Skip to main content
SpringerLink
Log in

The UEA Small RNA Workbench: A Suite of Computational Tools for Small RNA Analysis

  • Protocol
  • First Online:
MicroRNA Detection and Target Identification

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1580))

Abstract

RNA silencing (RNA interference, RNAi) is a complex, highly conserved mechanism mediated by short, typically 20–24 nt in length, noncoding RNAs known as small RNAs (sRNAs). They act as guides for the sequence-specific transcriptional and posttranscriptional regulation of target mRNAs and play a key role in the fine-tuning of biological processes such as growth, response to stresses, or defense mechanism.

High-throughput sequencing (HTS) technologies are employed to capture the expression levels of sRNA populations. The processing of the resulting big data sets facilitated the computational analysis of the sRNA patterns of variation within biological samples such as time point experiments, tissue series or various treatments. Rapid technological advances enable larger experiments, often with biological replicates leading to a vast amount of raw data. As a result, in this fast-evolving field, the existing methods for sequence characterization and prediction of interaction (regulatory) networks periodically require adapting or in extreme cases, a complete redesign to cope with the data deluge. In addition, the presence of numerous tools focused only on particular steps of HTS analysis hinders the systematic parsing of the results and their interpretation.

The UEA small RNA Workbench (v1-4), described in this chapter, provides a user-friendly, modular, interactive analysis in the form of a suite of computational tools designed to process and mine sRNA datasets for interesting characteristics that can be linked back to the observed phenotypes. First, we show how to preprocess the raw sequencing output and prepare it for downstream analysis. Then we review some quality checks that can be used as a first indication of sources of variability between samples. Next we show how the Workbench can provide a comparison of the effects of different normalization approaches on the distributions of expression, enhanced methods for the identification of differentially expressed transcripts and a summary of their corresponding patterns. Finally we describe individual analysis tools such as PAREsnip, for the analysis of PARE (degradome) data or CoLIde for the identification of sRNA loci based on their expression patterns and the visualization of the results using the software. We illustrate the features of the UEA sRNA Workbench on Arabidopsis thaliana and Homo sapiens datasets.

*The authors wish it to be known that the first two authors should be regarded as joint first authors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fire A, Xu S, Montgomery MK, Kostas SA, Driver SE, Mello CC (1998) Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 391:806–811

    Article  CAS  PubMed  Google Scholar 

  2. Ruiz MT, Voinnet O, Baulcombe DC (1998) Initiation and maintenance of virus-induced gene silencing. Plant Cell 10:937–946

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Meister G, Tuschl T (2004) Mechanisms of gene silencing by double-stranded RNA. Nature 431:343–349

    Article  CAS  PubMed  Google Scholar 

  4. Ha M, Kim VN (2014) Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol 15:509–524

    Article  CAS  PubMed  Google Scholar 

  5. Jones-Rhoades MW, Bartel DP, Bartel B (2006) MicroRNAS and their regulatory roles in plants. Annu Rev Plant Biol 57:19–53

    Article  CAS  PubMed  Google Scholar 

  6. Allen E, Xie Z, Gustafson AM, Carrington JC (2005) microRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell 121:207–221

    Article  CAS  PubMed  Google Scholar 

  7. Talbert PB, Henikoff S (2006) Spreading of silent chromatin: inaction at a distance. Nat Rev Genet 7:793–803

    Article  CAS  PubMed  Google Scholar 

  8. Siomi MC, Sato K, Pezic D, Aravin AA (2011) PIWI-interacting small RNAs: the vanguard of genome defence. Nat Rev Mol Cell Biol 12:246–258

    Article  CAS  PubMed  Google Scholar 

  9. Gunawardane LS, Saito K, Nishida KM, Miyoshi K, Kawamura Y, Nagami T, Siomi H, Siomi MC (2007) A slicer-mediated mechanism for repeat-associated siRNA 5′ end formation in Drosophila. Science 315:1587–1590

    Article  CAS  PubMed  Google Scholar 

  10. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136:215–233

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Voinnet O (2009) Origin, biogenesis, and activity of plant microRNAs. Cell 136:669–687

    Article  CAS  PubMed  Google Scholar 

  12. Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12:87–98

    Article  CAS  PubMed  Google Scholar 

  13. Morozova O, Marra MA (2008) Applications of next-generation sequencing technologies in functional genomics. Genomics 92:255–264

    Article  CAS  PubMed  Google Scholar 

  14. Beckers M, Mohorianu I, Stocks MB, Applegate C, Dalmay T, Moulton V (2017) An interactive pipeline for quality checking, normalization and differential expression analysis of high throughput small RNA sequencing data. in preparation.

    Google Scholar 

  15. Stocks MB, Moxon S, Mapleson D, Woolfenden HC, Mohorianu I, Folkes L, Schwach F, Dalmay T, Moulton V (2012) The UEA sRNA workbench: a suite of tools for analysing and visualizing next generation sequencing microRNA and small RNA datasets. Bioinformatics 28:2059–2061

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Moxon S, Schwach F, Dalmay T, Maclean D, Studholme DJ, Moulton V (2008) A toolkit for analysing large-scale plant small RNA datasets. Bioinformatics 24:2252–2253

    Article  CAS  PubMed  Google Scholar 

  17. Folkes L, Moxon S, Woolfenden HC, Stocks MB, Szittya G, Dalmay T, Moulton V (2012) PAREsnip: a tool for rapid genome-wide discovery of small RNA/target interactions evidenced through degradome sequencing. Nucleic Acids Res 40:e103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Mohorianu I, Stocks MB, Wood J, Dalmay T, Moulton V (2013) CoLIde: a bioinformatics tool for CO-expression-based small RNA Loci Identification using high-throughput sequencing data. RNA Biol 10:1221–1230

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhang X, Zhu Y, Liu X, Hong X, Xu Y, Zhu P, Shen Y, Wu H, Ji Y, Wen X et al (2015) Plant biology. Suppression of endogenous gene silencing by bidirectional cytoplasmic RNA decay in Arabidopsis. Science 348:120–123

    Article  CAS  PubMed  Google Scholar 

  20. Camps C, Saini HK, Mole DR, Choudhry H, Reczko M, Guerra-Assuncao JA, Tian YM, Buffa FM, Harris AL, Hatzigeorgiou AG 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

    Article  PubMed  PubMed Central  Google Scholar 

  21. Montgomery TA, Yoo SJ, Fahlgren N, Gilbert SD, Howell MD, Sullivan CM, Alexander A, Nguyen G, Allen E, Ahn JH, Carrington JC (2008) AGO1-miR173 complex initiates phased siRNA formation in plants. Proc Natl Acad Sci U S A 105:20055–20062

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. German MA, Pillay M, Jeong DH, Hetawal A, Luo S, Janardhanan P, Kannan V, Rymarquis LA, Nobuta K, German R et al (2008) Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends. Nat Biotechnol 26:941–946

    Article  CAS  PubMed  Google Scholar 

  23. Berardini TZ, Reiser L, Li D, Mezheritsky Y, Muller R, Strait E, Huala E (2015) The Arabidopsis information resource: Making and mining the “gold standard” annotated reference plant genome. Genesis 53:474–485

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lamesch P, Berardini TZ, Li D, Swarbreck D, Wilks C, Sasidharan R, Muller R, Dreher K, Alexander DL, Garcia-Hernandez M et al (2012) The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res 40:D1202–D1210

    Article  CAS  PubMed  Google Scholar 

  25. Prufer K, Stenzel U, Dannemann M, Green RE, Lachmann M, Kelso J (2008) PatMaN: rapid alignment of short sequences to large databases. Bioinformatics 24:1530–1531

    Article  PubMed  PubMed Central  Google Scholar 

  26. Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25

    Article  PubMed  PubMed Central  Google Scholar 

  27. McCormick KP, Willmann MR, Meyers BC (2011) Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments. Silence 2:2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sorefan K, Pais H, Hall AE, Kozomara A, Griffiths-Jones S, Moulton V, Dalmay T (2012) Reducing ligation bias of small RNAs in libraries for next generation sequencing. Silence 3:4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Xu P, Billmeier M, Mohorianu I, Green D, Fraser WD, Dalmay T (2015) An improved protocol for small RNA library construction using High Definition adapters. Methods in Next Generation Sequencing 2:2084–7173

