Skip to main content

Validation of Chimeric Fusion Peptides Using Proteomics Data

  • Protocol
  • First Online:
Chimeric RNA

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2079))

Abstract

Chimeric RNAs as well as their fused protein products have therapeutic applications ranging from diagnostics to being used as therapeutic target. Many algorithms have been developed to identify chimeric RNAs, however, identification and validation of fused protein product of the chimeric RNA is still an emerging field. These chimeric proteins can be validated by searching and identifying them in publicly available proteomics datasets. Here we describe the detailed steps for (1) downloading and processing publicly available proteomics datasets, (2) developing fusion peptide database by performing in silico tryptic digestion of chimeric proteins, and (3) software used to identify chimeric peptides in the proteomics data.

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

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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. Wu P, Yang S, Singh S et al (2018) The landscape and implications of chimeric RNAs in cervical cancer. EBioMedicine 37:158–167

    Article  Google Scholar 

  2. Yang Z, Yu L, Wang Z (2016) PCA3 and TMPRSS2-ERG gene fusions as diagnostic biomarkers for prostate cancer. Chin J Cancer Res 28:65–71

    Article  CAS  Google Scholar 

  3. Løvf M, Nome T, Bruun J et al (2014) A novel transcript, VNN1-AB, as a biomarker for colorectal cancer. Int J Cancer 135:2077–2084

    Article  Google Scholar 

  4. Lee M, Lee K, Yu N et al (2017) ChimerDB 3.0: an enhanced database for fusion genes from cancer transcriptome and literature data mining. Nucleic Acids Res 45:D784–D789

    CAS  PubMed  Google Scholar 

  5. Gorohovski A, Tagore S, Palande V et al (2017) ChiTaRS-3.1-the enhanced chimeric transcripts and RNA-seq database matched with protein-protein interactions. Nucleic Acids Res 45:D790–D795

    Article  CAS  Google Scholar 

  6. Mitelman F, Johansson B, Mertens F (2007) The impact of translocations and gene fusions on cancer causation. Nat Rev Cancer 7:233–245

    Article  CAS  Google Scholar 

  7. Novo FJ, de Mendíbil IO, Vizmanos JL (2007) TICdb: a collection of gene-mapped translocation breakpoints in cancer. BMC Genomics 8:33

    Article  Google Scholar 

  8. Babiceanu M, Qin F, Xie Z et al (2016) Recurrent chimeric fusion RNAs in non-cancer tissues and cells. Nucleic Acids Res 44:2859–2872

    Article  Google Scholar 

  9. Xie Z, Babiceanu M, Kumar S et al (2016) Fusion transcriptome profiling provides insights into alveolar rhabdomyosarcoma. Proc Natl Acad Sci U S A 113:13126–13131

    Article  CAS  Google Scholar 

  10. Benelli M, Pescucci C, Marseglia G et al (2012) Discovering chimeric transcripts in paired-end RNA-seq data by using EricScript. Bioinformatics 28:3232–3239

    Article  CAS  Google Scholar 

  11. Jia W, Qiu K, He M et al (2013) SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data. Genome Biol 14:R12

    Article  Google Scholar 

  12. Li Y, Heavican TB, Vellichirammal NN et al (2017) ChimeRScope: a novel alignment-free algorithm for fusion transcript prediction using paired-end RNA-Seq data. Nucleic Acids Res 45:e120

    Article  CAS  Google Scholar 

  13. Kumar S, Vo AD, Qin F et al (2016) Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data. Sci Rep 6:21597

    Article  CAS  Google Scholar 

  14. Edwards NJ, Oberti M, Thangudu RR et al (2015) The CPTAC data portal: a resource for cancer proteomics research. J Proteome Res 14:2707–2713

    Article  CAS  Google Scholar 

  15. Kim M-S, Pinto SM, Getnet D et al (2014) A draft map of the human proteome. Nature 509:575–581

    Article  CAS  Google Scholar 

  16. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26:841–842

    Article  CAS  Google Scholar 

  17. Rice P, Longden I, Bleasby A (2000) EMBOSS: the european molecular biology open software suite. Trends Genet 16:276–277

    Article  CAS  Google Scholar 

  18. Kim S, Pevzner PA (2014) MS-GF+ makes progress towards a universal database search tool for proteomics. Nat Commun 5:5277

    Article  CAS  Google Scholar 

  19. Gaudet P, Michel P-A, Zahn-Zabal M et al (2017) The neXtProt knowledgebase on human proteins: 2017 update. Nucleic Acids Res 45:D177–D182

    Article  CAS  Google Scholar 

  20. Vizcaíno JA, Csordas A, Del-Toro N et al (2016) 2016 update of the PRIDE database and its related tools. Nucleic Acids Res 44:D447–D456

    Article  Google Scholar 

  21. Deutsch EW (2010) The peptideatlas project. Methods Mol Biol 604:285–296

    Article  CAS  Google Scholar 

  22. Zhang B, Wang J, Wang X et al (2014) Proteogenomic characterization of human colon and rectal cancer. Nature 513:382–387

    Article  CAS  Google Scholar 

  23. Eng JK, McCormack AL, Yates JR (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 5:976–989

    Article  CAS  Google Scholar 

  24. Perkins DN, Pappin DJ, Creasy DM et al (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20:3551–3567

    Article  CAS  Google Scholar 

  25. Xu T, Park SK, Venable JD et al (2015) ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity. J Proteome 129:16–24

    Article  CAS  Google Scholar 

  26. Ma B (2015) Novor: real-time peptide de novo sequencing software. J Am Soc Mass Spectrom 26:1885–1894

    Article  CAS  Google Scholar 

  27. Tran NH, Zhang X, Xin L et al (2017) De novo peptide sequencing by deep learning. Proc Natl Acad Sci U S A 114(31):8247–8252

    Article  CAS  Google Scholar 

  28. Kolbowski L, Combe C, Rappsilber J (2018) xiSPEC: web-based visualization, analysis and sharing of proteomics data. Nucleic Acids Res 46:W473–W478

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hui Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Singh, S., Li, H. (2020). Validation of Chimeric Fusion Peptides Using Proteomics Data. In: Li, H., Elfman, J. (eds) Chimeric RNA. Methods in Molecular Biology, vol 2079. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9904-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9904-0_9

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9903-3

  • Online ISBN: 978-1-4939-9904-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics