Abstract
The vital role of microRNAs (miRNAs) involved in gene expression regulation has been confirmed in many biological processes. With the growing power and reducing cost of next-generation sequencing, more and more researchers turn to apply this high-throughput method to solve their biological problems. For miRNAs with known sequences, their expression profiles can be generated from the sequencing data. It also allows us to identify some novel miRNAs and explore the sequence variations under different conditions. Currently, there are a handful of tools available to analyze the miRNA sequencing data with separated or combined features, such as reads preprocessing, mapping and differential expression analysis. However, to our knowledge, a hands-on guideline for miRNA sequencing data analysis covering all steps is not available. Here we will utilize a set of published tools to perform the miRNA analysis with detailed explanation. Particularly, the miRNA target prediction and annotation may provide useful information for further experimental verification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
He L, Hannon GJ (2004) MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet 5(7):522–531
Kim VN, Han J, Siomi MC (2009) Biogenesis of small RNAs in animals. Nat Rev Mol Cell Biol 10(2):126–139
Siomi H, Siomi MC (2010) Posttranscriptional regulation of microRNA biogenesis in animals. Mol Cell 38(3):323–332
Baran-Gale J et al (2015) Addressing bias in small RNA library preparation for sequencing: a new protocol recovers MicroRNAs that evade capture by current methods. Front Genet 6:352
Fuchs RT et al (2015) Bias in ligation-based small RNA sequencing library construction is determined by adaptor and RNA structure. PLoS One 10(5):e0126049
Wu J et al (2013) mirTools 2.0 for non-coding RNA discovery, profiling, and functional annotation based on high-throughput sequencing. RNA Biol 10(7):1087–1092
Zhu E et al (2010) mirTools: microRNA profiling and discovery based on high-throughput sequencing. Nucleic Acids Res 38(Web Server issue):W392–W397
Shi J et al (2015) mirPRo-a novel standalone program for differential expression and variation analysis of miRNAs. Sci Rep 5:14617
Sun Z et al (2014) CAP-miRSeq: a comprehensive analysis pipeline for microRNA sequencing data. BMC Genomics 15:423
Andres-Leon E, Nunez-Torres R, Rojas AM (2016) miARma-Seq: a comprehensive tool for miRNA, mRNA and circRNA analysis. Sci Rep 6:25749
Vitsios DM, Enright AJ (2015) Chimira: analysis of small RNA sequencing data and microRNA modifications. Bioinformatics 31(20):3365–3367
Dodt M et al (2012) FLEXBAR-flexible barcode and adapter processing for next-generation sequencing platforms. Biology (Basel) 1(3):895–905
Agarwal V et al (2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife 4:e05005
Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120(1):15–20
Griffiths-Jones S (2006) miRBase: the microRNA sequence database. Methods Mol Biol 342:129–138
Griffiths-Jones S (2010) miRBase: microRNA sequences and annotation. Curr Protoc Bioinformatics Chapter 12:Unit 12 9 1–Unit 12 910
Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39(Database issue):152–157
Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42(Database issue):D68–D73
An J et al (2013) miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res 41(2):727–737
Lorenz R et al (2011) ViennaRNA package 2.0. Algorithms Mol Biol 6:26
Camps C et al (2014) Integrated analysis of microRNA and mRNA expression and association with HIF binding reveals the complexity of microRNA expression regulation under hypoxia. Mol Cancer 13:28
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550
Shukla GC, Singh J, Barik S (2011) MicroRNAs: processing, maturation, target recognition and regulatory functions. Mol Cell Pharmacol 3(3):83–92
Enright AJ et al (2003) MicroRNA targets in Drosophila. Genome Biol 5(1):R1
Rehmsmeier M et al (2004) Fast and effective prediction of microRNA/target duplexes. RNA 10(10):1507–1517
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Fu, X., Dong, D. (2018). Bioinformatic Analysis of MicroRNA Sequencing Data. In: Wang, Y., Sun, Ma. (eds) Transcriptome Data Analysis. Methods in Molecular Biology, vol 1751. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7710-9_8
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7710-9_8
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7709-3
Online ISBN: 978-1-4939-7710-9
eBook Packages: Springer Protocols