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

This 3-day mentor-led workshop provides practical training in Python and R-based multi-omics data analysis workflows covering genomics, transcriptomics, and proteomics. Participants will learn how to process biological datasets, perform statistical analysis, visualize molecular patterns, identify significant biological features, and integrate multiple omics layers for research applications. The workshop combines programming fundamentals, bioinformatics workflows, data visualization, and computational interpretation using real-world biological datasets.

o provide participants with hands-on computational skills for analysing and interpreting multi-omics datasets using Python and R, enabling them to perform genomic, transcriptomic, and proteomic data analysis workflows for biomedical and life-science research.

• Understand the role of computational approaches in modern multi-omics research.
• Learn Python and R programming concepts relevant to biological data analysis.
• Handle, clean, and preprocess large-scale biological datasets.
• Analyse genomic datasets and interpret sequence-based information.
• Perform transcriptomics data analysis including expression profiling.
• Analyse proteomics datasets and identify protein-level changes.
• Apply statistical methods for biological data interpretation.
• Generate publication-quality biological visualizations.
• Perform differential expression and enrichment analysis.
• Integrate genomic, transcriptomic, and proteomic information.
• Develop reproducible bioinformatics workflows for research applications.

📅 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

• Understand modern multi-omics research workflows.
• Analyse biological datasets using Python and R.
• Process genomic, transcriptomic, and proteomic data.
• Perform exploratory and statistical data analysis.
• Generate publication-ready biological visualizations.
• Conduct differential expression and functional analysis.
• Interpret molecular signatures from omics datasets.
• Integrate multiple omics layers for biological discovery.
• Build reproducible computational workflows.
• Apply computational approaches in biomedical and life-science research.

Undergraduate and postgraduate students in Biotechnology, Bioinformatics, Computational Biology, Life Sciences, Biomedical Sciences, Genetics, Microbiology, and related fields. PhD scholars and research scientists working in genomics, transcriptomics, proteomics, molecular biology, systems biology, and biomedical research. Academicians and faculty members interested in applying computational approaches for biological data analysis and research. Industry professionals from biotechnology, pharmaceutical, healthcare, bioinformatics, precision medicine, and data-driven life-science domains.