Verification of a Parkinson's Disease Protein Signature by Multiple Reaction Monitoring.

OBJECTIVE: Integration of different ‘omics data (genomic, transcriptomic, proteomic) reveals novel discoveries into biological systems. Integration of these datasets is challenging however, involving use of multiple disparate software in a sequential manner. However, the use of multiple, disparate software in a sequential manner makes the integration of multi-omic data a serious challenge. We describe the extension of Galaxy for mass spectrometric-based proteomics software, enabling advanced multi-omic applications in proteogenomics and metaproteomics. We will demonstrate the benefits of Galaxy for these analyses, as well as its value for software developers seeking to publish new software. We will also share insights on the benefits of the Galaxy framework as a bioinformatics solution for proteomic/metabolomic core facilities. METHODS: Multiple datasets for proteogenomics research (3D-fractionated salivary dataset and oral pre-malignant lesion (OPML) dataset) and metaproteomics research (OPML dataset and Severe Early Childhood Caries (SECC) dataset). Software required for analytical steps such as peaklist generation, database generation (RNA-Seq derived and others), database search (ProteinPilot and X! tandem) and for quantitative proteomics were deployed, tested and optimized for use in workflows. The software are shared in Galaxy toolshed (http://toolshed.g2.bx.psu.edu/). Results: Usage of analytical workflows resulted in reliable identification of novel proteoforms (proteogenomics) or microorganisms (metaproteomics). Proteogenomics analysis identified novel proteoforms in the salivary dataset (51) and OPML dataset (38). Metaproteomics analysis led to microbial identification in OPML and SECC datasets using MEGAN software. As examples, workflows for proteogenomics analysis (http://z.umn.edu/pg140) and metaproteomic analysis (http://z.umn.edu/mp65) are available at the usegalaxyp.org website. Tutorials for workflow usage within Galaxy-P framework are also available (http://z.umn.edu/ppingp). CONCLUSIONS: We demonstrate the use of Galaxy for integrated analysis of multi-omic data, in an accessible, transparent and reproducible manner. Our results and experiences using this framework demonstrate the potential for Galaxy to be a unifying bioinformatics solution for ‘omics core facilities.