syllabus of b.sc. (bioinformatics) · · 2012-02-04syllabus of b.sc. (bioinformatics) subject-...

Syllabus of B.Sc. (Bioinformatics)

Subject- Bioinformatics (as one subject)

B.Sc. I Year

Semester I

Paper I: Mathematics 50 marks

Paper II: Introduction to Bioinformatics I 50 marks

Practicals 50 marks

Semester II

Paper I: Statistics 50 marks

Paper II: Introduction to Bioinformatics II 50 marks

Practicals 50 marks

B.Sc. II Year

Semester III

Paper I: Coordinate Geometry, and Vectors 50 marks

Paper II: Structural Bioinformatics I 50 marks

Practicals 50 marks

Semester IV

Paper I: Matrices and Transforms 50 marks

Paper II: Structural Bioinformatics II 50 marks

Practicals 50 marks

B.Sc. III Year

Semester V

Paper I: Genomics 50 marks

Paper II: Metabolomics 50 marks

Practicals 50 marks

Semester VI

Paper I: Drug Designing and Cheminformatics 50 marks

Paper II: Computer Graphics and Machine Learning 50 marks

Practicals 50 marks

15 Marks 2CCE + 35 Marks End Semester Examination=50 Marks for Each Paper

B.Sc. BIOINFORMATICS

I year

SEMESTER-I

Paper I –Mathematics

Total Allotted Hrs.-35 Max. marks-50

Unit I

Sets, Types of Sets, Subsets, Complement of Sets, nion and Intersection of Sets,

Difference of Sets, Demorgan’s Law, Cartesian product of Sets, Sets of numbers,

Unit II

Significant figures and decimal places.

Law of Indices, trigonometric ratios, Inverse trigonometric functions.

Unit III

Sequences and series AP, GP, HP, Logrithmic and Exponential Series.

Binomial theorem

Unit IV

Basics of Functions and Limits, Elementary Differentiation and Integration

Unit V

Basics of Probability, Permutation and Combination

I year

SEMESTER IPaper II –Introduction to Bioinformatics I


Unit I Introduction to bioinformatics

• What is bioinformatics and its relation with molecular biology.

• Examples of related tools, databases and software

• Applications of bioinformatics.

Unit II General Introduction of Databases

• Nucleic acid databases (NCBI, DDBJ, EMBL).

• Protein databases (Primary, Composite, Secondary).

• Specialized Genome databases: SGD, TIGR, ACeDB

• Structure databases (CATH, SCOP, PDBsum)

Unit III Representation of patterns and relationship

• Sequences, alignments, regular expression, hierarchies, and graphical

models (including Marcov chain and Bayes notes)

• Genetic variability and connections to clinical data.

Unit IV Data generation

• Generation of large scale molecular biology data (General

introduction of Genome sequencing, Protein sequencing, Gel

electrophoresis, NMR Spectroscopy, X-Ray Diffraction,

Microarray), Quality of data, private and future data sources.

Unit V Data storage and retrieval

• Flat files, relational, object oriented databases and controlled

vocabularies.

• Indices, Boolean, fuzzy, neighboring search

• Introduction to Metadata

Practical Course

1. Measure of central tendencies- Mean, Mode and Median.

2. Explore NCBI.

3. To read GenBank flat file format.

4. To read SWISSPROT entries.

5. Search for a disease and related gene in OMIM using Boolean operator

6. To read PDB entries.

7. To analyze sequence patterns (motif, Domain).

8. Find ORF using ORF finder.

References

1. Bioinformatics- Sequence and Genome analysis- David W. Mount

2. Introduction to Bioinformatics – Terasa K. Attwood

3. Principles of Biostatistics, Authors- Pagano et al.

4. