Molecular Data Analysis

Module of Quantitative Skills (MSc Biological Sciences)

Welcome

This course is designed to equip you with essential computational skills for modern biological research, from data visualization to advanced genomics analyses. The module will guide you through practical, hands-on approaches to analyzing biological data. Throughout this course, you’ll work with real-world datasets and industry-standard tools, building a strong foundation in reproducible computational biology.

Learning Objectives

By the end of this module, you will be able to:

  • Set up and configure a complete bioinformatics analysis environment in R
  • Create publication-quality data visualizations using modern R packages
  • Design and implement effective graphics for exploring complex biological datasets
  • Perform end-to-end RNA-seq analysis, from raw reads to differential expression
  • Interpret RNA-seq results and conduct functional enrichment analyses
  • Understand the principles and workflows of genome assembly
  • Apply quality control measures and validation strategies for genomic data
  • Integrate multiple bioinformatics tools into reproducible analysis pipelines
  • Document and communicate your analyses effectively

Course Structure

This module is organized into three main parts, with a preliminary setup section:

Setup

Before diving into the analyses, you’ll configure your computational environment with all necessary software, packages, and dependencies.

Part 1: Data Visualization in R

Starting with the basics: data visualization using ggplot2 in R. In addition, we will work on making our science reproducible using Github.

Part 2: RNA-seq Analysis

Explore transcriptomics through an RNA-seq workflow.

Part 3: Genome Assembly

Explore the challenges of reconstructing genomes from sequencing reads.

Prerequisites

  • Basic understanding of molecular biology concepts
  • Basic programming experience (R or similar language) is beneficial
  • Enthusiasm for learning computational approaches to biology!

Getting Started

Ready to begin? Head to the Setup section to prepare your computational environment, then proceed through the course parts in order. Enjoy!