Aim
Molecular Cancer Biology: CRISPR and Bioinformatics covers cancer biology foundations and the use of CRISPR and bioinformatics for gene function studies, pathway analysis, and interpretation of cancer datasets. Learn core concepts, workflow thinking, and a capstone analysis project (research/education use).
Program Objectives
- Cancer Biology Core: hallmarks, mutations, signaling, tumor microenvironment basics.
- Genomics View: oncogenes, tumor suppressors, drivers vs passengers.
- CRISPR Workflows: knockout/knockdown concepts, screening basics (overview).
- Bioinformatics: sequence/QC concepts, differential expression basics, pathway enrichment.
- Databases: public cancer datasets and gene/pathway resources (overview).
- Interpretation: evidence, limitations, reproducibility, reporting.
- Capstone: analyze a cancer dataset and propose CRISPR targets (academic/research framing).
Program Structure
Module 1: Molecular Cancer Biology Foundations
- Hallmarks of cancer and tumor evolution.
- DNA damage, repair pathways, and genomic instability.
- Oncogenes vs tumor suppressors; common alteration types.
- Signaling basics: growth, apoptosis, cell cycle control.
Module 2: Cancer Genomics and Data Types
- Data types: DNA variants, copy number, RNA expression, methylation (overview).
- Driver vs passenger mutations and evidence levels (intro).
- Biomarkers: prognostic vs predictive (concepts).
- Study design and confounders (sample type, batch effects).
Module 3: CRISPR for Cancer Research (Workflow)
- CRISPR basics: Cas enzymes, gRNA, PAM, repair outcomes (high-level).
- KO vs CRISPRi/CRISPRa concepts; when to use what.
- Off-target risk concepts and control strategies.
- Intro to pooled screening: library design concepts, readouts, hits.
Module 4: RNA-seq and Expression Analysis (Intro)
- RNA-seq workflow overview: FASTQ → counts → results.
- QC concepts: read quality, alignment/quantification checks.
- Differential expression concepts and volcano plots.
- Heatmaps and clustering for sample patterns (intro).
Module 5: Variant Interpretation (Intro)
- Variant types: SNVs, indels, CNVs; basic terminology.
- Annotation concepts: gene effect, protein change, pathogenicity scores (overview).
- Filtering logic: frequency, impact, evidence.
- Linking variants to pathways (concept).
Module 6: Pathway and Network Analysis
- Pathway enrichment basics: GO/KEGG/Reactome concepts.
- Gene set analysis and interpretation cautions.
- Network view: interaction graphs and hub genes (intro).
- Prioritization: combining expression + variants + pathways.
Module 7: Public Datasets and Reproducible Research
- Using public resources (TCGA/GTEx concepts) and sample metadata.
- Reproducible notebooks and reporting structure.
- Limits: batch effects, sample heterogeneity, missing clinical variables.
- Ethics: privacy basics and responsible claims.
Module 8: From Bioinformatics to CRISPR Target Plan
- Target selection criteria: evidence, druggability concepts, pathway role.
- Design a validation plan: controls, assays, and readouts (overview).
- Risk planning: off-target, compensation, context dependence.
- Writing a short research proposal.
Final Project
- Choose a cancer type or dataset (provided).
- Deliverables: cleaned analysis + DE summary + pathway results + target shortlist.
- Include: CRISPR strategy (KO/CRISPRi/CRISPRa) + off-target notes + validation plan.
Participant Eligibility
- UG/PG students in Biotechnology, Genetics, Bioinformatics, Molecular Biology
- PhD scholars and researchers in cancer biology or functional genomics
- Professionals in genomics, translational research, or biotech R&D
- Basic biology knowledge required; basic R/Python helpful
Program Outcomes
- Understand cancer biology with a genomics and pathway view.
- Explain CRISPR workflows used in cancer research.
- Interpret expression and pathway analysis outputs.
- Build a target shortlist and CRISPR validation plan for research.
Program Deliverables
- e-LMS Access: lessons, datasets, templates.
- Toolkit: analysis checklist, pathway worksheet, CRISPR target template.
- Capstone Support: feedback on target shortlist and report.
- Assessment: certification after capstone submission.
- e-Certification and e-Marksheet: digital credentials on completion.
Future Career Prospects
- Functional Genomics / CRISPR Research Trainee
- Bioinformatics Analyst (Cancer Genomics) - Entry-level
- Research Assistant (Molecular Oncology)
- Biotech R&D Associate (Genomics)
Job Opportunities
- Cancer Research Labs: genomics analysis and functional validation support.
- Biotech/Pharma R&D: target discovery and biomarker analytics teams.
- CROs/Genomics Companies: sequencing analysis and reporting roles.
- Universities/Hospitals: research data analysis (non-clinical) and study support.
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