Daniel Liebler
Faculty Member
Last active: 2/15/2016

Protein identification using customized protein sequence databases derived from RNA-Seq data.

Wang X, Slebos RJ, Wang D, Halvey PJ, Tabb DL, Liebler DC, Zhang B
J Proteome Res. 2012 11 (2): 1009-17

PMID: 22103967 · PMCID: PMC3727138 · DOI:10.1021/pr200766z

The standard shotgun proteomics data analysis strategy relies on searching MS/MS spectra against a context-independent protein sequence database derived from the complete genome sequence of an organism. Because transcriptome sequence analysis (RNA-Seq) promises an unbiased and comprehensive picture of the transcriptome, we reason that a sample-specific protein database derived from RNA-Seq data can better approximate the real protein pool in the sample and thus improve protein identification. In this study, we have developed a two-step strategy for building sample-specific protein databases from RNA-Seq data. First, the database size is reduced by eliminating unexpressed or lowly expressed genes according to transcript quantification. Second, high-quality nonsynonymous coding single nucleotide variations (SNVs) are identified based on RNA-Seq data, and corresponding protein variants are added to the database. Using RNA-Seq and shotgun proteomics data from two colorectal cancer cell lines SW480 and RKO, we demonstrated that customized protein sequence databases could significantly increase the sensitivity of peptide identification, reduce ambiguity in protein assembly, and enable the detection of known and novel peptide variants. Thus, sample-specific databases from RNA-Seq data can enable more sensitive and comprehensive protein discovery in shotgun proteomics studies.

MeSH Terms (14)

Amino Acid Sequence Base Sequence Cell Line, Tumor Computational Biology Databases, Protein Gene Expression Profiling Humans Molecular Sequence Data Peptide Mapping Peptides Proteins RNA Sequence Analysis, RNA Transcriptome

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