Advertisement

Using Bioinformatics Tools to Study the Role of microRNA in Cancer

  • Fabio PassettiEmail author
  • Natasha Andressa Nogueira Jorge
  • Alan Durham
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1168)

Abstract

High-throughput sequencing (HTS) has emerged as a promising method to study gene expression in neoplastic and normal tissues. Using HTS, many research groups have described transcript variants as well as discovering new transcribed loci and noncoding RNAs, including microRNAs. In oncology, expression profiling of microRNAs in matched tumor and normal tissues has been used to detect differential expression of microRNAs in cancer. We present one approach for laboratories with few bioinformatics support to assist in the analysis of microRNA HTS data focused in oncology. This approach can also be adapted to study other systems.

Key words

Bioinformatics High-throughput sequencing microRNA miRNA databases miRNA target 

Abbreviations

HTS

High-throughput sequencing

miRNA

microRNA

ncRNA

Noncoding RNA

Notes

Acknowledgements

F.P. acknowledges the support of Fundação Carlos Chagas de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) and Fundação do Câncer. F.P. and A.D. acknowledge the support of Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). N.A.N.J. is supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

References

  1. 1.
    Cuperus JT, Fahlgren N, Carrington JC (2011) Evolution and functional diversification of MIRNA genes. Plant Cell 23:431–442PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Giardine B, Riemer C, Hardison RC et al (2005) Galaxy: a platform for interactive large-scale genome analysis. Genome Res 15:1451–1455PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Blankenberg D, Von Kuster G, Coraor N et al (2010) Galaxy: a web-based genome analysis tool for experimentalists. Curr Protoc Mol Biol 19(10):1–21Google Scholar
  4. 4.
    Goecks J, Nekrutenko A, Taylor J et al (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol 11:R86PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Burge SW, Daub J, Eberhardt R et al (2013) Rfam 11.0: 10 years of RNA families. Nucleic Acids Res 41:D226–D232PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39:D152–D157PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Grimson A, Farh KK, Johnston WK et al (2007) MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 27:91–105PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Papadopoulos GL, Reczko M, Simossis VA et al (2009) The database of experimentally supported targets: a functional update of TarBase. Nucleic Acids Res 37:D155–D158PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Witten D, Tibshirani R, Gu SG et al (2010) Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls. BMC Biol 8:58PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Leinonen R, Sugawara H, Shumway M (2011) The sequence read archive. Nucleic Acids Res 39:D19–D21PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Creighton CJ, Reid JG, Gunaratne PH (2009) Expression profiling of microRNAs by deep sequencing. Brief Bioinform 10:490–497PubMedCentralPubMedCrossRefGoogle Scholar
  13. 13.
    Givan SA, Bottoms CA, Spollen WG (2012) Computational analysis of RNA-seq. Methods Mol Biol 883:201–219PubMedCrossRefGoogle Scholar
  14. 14.
    Lindner R, Friedel CC (2012) A comprehensive evaluation of alignment algorithms in the context of RNA-seq. PLoS One 7:e52403PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Li H, Handsaker B, Wysoker A et al (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Hoffmann S (2011) Computational analysis of high throughput sequencing data. Methods Mol Biol 719:199–217PubMedCrossRefGoogle Scholar
  17. 17.
    Majer A, Caligiuri KA, Booth SA (2013) A user-friendly computational workflow for the analysis of microRNA deep sequencing data. Methods Mol Biol 936:35–45PubMedCrossRefGoogle Scholar
  18. 18.
    Mituyama T, Yamada K, Hattori E et al (2009) The Functional RNA Database 3.0: databases to support mining and annotation of functional RNAs. Nucleic Acids Res 37:D89–D92PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Paschoal AR, Maracaja-Coutinho V, Setubal JC et al (2012) Non-coding transcription characterization and annotation: a guide and web resource for non-coding RNA databases. RNA Biol 9:274–282PubMedCrossRefGoogle Scholar
  20. 20.
    Yang Z, Ren F, Liu C et al (2010) dbDEMC: a database of differentially expressed miRNAs in human cancers. BMC Genomics 11(Suppl 4):S5PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Qiu X, Dong S, Qiao F et al (2013) HBx-mediated miR-21 upregulation represses tumor-suppressor function of PDCD4 in hepatocellular carcinoma. Oncogene 32:3296–3305PubMedCrossRefGoogle Scholar
  22. 22.
    Deftereos G, Corrie SR, Feng Q et al (2011) Expression of mir-21 and mir-143 in cervical specimens ranging from histologically normal through to invasive cervical cancer. PLoS One 6:e28423PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Buscaglia LE, Li Y (2011) Apoptosis and the target genes of microRNA-21. Chin J Cancer 30:371–380PubMedCentralPubMedCrossRefGoogle Scholar
  24. 24.
    Dreszer TR, Karolchik D, Zweig AS et al (2012) The UCSC Genome Browser database: extensions and updates 2011. Nucleic Acids Res 40:1–6CrossRefGoogle Scholar
  25. 25.
    Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136:215–233PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Fabio Passetti
    • 1
    Email author
  • Natasha Andressa Nogueira Jorge
    • 1
  • Alan Durham
    • 2
  1. 1.Bioinformatics Unit, Clinical Research CoordinationInstituto Nacional de Câncer (INCA)Rio de JaneiroBrazil
  2. 2.Department of Computer ScienceInstitute of Mathematics and Statistics, Universidade de São Paulo (USP)São PauloBrazil

Personalised recommendations