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In Silico Analysis of Micro-RNA Sequencing Data

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RNA Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2284))

Abstract

High-throughput sequencing for micro-RNAs (miRNAs) to obtain expression estimates is a central method of molecular biology. Surprisingly, there are a number of different approaches to converting sequencing output into micro-RNA counts. Each has their own strengths and biases that impact on the final data that can be obtained from a sequencing run. This chapter serves to make the reader aware of the trade-offs one must consider in analyzing small RNA sequencing data. It then compares two methods, miRge2.0 and the sRNAbench and the steps utilized to output data from their tools.

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References

  1. Halushka MK, Fromm B, Peterson KJ, McCall MN (2018) Big strides in cellular micro-RNA expression. Trends in genetics : TIG 34(3):165–167. https://doi.org/10.1016/j.tig.2017.12.015

    Article  CAS  PubMed  Google Scholar 

  2. Pliatsika V, Loher P, Magee R, Telonis AG, Londin E, Shigematsu M, Kirino Y, Rigoutsos I (2018) MINTbase v2.0: a comprehensive database for tRNA-derived fragments that includes nuclear and mitochondrial fragments from all The Cancer Genome Atlas projects. Nucleic Acids Res 46(D1):D152–D159. https://doi.org/10.1093/nar/gkx1075

    Article  CAS  PubMed  Google Scholar 

  3. Bartel DP (2018) Metazoan Micro-RNAs. Cell 173(1):20–51. https://doi.org/10.1016/j.cell.2018.03.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Mendell JT, Olson EN (2012) Micro-RNAs in stress signaling and human disease. Cell 148(6):1172–1187. https://doi.org/10.1016/j.cell.2012.02.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Calin GA, Croce CM (2006) Micro-RNA-cancer connection: the beginning of a new tale. Cancer Res 66(15):7390–7394. https://doi.org/10.1158/0008-5472.CAN-06-0800

    Article  CAS  PubMed  Google Scholar 

  6. Baras AS, Mitchell CJ, Myers JR, Gupta S, Weng LC, Ashton JM, Cornish TC, Pandey A, Halushka MK (2015) miRge – a multiplexed method of processing small RNA-Seq data to determine micro-RNA entropy. PLoS One 10(11):e0143066. https://doi.org/10.1371/journal.pone.0143066

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Neilsen CT, Goodall GJ, Bracken CP (2012) IsomiRs—the overlooked repertoire in the dynamic micro-RNAome. Trends Genet 28(11):544–549. https://doi.org/10.1016/j.tig.2012.07.005

    Article  CAS  PubMed  Google Scholar 

  8. McCall MN, Kim MS, Adil M, Patil AH, Lu Y, Mitchell CJ, Leal-Rojas P, Xu J, Kumar M, Dawson VL, Dawson TM, Baras AS, Rosenberg AZ, Arking DE, Burns KH, Pandey A, Halushka MK (2017) Toward the human cellular micro-RNAome. Genome Res 27(10):1769–1781. https://doi.org/10.1101/gr.222067.117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Fromm B, Domanska D, Hoye E, Ovchinnikov V, Kang W, Aparicio-Puerta E, Johansen M, Flatmark K, Mathelier A, Hovig E, Hackenberg M, Friedlander MR, Peterson KJ (2020) MirGeneDB 2.0: the metazoan micro-RNA complement. Nucleic Acids Res 48(D1):D132–D141. https://doi.org/10.1093/nar/gkz885

    Article  CAS  PubMed  Google Scholar 

  10. Kozomara A, Birgaoanu M, Griffiths-Jones S (2019) miRBase: from micro-RNA sequences to function. Nucleic Acids Res 47(D1):D155–D162. https://doi.org/10.1093/nar/gky1141

    Article  CAS  PubMed  Google Scholar 

  11. Lukasik A, Wojcikowski M, Zielenkiewicz P (2016) Tools4miRs – one place to gather all the tools for miRNA analysis. Bioinformatics 32(17):2722–2724. https://doi.org/10.1093/bioinformatics/btw189

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sakaram S, Craig MP, Hill NT, Aljagthmi A, Garrido C, Paliy O, Bottomley M, Raymer M, Kadakia MP (2018) Identification of novel DeltaNp63alpha-regulated miRNAs using an optimized small RNA-Seq analysis pipeline. Sci Rep 8(1):10069. https://doi.org/10.1038/s41598-018-28168-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kang W, Eldfjell Y, Fromm B, Estivill X, Biryukova I, Friedlander MR (2018) miRTrace reveals the organismal origins of micro-RNA sequencing data. Genome Biol 19(1):213. https://doi.org/10.1186/s13059-018-1588-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yang A, Bofill-De Ros X, Shao TJ, Jiang M, Li K, Villanueva P, Dai L, Gu S (2019) 3′ uridylation confers miRNAs with non-canonical target repertoires. Mol Cell 75(3):511–522. e514. https://doi.org/10.1016/j.molcel.2019.05.014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Desvignes T, Loher P, Eilbeck K, Ma J, Urgese G, Fromm B, Sydes J, Aparicio-Puerta E, Barrera V, Espin R, Thibord F, Ros XB, Londin E, Telonis AG, Ficarra E, Friedlander MR, Postlethwait JH, Rigoutsos I, Hackenberg M, Vlachos IS, Halushka MK, Pantano L (2019) Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API. Bioinformatics 36:698. https://doi.org/10.1093/bioinformatics/btz675

    Article  CAS  PubMed Central  Google Scholar 

  16. Wang X, Liu XS (2011) Systematic curation of miRBase annotation using integrated small RNA high-throughput sequencing data for C. elegans and drosophila. Front Genet 2:25. https://doi.org/10.3389/fgene.2011.00025

    Article  PubMed  PubMed Central  Google Scholar 

  17. Tarver JE, Donoghue PC, Peterson KJ (2012) Do miRNAs have a deep evolutionary history? BioEssays 34(10):857–866. https://doi.org/10.1002/bies.201200055

    Article  CAS  PubMed  Google Scholar 

  18. Taylor RS, Tarver JE, Hiscock SJ, Donoghue PC (2014) Evolutionary history of plant micro-RNAs. Trends Plant Sci 19(3):175–182. https://doi.org/10.1016/j.tplants.2013.11.008

    Article  CAS  PubMed  Google Scholar 

  19. Chiang HR, Schoenfeld LW, Ruby JG, Auyeung VC, Spies N, Baek D, Johnston WK, Russ C, Luo S, Babiarz JE, Blelloch R, Schroth GP, Nusbaum C, Bartel DP (2010) Mammalian micro-RNAs: experimental evaluation of novel and previously annotated genes. Genes Dev 24(10):992–1009. https://doi.org/10.1101/gad.1884710

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ludwig N, Becker M, Schumann T, Speer T, Fehlmann T, Keller A, Meese E (2017) Bias in recent miRBase annotations potentially associated with RNA quality issues. Sci Rep 7(1):5162. https://doi.org/10.1038/s41598-017-05070-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Londin E, Loher P, Telonis AG, Quann K, Clark P, Jing Y, Hatzimichael E, Kirino Y, Honda S, Lally M, Ramratnam B, Comstock CE, Knudsen KE, Gomella L, Spaeth GL, Hark L, Katz LJ, Witkiewicz A, Rostami A, Jimenez SA, Hollingsworth MA, Yeh JJ, Shaw CA, McKenzie SE, Bray P, Nelson PT, Zupo S, Van Roosbroeck K, Keating MJ, Calin GA, Yeo C, Jimbo M, Cozzitorto J, Brody JR, Delgrosso K, Mattick JS, Fortina P, Rigoutsos I (2015) Analysis of 13 cell types reveals evidence for the expression of numerous novel primate- and tissue-specific micro-RNAs. Proc Natl Acad Sci U S A 112(10):E1106–E1115. https://doi.org/10.1073/pnas.1420955112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Alles J, Fehlmann T, Fischer U, Backes C, Galata V, Minet M, Hart M, Abu-Halima M, Grasser FA, Lenhof HP, Keller A, Meese E (2019) An estimate of the total number of true human miRNAs. Nucleic Acids Res 47(7):3353–3364. https://doi.org/10.1093/nar/gkz097

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hou XS, Han CQ, Zhang W (2018) MiR-1182 inhibited metastasis and proliferation of ovarian cancer by targeting hTERT. Eur Rev Med Pharmacol Sci 22(6):1622–1628. https://doi.org/10.26355/eurrev_201803_14569

    Article  PubMed  Google Scholar 

  24. Zhang D, Xiao YF, Zhang JW, Xie R, Hu CJ, Tang B, Wang SM, Wu YY, Hao NB, Yang SM (2015) miR-1182 attenuates gastric cancer proliferation and metastasis by targeting the open reading frame of hTERT. Cancer Lett 360(2):151–159. https://doi.org/10.1016/j.canlet.2015.01.044

    Article  CAS  PubMed  Google Scholar 

  25. de Rie D, Abugessaisa I, Alam T, Arner E, Arner P, Ashoor H, Astrom G, Babina M, Bertin N, Burroughs AM, Carlisle AJ, Daub CO, Detmar M, Deviatiiarov R, Fort A, Gebhard C, Goldowitz D, Guhl S, Ha TJ, Harshbarger J, Hasegawa A, Hashimoto K, Herlyn M, Heutink P, Hitchens KJ, Hon CC, Huang E, Ishizu Y, Kai C, Kasukawa T, Klinken P, Lassmann T, Lecellier CH, Lee W, Lizio M, Makeev V, Mathelier A, Medvedeva YA, Mejhert N, Mungall CJ, Noma S, Ohshima M, Okada-Hatakeyama M, Persson H, Rizzu P, Roudnicky F, Saetrom P, Sato H, Severin J, Shin JW, Swoboda RK, Tarui H, Toyoda H, Vitting-Seerup K, Winteringham L, Yamaguchi Y, Yasuzawa K, Yoneda M, Yumoto N, Zabierowski S, Zhang PG, Wells CA, Summers KM, Kawaji H, Sandelin A, Rehli M, Consortium F, Hayashizaki Y, Carninci P, Forrest ARR, de Hoon MJL (2017) An integrated expression atlas of miRNAs and their promoters in human and mouse. Nat Biotechnol 35(9):872–878. https://doi.org/10.1038/nbt.3947

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kleaveland B, Shi CY, Stefano J, Bartel DP (2018) A network of noncoding regulatory RNAs acts in the mammalian brain. Cell 174(2):350–362. e317. https://doi.org/10.1016/j.cell.2018.05.022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kim B, Jeong K, Kim VN (2017) Genome-wide mapping of DROSHA cleavage sites on primary micro-RNAs and noncanonical substrates. Mol Cell 66(2):258–269. e255. https://doi.org/10.1016/j.molcel.2017.03.013

    Article  CAS  PubMed  Google Scholar 

  28. Lu Y, Baras AS, Halushka MK (2018) miRge 2.0 for comprehensive analysis of micro-RNA sequencing data. BMC Bioinformatics 19(1):275. https://doi.org/10.1186/s12859-018-2287-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal 17(1):10–12. https://doi.org/10.14806/ej.17.1.200

    Google Scholar 

  30. 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(3):R25. https://doi.org/10.1186/gb-2009-10-3-r25

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Hackenberg M, Sturm M, Langenberger D, Falcon-Perez JM, Aransay AM (2009) miRanalyzer: a micro-RNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res 37(Web Server issue):W68–W76. https://doi.org/10.1093/nar/gkp347

  32. Hackenberg M, Rodriguez-Ezpeleta N, Aransay AM (2011) miRanalyzer: an update on the detection and analysis of micro-RNAs in high-throughput sequencing experiments. Nucleic Acids Res 39(Web Server issue):W132–W138. https://doi.org/10.1093/nar/gkr247

  33. Rueda A, Barturen G, Lebron R, Gomez-Martin C, Alganza A, Oliver JL, Hackenberg M (2015) sRNAtoolbox: an integrated collection of small RNA research tools. Nucleic Acids Res 43(W1):W467–W473. https://doi.org/10.1093/nar/gkv555

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Aparicio-Puerta E, Lebron R, Rueda A, Gomez-Martin C, Giannoukakos S, Jaspez D, Medina JM, Zubkovic A, Jurak I, Fromm B, Marchal JA, Oliver J, Hackenberg M (2019) sRNAbench and sRNAtoolbox 2019: intuitive fast small RNA profiling and differential expression. Nucleic Acids Res 47(W1):W530–W535. https://doi.org/10.1093/nar/gkz415

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106. https://doi.org/10.1186/gb-2010-11-10-r106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 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. https://doi.org/10.1186/s13059-014-0550-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140. https://doi.org/10.1093/bioinformatics/btp616

    Article  CAS  PubMed  Google Scholar 

  38. Tarazona S, Furio-Tari P, Turra D, Pietro AD, Nueda MJ, Ferrer A, Conesa A (2015) Data quality aware analysis of differential expression in RNA-seq with NOISeq R/bioc package. Nucleic Acids Res 43(21):e140. https://doi.org/10.1093/nar/gkv711

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Galili T, O’Callaghan A, Sidi J, Sievert C (2018) Heatmaply: an R package for creating interactive cluster heatmaps for online publishing. Bioinformatics 34(9):1600–1602. https://doi.org/10.1093/bioinformatics/btx657

    Article  CAS  PubMed  Google Scholar 

  40. Conway JR, Lex A, Gehlenborg N (2017) UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33(18):2938–2940. https://doi.org/10.1093/bioinformatics/btx364

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Genetics, University of Granada, Granada, Spain

    Ernesto Aparicio-Puerta & Michael Hackenberg

  2. Department of Molecular Biosciences, Stockholm University, Stockholm, Sweden

    Bastian Fromm

  3. Department of Pathology, Johns Hopkins University, Baltimore, MD, USA

    Marc K. Halushka

Authors
  1. Ernesto Aparicio-Puerta
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  2. Bastian Fromm
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  3. Michael Hackenberg
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  4. Marc K. Halushka
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Corresponding author

Correspondence to Marc K. Halushka .

Editor information

Editors and Affiliations

  1. Dipartimento Di Bioscienze Biotecnologie E Biofarmaceutica, Università degli Studi di Bari Aldo Moro, Bari, Italy

    Ernesto Picardi

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Aparicio-Puerta, E., Fromm, B., Hackenberg, M., Halushka, M.K. (2021). In Silico Analysis of Micro-RNA Sequencing Data. In: Picardi, E. (eds) RNA Bioinformatics. Methods in Molecular Biology, vol 2284. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1307-8_13

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  • DOI: https://doi.org/10.1007/978-1-0716-1307-8_13

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1306-1

  • Online ISBN: 978-1-0716-1307-8

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