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B-SC-HONS in Computational Biology Bioinformatics at JSS Academy of Higher Education & Research
Mysuru, Karnataka
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About the Specialization
What is Computational Biology & Bioinformatics at JSS Academy of Higher Education & Research Mysuru?
This B.Sc (Hons.) Computational Biology & Bioinformatics program at JSS Academy of Higher Education and Research focuses on equipping students with interdisciplinary skills in biology, computer science, and data analysis. Given India''''s burgeoning biotechnology and pharmaceutical sectors, this program addresses the critical need for professionals who can leverage computational tools to interpret vast biological data, driving advancements in research, diagnostics, and drug discovery. Its curriculum is designed to foster both theoretical understanding and practical application, preparing students for the evolving demands of the Indian scientific landscape.
Who Should Apply?
This program is ideal for fresh graduates with a 10+2 science background (PCB/PCM/Computer Science/Biotechnology) eager to enter the high-growth field of bioinformatics. It also caters to individuals passionate about solving biological challenges using computational approaches, those seeking a strong foundation for advanced studies, and aspiring researchers or data analysts in life sciences. The curriculum is structured to support students in developing a robust skill set from foundational biology to advanced machine learning for biological data.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including Bioinformatics Scientist, Data Analyst (Life Sciences), Cheminformatician, Clinical Data Manager, and Research Assistant in leading pharmaceutical, biotechnology, and academic institutions. Entry-level salaries typically range from INR 3-6 lakhs per annum, with experienced professionals earning INR 8-15+ lakhs. The program aligns with industry demands, offering growth trajectories in R&D, clinical research, and data science within Indian and global companies, potentially leading to professional certifications in areas like data science or specific bioinformatics tools.
Student Success Practices
Foundation Stage
Build Strong Computational & Biological Foundations- (Semester 1-2)
Focus diligently on core subjects like Cell Biology, Biochemistry, Mathematics, C++ Programming, and Biostatistics. Understand the underlying principles of biology and develop logical thinking through programming. Form study groups to discuss complex topics and clarify doubts.
Tools & Resources
Khan Academy for concepts, GeeksforGeeks/HackerRank for coding practice, Jupyter Notebook for interactive learning
Career Connection
A solid foundation in both domains is crucial for all advanced bioinformatics applications and ensures readiness for complex problem-solving in future roles like a Bioinformatics Analyst.
Cultivate Practical Lab Skills- (Semester 1-2)
Actively participate in all practical sessions for Cell Biology, Biochemistry, Molecular Biology, and Microbiology. Document experiments meticulously, understand the ''''why'''' behind each step, and practice data interpretation. Seek opportunities for extra lab time if available.
Tools & Resources
Lab manuals, Virtual lab simulations (if provided), YouTube channels for protocol demonstrations
Career Connection
Strong wet-lab skills complement computational expertise, making graduates versatile for R&D positions where data generation and analysis are intertwined.
Engage with Early Bioinformatics Tools- (Semester 1-2)
Start exploring basic bioinformatics databases and tools like NCBI, UniProt, and BLAST even before formal teaching. Familiarize yourself with how biological data is stored and retrieved. Attend introductory workshops or webinars on bioinformatics basics.
Tools & Resources
NCBI website, UniProt database, BLAST tutorial, Coursera/edX introductory bioinformatics courses
Career Connection
Early exposure builds confidence and a foundational understanding of the digital landscape of biology, which is essential for any computational biology role.
Intermediate Stage
Develop Robust Programming & Data Skills- (Semester 3-5)
Master Python and R for biological data analysis. Work on mini-projects involving data cleaning, visualization, and basic statistical analysis using biological datasets. Contribute to open-source bioinformatics projects or participate in coding challenges specific to biology.
Tools & Resources
Biopython, Bioconductor (R), Kaggle datasets (biological), GitHub
Career Connection
Proficiency in Python and R is a core requirement for almost all bioinformatics, data science, and computational biology roles in industry and academia.
Seek Internships and Research Projects- (Semester 3-5)
Actively look for summer internships or short-term research projects at university labs, research institutes (like IISc, NCBS, CCMB, NII in India), or biotech companies. This provides real-world experience, helps in networking, and clarifies career interests.
Tools & Resources
University career services, Institute websites, LinkedIn
Career Connection
Internships are critical for gaining practical experience, building a professional network, and often lead to pre-placement offers or strong recommendation letters for higher studies.
Specialize in a Niche & Build a Portfolio- (Semester 3-5)
As you delve into subjects like Genomics, Proteomics, Structural Bioinformatics, or Cheminformatics, identify an area of deep interest. Work on a focused project or start a personal portfolio of computational analyses (e.g., protein modeling, genome annotation) to showcase your specialized skills.
Tools & Resources
GitHub for portfolio, Specific software like PyMOL, GROMACS, BLAST+, Pathway analysis tools
Career Connection
A specialized portfolio demonstrates expertise to potential employers or PhD advisors, setting you apart in a competitive job market for roles like Structural Bioinformatician or Genomic Analyst.
Advanced Stage
Excel in Capstone Project & Thesis Work- (Semester 6)
Dedicate significant effort to your final year project. Choose a challenging problem, formulate clear objectives, and apply all learned computational and biological techniques. Present your findings professionally and prepare a comprehensive report, treating it as your major portfolio piece.
Tools & Resources
Relevant bioinformatics software, Statistical packages, Scientific writing tools (e.g., LaTeX), Presentation software
Career Connection
The project is a direct demonstration of your ability to conduct independent research, solve complex problems, and articulate scientific findings, crucial for research and development positions.
Master Machine Learning & Big Data in Biology- (Semester 6)
Deepen your understanding and practical skills in Machine Learning for Bioinformatics and Big Data Analytics. Work on projects that involve large-scale biological datasets, applying advanced algorithms for prediction, classification, or pattern recognition.
Tools & Resources
scikit-learn, TensorFlow/Keras, PyTorch, Hadoop, Spark, Cloud platforms (AWS/Azure/GCP)
Career Connection
These skills are highly sought after for roles in AI-driven drug discovery, precision medicine, and large-scale genomic data analysis in Indian and international companies.
Network & Prepare for Placements/Higher Education- (Semester 6)
Attend industry conferences, workshops, and seminars. Connect with alumni and professionals on LinkedIn. Refine your resume/CV and practice interview skills, focusing on both technical knowledge and problem-solving. Research potential employers or universities for Master''''s/PhD programs.
Tools & Resources
LinkedIn, University career fairs, Mock interviews, Online resume builders
Career Connection
Effective networking and robust preparation significantly enhance placement prospects or admission into top-tier graduate programs in India and abroad.
Program Structure and Curriculum
Eligibility:
- Pass in 10+2 / PUC or Equivalent Examination with 40% aggregate marks in PCB / PCMB / PCM / Computer Science / Statistics / Electronics / Biotechnology / Biochemistry with English as one of the languages.
Duration: 3 years (6 semesters)
Credits: 140 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CBB 101 | Cell Biology & Genetics | Core | 4 | Cell structure and function, Cell division (Mitosis, Meiosis), Mendelian inheritance, Gene interactions, Chromosomal aberrations |
| CBB 102 | Biochemistry | Core | 4 | Biomolecules (Carbohydrates, Proteins, Lipids), Enzymes and enzyme kinetics, Metabolism (Glycolysis, Krebs cycle), Photosynthesis, Respiration |
| CBB 103 | Mathematics for Biological Sciences | Core | 4 | Algebra and functions, Calculus (Differentiation, Integration), Matrices and determinants, Differential equations, Probability and set theory |
| CBB 104 | General Microbiology | Core | 4 | Microbial classification and diversity, Bacterial growth and nutrition, Sterilization and disinfection, Industrial microbiology, Virology |
| CBB 105 | Cell Biology & Genetics Practical | Core | 2 | Microscopy techniques, Cell staining and counting, Observation of mitosis, Pedigree analysis, Blood grouping |
| CBB 106 | Biochemistry Practical | Core | 2 | Qualitative tests for biomolecules, Estimation of proteins and carbohydrates, Enzyme activity assays, Chromatography techniques, Spectrophotometry |