    Article  Google Scholar 

  30. Mohorianu I, Schwach F, Jing R, Lopez-Gomollon S, Moxon S, Szittya G, Sorefan K, Moulton V, Dalmay T (2011) Profiling of short RNAs during fleshy fruit development reveals stage-specific sRNAome expression patterns. Plant J 67:232–246

    Article  CAS  PubMed  Google Scholar 

  31. Mantha S, Roizen MF, Fleisher LA, Thisted R, Foss J (2000) Comparing methods of clinical measurement: reporting standards for bland and altman analysis. Anesth Analg 90:593–602

    Article  CAS  PubMed  Google Scholar 

  32. Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Castel D, Estelle J et al (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 14:671–683

    Article  CAS  PubMed  Google Scholar 

  33. Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND, Betel D (2013) Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol 14:R95

    Article  PubMed  PubMed Central  Google Scholar 

  34. Soneson C, Delorenzi M (2013) A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 14:91

    Article  PubMed  PubMed Central  Google Scholar 

  35. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5:621–628

    Article  CAS  PubMed  Google Scholar 

  36. Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11:R25

    Article  PubMed  PubMed Central  Google Scholar 

  37. Bullard JH, Purdom E, Hansen KD, Dudoit S (2010) Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 11:94

    Article  PubMed  PubMed Central  Google Scholar 

  38. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550

    Article  PubMed  PubMed Central  Google Scholar 

  40. Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185–193

    Article  CAS  PubMed  Google Scholar 

  41. Mohorianu I, Bretman A, Smith D, Fowler E, Dalmay T, Chapman T (2016) New approaches for analysing RNA-seq data: sampling-based normalization and hierarchical differential expression. in preparation.

    Google Scholar 

  42. Li J, Witten DM, Johnstone IM, Tibshirani R (2012) Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics 13:523–538

    Article  PubMed  Google Scholar 

  43. Cleaveland W (1979) Robust locally weighted regression and smoothing scatterplot. J Am Stat Assoc 74:829–836

    Article  Google Scholar 

  44. Cleaveland W (1981) LOWESS: a program for smoothing scatterplots by robust locally weighted regression. The American Statistician 35:54–60

    Article  Google Scholar 

  45. Lopez-Gomollon S, Mohorianu I, Szittya G, Moulton V, Dalmay T (2012) Diverse correlation patterns between microRNAs and their targets during tomato fruit development indicates different modes of microRNA actions. Planta 236:1875–1887

    Article  CAS  PubMed  Google Scholar 

  46. Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42:D68–D73

    Article  CAS  PubMed  Google Scholar 

  47. Hofacker IL, Lorenz R (2014) Predicting RNA structure: advances and limitations. Methods Mol Biol 1086:1–19

    Article  CAS  PubMed  Google Scholar 

  48. Lorenz R, Hofacker IL, Stadler PF (2016) RNA folding with hard and soft constraints. Algorithms Mol Biol 11:8

    Article  PubMed  PubMed Central  Google Scholar 

  49. Bonnet E, Wuyts J, Rouze P, Van de Peer Y (2004) Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences. Bioinformatics 20:2911–2917

    Article  CAS  PubMed  Google Scholar 

  50. Peragine A, Yoshikawa M, Wu G, Albrecht HL, Poethig RS (2004) SGS3 and SGS2/SDE1/RDR6 are required for juvenile development and the production of trans-acting siRNAs in Arabidopsis. Genes Dev 18:2368–2379

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Vazquez F, Vaucheret H, Rajagopalan R, Lepers C, Gasciolli V, Mallory AC, Hilbert JL, Bartel DP, Crete P (2004) Endogenous trans-acting siRNAs regulate the accumulation of Arabidopsis mRNAs. Mol Cell 16:69–79

    Article  CAS  PubMed  Google Scholar 

  52. Fei Q, Xia R, Meyers BC (2013) Phased, secondary, small interfering RNAs in posttranscriptional regulatory networks. Plant Cell 25:2400–2415

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yifhar T, Pekker I, Peled D, Friedlander G, Pistunov A, Sabban M, Wachsman G, Alvarez JP, Amsellem Z, Eshed Y (2012) Failure of the tomato trans-acting short interfering RNA program to regulate AUXIN RESPONSE FACTOR3 and ARF4 underlies the wiry leaf syndrome. Plant Cell 24:3575–3589

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Chen HM, Li YH, Wu SH (2007) Bioinformatic prediction and experimental validation of a microRNA-directed tandem trans-acting siRNA cascade in Arabidopsis. Proc Natl Acad Sci U S A 104:3318–3323

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Friedlander MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N (2008) Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26:407–415

    Article  PubMed  Google Scholar 

  56. Guerra-Assuncao JA, Enright AJ (2010) MapMi: automated mapping of microRNA loci. BMC Bioinformatics 11:133

    Article  PubMed  PubMed Central  Google Scholar 

  57. Molnar A, Schwach F, Studholme DJ, Thuenemann EC, Baulcombe DC (2007) miRNAs control gene expression in the single-cell alga Chlamydomonas reinhardtii. Nature 447:1126–1129

    Article  CAS  PubMed  Google Scholar 

  58. Tomato Genome Consortium (2012) The tomato genome sequence provides insights into fleshy fruit evolution. Nature 485:635–641

    Article  Google Scholar 

  59. Helt GA, Nicol JW, Erwin E, Blossom E, Blanchard SG Jr, Chervitz SA, Harmon C, Loraine AE (2009) Genoviz Software Development Kit: Java tool kit for building genomics visualization applications. BMC Bioinformatics 10:266

    Article  PubMed  PubMed Central  Google Scholar 

  60. German MA, Luo S, Schroth G, Meyers BC, Green PJ (2009) Construction of Parallel Analysis of RNA Ends (PARE) libraries for the study of cleaved miRNA targets and the RNA degradome. Nat Protoc 4:356–362

    Article  CAS  PubMed  Google Scholar 

  61. Zhai J, Arikit S, Simon SA, Kingham BF, Meyers BC (2014) Rapid construction of parallel analysis of RNA end (PARE) libraries for Illumina sequencing. Methods 67:84–90

    Article  CAS  PubMed  Google Scholar 

  62. Addo-Quaye C, Eshoo TW, Bartel DP, Axtell MJ (2008) Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome. Curr Biol 18:758–762

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Addo-Quaye C, Miller W, Axtell MJ (2009) CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics 25:130–131

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

We thank Matthew Beckers, Tamas Dalmay, Frank Schwach, Simon Moxon, Hugh Woolfenden, Helio Pais, and Daniel Mapleson for their contributions to the UEA sRNA Workbench.

Author information

Author notes

    Authors and Affiliations

    1. School of Biological Sciences, University of East Anglia, Norwich, UK

      Irina Mohorianu

    2. School of Computing Sciences, University of East Anglia, Norwich, UK

      Irina Mohorianu, Matthew Benedict Stocks, Christopher Steven Applegate & Vincent Moulton

    3. The Earlham Institute, Norwich, UK

      Leighton Folkes

    Authors
    1. Irina Mohorianu
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Matthew Benedict Stocks
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Christopher Steven Applegate
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Leighton Folkes
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Vincent Moulton
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Vincent Moulton .

    Editor information

    Editors and Affiliations

    1. School of Biological Sciences, University of East Anglia, Norwich, United Kingdom

      Tamas Dalmay

    Rights and permissions

    Reprints and permissions

    Copyright information

    © 2017 Springer Science+Business Media LLC

    About this protocol

    Cite this protocol

    Mohorianu, I., Stocks, M.B., Applegate, C.S., Folkes, L., Moulton, V. (2017). The UEA Small RNA Workbench: A Suite of Computational Tools for Small RNA Analysis. In: Dalmay, T. (eds) MicroRNA Detection and Target Identification. Methods in Molecular Biology, vol 1580. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6866-4_14

    Download citation

    • DOI: https://doi.org/10.1007/978-1-4939-6866-4_14

    • Published:

    • Publisher Name: Humana Press, New York, NY

    • Print ISBN: 978-1-4939-6864-0

    • Online ISBN: 978-1-4939-6866-4

    • eBook Packages: Springer Protocols

    Publish with us

    Policies and ethics

    Search

    Navigation