De novo derivation of proteomes from transcriptomes for transcript and protein identification

Vanessa C Evans, Gary Barker, Kate J Heesom, Jun Fan, Conrad Bessant, David A Matthews

Research output: Contribution to journalArticle (Academic Journal)peer-review

136 Citations (Scopus)


Identification of proteins by tandem mass spectrometry requires a reference protein database, but these are only available for model species. Here we demonstrate that, for a non-model species, the sequencing of expressed mRNA can generate a protein database for mass spectrometry-based identification. This combination of high-throughput sequencing and protein identification technologies allows detection of genes and proteins. We use human cells infected with human adenovirus as a complex and dynamic model to demonstrate the robustness of this approach. Our proteomics informed by transcriptomics (PIT) technique identifies >99% of over 3,700 distinct proteins identified using traditional analysis that relies on comprehensive human and adenovirus protein lists. We show that this approach can also be used to highlight genes and proteins undergoing dynamic changes in post-transcriptional protein stability.
Original languageEnglish
Pages (from-to)1207-11
Number of pages5
JournalNature Methods
Issue number12
Publication statusPublished - Dec 2012


  • Software
  • Adenoviridae
  • Animals
  • Polymorphism, Single Nucleotide
  • Cricetulus
  • Arginine
  • HeLa Cells
  • Humans
  • Lysine
  • Tandem Mass Spectrometry
  • Sequence Analysis, Protein
  • RNA, Messenger
  • Nuclear Proteins
  • RNA-Binding Proteins
  • Databases, Protein
  • Proteomics
  • Proteome
  • Chromatography, Liquid
  • CHO Cells
  • Nitrogen Isotopes
  • Transcriptome
  • Carbon Isotopes
  • Cricetinae


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