Abstract
Small RNAs are important transcriptional regulators within cells. With the advent of powerful Next Generation Sequencing platforms, sequencing small RNAs seems to be an obvious choice to understand their expression and its downstream effect. Additionally, sequencing provides an opportunity to identify novel and polymorphic miRNA. However, the biggest challenge is the appropriate data analysis pipeline, which is still in phase of active development by various academic groups. This chapter describes basic and advanced steps for small RNA sequencing analysis including quality control, small RNA alignment and quantification, differential expression analysis, novel small RNA identification, target prediction, and downstream analysis. We also provide a list of various resources for small RNA analysis.
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References
Hackenberg M, Sturm M, Langenberger D et al (2009) miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res 37: 68–76
Zhu E, Zhao F, Xu G et al (2010) mirTools: microRNA profiling and discovery based on high-throughput sequencing. Nucleic Acids Res 38:392–397
Wu J, Liu Q, Wang X et al (2013) mirTools 2.0 for non-coding RNA discovery, profiling and functional annotation based on high-throughput sequencing. RNA Biol 10:1087–1092
Huang PJ, Liu YC, Lee CC et al (2010) DSAP: deep-sequencing small RNA analysis pipeline. Nucleic Acids Res 38:385–391
Langmead B, Trapnell C, Pop M et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25
Li H, Durbin R (2009) Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics 25:1754–1760
Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A et al (2006) miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34:140–144
Griffiths-Jones S (2004) The microRNA registry. Nucleic Acids Res 32:D109–D111
Griffiths-Jones S, Saini HK, van Dongen S et al (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res 36:D154–D158
Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39: D152–D157
An J, Lai J, Lehman ML et al (2013) miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res 41:727–737
Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106
Betel D, Wilson M, Gabow A et al (2008) The microRNA.org resource: targets and expression. Nucleic Acids Res 36:D149–D153
Garcia DM, Baek D, Shin C et al (2011) Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol 18: 1139–1146
Grimson A, Farh KK, Johnston WK et al (2007) MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 27:91–105
Friedman RC, Farh KK, Burge CB et al (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19:92–105
Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15–20
Krek A, Grun D, Poy MN et al (2005) Combinatorial microRNA target predictions. Nat Genet 37:495–500
Wang X (2008) miRDB: a microRNA target prediction and functional annotation database with a wiki interface. RNA 14:1012–1017
Sun X, Dong B, Yin L et al (2013) PMTED: a plant microRNA target expression database. BMC Bioinformatics 14:174
Bhartiya D, Laddha SV, Mukhopadhyay A et al (2011) miRvar: a comprehensive database for genomic variations in microRNAs. Hum Mutat 32:E2226–E2245
Wang WC, Lin FM, Chang WC et al (2009) miRExpress: analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinformatics 10:328
Hendrix D, Levine M, Shi W (2010) miRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data. Genome Biol 11:R39
Mathelier A, Carbone A (2010) MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data. Bioinformatics 26: 2226–2234
Ronen R, Gan I, Modai S et al (2010) miRNAkey: a software for microRNA deep sequencing analysis. Bioinformatics 26:2615–2616
Mackowiak SD (2011) Identification of novel and known miRNAs in deep-sequencing data with miRDeep2. Curr Protoc Bioinformatics 12, Unit 12.10
Humphreys DT, Suter CM (2013) miRspring: a compact standalone research tool for analyzing miRNA-seq data. Nucleic Acids Res 41:147
Li H, Handsaker B, Wysoker A et al (2009) The sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079
Burge SW, Daub J, Eberhardt R et al (2013) Rfam 11.0: 10 years of RNA families. Nucleic Acids Res 41:D226–D232
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Mehta, J.P. (2014). Sequencing Small RNA: Introduction and Data Analysis Fundamentals. In: Alvarez, M., Nourbakhsh, M. (eds) RNA Mapping. Methods in Molecular Biology, vol 1182. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1062-5_9
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DOI: https://doi.org/10.1007/978-1-4939-1062-5_9
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