B.Tech in Bioinformatics semester-wise syllabus 

Semester 1: Foundation Courses

1. Mathematics-I

   • Calculus, matrices, differential equations.

2. Physics / Chemistry

   • Basics relevant to biomolecules (atomic structure, thermodynamics).

3. Introduction to Biology

   • Cell biology, biomolecules (DNA, RNA, proteins), genetics.

4. Programming Fundamentals (C/Python)

   • Loops, functions, file handling, basics of algorithms.

5. English & Communication Skills

6. Lab:

   • Programming lab (C/Python), biology lab (microscopy, DNA isolation).

Semester 2: Core Basics

1. Mathematics-II

   • Probability, statistics, linear algebra.

2. Biochemistry

   • Enzymes, metabolic pathways (glycolysis, TCA cycle).

3. Data Structures & Algorithms

   • Stacks, queues, trees, sorting algorithms.

4. Digital Logic & Microprocessors

5. Principles of Biotechnology

   • PCR, cloning, recombinant DNA technology.

6. Lab:

   • Data structures lab, biochemistry lab.

Semester 3: Introduction to Bioinformatics

1. Molecular Biology

   • Central dogma, gene regulation, PCR techniques.

2. Database Management Systems (DBMS)

   • SQL, bioinformatics databases (NCBI, PDB, UniProt).

3. Object-Oriented Programming (Java/C++)

4. Biostatistics

   • Hypothesis testing, regression, p-values.

5. Lab:

   • Molecular biology techniques, DBMS/SQL lab.

Semester 4: Computational Biology

1. Genomics & Proteomics

   • Genome sequencing methods, protein structure prediction.

2. Bioinformatics Algorithms

   • Sequence alignment (Needleman-Wunsch, BLAST, FASTA).

3. Operating Systems & Linux

   • Bash scripting for bioinformatics pipelines.

4. Structural Bioinformatics

   • Protein folding, PDB files, RasMol/PyMol.

5. Lab:

   • NGS data analysis, Linux commands lab.

Semester 5: Advanced Bioinformatics

1. Systems Biology

   • Metabolic networks, SBML, computational modeling.

2. Machine Learning in Bioinformatics

   • SVM, neural networks for gene expression analysis.

3. Pharmacogenomics & Drug Design

   • Molecular docking, QSAR, drug-target interactions.

4. Elective-I (e.g., Cheminformatics, AI in Biology)

5. Lab:

   • Drug discovery tools (AutoDock, GROMACS).

Semester 6: Applications & Tools

1. Next-Generation Sequencing (NGS) Analysis

   • RNA-seq, ChIP-seq, variant calling.

2. Big Data in Biology

   • Hadoop/Spark for genomic datasets.

3. Immunoinformatics

   • Epitope prediction, vaccine design.

4. Elective-II (e.g., Cancer Bioinformatics, Metagenomics)

5. Lab:

   • NGS pipeline (Bowtie, TopHat, Cufflinks).

Semester 7: Research & Specialization

1. Clinical Bioinformatics

   • Biomarkers, personalized medicine.

2. Cloud Computing for Bioinformatics

   • AWS/GCP for scalable analysis.

3. Ethics & IPR in Bioinformatics

4. Major Project-I (Literature review + Proposal)

Semester 8: Capstone & Industry Readiness

1. Major Project-II (Implementation & Thesis)

   • Develop a tool/model (e.g., ML-based disease prediction).

2. Seminar & Viva

3. Industry Case Studies

Key Labs & Tools Covered:

• Programming: Python/R, Bioconductor.

• Databases: MySQL, MongoDB for biological data.

• Structural Analysis: PyMol, RasMol, GROMACS.

• NGS Tools: BWA, SAMtools, GATK.

• ML Libraries: Scikit-learn, TensorFlow.