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Click on the Project for the details and description, submit the project documents for each project using the Project Submission Form

Foundation Level (Beginner-Friendly)

1. Genomic Variant Analysis Pipeline
Create a comprehensive pipeline for analyzing human genomic variants to identify disease-associated mutations and their clinical significance.

Key Components:

  • Process raw sequencing data (FASTQ) using quality control tools

  • Align sequences to reference genome using BWA or similar aligner

  • Call variants using GATK best practices pipeline

  • Annotate variants using databases like dbSNP, ClinVar, and gnomAD

  • Generate comprehensive reports with clinical interpretations

Skills Gained: Next-generation sequencing analysis, variant calling, clinical genomics, bioinformatics pipelines

2. Protein Structure Prediction and Analysis
Develop a system for predicting protein structures and analyzing their functional implications using machine learning approaches.

Key Features:

  • Implement homology modeling using tools like MODELLER

  • Predict secondary structure using deep learning methods

  • Analyze protein-protein interactions and binding sites

  • Visualize structures using PyMOL or similar tools

  • Assess structural quality and reliability metrics

Skills Gained: Structural bioinformatics, protein modeling, machine learning, molecular visualization

Medium Level (Intermediate)

3. Multi-Omics Data Integration Platform
Build a comprehensive platform that integrates genomics, transcriptomics, and proteomics data to understand disease mechanisms.

Key Components:

  • Develop data preprocessing pipelines for different omics types

  • Implement dimensionality reduction and clustering algorithms

  • Create network analysis tools for pathway reconstruction

  • Build machine learning models for biomarker discovery

  • Design interactive visualization dashboard for multi-omics exploration

Skills Gained: Multi-omics integration, systems biology, network analysis, biomarker discovery

4. Pharmacogenomics Decision Support System
Create an AI-powered system that predicts drug responses based on individual genetic profiles to enable personalized medicine.

Key Features:

  • Curate pharmacogenomic databases (PharmGKB, DrugBank)

  • Develop machine learning models for drug response prediction

  • Implement clinical decision support algorithms

  • Create patient-specific treatment recommendations

  • Build physician interface with actionable insights

Skills Gained: Pharmacogenomics, personalized medicine, clinical decision support, drug discovery

Expert Level (Advanced)

5. AI-Powered Drug Discovery Platform
Develop a comprehensive drug discovery platform using artificial intelligence to identify novel therapeutic compounds.

Key Components:

  • Implement molecular representation learning using graph neural networks

  • Develop generative models for novel compound design

  • Create virtual screening pipelines using molecular docking

  • Build ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) prediction models

  • Design optimization algorithms for lead compound improvement

Skills Gained: Drug discovery, molecular modeling, generative AI, cheminformatics

6. Single-Cell Multi-Omics Analysis Platform
Build an advanced platform for analyzing single-cell genomics and epigenomics data to understand cellular heterogeneity and development.

Key Features:

  • Implement state-of-the-art single-cell RNA-seq analysis workflows

  • Develop trajectory inference algorithms for developmental biology

  • Create multi-modal integration methods (scRNA-seq + scATAC-seq)

  • Build cell type annotation and marker gene discovery tools

  • Design interactive visualization tools for single-cell data exploration

Skills Gained: Single-cell genomics, developmental biology, advanced statistics, cellular heterogeneity analysis

7. Evolutionary Genomics and Phylogenetic Analysis
Develop a comprehensive platform for studying evolutionary relationships and genomic evolution across species.

Key Components:

  • Implement comparative genomics pipelines for multiple species

  • Develop phylogenetic reconstruction algorithms

  • Create tools for detecting positive selection and molecular evolution

  • Build ancestral sequence reconstruction methods

  • Design visualization tools for evolutionary relationships

Skills Gained: Evolutionary biology, phylogenetics, comparative genomics, molecular evolution

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