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J Proteome Res. 2016 Dec 2;15(12):4686-4695. Epub 2016 Nov 10.

Flexible Data Analysis Pipeline for High-Confidence Proteogenomics.

Author information

1
School of Informatics, Communications, and Media, University of Applied Sciences Upper Austria , Hagenberg 4232, Austria.

Abstract

Proteogenomics leverages information derived from proteomic data to improve genome annotations. Of particular interest are "novel" peptides that provide direct evidence of protein expression for genomic regions not previously annotated as protein-coding. We present a modular, automated data analysis pipeline aimed at detecting such "novel" peptides in proteomic data sets. This pipeline implements criteria developed by proteomics and genome annotation experts for high-stringency peptide identification and filtering. Our pipeline is based on the OpenMS computational framework; it incorporates multiple database search engines for peptide identification and applies a machine-learning approach (Percolator) to post-process search results. We describe several new and improved software tools that we developed to facilitate proteogenomic analyses that enhance the wealth of tools provided by OpenMS. We demonstrate the application of our pipeline to a human testis tissue data set previously acquired for the Chromosome-Centric Human Proteome Project, which led to the addition of five new gene annotations on the human reference genome.

KEYWORDS:

bioinformatics; genome annotation; mass spectrometry; proteogenomics; testis; workflow

PMID:
27786492
PMCID:
PMC5703597
DOI:
10.1021/acs.jproteome.6b00765
[Indexed for MEDLINE]
Free PMC Article

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