ProteoRE

ProteoRE provides an environment for functional analysis and interpretation of proteomics and transcriptomics data in biomedical research.

Key Features:

• Platform: Implemented on the Galaxy framework to integrate and run modular analysis tools.
• Tool organization: Tools are organized into five subsections: data manipulation; species-specific database annotation for human and mouse; functional analysis; pathway analysis; and graphical representations.
• Functional annotation and enrichment: Supports functional annotation and Gene Ontology (GO)-based enrichment analyses for proteomics and transcriptomics datasets.
• Pathway analysis and visualization: Provides pathway analysis tools and graphical representations for interpreting functional results.
• Biomarker discovery: Implements knowledge-based approaches using large-scale biological data, retrieval of experimental data from public databases, and filters based on disease pathophysiology to support mechanistic biomarker identification and candidate selection for MS-based proteomics assessment.
• Workflows for differential proteins: Includes stepwise workflows and protocols for analysis of differentially expressed proteins and reproducible biomarker selection.

Scientific Applications:

• Functional interpretation: Translating large protein lists from proteomics and transcriptomics experiments into biological insights using GO and pathway analyses.
• Pathway mapping: Identification and contextualization of altered pathways from proteomic datasets.
• Biomarker identification: Knowledge-driven selection of mechanistic biomarkers and tissue-leakage biomarker candidates, including applications to myocardial infarction.
• MS-based candidate assessment: Generation of candidate lists suitable for downstream assessment by MS-based proteomics.

Methodology:

Implemented on the Galaxy framework and composed of tools for data manipulation, species-specific database annotation (human and mouse), functional annotation, Gene Ontology (GO) enrichment, pathway analysis, graphical representation, retrieval of experimental data from public databases, and filtering based on disease pathophysiology for biomarker selection.