Flexible Data Analysis Pipeline for High-Confidence Proteogenomics.
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ICR Authors
Authors
Weisser, H
Wright, JC
Mudge, JM
Gutenbrunner, P
Choudhary, JS
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