Flexible Data Analysis Pipeline for High-Confidence Proteogenomics.

Loading...
Thumbnail Image

Embargo End Date

Authors

Weisser, H
Wright, JC
Mudge, JM
Gutenbrunner, P
Choudhary, JS

Document Type

Journal Article

Date

2016-12-02

Date Accepted

2016-10-27

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.

Citation

Journal of proteome research, 2016, 15 (12), pp. 4686 - 4695

Source Title

Publisher

AMER CHEMICAL SOC

ISSN

1535-3893

eISSN

1535-3907

Collections

Research Team

Functional Proteomics Group

Notes