| CBB 107 | General Microbiology Practical | Core | 2 | Sterilization methods, Media preparation, Bacterial staining techniques, Isolation and enumeration of microbes, Antibiotic sensitivity testing |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CBB 201 | Molecular Biology | Core | 4 | DNA structure and replication, Transcription and RNA processing, Translation and protein synthesis, Gene regulation (Prokaryotic, Eukaryotic), Mutations and DNA repair |
| CBB 202 | Object-Oriented Programming using C++ | Core | 4 | Introduction to OOP concepts, Classes and objects, Inheritance and polymorphism, Operator overloading, File handling and templates |
| CBB 203 | Fundamentals of Bioinformatics | Core | 4 | Biological databases (NCBI, UniProt, PDB), Sequence alignment (BLAST, FASTA), Phylogenetic analysis, Gene prediction, Drug target identification |
| CBB 204 | Biostatistics | Core | 4 | Data collection and presentation, Measures of central tendency and dispersion, Probability distributions, Hypothesis testing (t-test, ANOVA, Chi-square), Correlation and regression |
| CBB 205 | Molecular Biology Practical | Core | 2 | DNA isolation and quantification, Agarose gel electrophoresis, PCR amplification, Plasmid isolation, Restriction digestion |
| CBB 206 | Object-Oriented Programming using C++ Practical | Core | 2 | C++ program development, Implementation of classes and objects, Inheritance and polymorphism examples, Data structure implementation (lists, stacks), File input/output operations |
| CBB 207 | Fundamentals of Bioinformatics Practical | Core | 2 | Database navigation and searching, Sequence retrieval and format conversion, BLAST and FASTA searches, Multiple sequence alignment, Phylogenetic tree construction |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CBB 301 | Immunology | Core | 4 | Innate and adaptive immunity, Antigens and antibodies, Major Histocompatibility Complex, Hypersensitivity reactions, Autoimmunity and immunodeficiency |
| CBB 302 | Genomics & Proteomics | Core | 4 | Genome sequencing strategies, Genome annotation, Transcriptomics and gene expression analysis, Proteomics technologies (2D-PAGE, Mass spectrometry), Protein-protein interaction networks |
| CBB 303 | Database Management Systems | Core | 4 | Relational database model, SQL queries (DDL, DML), Database design and ER modeling, Normalization, Transaction management |
| CBB 304 | Python Programming for Bioinformatics | Core | 4 | Python basics and data structures, Functions and modules, Biopython library, Data manipulation with Pandas, Web scraping for biological data |
| CBB 305 | Immunology Practical | Core | 2 | Antigen-antibody reactions (Agglutination, Precipitation), ELISA technique, Immunodiffusion, Blood cell counting, Phagocytosis assay |
| CBB 306 | Genomics & Proteomics Practical | Core | 2 | Genome browser usage (UCSC, Ensembl), Protein identification using mass spectrometry data, Gene expression analysis (microarray, RNA-seq basics), Proteomics database search, Functional enrichment analysis |
| CBB 307 | Database Management Systems Practical | Core | 2 | Creating and managing databases, Implementing SQL queries for data retrieval and manipulation, Designing E-R diagrams, Applying normalization techniques, Developing simple database applications |
| CBB 308 | Python Programming for Bioinformatics Practical | Core | 2 | Biopython for sequence manipulation, File parsing and data extraction, Data visualization using Matplotlib, Developing small bioinformatics scripts, Accessing web services with Python |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CBB 401 | Recombinant DNA Technology | Core | 4 | Cloning vectors and restriction enzymes, Gene cloning strategies, Gene libraries (Genomic, cDNA), PCR and its applications, Gene editing technologies (CRISPR) |
| CBB 402 | Structural Bioinformatics & Drug Design | Core | 4 | Protein structure prediction (Homology modeling), Molecular visualization tools, Ligand-protein docking, Virtual screening methods, Quantitative Structure-Activity Relationship (QSAR) |
| CBB 403 | Perl Programming | Core | 4 | Perl syntax and data types, Regular expressions for pattern matching, File input/output operations, Subroutines and modules, Bioperl library |
| CBB 404 | Cheminformatics | Core | 4 | Chemical data representation (SMILES, SDF), Molecular descriptors, Chemical databases (PubChem, ChEMBL), Similarity searching, Pharmacophore modeling |
| CBB 405 | Recombinant DNA Technology Practical | Core | 2 | Plasmid DNA isolation, Restriction digestion and ligation, Bacterial transformation, Gel electrophoresis for DNA fragments, Colony PCR |
