miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments - PubMed (original) (raw)
. 2009 Jul;37(Web Server issue):W68-76.
doi: 10.1093/nar/gkp347. Epub 2009 May 11.
Affiliations
- PMID: 19433510
- PMCID: PMC2703919
- DOI: 10.1093/nar/gkp347
miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments
Michael Hackenberg et al. Nucleic Acids Res. 2009 Jul.
Abstract
Next-generation sequencing allows now the sequencing of small RNA molecules and the estimation of their expression levels. Consequently, there will be a high demand of bioinformatics tools to cope with the several gigabytes of sequence data generated in each single deep-sequencing experiment. Given this scene, we developed miRanalyzer, a web server tool for the analysis of deep-sequencing experiments for small RNAs. The web server tool requires a simple input file containing a list of unique reads and its copy numbers (expression levels). Using these data, miRanalyzer (i) detects all known microRNA sequences annotated in miRBase, (ii) finds all perfect matches against other libraries of transcribed sequences and (iii) predicts new microRNAs. The prediction of new microRNAs is an especially important point as there are many species with very few known microRNAs. Therefore, we implemented a highly accurate machine learning algorithm for the prediction of new microRNAs that reaches AUC values of 97.9% and recall values of up to 75% on unseen data. The web tool summarizes all the described steps in a single output page, which provides a comprehensive overview of the analysis, adding links to more detailed output pages for each analysis module. miRanalyzer is available at http://web.bioinformatics.cicbiogune.es/microRNA/.
Figures
Figure 1.
Histogram of miRanalyzer scores. Known microRNAs are colored in red, all other data are colored in blue. The insert is a close-up for candidates with scores better than 0.65.
Figure 2.
The summary page of miRanalyzer: five boxes are shown which correspond to summary & state of the process, analysis of known microRNA, matches against transcribed sequences, and detection of new microRNAs and summary of unmatched sequences.
Similar articles
- miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments.
Hackenberg M, Rodríguez-Ezpeleta N, Aransay AM. Hackenberg M, et al. Nucleic Acids Res. 2011 Jul;39(Web Server issue):W132-8. doi: 10.1093/nar/gkr247. Epub 2011 Apr 22. Nucleic Acids Res. 2011. PMID: 21515631 Free PMC article. - miRBase: integrating microRNA annotation and deep-sequencing data.
Kozomara A, Griffiths-Jones S. Kozomara A, et al. Nucleic Acids Res. 2011 Jan;39(Database issue):D152-7. doi: 10.1093/nar/gkq1027. Epub 2010 Oct 30. Nucleic Acids Res. 2011. PMID: 21037258 Free PMC article. - DSAP: deep-sequencing small RNA analysis pipeline.
Huang PJ, Liu YC, Lee CC, Lin WC, Gan RR, Lyu PC, Tang P. Huang PJ, et al. Nucleic Acids Res. 2010 Jul;38(Web Server issue):W385-91. doi: 10.1093/nar/gkq392. Epub 2010 May 16. Nucleic Acids Res. 2010. PMID: 20478825 Free PMC article. - miRBase: the microRNA sequence database.
Griffiths-Jones S. Griffiths-Jones S. Methods Mol Biol. 2006;342:129-38. doi: 10.1385/1-59745-123-1:129. Methods Mol Biol. 2006. PMID: 16957372 Review. - Expression profiling of microRNAs by deep sequencing.
Creighton CJ, Reid JG, Gunaratne PH. Creighton CJ, et al. Brief Bioinform. 2009 Sep;10(5):490-7. doi: 10.1093/bib/bbp019. Epub 2009 Mar 30. Brief Bioinform. 2009. PMID: 19332473 Free PMC article. Review.
Cited by
- Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation.
Williamson V, Kim A, Xie B, McMichael GO, Gao Y, Vladimirov V. Williamson V, et al. Brief Bioinform. 2013 Jan;14(1):36-45. doi: 10.1093/bib/bbs010. Epub 2012 Mar 24. Brief Bioinform. 2013. PMID: 23334922 Free PMC article. - The discovery approaches and detection methods of microRNAs.
Huang Y, Zou Q, Wang SP, Tang SM, Zhang GZ, Shen XJ. Huang Y, et al. Mol Biol Rep. 2011 Aug;38(6):4125-35. doi: 10.1007/s11033-010-0532-1. Epub 2010 Nov 25. Mol Biol Rep. 2011. PMID: 21107708 Review. - Evaluation of Bioinformatics Approaches for Next-Generation Sequencing Analysis of microRNAs with a Toxicogenomics Study Design.
Bisgin H, Gong B, Wang Y, Tong W. Bisgin H, et al. Front Genet. 2018 Feb 6;9:22. doi: 10.3389/fgene.2018.00022. eCollection 2018. Front Genet. 2018. PMID: 29467792 Free PMC article. - Wolbachia infection modifies the profile, shuttling and structure of microRNAs in a mosquito cell line.
Mayoral JG, Etebari K, Hussain M, Khromykh AA, Asgari S. Mayoral JG, et al. PLoS One. 2014 Apr 23;9(4):e96107. doi: 10.1371/journal.pone.0096107. eCollection 2014. PLoS One. 2014. PMID: 24759922 Free PMC article. - IsomiRage: From Functional Classification to Differential Expression of miRNA Isoforms.
Muller H, Marzi MJ, Nicassio F. Muller H, et al. Front Bioeng Biotechnol. 2014 Sep 29;2:38. doi: 10.3389/fbioe.2014.00038. eCollection 2014. Front Bioeng Biotechnol. 2014. PMID: 25325056 Free PMC article.
References
- Kim VN, Nam JW. Genomics of microRNA. Trends Genet. 2006;22:165–173. - PubMed
- Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T. Identification of novel genes coding for small expressed RNAs. Science. 2001;294:853–858. - PubMed
- Bagasra O, Prilliman KR. RNA interference: the molecular immune system. J. Mol. Histol. 2004;35:545–553. - PubMed
- Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, et al. MicroRNA expression profiles classify human cancers. Nature. 2005;435:834–838. - PubMed
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources