Next-Generation Microbiome Therapeutics: AI, NGS & Python-Based Analysis

To provide hands-on training in AI, Python, bioinformatics, and NGS-based microbiome analysis, focusing on 16S rRNA sequencing, shotgun metagenomics, and microbial biomarker discovery using Google Colab for microbiome therapeutic research.

• NGS & Microbiome Profiling: Explore 16S rRNA sequencing and shotgun metagenomics to understand gut microbial diversity and composition.
• Python & Bioinformatics: Analyze microbiome datasets using Google Colab, QIIME 2, Biopython, and Python-based visualization tools.
• AI & Machine Learning: Apply machine learning techniques to identify microbial biomarkers and explore disease-associated microbiome patterns.
• Probiotic Discovery & Metabolic Analysis: Investigate beneficial microbes, metabolic pathways, and functional profiles relevant to probiotic and postbiotic research.
• Microbiome Therapeutics & FMT: Evaluate microbiome-based interventions, including probiotics, prebiotics, postbiotics, and FMT, through computational and data-driven approaches.

📅 Day 0: Workshop Orientation: Introduction to AI, Python & NGS-Based Microbiome Therapeutics

1. Introduction to gut microbiome research and next-generation microbiome therapeutics.

2. Overview of 16S rRNA sequencing, shotgun metagenomics, and their applications in microbiome analysis.

3. Role of artificial intelligence, machine learning, and Python in microbial profiling and therapeutic discovery.

4. Introduction to NCBI SRA, QIIME 2, Google Colab, and publicly available microbiome datasets.

5. Workshop workflow overview, Python environment setup, and preparation for three-day hands-on sessions.

📅 Day 1:  Next-Generation Sequencing & Python-Based Gut Microbiome Profiling

🧪 Hands-On Activity:

Analyze curated shotgun metagenomic functional profiles using Python, train a Random Forest model, visualize important microbial features, and develop a Probiotic–Metabolic Function Prioritization Matrix.

💻 Tools: NCBI Genome | NCBI BLAST | BAGEL4 | Google Colab | FastQC | QIIME 2 | DADA2 | SILVA | Biopython | Pandas | Scikit-bio | Seaborn

📅 Day 2: AI-Driven Shotgun Metagenomics, Probiotic Discovery & Metabolic Profiling

🧪 Hands-On Activity:

Analyze curated shotgun metagenomic functional profiles using Python, train a Random Forest model, visualize important microbial features, and develop a Probiotic–Metabolic Function Prioritization Matrix.

💻 Tools: PubMed | KEGG | NCBI Taxonomy | gutMGene | Google Colab | MetaPhlAn | HUMAnN | KEGG | CAZy | AMRFinderPlus | Scikit-learn | SHAP

📅 Day 3: AI-Based Microbiome Therapeutics, Postbiotic Analysis & FMT Response Prediction

• Therapeutic applications of probiotics, prebiotics, postbiotics, and fecal microbiota transplantation (FMT).
• Analysis of microbial metabolites, including acetate, propionate, butyrate, and other bioactive compounds.
• Integration of microbiome composition, functional pathways, and metabolite-associated evidence.
• Comparative analysis of pre- and post-intervention microbiome profiles, including FMT datasets.
• AI-based prediction of microbiome intervention responses using curated clinical and microbial features.
• Evaluation of model performance, data leakage, clinical limitations, and therapeutic safety.
• Development of an AI-assisted Microbiome Therapeutic Candidate Ranking Framework.

🧪 Hands-On Activity:

Use Python and machine learning to compare microbiome intervention datasets, explore microbial response patterns, and rank potential therapeutic strategies based on functional, safety, and clinical evidence.

💻 Tools: PubMed | KEGG | HMDB | gutMGene | NCBI SRA | Google Colab | MetaPhlAn | HUMAnN | CAZy | Scikit-learn | SHAP

• Students and Research Scholars: Undergraduate, postgraduate, PhD, and postdoctoral researchers in biotechnology, microbiology, bioinformatics, and life sciences.
• Microbiome and Gut Health Researchers: Professionals studying gut microbiota, host–microbe interactions, microbial metabolites, and microbiome-based interventions.
• Biotechnology and Pharmaceutical Professionals: Researchers involved in probiotic development, microbial therapeutics, functional genomics, and drug discovery.
• Food Science and Nutrition Researchers: Professionals working on functional foods, probiotics, prebiotics, fermentation, and microbiome-associated nutrition.
• Bioinformatics and Computational Biology Professionals: Individuals interested in microbial genomics, microbiome data analysis, Python, and AI-assisted biological research.
• Healthcare and Clinical Researchers: Gastroenterology, precision medicine, and translational research professionals exploring microbiome-based therapeutic evidence.
• Academicians and Educators: Faculty members seeking to integrate computational microbiome analysis and hands-on bioinformatics into teaching and research.

Prerequisites: Basic knowledge of biology or microbiology is recommended. Prior Python or bioinformatics experience is helpful but not mandatory, as guided datasets and computational workflows will be provided.

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