Gut Microbiome Multi-Omics: Shotgun Metagenomics, Metabolomics & Precision Probiotics

To equip participants with practical knowledge and computational skills to analyze gut microbiome metagenomic and metabolomic data, integrate multi-omics datasets, and translate microbial signatures into data-driven precision probiotic strategies.

• Understand the fundamentals of gut microbiome research and shotgun metagenomics.
• Perform taxonomic and functional profiling of microbiome datasets.
• Explore key gut microbial metabolites and metabolomics workflows.
• Apply statistical and machine learning approaches to microbiome data.
• Integrate metagenomic and metabolomic datasets.
• Identify microbial, metabolic, and functional biomarkers.
• Understand microbe–metabolite relationships.
• Explore data-driven approaches for precision probiotic candidate prioritization.
• Gain hands-on experience with open-source and freely accessible bioinformatics tools.

📅 Day 1: Shotgun Metagenomics for Gut Microbiome Profiling

• Focus: Understanding gut microbiome profiling using shotgun metagenomics, taxonomic analysis, microbial diversity, and functional pathway interpretation.
• Introduction to gut microbiome and host–microbe interactions in health, disease, nutrition, and therapeutic research.
• Understanding the difference between 16S rRNA sequencing and shotgun metagenomics for microbiome analysis.
• Working with FASTQ data, quality control, preprocessing, and sequence-read cleaning for microbiome datasets.
• Taxonomic profiling of gut microbiome samples at species and strain-level resolution.
• Microbial abundance, diversity analysis, functional gene profiling, and pathway-level interpretation.
• Interpreting microbiome signatures linked with gut health, dysbiosis, disease states, and probiotic relevance.

🛠️ Hands-on:

• Taxonomic and functional profiling of a gut microbiome dataset.

🧰 Tools Covered: Google Colab, Python, FastQC, fastp, Kraken2, Bracken, MetaPhlAn, HUMAnN, pandas, Matplotlib

📅 Day 2: Gut Metabolomics & Microbe–Metabolite Analysis

• Focus: Exploring gut metabolomics, microbial metabolites, data normalization, biomarker discovery, and microbe–metabolite association concepts.
• Introduction to targeted and untargeted metabolomics for gut microbiome and host-response research.
• Overview of LC-MS, GC-MS, and NMR-based metabolomics platforms used in gut metabolite profiling.
• Understanding metabolomics data preprocessing, normalization, scaling, and quality assessment.
• Studying key gut-related metabolites such as SCFAs, bile acids, tryptophan metabolites, and TMA/TMAO.
• Applying PCA, sample clustering, and differential metabolite analysis for metabolic signature identification.
• Understanding biomarker discovery and microbe–metabolite association concepts in gut health and disease research.

🛠️ Hands-on:

• Identification and visualization of key gut metabolomic signatures.

🧰 Tools Covered: Google Colab, Python, pandas, NumPy, SciPy, scikit-learn, Matplotlib, MetaboAnalyst, GNPS

📅 Day 3: Multi-Omics Integration, Machine Learning & Precision Probiotics

• Focus: Integrating metagenomics and metabolomics data to discover biomarkers, build prediction models, and prioritize precision probiotic candidates.
• Integration of metagenomics and metabolomics data for gut microbiome multi-omics analysis.
• Microbe–metabolite correlation analysis for identifying functional relationships between microbes and metabolites.
• Multi-omics biomarker discovery, feature selection, and data integration strategies.
• Random Forest-based prediction, ROC-AUC analysis, and model evaluation for gut microbiome datasets.
• Understanding feature importance and introduction to SHAP for interpretable machine learning.
• Identification of microbial and metabolic functional deficits linked with gut health and disease-associated signatures.
• Precision probiotic candidate prioritization, next-generation probiotics, synbiotics, and microbial consortia concepts.

🛠️ Hands-on:

• Build a multi-omics model to identify key microbial/metabolite features and prioritize probiotic candidates.

🧰 Tools Covered: Google Colab, Python, pandas, scikit-learn, SHAP, SciPy, Matplotlib, NetworkX, MOFA2, mixOmics

• Researchers and Research Scholars
• PhD and Postdoctoral Researchers
• Academicians and Faculty Members
• Microbiologists and Microbiome Researchers
• Bioinformaticians and Computational Biologists
• Biotechnology and Life Science Professionals
• Metabolomics and Omics Researchers
• Nutrition and Food Science Researchers
• Probiotic and Functional Food Researchers
• Pharmaceutical and Biotechnology R&D Professionals
• Scientists working in host–microbe interactions and microbial therapeutics
• Professionals interested in multi-omics, machine learning, and precision microbiome research

By the end of the workshop, participants will be able to:

• Interpret shotgun metagenomics datasets and microbial abundance profiles.
• Analyze functional genes and metabolic pathways in the gut microbiome.
• Process and visualize gut metabolomics datasets.
• Identify important metabolites and microbial biomarkers.
• Perform microbe–metabolite correlation analysis.
• Apply PCA, Random Forest, ROC-AUC, and feature importance analysis.
• Integrate microbiome and metabolomics datasets for multi-omics interpretation.
• Identify potential functional and metabolic dysbiosis signatures.
• Prioritize potential probiotic strains or microbial consortia based on multi-omics evidence.
• Develop reproducible workflows using Google Colab and open-source tools.