Display options
Cite
Veneziano D, Nigita G, Ferro A. Computational Approaches for the Analysis of ncRNA through Deep Sequencing Techniques. Front Bioeng Biotechnol. 2015;3:77doi: 10.3389/fbioe.2015.00077.
Veneziano, D., Nigita, G., & Ferro, A. (2015). Computational Approaches for the Analysis of ncRNA through Deep Sequencing Techniques. Frontiers in bioengineering and biotechnology, 377. https://doi.org/10.3389/fbioe.2015.00077
Veneziano, Dario, et al. "Computational Approaches for the Analysis of ncRNA through Deep Sequencing Techniques." Frontiers in bioengineering and biotechnology vol. 3 (2015): 77. doi: https://doi.org/10.3389/fbioe.2015.00077
Veneziano D, Nigita G, Ferro A. Computational Approaches for the Analysis of ncRNA through Deep Sequencing Techniques. Front Bioeng Biotechnol. 2015 Jun 03;3:77. doi: 10.3389/fbioe.2015.00077. eCollection 2015. PMID: 26090362; PMCID: PMC4453482.
Copy Download .nbib Format: NLM
Share it on

Front Bioeng Biotechnol. 2015 Jun 03;3:77. doi: 10.3389/fbioe.2015.00077. eCollection 2015.

Computational Approaches for the Analysis of ncRNA through Deep Sequencing Techniques.

Frontiers in bioengineering and biotechnology

Dario Veneziano, Giovanni Nigita, Alfredo Ferro

Affiliations

  1. Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University , Columbus, OH , USA.
  2. Department of Clinical and Molecular Biomedicine, University of Catania , Catania , Italy.

PMID: 26090362 PMCID: PMC4453482 DOI: 10.3389/fbioe.2015.00077

Abstract

The majority of the human transcriptome is defined as non-coding RNA (ncRNA), since only a small fraction of human DNA encodes for proteins, as reported by the ENCODE project. Several distinct classes of ncRNAs, such as transfer RNA, microRNA, and long non-coding RNA, have been classified, each with its own three-dimensional folding and specific function. As ncRNAs are highly abundant in living organisms and have been discovered to play important roles in many biological processes, there has been an ever increasing need to investigate the entire ncRNAome in further unbiased detail. Recently, the advent of next-generation sequencing (NGS) technologies has substantially increased the throughput of transcriptome studies, allowing an unprecedented investigation of ncRNAs, as regulatory pathways and novel functions involving ncRNAs are now also emerging. The huge amount of transcript data produced by NGS has progressively required the development and implementation of suitable bioinformatics workflows, complemented by knowledge-based approaches, to identify, classify, and evaluate the expression of hundreds of ncRNAs in normal and pathological conditions, such as cancer. In this mini-review, we present and discuss current bioinformatics advances in the development of such computational approaches to analyze and classify the ncRNA component of human transcriptome sequence data obtained from NGS technologies.

Keywords: RNA-seq; bioinformatics; circRNA; lncRNA; miRNA

References

  1. Nucleic Acids Res. 2013 Aug;41(15):e147 - PubMed
  2. Nat Rev Genet. 2009 Jan;10(1):57-63 - PubMed
  3. Cell. 2007 Jun 29;129(7):1401-14 - PubMed
  4. Bioinformatics. 2012 Dec 1;28(23):3147-9 - PubMed
  5. BMC Bioinformatics. 2012 Dec 13;13:331 - PubMed
  6. Nature. 2007 Jun 14;447(7146):799-816 - PubMed
  7. Bioinformatics. 2010 Jan 1;26(1):139-40 - PubMed
  8. Nature. 1998 Feb 19;391(6669):806-11 - PubMed
  9. Genome Biol. 2015 Jan 13;16:4 - PubMed
  10. Genes Dev. 2011 Sep 15;25(18):1915-27 - PubMed
  11. Genome Res. 2012 Sep;22(9):1775-89 - PubMed
  12. BMC Bioinformatics. 2010 Aug 10;11:422 - PubMed
  13. Genome Res. 2011 Feb;21(2):276-85 - PubMed
  14. BMC Genomics. 2010 Feb 01;11:77 - PubMed
  15. Cell. 2005 Jan 14;120(1):15-20 - PubMed
  16. Nature. 2013 Mar 21;495(7441):333-8 - PubMed
  17. Genome Biol. 2010;11(10):R106 - PubMed
  18. Bioinformatics. 2004 Nov 22;20(17):2911-7 - PubMed
  19. J Genet Genomics. 2011 Nov 20;38(11):505-13 - PubMed
  20. BMC Genomics. 2015;16 Suppl 3:S2 - PubMed
  21. Curr Protoc Bioinformatics. 2011 Dec;Chapter 12:Unit 12.10. - PubMed
  22. PLoS One. 2012;7(2):e30733 - PubMed
  23. Biopolymers. 2007 Dec 5-15;87(5-6):275-8 - PubMed
  24. Clin Biochem Rev. 2011 Nov;32(4):177-95 - PubMed
  25. Front Plant Sci. 2014 Dec 23;5:708 - PubMed
  26. EMBO Rep. 2001 Nov;2(11):986-91 - PubMed
  27. Cell. 1993 Dec 3;75(5):843-54 - PubMed
  28. Bioinformatics. 2012 Jul 15;28(14):1925-7 - PubMed
  29. BMC Genomics. 2013;14 Suppl 2:S7 - PubMed
  30. Bioinformatics. 2015 Jul 1;31(13):2199-201 - PubMed
  31. Nucleic Acids Res. 2011 Jul;39(Web Server issue):W112-7 - PubMed
  32. PLoS Comput Biol. 2014 Mar 20;10(3):e1003517 - PubMed
  33. Nucleic Acids Res. 2012 Jan;40(1):37-52 - PubMed
  34. RNA. 2013 Feb;19(2):141-57 - PubMed
  35. Nat Biotechnol. 2008 Apr;26(4):407-15 - PubMed
  36. Genome Biol. 2009;10(3):R25 - PubMed
  37. Trends Genet. 2008 Jul;24(7):344-52 - PubMed
  38. Nucleic Acids Res. 2013 Aug;41(14):e137 - PubMed
  39. Nat Biotechnol. 2010 May;28(5):503-10 - PubMed
  40. BMC Biol. 2011 May 31;9:34 - PubMed
  41. Genome Res. 2012 Mar;22(3):577-91 - PubMed
  42. Bioinformatics. 2010 Oct 15;26(20):2615-6 - PubMed
  43. PLoS One. 2012;7(2):e31630 - PubMed
  44. Nature. 1970 Aug 8;227(5258):561-3 - PubMed
  45. Nucleic Acids Res. 2012 Jun;40(11):e86 - PubMed
  46. PLoS One. 2013 Oct 25;8(10):e77596 - PubMed
  47. Nat Rev Genet. 2001 Dec;2(12):919-29 - PubMed
  48. Cell. 2009 Feb 20;136(4):629-41 - PubMed
  49. Nucleic Acids Res. 2008 Dec;36(21):e141 - PubMed
  50. Nucleic Acids Res. 2013 Jan;41(2):727-37 - PubMed
  51. Genome Biol. 2014;15(12):550 - PubMed

Publication Types