Genomics, Systems Biology & Bioinformatics

Molecular Data Integration and Computational Modeling

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. It is a unified framework spanning the harmonized Genomics, Systems Biology and Bioinformatics and Multi-Omics Technology in Animal Production outlines.

Start with the learning pathway for core work, prerequisites and suggested study time. Use the unified course map to explore the reusable teaching blocks and the two course pathways.

Apply the methods in three real-data case studies, then use the assessment guide to build a defensible report. The visual guide and videos and animations explain recurring concepts.

Core Themes

The material is organized around seven interconnected themes:

  1. Molecular Technologies
    • DNA sequencing technologies
    • Genotyping platforms
    • RNA-seq and transcriptomics
    • Epigenomic profiling
    • Proteomics and metabolomics
    • Microbiomics and high-throughput phenotyping
  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
  6. Experimental Design and Sampling
    • Biological replication and experimental units
    • Matched animals, tissues and collection times
    • Batch allocation, missingness and assay-specific quality
  7. Animal-Production Interpretation and Responsible Research
    • Production, health, adaptation and sustainability questions
    • Independent validation and decision-time relevance
    • Data governance, research integrity and responsible AI

Learning Objectives

After completing the course, students should be able to:

  • Explain how sequencing and other omics technologies generate molecular data
  • Design matched sampling and identify the appropriate experimental unit
  • 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
  • Distinguish association, prediction and causal evidence
  • Document reproducibility, data governance and analysis limitations

Course Materials

Read the lecture overview, then browse the teaching modules and practicals with exercises and solutions. The two harmonized outlines use different pathways through these shared resources.

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

Each module and practical connects:

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

The collection includes 18 modules, 31 hypothetical practicals and three real-data case studies, covering both harmonized outlines through shared sources. Reproduce a worked example, predict the effect of changing a parameter, and attempt the exercises before opening the suggested answers.

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.

Course Hub

Return to the gteach teaching-materials hub.