Skip to main content
SpringerLink
Log in

Computational Methods for Predicting Mature microRNAs

  • Protocol
  • First Online:
miRNomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2257))

Abstract

Tiny single-stranded noncoding RNAs with size 19–27 nucleotides serve as microRNAs (miRNAs), which have emerged as key gene regulators in the last two decades. miRNAs serve as one of the hallmarks in regulatory pathways with critical roles in human diseases. Ever since the discovery of miRNAs, researchers have focused on how mature miRNAs are produced from precursor mRNAs. Experimental methods are faced with notorious challenges in terms of experimental design, since it is time consuming and not cost-effective. Hence, different computational methods have been employed for the identification of miRNA sequences where most of them labeled as miRNA predictors are in fact pre-miRNA predictors and provide no information about the putative miRNA location within the pre-miRNA. This chapter provides an update and the current state of the art in this area covering various methods and 15 software suites used for prediction of mature miRNA.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Esquela-Kerscher A, Slack FJ (2006) Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer 6(4):259–269

    Article  CAS  Google Scholar 

  2. Small EM, Olson EN (2011) Pervasive roles of microRNAs in cardiovascular biology. Nature 469(7330):336–342

    Article  CAS  Google Scholar 

  3. Nam JW et al (2005) Human microRNA prediction through a probabilistic co-learning model of sequence and structure. Nucleic Acids Res 33(11):3570–3581

    Article  CAS  Google Scholar 

  4. Tijsterman M, Plasterk RH (2004) Dicers at RISC; the mechanism of RNAi. Cell 117(1):1–3

    Article  CAS  Google Scholar 

  5. Brennecke J et al (2005) Principles of microRNA-target recognition. PLoS Biol 3(3):e85

    Article  Google Scholar 

  6. Cui H, Zhai J, Ma C (2015) miRLocator: machine learning-based prediction of mature microRNAs within plant pre-miRNA sequences. PLoS One 10(11):e0142753

    Article  Google Scholar 

  7. Sacar MD, Allmer J (2014) Machine learning methods for microRNA gene prediction. Methods Mol Biol 1107:177–187

    Article  CAS  Google Scholar 

  8. Lim LP et al (2003) The microRNAs of Caenorhabditis elegans. Genes Dev 17(8):991–1008

    Article  CAS  Google Scholar 

  9. Artzi S, Kiezun A, Shomron N (2008) miRNAminer: a tool for homologous microRNA gene search. BMC Bioinformatics 9:39

    Article  Google Scholar 

  10. Nam JW et al (2006) ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs. Nucleic Acids Res 34(Web Server issue):W455–W458

    Article  CAS  Google Scholar 

  11. Huang TH et al (2007) MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans. BMC Bioinformatics 8:341

    Article  Google Scholar 

  12. Zhang T et al (2019) miRLocator: a Python implementation and web server for predicting miRNAs from pre-miRNA sequences. Methods Mol Biol 1932:89–97

    Article  CAS  Google Scholar 

  13. Tempel S, Tahi F (2012) A fast ab-initio method for predicting miRNA precursors in genomes. Nucleic Acids Res 40(11):e80

    Article  CAS  Google Scholar 

  14. Xue C et al (2005) Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics 6:310

    Article  Google Scholar 

  15. Jiang P et al (2007) MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res 35(Web Server issue):W339–W344

    Article  Google Scholar 

  16. Wu Y et al (2011) MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences. BMC Bioinformatics 12:107

    Article  CAS  Google Scholar 

  17. Gkirtzou K et al (2010) MatureBayes: a probabilistic algorithm for identifying the mature miRNA within novel precursors. PLoS One 5(8):e11843

    Article  Google Scholar 

  18. Leclercq M, Diallo AB, Blanchette M (2013) Computational prediction of the localization of microRNAs within their pre-miRNA. Nucleic Acids Res 41(15):7200–7211

    Article  CAS  Google Scholar 

  19. Li J et al (2015) MatPred: computational identification of mature microRNAs within novel pre-MicroRNAs. Biomed Res Int 2015:546763

