1. Introduction to Bioinformatics

• Theory:
  • History and evolution of bioinformatics
  • Key concepts: databases, sequence alignment, and gene expression
  • Overview of bioinformatics tools and software
• Practical:
  • Hands-on with bioinformatics databases (NCBI, EMBL, SRA, GEO, Pubchem, ENA)
  • Basic sequence alignment exercises using BLAST and Clustal Omega

2. Computational Genomics

• Theory:
  • Genomic data types: DNA, RNA, and protein sequences
  • Genome assembly and annotation
  • Comparative genomics and evolutionary studies
  • Concept of Primer Designing
• Practical:
  • Genome assembly using tools like SPAdes or SOAPdenovo
  • Functional annotation using tools like InterProScan or Pfam
  • Exercise of Primer Designing with PRIMERBLAST or Primer3

3. Statistical Methods for Bioinformatics

• Theory:
  • Basic statistics and probability
  • Statistical methods for analyzing high-dimensional data
  • Multiple testing correction and false discovery rates
• Practical:
  • Statistical analysis using R or Python
  • Application of statistical methods to genomic datasets

4. Bioinformatics Programming

• Theory:
  • Programming concepts and best practices
  • Scripting languages in bioinformatics (Python, Perl)
  • Introduction to data structures and algorithms
• Practical:
  • Writing scripts for data processing and analysis
  • Developing custom bioinformatics tools

5. Advanced Sequence Analysis

• Theory:
  • Advanced sequence alignment techniques
  • Phylogenetic analysis
  • Structural bioinformatics
• Practical:
  • Building and interpreting phylogenetic trees
  • Structural modeling with tools like PyMOL or Chimera

6. Linux for Bioinformatics

• Theory:
  • Introduction to Linux operating systems
  • Command-line tools and scripting
  • Linux file systems and permissions
• Practical:
  • Navigating and managing files in Linux
  • Writing shell scripts for bioinformatics tasks

7. Introduction to SQL for Bioinformatics

• Theory:
  • Basics of SQL and relational databases
  • Querying and managing bioinformatics databases
• Practical:
  • Writing SQL queries to retrieve and manipulate data
  • Integrating SQL databases with bioinformatics tools

8. Project Work I

• Practical:
  • Begin a research project involving bioinformatics analysis

Regular progress reports and mentorship sessions

1. Genomic Data Analysis and Interpretation

• Theory:
  • High-throughput sequencing technologies (NGS, RNA-Seq)
  • Data preprocessing and quality control
  • Interpretation of genomic variants
• Practical:
  • Analysis of NGS data using tools like SAMTools or Bowtie2
  • Variant calling and annotation

2. Systems Biology and Network Analysis

• Theory:
  • Introduction to systems biology
  • Protein Modeling
  • Gene regulatory networks and protein interaction networks
  • Pathway analysis
• Practical:
  • Network analysis using Cytoscape or STRING
  • Protein Modeling with Modeller
  • Pathway analysis using KEGG

3. Machine Learning and AI in Bioinformatics

• Theory:
  • Basics of machine learning and artificial intelligence
  • Supervised and unsupervised learning techniques
  • Deep learning and neural networks
• Practical:
  • Implementing machine learning algorithms with scikit-learn or TensorFlow
  • Application of AI techniques to genomic data

4. Advanced Computational Genomics Techniques

• Theory:
  • Emerging technologies and techniques in computational genomics
  • Integrative genomics approaches
• Practical:
  • Hands-on with cutting-edge tools and techniques
  • Analysis of complex multi-omic datasets

5. R Language for Bioinformatics

• Theory:
  • Introduction to R language and its application in bioinformatics
  • Data manipulation and visualization in R
  • Bioinformatics packages and libraries in R
• Practical:
  • Data analysis and visualization using R
  • Applying R packages for genomic data analysis

6. Cloud Computing with AWS for Bioinformatics

• Theory:
  • Basics of cloud computing and AWS
  • AWS services relevant to bioinformatics (e.g., EC2, S3, Lambda)
  • Data storage and management in the cloud
• Practical:
  • Setting up and using AWS resources for bioinformatics workflows
  • Managing large-scale genomic data using AWS

7. Personalized Genomics and Medicine

• Theory:
  • Concepts of personalized genomics
  • Applications in precision medicine
  • Ethical and social implications
• Practical:
  • Case studies on personalized medicine
  • Data analysis related to personalized genomics

8. Scientific Communication and Research Skills

• Theory:
  • Writing research papers and grants
  • Presentation skills and conference etiquette
• Practical:
  • Preparing and presenting research findings
  • Peer review and feedback sessions

9. Project Work II

• Practical:
  • Finalization of research project initiated in Semester 1
  • Data analysis, interpretation, and presentation of results
  • Preparation of a final report and presentation

10. Industry/Research Internship (Optional)

• Practical:
  • Placement in a relevant industry or research institution
  • Application of learned skills in a real-world setting
  • Final report and presentation of internship experience

Domain Project (6 Months) + Paper Publication + Placement