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Motif Discovery from CLIP Experiments

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RNA Bioinformatics

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

Abstract

RNA primary and secondary motif discovery is an important step in the annotation and characterization of unknown interaction dynamics between RNAs and RNA-Binding Proteins, and several methods have been developed to meet the need of fast and efficient discovery of interaction motifs. Recent advances have increased the amount of data produced by experimental assays and there is no available method suitable for the analysis of all type of results. Here we present a simple workflow to help choosing the more appropriate method, depending on the starting situation, among the three algorithms that best cover the landscape of approaches. A detailed analysis is presented to highlight the need for different algorithms in different working settings. In conclusion, the proposed workflow depends on the nature of the starting data and on the availability of RNA annotations.

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References

  1. Mattei E, Ausiello G, Ferrè F, Helmer-Citterich M (2014) A novel approach to represent and compare RNA secondary structures. Nucleic Acids Res 42:6146–6157. https://doi.org/10.1093/nar/gku283

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Sasse A, Laverty KU, Hughes TR, Morris QD (2018) Motif models for RNA-binding proteins. Curr Opin Struct Biol 53:115–123. https://doi.org/10.1016/j.sbi.2018.08.001

    Article  CAS  PubMed  Google Scholar 

  3. Maticzka D, Lange SJ, Costa F, Backofen R (2014) GraphProt: modeling binding preferences of RNA-binding proteins. Genome Biol 15:R17. https://doi.org/10.1186/gb-2014-15-1-r17

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hiller M, Pudimat R, Busch A, Backofen R (2006) Using RNA secondary structures to guide sequence motif finding towards single-stranded regions. Nucleic Acids Res 34:e117. https://doi.org/10.1093/nar/gkl544

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Pietrosanto M, Mattei E, Helmer-Citterich M, Ferrè F (2016) A novel method for the identification of conserved structural patterns in RNA: from small scale to high-throughput applications. Nucleic Acids Res 44:8600–8609. https://doi.org/10.1093/nar/gkw750

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Adinolfi M, Pietrosanto M, Parca L et al (2019) Discovering sequence and structure landscapes in RNA interaction motifs. Nucleic Acids Res 47:4958–4969. https://doi.org/10.1093/nar/gkz250

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Rabani M, Kertesz M, Segal E (2011) Computational prediction of RNA structural motifs involved in post-transcriptional regulatory processes. Methods Mol Biol 714:467–479

    Article  CAS  Google Scholar 

  8. Yao Z, Weinberg Z, Ruzzo WL (2006) CMfinder--a covariance model based RNA motif finding algorithm. Bioinformatics 22. https://doi.org/10.1093/bioinformatics/btk008

  9. Kazan H, Ray D, Chan ET et al (2010) RNAcontext: a new method for learning the sequence and structure binding preferences of RNA-binding proteins. PLoS Comput Biol 6:28. https://doi.org/10.1371/journal.pcbi.1000832

    Article  CAS  Google Scholar 

  10. Cook KB, Vembu S, Ha KCH et al (2017) RNAcompete-S: combined RNA sequence/structure preferences for RNA binding proteins derived from a single-step in vitro selection. Methods 126:18–28. https://doi.org/10.1016/j.ymeth.2017.06.024

    Article  CAS  PubMed  Google Scholar 

  11. Heller D, Krestel R, Ohler U et al (2017) ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data. Nucleic Acids Res 45:11004–11018. https://doi.org/10.1093/nar/gkx756

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Polishchuk M, Paz I, Kohen R et al (2017) A combined sequence and structure based method for discovering enriched motifs in RNA from in vivo binding data. Methods 118–119:73–81. https://doi.org/10.1016/j.ymeth.2017.03.003

    Article  CAS  PubMed  Google Scholar 

  13. Kalvari I, Argasinska J, Quinones-Olvera N et al (2017) Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families. Nucleic Acids Res 46:335–342. https://doi.org/10.1093/nar/gkx1038

    Article  CAS  Google Scholar 

  14. Gruber AR, Lorenz R, Bernhart SH et al (2008) The Vienna RNA websuite. Nucleic Acids Res 36:W70–W74. https://doi.org/10.1093/nar/gkn188

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Quinlan AR (2014) BEDTools: the Swiss-army tool for genome feature analysis. Curr Protoc Bioinformatics 47:11.12.1–11.12.34. https://doi.org/10.1002/0471250953.bi1112s47

    Article  Google Scholar 

  16. Pietrosanto M, Adinolfi M, Casula R et al (2018) BEAM web server: a tool for structural RNA motif discovery. Bioinformatics 34:1058–1060. https://doi.org/10.1093/bioinformatics/btx704

    Article  CAS  PubMed  Google Scholar 

  17. Will S, Grüning B, Backofen R et al (2017) Recent advances in RNA folding. J Biotechnol 261:97–104. https://doi.org/10.1016/j.jbiotec.2017.07.007

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Rome, Italy

    Marco Pietrosanto, Gabriele Ausiello & Manuela Helmer-Citterich

Authors
  1. Marco Pietrosanto
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  2. Gabriele Ausiello
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  3. Manuela Helmer-Citterich
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Corresponding author

Correspondence to Manuela Helmer-Citterich .

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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Pietrosanto, M., Ausiello, G., Helmer-Citterich, M. (2021). Motif Discovery from CLIP Experiments. 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_3

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  • DOI: https://doi.org/10.1007/978-1-0716-1307-8_3

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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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