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

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

13 Citations (Scopus)


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 signi fi cance 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 signi fi cance 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 dif fi cult 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.

Original languageEnglish
Title of host publicationProteomics for Biomarker Discovery
EditorsMing Zhou, Timothy Veenstra
Place of PublicationNew York
PublisherSpringer, Springer Nature
Number of pages28
ISBN (Electronic)9781627033602
ISBN (Print)9781627033596
Publication statusPublished - 2013

Publication series

NameMethods in Molecular Biology
ISSN (Print)10643745

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