| CBB 406 | Structural Bioinformatics & Drug Design Practical | Core | 2 | PDB database navigation and analysis, Protein structure visualization with PyMOL, Homology modeling using online tools, Molecular docking simulation, Ligand preparation for docking |
| CBB 407 | Perl Programming Practical | Core | 2 | Bioperl scripting for sequence manipulation, Extracting information from GenBank/FASTA files, Pattern matching using regular expressions, Writing scripts for data parsing, Developing simple bioinformatics tools |
| CBB 408 | Cheminformatics Practical | Core | 2 | Handling chemical file formats (SMILES, SDF), Calculating molecular properties, Searching chemical databases, 2D/3D structure visualization, Virtual library generation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CBB 501 | Systems Biology | Core | 4 | Biological networks (Gene regulatory, Metabolic), Pathway analysis, Flux balance analysis, Modeling and simulation of biological systems, Omics data integration |
| CBB 502 | Data Science with R | Core | 4 | R programming fundamentals, Data import, cleaning, and manipulation, Data visualization with ggplot2, Statistical tests in R, Introduction to machine learning in R |
| CBB 503 | Molecular Modeling & Dynamics | Core | 4 | Force fields in molecular mechanics, Energy minimization techniques, Molecular dynamics simulations, Conformational analysis, Free energy calculations |
| CBB 504 | Next Generation Sequencing Data Analysis (DSE-1 Elective Example) | Elective | 4 | NGS platforms and data formats, Read quality control and preprocessing, Read mapping and alignment, Variant calling and annotation, RNA-Seq data analysis |
| CBB 507 | Systems Biology Practical | Core | 2 | Network visualization using Cytoscape, Pathway database exploration (KEGG, Reactome), Metabolic modeling tools, Simulation of biological systems, Gene regulatory network analysis |
| CBB 508 | Data Science with R Practical | Core | 2 | R scripting for data analysis, Data visualization techniques, Performing statistical tests (t-test, ANOVA), Implementing basic machine learning models, Generating reports with R Markdown |
| CBB 509 | Molecular Modeling & Dynamics Practical | Core | 2 | Setting up force fields, Performing energy minimization, Running molecular dynamics simulations, Trajectory analysis and visualization, Using software like GROMACS/AMBER |
| CBB 510 | Next Generation Sequencing Data Analysis Practical (DSE-1 Elective Example) | Elective | 2 | Processing NGS raw reads, Alignment to reference genomes, Identifying genetic variants, Differential gene expression analysis, Metagenomics data pipeline |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CBB 601 | Big Data Analytics in Biology | Core | 4 | Big data concepts and challenges in biology, Hadoop and Spark ecosystems, Cloud computing for bioinformatics, Data warehousing and ETL processes, Biological big data case studies |
| CBB 602 | Machine Learning for Bioinformatics | Core | 4 | Supervised and unsupervised learning, Deep learning basics, Model evaluation and cross-validation, Applications in gene expression, protein function prediction, Clustering and classification algorithms |
| CBB 603 | Pharma-Bioinformatics (DSE-2 Elective Example) | Elective | 4 | Target identification and validation, Lead optimization and ADMET prediction, Toxicity prediction models, Clinical trials data analysis, Pharmacogenomics |
| CBB 606 | Big Data Analytics in Biology Practical | Core | 2 | Working with Hadoop Distributed File System (HDFS), Spark programming for data processing, Utilizing cloud-based bioinformatics services, Large-scale genomic data handling, Implementing data parallelization techniques |
| CBB 607 | Machine Learning for Bioinformatics Practical | Core | 2 | Implementing classification and regression models, Using scikit-learn for machine learning tasks, Introduction to deep learning frameworks (TensorFlow/Keras), Feature engineering for biological data, Model training and hyperparameter tuning |
| CBB 608 | Pharma-Bioinformatics Practical (DSE-2 Elective Example) | Elective | 2 | Utilizing drug discovery databases, Predicting ADMET properties using software tools, QSAR model development and validation, Analyzing clinical trial data, Pharmacogenomics data interpretation |
| CBB 609 | Project | Core | 4 | Problem identification and literature review, Methodology design and data collection, Computational data analysis, Interpretation of results, Scientific report writing and presentation |
