Semester-wise syllabus for an M.Tech in Bioinformatics

 

Semester 1: Core Foundations

1. Introduction to Bioinformatics 

   - History, scope, biological databases (NCBI, PDB, UniProt), sequence alignment tools (BLAST, ClustalW), and ontology (Gene Ontology, KEGG). 

   2. Molecular Biology and Biochemistry 

   - DNA/RNA structure, gene expression, protein synthesis, metabolic pathways, and genomics basics. 

3. Programming for Bioinformatics 

   - Python/R programming, Biopython/Bioconductor, data structures, and scripting for biological data analysis. 

4. Biostatistics and Data Analysis 

   - Probability, hypothesis testing, regression, ANOVA, and tools like R/SPSS for biological datasets. 

5. Lab 1: Computational Tools 

   - Hands-on with sequence alignment, database searching, and basic scripting (e.g., automating BLAST workflows). 

 

Semester 2: Advanced Methods & Applications

1. Algorithms in Bioinformatics 

   - Dynamic programming (Smith-Waterman, Needleman-Wunsch), phylogenetic tree construction, and hidden Markov models (HMMs). 

2. Structural Bioinformatics

   - Protein structure prediction (homology modeling, threading), molecular docking, and tools like PyMOL, GROMACS. 

3. Genomics and Proteomics 

   - NGS data analysis (RNA-seq, ChIP-seq), genome assembly, proteomics workflows (MALDI-TOF, mass spectrometry). 

 

4. Systems Biology 

   - Network modeling (gene regulatory networks, metabolic pathways), and tools like Cytoscape, CellDesigner. 

5. Lab 2: Omics Data Analysis 

   - RNA-seq pipeline (FastQC, HISAT2, DESeq2), protein structure visualization, and pathway analysis. 

6. Elective 1 

   - Example: Drug Discovery & Cheminformatics or Next-Generation Sequencing Technologies. 

 

Semester 3: Specializations & Dissertation Prep

1. Machine Learning in Bioinformatics 

   - Classification, clustering, neural networks, and applications in gene prediction, biomarker discovery. 

2. Clinical Bioinformatics 

   - Personalized medicine, cancer genomics, variant analysis (SNPs, CNVs), and tools like ANNOVAR, GATK. 

3. Elective 2 

   - Example: Metagenomics & Microbiome Analysis or Pharmacogenomics. 

4. Elective 3 

   - Example: AI for Drug Design or Single-Cell Genomics.  

5. Dissertation Phase 1 

   - Literature review, research proposal, and initial data collection. 

6. Lab 3: Advanced Tools 

   - Machine learning frameworks (TensorFlow, scikit-learn) and cloud-based analysis (AWS, Galaxy Platform). 

 

Semester 4: Dissertation & Industry Training

1. Dissertation Phase 2 

   - Full-time research, implementation, thesis writing, and defense. 

   - Example projects: 

     - Developing a pipeline for cancer mutation detection. 

     - Designing an AI model for protein-ligand interaction prediction. 

2. Industry Internship (Optional) 

   - Practical training in biotech/pharma companies or research labs. 

 

Elective Options 

- Chemoinformatics: Molecular docking, QSAR, virtual screening. 

- Evolutionary Bioinformatics: Phylogenetics, molecular evolution. 

- Big Data in Biology: Hadoop/Spark for genomic datasets. 

- Immunoinformatics: Vaccine design, epitope prediction. 

- Ethics in Bioinformatics: Data privacy, genomic ethics. 

 

Key Tools & Technologies 

- Programming: Python, R, Perl, SQL. 

- Tools: BLAST, PyMOL, AutoDock, Galaxy, GROMACS, Bioconductor. 

- Cloud Platforms: AWS, Google Cloud for large-scale data analysis. 

- ML Libraries: TensorFlow, Keras, scikit-learn.