Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Multi-omics data analysis, crucial for understanding complex biological systems, often requires specialized programming skills and high-performance computing.
• Existing tools for gene-set pathway analysis present significant challenges for researchers lacking coding expertise.
• The complexity of multi-omics data preparation and analysis hinders broader accessibility in big data research.

Purpose of the Study:

• To develop an accessible, automated workflow for multi-omics pathway analysis.
• To simplify the process of data preparation, dimensionality reduction, and pathway analysis for various omics data types.
• To provide a user-friendly interface for researchers to identify distinct biological pathways in specific subgroups.

Main Methods:

• Introduction of an automated multi-omics pathway workflow with a graphical user interface (GUI) for Multivariate Single Sample Gene Set Analysis (MOGSA).
• The workflow integrates tools for data preparation, dimensionality reduction, and MOGSA pathway analysis across copy number alteration, transcriptomics, proteomics, and phosphoproteomics data.
• An additional workflow facilitates data download and preprocessing from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC).

Main Results:

• The workflow generates distinct pathway information for user-defined subgroups, visualized through heatmaps, graphs, and tables.
• Identifies overactivated or deactivated pathways within specific sample groups.
• Provides actionable insights for potential therapeutic targeting.

Conclusions:

• The Multi-omics Pathway Workflow requires no coding experience, making advanced analysis accessible to a wider research community.
• Enables users to analyze their own data or preprocess public datasets from TCGA and CPTAC.
• Facilitates the discovery of distinct pathways critical for effective therapeutic strategies.