    PubMed  PubMed Central  Google Scholar 

  20. Yones CA et al (2015) miRNAfe: a comprehensive tool for feature extraction in microRNA prediction. Biosystems 138:1–5

    Article  CAS  Google Scholar 

  21. Ding J, Zhou S, Guan J (2010) MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features. BMC Bioinformatics 11:S11

    Article  Google Scholar 

  22. Raad J, Stegmayer G, Milone DH (2020) Complexity measures of the mature miRNA for improving pre-miRNAs prediction. Bioinformatics 36(8):2319–2327

    Article  CAS  Google Scholar 

  23. Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions, and reversals. Sov Phys Dokl 10(8):707–710

    Google Scholar 

  24. Marques YB et al (2016) Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction. BMC Bioinformatics 17(Suppl 18):474

    Article  Google Scholar 

  25. Terai G et al (2012) Prediction of conserved precursors of miRNAs and their mature forms by integrating position-specific structural features. PLoS One 7(9):e44314

    Article  CAS  Google Scholar 

  26. He C et al (2012) MiRmat: mature microRNA sequence prediction. PLoS One 7(12):e51673

    Article  CAS  Google Scholar 

  27. Xuan P et al (2011) MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs. PLoS One 6(11):e27422

    Article  CAS  Google Scholar 

  28. Hu X, Ma C, Zhou Y (2013) A novel two-layer SVM model in miRNA Drosha processing site detection. BMC Syst Biol 7(Suppl 4):S4

    Article  Google Scholar 

  29. Sheng Y, Engstrom PG, Lenhard B (2007) Mammalian microRNA prediction through a support vector machine model of sequence and structure. PLoS One 2(9):e946

    Article  Google Scholar 

  30. Yousef M et al (2006) Combining multi-species genomic data for microRNA identification using a naive Bayes classifier. Bioinformatics 22(11):1325–1334

    Article  CAS  Google Scholar 

  31. Wang X et al (2005) MicroRNA identification based on sequence and structure alignment. Bioinformatics 21(18):3610–3614

    Article  CAS  Google Scholar 

  32. Jones-Rhoades MW, Bartel DP (2004) Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell 14(6):787–799

    Article  CAS  Google Scholar 

  33. Gao D et al (2013) miREval 2.0: a web tool for simple microRNA prediction in genome sequences. Bioinformatics 29(24):3225–3226

    Article  CAS  Google Scholar 

  34. Akgül B, Stadler PF, Hawkins LJ, Hadj-Moussa H, Storey KB, Ergin K, Çetinkaya R, Paschoal AR, Nachtigall PG, Tutar Y, Yousef M, Allmer J (2021) 44 Current challenges in miRNomics. In: Allmer J, Yousef M (eds) miRNomics: microRNA biology and computational analysis. Methods in molecular biology, vol 2257. Springer, New York

    Google Scholar 

Download references

Acknowledgments

AK is a recipient of Ramalingaswami Re-Retry Faculty Fellowship (Grant; BT/RLF/Re-entry/38/2017) from Department of Biotechnology (DBT), Government of India (GOI).

Author information

Authors and Affiliations

  1. Department of Information System, Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel

    Malik Yousef

  2. Rudolf‑Zenker Institute of Experimental Surgery, Rostock University Medical Center, Rostock, Germany

    Alisha Parveen

  3. Institute of Bioinformatics, Bangalore, India

    Abhishek Kumar

  4. Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India

    Abhishek Kumar

Authors
  1. Malik Yousef
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alisha Parveen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abhishek Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhishek Kumar .

Editor information

Editors and Affiliations

  1. Medical Informatics and Bioinformatics, Institute for Measurement Engineering and Sensor Technology, Hochschule Ruhr West, University of Applied Sciences, MĂĽlheim adR, Germany

    Jens Allmer

  2. Department of Information System, Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel

    Malik Yousef

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Yousef, M., Parveen, A., Kumar, A. (2022). Computational Methods for Predicting Mature microRNAs. In: Allmer, J., Yousef, M. (eds) miRNomics. Methods in Molecular Biology, vol 2257. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1170-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1170-8_9

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1169-2

  • Online ISBN: 978-1-0716-1170-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation