Skip to main content
SpringerLink
Log in

RBPmap: A Tool for Mapping and Predicting the Binding Sites of RNA-Binding Proteins Considering the Motif Environment

  • Protocol
  • First Online:
Post-Transcriptional Gene Regulation

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2404))

Abstract

RNA-binding proteins (RBPs) play a key role in post-transcriptional regulation via binding to coding and non-coding RNAs. Recent development in experimental technologies, aimed to identify the targets of RBPs, has significantly broadened our knowledge on protein-RNA interactions. However, for many RBPs in many organisms and cell types, experimental RNA-binding data is not available. In this chapter we describe a computational approach, named RBPmap, available as a web service via http://rbpmap.technion.ac.il/ and as a stand-alone version for download. RBPmap was designed for mapping and predicting the binding sites of any RBP within a nucleic acid sequence, given the availability of an experimentally defined binding motif of the RBP. The algorithm searches for a sub-sequence that significantly matches the RBP motif, considering the clustering propensity of other weak matches within the motif environment. Here, we present different applications of RBPmap for discovering the involvement of RBPs and their targets in a variety of cellular processes, in health and disease states. Finally, we demonstrate the performance of RBPmap in predicting the binding targets of RBPs in large-scale RNA-binding data, reinforcing the strength of the tool in distinguishing cognate binding sites from weak motifs.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
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. Hentze MW, Castello A, Schwarzl T, Preiss T (2018) A brave new world of RNA-binding proteins. Nat Rev Mol Cell Biol 19(5):327–341. https://doi.org/10.1038/nrm.2017.130

    Article  CAS  PubMed  Google Scholar 

  2. Mittal N, Roy N, Babu MM, Janga SC (2009) Dissecting the expression dynamics of RNA-binding proteins in posttranscriptional regulatory networks. Proc Natl Acad Sci U S A 106(48):20300–20305. https://doi.org/10.1073/pnas.0906940106

    Article  PubMed  PubMed Central  Google Scholar 

  3. Conlon EG, Manley JL (2017) RNA-binding proteins in neurodegeneration: mechanisms in aggregate. Genes Dev 31(15):1509–1528. https://doi.org/10.1101/gad.304055.117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gebauer F, Schwarzl T, Valcárcel J, Hentze MW (2021) RNA-binding proteins in human genetic disease. Nat Rev Genet 22(3):185–198. https://doi.org/10.1038/s41576-020-00302-y

    Article  CAS  PubMed  Google Scholar 

  5. Dasti A, Cid-Samper F, Bechara E, Tartaglia GG (2020) RNA-centric approaches to study RNA-protein interactions in vitro and in silico. Methods 178:11–18. https://doi.org/10.1016/j.ymeth.2019.09.011

    Article  CAS  PubMed  Google Scholar 

  6. Nechay M, Kleiner RE (2020) High-throughput approaches to profile RNA-protein interactions. Curr Opin Chem Biol 54:37–44. https://doi.org/10.1016/j.cbpa.2019.11.002

    Article  CAS  PubMed  Google Scholar 

  7. Van Nostrand EU, Pratt GU, Shishkin AA, Gelboin-Burkhart CU, Fang MU, Sundararaman BU, Blue SU, Nguyen TU, Surka C, Elkins KU, Stanton RU, Rigo F, Guttman M, Yeo GU (2016) Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat Methods 13(6):508–514. https://doi.org/10.1038/nmeth.3810

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Van Nostrand EL, Pratt GA, Yee BA, Wheeler EC, Blue SM, Mueller J, Park SS, Garcia KE, Gelboin-Burkhart C, Nguyen TB, Rabano I, Stanton R, Sundararaman B, Wang R, Fu XD, Graveley BR, Yeo GW (2020) Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins. Genome Biol 21(1):90. https://doi.org/10.1186/s13059-020-01982-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Van Nostrand EL, Freese P, Pratt GA, Wang X, Wei X, Xiao R, Blue SM, Chen JY, Cody NAL, Dominguez D, Olson S, Sundararaman B, Zhan L, Bazile C, Bouvrette LPB, Bergalet J, Duff MO, Garcia KE, Gelboin-Burkhart C, Hochman M, Lambert NJ, Li H, McGurk MP, Nguyen TB, Palden T, Rabano I, Sathe S, Stanton R, Su A, Wang R, Yee BA, Zhou B, Louie AL, Aigner S, Fu XD, Lecuyer E, Burge CB, Graveley BR, Yeo GW (2020) A large-scale binding and functional map of human RNA-binding proteins. Nature 583(7818):711–719. https://doi.org/10.1038/s41586-020-2077-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Uhl M, Houwaart T, Corrado G, Wright PR, Backofen R (2017) Computational analysis of CLIP-seq data. Methods 118-119:60–72. https://doi.org/10.1016/j.ymeth.2017.02.006

    Article  CAS  PubMed  Google Scholar 

  11. Dvir S, Argoetti A, Mandel-Gutfreund Y (2018) Ribonucleoprotein particles: advances and challenges in computational methods. Curr Opin Struct Biol 53:124–130. https://doi.org/10.1016/j.sbi.2018.08.002

    Article  CAS  PubMed  Google Scholar 

  12. Hiller MG, Pudimat R, Busch A, Backofen R (2006) Using RNA secondary structures to guide sequence motif finding towards single-stranded regions. Nucleic Acids Res 34(17):e117. https://doi.org/10.1093/nar/gkl544

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Paz I, Akerman M, Dror I, Kosti I, Mandel-Gutfreund Y (2010) SFmap: a web server for motif analysis and prediction of splicing factor binding sites. Nucleic Acids Res 38(Web Server issue):W281–W285. https://doi.org/10.1093/nar/gkq444

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Li XC, Kazan H, Lipshitz HD, Morris QD (2014) Finding the target sites of RNA-binding proteins. Wiley Interdis Rev RNA 5(1):111–130. https://doi.org/10.1002/wrna.1201

    Article  CAS  Google Scholar 

  15. 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 

  16. Paz I, Kosti I, Ares M Jr, Cline M, Mandel-Gutfreund Y (2014) RBPmap: a web server for mapping binding sites of RNA-binding proteins. Nucleic Acids Res 42(Web Server issue):W361–W367. https://doi.org/10.1093/nar/gku406

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kazan H, Morris Q (2013) RBPmotif: a web server for the discovery of sequence and structure preferences of RNA-binding proteins. Nucleic Acids Res 41(Web Server issue):W180–W186. https://doi.org/10.1093/nar/gkt463

    Article  PubMed  PubMed Central  Google Scholar 

  18. Kazan H, Ray D, Chan ET, Hughes TR, Morris Q (2010) RNAcontext: a new method for learning the sequence and structure binding preferences of RNA-binding proteins. PLoS Comput Biol 6:e1000832. https://doi.org/10.1371/journal.pcbi.1000832

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Orenstein Y, Shamir R (2017) Modeling protein-DNA binding via high-throughput in vitro technologies. Brief Funct Genomics 16(3):171–180. https://doi.org/10.1093/bfgp/elw030

    Article  CAS  PubMed  Google Scholar 

  21. Polishchuk M, Paz I, Kohen R, Mesika R, Yakhini Z, Mandel-Gutfreund Y (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 

  22. Polishchuk M, Paz I, Yakhini Z, Mandel-Gutfreund Y (2018) SMARTIV: combined sequence and structure de-novo motif discovery for in-vivo RNA binding data. Nucleic Acids Res 46(W1):W221–w228. https://doi.org/10.1093/nar/gky453

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Akerman M, David-Eden H, Pinter RY, Mandel-Gutfreund Y (2009) A computational approach for genome-wide mapping of splicing factor binding sites. Genome Biol 10(3):R30. https://doi.org/10.1186/gb-2009-10-3-r30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ray D, Kazan H, Cook KB, Weirauch MT, Najafabadi HS, Li X, Gueroussov S, Albu M, Zheng H, Yang A, Na H, Irimia M, Matzat LH, Dale RK, Smith SA, Yarosh CA, Kelly SM, Nabet B, Mecenas D, Li W, Laishram RS, Qiao M, Lipshitz HD, Piano F, Corbett AH, Carstens RP, Frey BJ, Anderson RA, Lynch KW, Penalva LO, Lei EP, Fraser AG, Blencowe BJ, Morris QD, Hughes TR (2013) A compendium of RNA-binding motifs for decoding gene regulation. Nature 499(7457):172–177. https://doi.org/10.1038/nature12311

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Dominguez D, Freese P, Alexis MS, Su A, Hochman M, Palden T, Bazile C, Lambert NJ, Van Nostrand EL, Pratt GA, Yeo GW, Graveley BR, Burge CB (2018) Sequence, structure, and context preferences of human RNA binding proteins. Mol Cell 70(5):854–867.e859. https://doi.org/10.1016/j.molcel.2018.05.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lambert N, Robertson A, Jangi M, McGeary S, Sharp PA, Burge CB (2014) RNA Bind-n-Seq: quantitative assessment of the sequence and structural binding specificity of RNA binding proteins. Mol Cell 54(5):887–900. https://doi.org/10.1016/j.molcel.2014.04.016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kent WJ (2002) BLAT—the BLAST-like alignment tool. Genome Res 12(4):656–664. https://doi.org/10.1101/gr.229202

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ray D, Kazan H, Chan ET, Pena Castillo L, Chaudhry S, Talukder S, Blencowe BJ, Morris Q, Hughes TR (2009) Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins. Nat Biotechnol 27(7):667–670. https://doi.org/10.1038/nbt.1550

    Article  CAS  PubMed  Google Scholar 

  29. Bailey TL, Johnson J, Grant CE, Noble WS (2015) The MEME suite. Nucleic Acids Res 43(W1):W39–W49. https://doi.org/10.1093/nar/gkv416

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Fiszbein A, Krick KS, Begg BE, Burge CB (2019) Exon-mediated activation of transcription starts. Cell 179(7):1551–1565.e1517. https://doi.org/10.1016/j.cell.2019.11.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Takahashi K, Yamanaka S (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126(4):663–676. https://doi.org/10.1016/j.cell.2006.07.024

    Article  CAS  PubMed  Google Scholar 

  32. Tang SJ, Shen H, An O, Hong H, Li H, Song Y, Han J, Tay DJT, Ng VHE, Bellido Molias F, Leong KW, Pitcheshwar P, Yang H, Chen L (2020) Cis- and trans-regulations of pre-mRNA splicing by RNA editing enzymes influence cancer development. Nat Commun 11(1):799. https://doi.org/10.1038/s41467-020-14621-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Singer RA, Arnes L, Cui Y, Wang J, Gao Y, Guney MA, Burnum-Johnson KE, Rabadan R, Ansong C, Orr G, Sussel L (2019) The long noncoding RNA Paupar modulates PAX6 regulatory activities to promote alpha cell development and function. Cell Metab 30(6):1091–1106.e1098. https://doi.org/10.1016/j.cmet.2019.09.013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Siam A, Baker M, Amit L, Regev G, Rabner A, Najar RA, Bentata M, Dahan S, Cohen K, Araten S, Nevo Y, Kay G, Mandel-Gutfreund Y, Salton M (2019) Regulation of alternative splicing by p300-mediated acetylation of splicing factors. RNA 25(7):813–824. https://doi.org/10.1261/rna.069856.118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chen M, Lyu G, Han M, Nie H, Shen T, Chen W, Niu Y, Song Y, Li X, Li H, Chen X, Wang Z, Xia Z, Li W, Tian XL, Ding C, Gu J, Zheng Y, Liu X, Hu J, Wei G, Tao W, Ni T (2018) 3' UTR lengthening as a novel mechanism in regulating cellular senescence. Genome Res 28(3):285–294. https://doi.org/10.1101/gr.224451.117

    Article  CAS  PubMed Central  Google Scholar 

  36. Vogt C, Hackmann C, Rabner A, Koste L, Santag S, Kati S, Mandel-Gutfreund Y, Schulz TF, Bohne J (2015) ORF57 overcomes the detrimental sequence bias of Kaposi’s sarcoma-associated herpesvirus lytic genes. J Virol 89(9):5097–5109. https://doi.org/10.1128/jvi.03264-14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mukherjee M, Goswami S (2020) Global cataloguing of variations in untranslated regions of viral genome and prediction of key host RNA binding protein-microRNA interactions modulating genome stability in SARS-CoV-2. PLoS One 15(8):e0237559. https://doi.org/10.1371/journal.pone.0237559

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Orlandini von Niessen AG, Poleganov MA, Rechner C, Plaschke A, Kranz LM, Fesser S, Diken M, Löwer M, Vallazza B, Beissert T, Bukur V, Kuhn AN, Türeci Ö, Sahin U (2019) Improving mRNA-based therapeutic gene delivery by expression-augmenting 3' UTRs identified by cellular library screening. Mol Ther 27(4):824–836. https://doi.org/10.1016/j.ymthe.2018.12.011

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The maintenance of RBPmap and the development of new features added to version 1.2 were supported by the ISF grant number 1182/16. We would like to thank Tamar Hashimshony for helpful comments on this chapter.

Author information

Authors and Affiliations

  1. Faculty of Biology, Technion—Israel Institute of Technology, Haifa, Israel

    Inbal Paz, Amir Argoetti, Noa Cohen, Niv Even & Yael Mandel-Gutfreund

  2. Department of Computer Sciences, Technion—Israel Institute of Technology, Haifa, Israel

    Noa Cohen, Niv Even & Yael Mandel-Gutfreund

Authors
  1. Inbal Paz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Argoetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Noa Cohen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Niv Even
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yael Mandel-Gutfreund
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yael Mandel-Gutfreund .

Editor information

Editors and Affiliations

  1. Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy

    Erik Dassi

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Paz, I., Argoetti, A., Cohen, N., Even, N., Mandel-Gutfreund, Y. (2022). RBPmap: A Tool for Mapping and Predicting the Binding Sites of RNA-Binding Proteins Considering the Motif Environment. In: Dassi, E. (eds) Post-Transcriptional Gene Regulation. Methods in Molecular Biology, vol 2404. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1851-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1851-6_3

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1850-9

  • Online ISBN: 978-1-0716-1851-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation