Genomics, Systems Biology & Bioinformatics

Molecular Data Integration and Computational Modeling

Author
Affiliation

Peter Sørensen

Center for Quantitative Genetics and Genomics, Aarhus University, Denmark

Course Overview

This course provides a conceptual and computational framework for analyzing molecular data across biological scales, from genome to transcriptome to systems-level organization.

The focus is on understanding how genetic and environmental variation influence molecular phenotypes and complex traits through modern high-throughput technologies and statistical modeling.

The course integrates biological interpretation, statistical methodology, and practical implementation in R.

Core Themes

The material is organized around five interconnected themes:

  1. Molecular Technologies
    • DNA sequencing technologies
    • Genotyping platforms
    • RNA-seq and transcriptomics
    • Epigenomic and multi-omics profiling
  2. Genomic Association and Mapping
    • GWAS principles
    • Population structure and confounding
    • Fine-mapping
    • Functional annotation
  3. Multi-Omics Integration
    • Transcriptome-wide association
    • eQTL and regulatory variation
    • Integrative modeling across data layers
  4. Systems-Level Modeling
    • Gene regulatory networks
    • Pathway analysis
    • Network-based interpretation
    • Causal modeling concepts
  5. Computational and Statistical Methods
    • High-dimensional regression
    • Regularization and shrinkage
    • Machine learning approaches
    • Reproducible workflows in R

Learning Objectives

After completing the course, students should be able to:

  • Explain how sequencing technologies generate molecular data
  • Evaluate appropriate statistical models for genomic data
  • Analyze high-dimensional datasets using R
  • Interpret GWAS and fine-mapping results
  • Integrate multi-omics datasets conceptually and computationally
  • Connect molecular variation to complex phenotypes

Structure of the Book

The course progresses from molecular data generation to systems-level interpretation.

Each chapter includes:

  • Conceptual background
  • Statistical framework
  • Applied examples
  • Executable R code
  • Visualizations and case studies

Students are encouraged to actively reproduce analyses and explore parameter choices to deepen understanding.

Prerequisites

Students are expected to have:

  • Basic molecular biology knowledge
  • Introductory statistics
  • Familiarity with R

Prior exposure to linear models and quantitative genetics is beneficial but not required.