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BMC Bioinformatics. 2016 Jun 17;17(1):244. doi: 10.1186/s12859-016-1133-3.

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

Author information

1
BGI-Shenzhen, Shenzhen, 518083, China.
2
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA.
3
BGI-Shenzhen, Shenzhen, 518083, China. siqiliu@genomics.cn.
4
Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China. siqiliu@genomics.cn.

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.

KEYWORDS:

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

PMID:
27316337
PMCID:
PMC4912784
DOI:
10.1186/s12859-016-1133-3
[Indexed for MEDLINE]
Free PMC Article

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