, et al. Ensembl 2024. Nucleic Acids Res. 2024;52: D891–D899. doi:10.1093/nar/gkad1049Miga KH, Newton Y, Jain M, et al. Centromere reference models for human chromosomes X and Y satellite arrays. Genome Res. 2014;24:697-707. doi:10.1101/gr.159624.113.Zhang H. Overview of sequence data formats. Methods Mol Biol. 2016;1418: 3–17. doi:10.1007/978-1-4939-3578-9_1Aganezov S, Yan SM, Soto DC, et al. A complete reference genome improves analysis of human genetic variation. Science. 2022;376:eabl3533. doi:10.1126/science.abl3533.Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29: 15–21. doi:10.1093/bioinformatics/bts635Luu P-L, Ong P-T, Dinh T-P, et al. Benchmark study comparing liftover tools for genome conversion of epigenome sequencing data. NAR Genom Bioinform. 2020;2:lqaa054. doi:10.1093/nargab/lqaa054.Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37: 907–915. doi:10.1038/s41587-019-0201-4Conesa A, Madrigal P, Tarazona S, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13. doi:10.1186/s13059-016-0881-8.Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10. doi:10.1093/gigascience/giab008Harrison PW, Amode MR, Austine-Orimoloye O, et al. Ensembl 2024. Nucleic Acids Res. 2024;52:D891-9. doi:10.1093/nar/gkad1049.Sai Lakshmi S, Agrawal S. piRNABank: a web resource on classified and clustered Piwi-interacting RNAs. Nucleic Acids Res. 2008;36: D173–7. doi:10.1093/nar/gkm696Zhang H. Overview of sequence data formats. Methods Mol Biol. 2016;1418:3-17. doi:10.1007/978-1-4939-3578-9_1.Chen X, Han P, Zhou T, Guo X, Song X, Li Y. circRNADb: A comprehensive database for human circular RNAs with protein-coding annotations. Sci Rep. 2016;6. doi:10.1038/srep34985Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15-21. doi:10.1093/bioinformatics/bts635.Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40: 37–52. doi:10.1093/nar/gkr688Kim D, Paggi JM, Park C, et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37:907-15. doi:10.1038/s41587-019-0201-4.Tav C, Tempel S, Poligny L, Tahi F. miRNAFold: a web server for fast miRNA precursor prediction in genomes. Nucleic Acids Res. 2016;44: W181–4. doi:10.1093/nar/gkw459Bezuglov V, Stupnikov A, Skakov I, et al. Approaches for sRNA analysis of human RNA-seq data: Comparison, benchmarking. Int J Mol Sci. 2023;24:4195. doi:10.3390/ijms24044195.Vitsios DM, Kentepozidou E, Quintais L, Benito-Gutiérrez E, van Dongen S, Davis MP, et al. Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests. Nucleic Acids Res. 2017;45: e177. doi:10.1093/nar/gkx836Ziemann M, Kaspi A, El-Osta A. Evaluation of microRNA alignment techniques. RNA. 2016;22:1120-38. doi:10.1261/rna.055509.115.Chatterjee A, Ahn A, Rodger EJ, Stockwell PA, Eccles MR. A guide for designing and analyzing RNA-Seq data. Methods Mol Biol. 2018;1783: 35–80. doi:10.1007/978-1-4939-7834-2_3Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10. doi:10.1093/gigascience/giab008.Hans FP, Moser M, Bode C, Grundmann S. MicroRNA regulation of angiogenesis and arteriogenesis. Trends Cardiovasc Med. 2010;20: 253–262. doi:10.1016/j.tcm.2011.12.001Sai Lakshmi S, Agrawal S. piRNABank: a web resource on classified and clustered Piwi-interacting RNAs. Nucleic Acids Res. 2008;36:D173-7. doi:10.1093/nar/gkm696.Khan J, Lieberman JA, Lockwood CM. Variability in, variability out: best practice recommendations to standardize pre-analytical variables in the detection of circulating and tissue microRNAs. Clin Chem Lab Med. 2017;55: 608–621. doi:10.1515/cclm-2016-0471Chen X, Han P, Zhou T, et al. circRNADb: A comprehensive data-base for human circular RNAs with protein-coding annotations. Sci Rep. 2016;6. doi:10.1038/srep34985.Enright A, John B, Gaul U, Tuschl T, Sander C, Marks D. MicroRNA Targets in Drosophila. Genome Biol. 2003;4: P8. doi:10.1186/gb-2003-4-11-p8Friedländer MR, Mackowiak SD, Li N, et al. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40:37-52. doi:10.1093/nar/gkr688.Friedländer MR, Mackowiak SD, Li N, et al. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40:37-52. doi:10.1093/nar/gkr688.Tav C, Tempel S, Poligny L, et al. miRNAFold: a web server for fast miRNA precursor prediction in genomes. Nucleic Acids Res. 2016;44:W181-4. doi:10.1093/nar/gkw459.Tav C, Tempel S, Poligny L, et al. miRNAFold: a web server for fast miRNA precursor prediction in genomes. Nucleic Acids Res. 2016;44:W181-4. doi:10.1093/nar/gkw459.Vitsios DM, Kentepozidou E, Quintais L, et al. Mirnovo: genomefree prediction of microRNAs from small RNA sequencing data and single-cells using decision forests. Nucleic Acids Res. 2017;45:e177. doi:10.1093/nar/gkx836.Vitsios DM, Kentepozidou E, Quintais L, et al. Mirnovo: genomefree prediction of microRNAs from small RNA sequencing data and single-cells using decision forests. Nucleic Acids Res. 2017;45:e177. doi:10.1093/nar/gkx836.Agnelli L, Bortoluzzi S, Pruneri G. Bioinformatic pipelines to analyze lncRNAs RNAseq data. Methods Mol Biol. 2021;2348:55-69. doi:10.1007/978-1-0716-1581-2_4.Agnelli L, Bortoluzzi S, Pruneri G. Bioinformatic pipelines to analyze lncRNAs RNAseq data. Methods Mol Biol. 2021;2348:55-69. doi:10.1007/978-1-0716-1581-2_4.Chatterjee A, Ahn A, Rodger EJ, et al. A guide for designing and analyzing RNA-Seq data. Methods Mol Biol. 2018;1783:35-80. doi:10.1007/978-1-4939-7834-2_3.Chatterjee A, Ahn A, Rodger EJ, et al. A guide for designing and analyzing RNA-Seq data. Methods Mol Biol. 2018;1783:35-80. doi:10.1007/978-1-4939-7834-2_3.Hans FP, Moser M, Bode C, et al. MicroRNA regulation of angiogenesis and arteriogenesis. Trends Cardiovasc Med. 2010;20:253-62. doi:10.1016/j.tcm.2011.12.001.Hans FP, Moser M, Bode C, et al. MicroRNA regulation of angiogenesis and arteriogenesis. Trends Cardiovasc Med. 2010;20:253-62. doi:10.1016/j.tcm.2011.12.001.Khan J, Lieberman JA, Lockwood CM. Variability in, variability out: best practice recommendations to standardize pre-analytical variables in the detection of circulating and tissue microRNAs. Clin Chem Lab Med. 2017;55:608-21. doi:10.1515/cclm-2016-0471.Khan J, Lieberman JA, Lockwood CM. Variability in, variability out: best practice recommendations to standardize pre-analytical variables in the detection of circulating and tissue microRNAs. Clin Chem Lab Med. 2017;55:608-21. doi:10.1515/cclm-2016-0471.Enright A, John B, Gaul U, et al. MicroRNA Targets in Drosophila. Genome Biol. 2003;4:P8. doi:10.1186/gb-2003-4-11-p8.Enright A, John B, Gaul U, et al. MicroRNA Targets in Drosophila. Genome Biol. 2003;4:P8. doi:10.1186/gb-2003-4-11-p8.Agarwal V, Bell GW, Nam J-W, et al. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015;4. doi:10.7554/eLife.05005.Agarwal V, Bell GW, Nam J-W, et al. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015;4. doi:10.7554/eLife.05005.Cihan M, Andrade-Navarro MA. Detection of features predictive of microRNA targets by integration of network data. PLoS One. 2022;17:e0269731. doi:10.1371/journal.pone.0269731.Cihan M, Andrade-Navarro MA. Detection of features predictive of microRNA targets by integration of network data. PLoS One. 2022;17:e0269731. doi:10.1371/journal.pone.0269731.Geistlinger L, Csaba G, Santarelli M, et al. Toward a gold standard for benchmarking gene set enrichment analysis. Brief Bioinform. 2021;22:545-56. doi:10.1093/bib/bbz158.Geistlinger L, Csaba G, Santarelli M, et al. Toward a gold standard for benchmarking gene set enrichment analysis. Brief Bioinform. 2021;22:545-56. doi:10.1093/bib/bbz158.

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