Python and R for Multi-Omics Data Analysis: Genomics, Transcriptomics and Proteomics

Aim

Workshop Objectives

Workshop Structure

📅 Day 1: Python & R Foundations for Biological Data Analysis

• Introduction to computational biology and multi-omics data analysis
• Overview of genomics, transcriptomics, and proteomics datasets
• Role of Python and R in bioinformatics research workflows
• Python fundamentals: variables, data structures, functions, libraries, and biological data handling
• Data manipulation and analysis using pandas and NumPy
• R programming fundamentals, packages, scripting, and Bioconductor ecosystem
• Importing, organising, and structuring biological datasets
• Data preprocessing, cleaning, missing-value handling, and normalization
• Exploratory data analysis (EDA) for biological datasets
• Biological data visualization and interpretation principles

🛠️ Hands-on:

• Load and organise biological datasets using Python and R
• Perform data cleaning, preprocessing, and normalization
• Generate statistical summaries and exploratory visualisations
• Analyse biological patterns through data visualization

🧰 Tools Covered:

Python, R, Jupyter Notebook, RStudio, pandas, NumPy, ggplot2, matplotlib

📅 Day 2: Genomics & Transcriptomics Data Analysis

• Introduction to genomic data formats and sequence-based datasets
• FASTA, FASTQ and genomic data processing concepts
• Quality assessment, preprocessing, and sequence statistics analysis
• Genomic feature analysis, annotation concepts, and variant interpretation
• Introduction to RNA-seq workflow and gene-expression analysis
• Gene-expression matrices, count data, and normalization approaches
• Differential gene-expression analysis and identification of significant genes
• Visualization of expression patterns using heatmaps and volcano plots
• Functional enrichment analysis and pathway interpretation
• Biological interpretation of transcriptomic signatures and molecular changes

🛠️ Hands-on:

• Analyse genomic datasets and sequence-based information
• Explore RNA-seq expression profiles
• Perform differential expression analysis
• Generate heatmaps, volcano plots, and expression summaries
• Identify important genes and biological pathways

🧰 Tools Covered:

R, Bioconductor, DESeq2, edgeR, ggplot2, Python, pandas, Scanpy, genomic and transcriptomic datasets

📅 Day 3: Proteomics Analysis & Multi-Omics Integration

• Introduction to proteomics datasets and quantitative protein analysis
• Protein abundance profiling, normalization, and quality assessment
• Differential protein analysis and functional annotation
• Principles of multi-omics data integration
• Linking genomic, transcriptomic, and proteomic information
• Correlation-based molecular analysis and biological pattern identification
• Network-based interpretation of molecular interactions
• Biomarker discovery approaches using integrated omics data
• Machine-learning concepts for omics-based classification
• Development of reproducible computational workflows for multi-omics research

🛠️ Hands-on:

• Analyse quantitative proteomics datasets
• Perform protein annotation and functional analysis
• Integrate genomics, transcriptomics, and proteomics datasets
• Visualize molecular networks and biological relationships
• Identify potential biomarkers and molecular signatures

🧰 Tools Covered:

Python, R, Cytoscape, STRING, pandas, scikit-learn, ggplot2, Plotly, Google Colab

Who Should Enrol?

Important Dates

Workshop Outcomes