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

Trinh Nguyen1, Xiaopeng Bian1, David Roberson2

  • 1The Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA.

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Summary
This summary is machine-generated.

This study introduces an automated multi-omics pathway workflow, simplifying complex data analysis for researchers. The tool identifies distinct biological pathways, aiding in targeted therapeutic strategies.

Keywords:
CGCMulti-omicsTCGAautomatic workflowcloudpathway analysis

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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.