Multi-Omics Data Analysis

This course teaches data-specific computational analyses and integrative analysis approaches across omics datasets, including transcriptomics, proteomics, metabolomics, single-cell RNA sequencing, spatial transcriptomics, and machine learning approaches for multi-omics integration. Course code: MODA Instructor: Dr. Fadhl Alakwaa and Dr. Mohamed Hamed Group leader, Rostock University, Germany; Bioinformatician and Researcher, Department of Medicine, Stanford University, USA. Workload: 12 lectures, 3 hours each. Total workload: 48 hours: 36 hours of lectures and tutorials and 12 hours of self studies. Entrance requirements: Basic knowledge of biology and Bioinformatics I. Used media: PowerPoint presentation

Objectives

• Understand different omics data types and functional genomics applications
• Perform data-specific computational analyses for high-throughput biological data
• Analyze omics data using R and Bioconductor packages
• Develop and apply integrative bioinformatics methods
• Use machine learning concepts to integrate biological features from heterogeneous omics data

Competences to be Developed

• Data-specific computational analysis pipelines
• R language and Bioconductor for omics analysis
• Integrative bioinformatics methods
• Machine learning basics for heterogeneous omics integration
• Research project interpretation, manuscript writing, and scientific discussion

Assessment

• Finalize a research project applying learned methods
• Compile outcomes as a high-quality scientific article
• Present, discuss, and review projects in the last lecture

Assessment and completion

• Quizzes: 20%
• Assessments: 30%
• Final / Graduation Project: 50%
• Overall passing grade: 60%
• Minimum project grade: 50%
• All mandatory components must be completed
• One reassessment or resubmission opportunity is available for a failed mandatory assessment or final project