PGA: An R/Bioconductor package for identification of novel peptides using a customized database derived from RNA-Seq

Bo Wen, Shaohang Xu, Ruo Zhou, Bing Zhang, Xiaojing Wang, Xin Liu, Xun Xu, Siqi Liu

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

Background: Peptide identification based upon mass spectrometry (MS) is generally achieved by comparison of the experimental mass spectra with the theoretically digested peptides derived from a reference protein database. Obviously, this strategy could not identify peptide and protein sequences that are absent from a reference database. A customized protein database on the basis of RNA-Seq data is thus proposed to assist with and improve the identification of novel peptides. Correspondingly, development of a comprehensive pipeline, which provides an end-to-end solution for novel peptide detection with the customized protein database, is necessary. Results: A pipeline with an R package, assigned as a PGA utility, was developed that enables automated treatment to the tandem mass spectrometry (MS/MS) data acquired from different MS platforms and construction of customized protein databases based on RNA-Seq data with or without a reference genome guide. Hence, PGA can identify novel peptides and generate an HTML-based report with a visualized interface. On the basis of a published dataset, PGA was employed to identify peptides, resulting in 636 novel peptides, including 510 single amino acid polymorphism (SAP) peptides, 2 INDEL peptides, 49 splice junction peptides, and 75 novel transcript-derived peptides. The software is freely available from http://bioconductor.org/packages/PGA/ , and the example reports are available at http://wenbostar.github.io/PGA/. Conclusions: The pipeline of PGA, aimed at being platform-independent and easy-to-use, was successfully developed and shown to be capable of identifying novel peptides by searching the customized protein database derived from RNA-Seq data.

Original languageEnglish (US)
Article number244
JournalBMC bioinformatics
Volume17
Issue number1
DOIs
StatePublished - Jun 17 2016
Externally publishedYes

Keywords

  • MS/MS
  • Peptide identification
  • Proteogenomics
  • Proteomics
  • RNA-Seq

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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