About Workshop
This 3-day online hands-on workshop introduces participants to the fundamentals of modern bioinformatics and AI-assisted biological data analysis workflows. Participants will learn how biological data such as DNA, RNA, and protein information is generated, accessed, analyzed, and interpreted using widely used bioinformatics databases, computational tools, and artificial intelligence systems.
The workshop integrates essential theoretical concepts with practical demonstrations using globally recognized platforms such as NCBI, Ensembl, UniProt, g:Profiler, STRING database, and AI tools like ChatGPT and Gemini. By the end of the workshop, participants will understand the complete bioinformatics workflow—from biological data exploration and sequence analysis to functional interpretation and AI-assisted discovery in genomics and disease research.
Aim
To provide participants with practical knowledge of modern bioinformatics workflows, genomic data analysis, and AI-assisted biological interpretation for applications in biotechnology, healthcare, and life science research.
What Participants Will Learn
- Understand the fundamentals of biological data and bioinformatics.
- Learn how genomic, transcriptomic, and proteomic data are generated and analyzed.
- Explore major biological databases such as NCBI, Ensembl, and UniProt.
- Perform basic sequence analysis and interpret biological data.
- Understand gene expression concepts and functional genomics.
- Learn pathway and network-based biological interpretation.
- Utilize AI tools for biological data analysis and scientific interpretation.
- Develop a complete data-to-discovery workflow using real biological datasets.
Structure
📅 Day 1: Integrated Genomic Data Mining and Variant Interpretation
Core Objective: Build an integrated genomic-data workflow for investigating genes, transcripts, proteins, variants, and disease associations.- Advanced navigation of genomic, transcriptomic, proteomic, and disease databases
- Gene, transcript, isoform, exon, regulatory-region, and protein annotation
- Genome-build awareness, coordinates, identifiers, and cross-database mapping
- Sequence retrieval, similarity searching, and comparative sequence interpretation
- Variant annotation, consequence prediction, and functional-impact assessment
- AI-assisted literature mining with evidence verification and source validation
🧪 Hands-on Session
- Retrieve and integrate gene, transcript, protein, variant, and disease information for a selected biological target.
- Perform sequence-similarity and variant-impact analysis and prepare an evidence-based genomic annotation table.
🧰 Tools Covered
NCBI, Ensembl, UniProt, NCBI BLAST, Ensembl Variant Effect Predictor📅 Day 2: Transcriptomic Analysis and AI-Assisted Biomarker Discovery
Core Objective: Transform gene-expression data into statistically and biologically meaningful disease-associated signatures.- Experimental design, metadata assessment, and transcriptomic dataset selection
- Gene-expression normalization, quality assessment, and sample comparison
- Differential-expression analysis and statistical significance interpretation
- Fold change, adjusted p-value, multiple testing, and expression-pattern assessment
- Identification of candidate biomarkers and disease-associated gene signatures
- AI-assisted interpretation of expression results, biological relevance, and research hypotheses
🧪 Hands-on Session
- Analyze a disease-versus-control gene-expression dataset and identify significantly differentially expressed genes.
- Prioritize candidate biomarkers using statistical evidence, biological databases, and AI-assisted interpretation.
🧰 Tools Covered
NCBI GEO, GEO2R, Ensembl, UniProt, Excel or Google Sheets📅 Day 3: Functional Enrichment, Network Biology and Intelligent Discovery
Core Objective: Convert gene lists into pathways, interaction networks, disease mechanisms, and testable biological hypotheses.- Gene Ontology, pathway databases, and functional-annotation frameworks
- Over-representation analysis and enrichment-result interpretation
- Biological pathway mapping and disease-mechanism reconstruction
- Protein–protein interaction network construction and confidence assessment
- Hub-gene, functional-module, and candidate-target prioritization
- AI-assisted integration of genomic, expression, pathway, and network findings
🧪 Hands-on Session
- Perform functional enrichment and pathway analysis on a prioritized disease-associated gene list.
- Construct and interpret a protein-interaction network and prepare an AI-supported biological discovery summary.
🧰 Tools Covered
g: Profiler, STRING Database, KEGG, Cytoscape AwarenessImportant Dates
Registration Ends
4:00 PM
Workshop Dates
2026-07-27
5:00 PM
5:00 PM
What You Will Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Outcomes
- Understand the complete workflow of modern bioinformatics from biological data acquisition to interpretation.
- Confidently navigate and extract information from major biological databases such as NCBI, Ensembl, and UniProt.
- Analyze DNA, RNA, and protein sequence data using standard bioinformatics tools.
- Interpret gene expression patterns and understand their relevance in disease and biological systems.
- Perform basic functional annotation and pathway-based analysis of gene sets.
- Understand how genomic data contributes to disease research and biomedical discoveries.
- Apply AI tools to assist in biological data interpretation, literature understanding, and research problem-solving.
- Develop a systems-level understanding of how bioinformatics supports modern biotechnology, healthcare, and drug discovery.
- Gain exposure to real-world research workflows used in academic and industry settings.
Who Should Attend
- Undergraduate and postgraduate students in Biotechnology, Bioinformatics, Genetics, Molecular Biology, Life Sciences, Pharmacy, and related fields.
- PhD scholars, researchers, academicians, and biotechnology professionals.
- Bioinformatics enthusiasts interested in genomics, AI, and computational biology.
Prerequisite: Basic knowledge of molecular biology and genetics is recommended. No prior experience in bioinformatics or programming is required.
Deliverables
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience