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University of Mumbai Semester 5 (TE Third Year) Bioinformatics 1 Revised Syllabus
University of Mumbai Semester 5 (TE Third Year) Bioinformatics 1 and their Unit wise marks distribution
Units and Topics
| # | Unit/Topic | Marks |
|---|---|---|
| 100 | Introduction to Bioinformatics | - |
| 200 | Databases | - |
| 300 | Alignment | - |
| 400 | Visualization | - |
| 500 | Proteins | - |
| 600 | Drug | - |
| Total | - |
Syllabus
100 Introduction to Bioinformatics
- Types of biological data
- Sequencing Methods: DNA (Maxim Gilbert Method, Sangers Method) & Protein (MS-MS Analysis), Genomic Sequencing
- ESTs and SNPs
- Applications of bioinformatics.
200 Databases
- Types of databases
- Based on storage techniques (Flat , Relational, Object Oriented)
- Based on data (Primary, Secondary , Specialized)
- Search engines:- Entrez & SRS Sequence databases: NCBI , EMBL , DDBJ
- Structural databases : PDB
- Protein Databases : PIR ,SWISS PROT
- Other Databases : KEGG , TrEMBL , EBI
300 Alignment
- Global Alignment
- Local Alignment
- Pair Wise Sequence Alignment
- Dot Matrix Alignment
- Dynamic programming Methods : Needleman Wunch Algorithm, Smith Waterman Algorithm
- Heuristic Method : BLAST ,FASTA
- Amino Acid Substitution Matrices: PAM , BLOSSUM
- Multiple Alignment: CLUSTAL W
- Phylogenetic Analysis.
400 Visualization
- Methods for representing biological data, Rasmol, Swiss PDB, 3D Structure Viewers.
500 Proteins
- Structure
- Classification
- Classification databases
- Protein Structure prediction :-
- Primary Structure Prediction
- Secondary Structure Prediction
- Tertiary Structure Prediction
- Homology Modelling
- Chao-Fasman Algorithm
- Neural Networks
- Ab-Initio Modelling
- Fold recognition (Threading)
600 Drug
- Drug discovery Markov chains
- Hidden markov models.
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