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Plant Proteogenomics: From Protein Extraction to Improved Gene Predictions

  • Brett Chapman
  • Natalie Castellana
  • Alex Apffel
  • Ryan Ghan
  • Grant R. Cramer
  • Matthew Bellgard
  • Paul A. Haynes
  • Steven C. Van Sluyter
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1002)

Abstract

Historically many genome annotation strategies have lacked experimental evidence at the protein level, which and have instead relied heavily on ab initio gene prediction tools, which consequently resulted in many incorrectly annotated genomic sequences. Proteogenomics aims to address these issues using mass spectrometry (MS)-based proteomics, genomic mapping, and providing statistical significance measures such as false discovery rates (FDRs) to validate the mapped peptides. Presented here is a tool capable of meeting this goal, the UCSD proteogenomic pipeline, which maps peptide-spectrum matches (PSMs) to the genome using the Inspect MS/MS database search tool and assigns a statistical significance to the match using a target-decoy search approach to assign estimated FDRs. This pipeline also provides the option of using a more reliable approach to proteogenomics by determining the precise false-positive rates (FPRs) and p-values of each PSM by calculating their spectral probabilities and rescoring each PSM accordingly. In addition to the protein prediction challenges in the rapidly growing number of sequenced plant genomes, it is difficult to extract high-quality protein samples from many plant species. For that reason, this chapter contains methods for protein extraction and trypsin digestion that reliably produce samples suitable for proteogenomic analysis.

Key words

Proteogenomics Proteomics Peptide identification Protein extraction Trypsin digest False discovery rate False-positive rate Posterior error probability Local false discovery rate p-Value q-Value Inspect MS-GF 

Notes

Acknowledgments

The authors acknowledge funding support from the Australian Research Council and the NSF Grape Research Coordination Network. P.A.H. acknowledges Robert Black for continued support and encouragement.

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Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Brett Chapman
    • 1
  • Natalie Castellana
    • 2
  • Alex Apffel
    • 3
  • Ryan Ghan
    • 4
  • Grant R. Cramer
    • 4
  • Matthew Bellgard
    • 1
  • Paul A. Haynes
    • 5
  • Steven C. Van Sluyter
    • 5
  1. 1.Centre for Comparative GenomicsMurdoch UniversityPerthAustralia
  2. 2.University of California San DiegoLa JollaUSA
  3. 3.Agilent LaboratoriesSanta ClaraUSA
  4. 4.University of NevadaRenoUSA
  5. 5.Macquarie UniversityNorth RydeAustralia

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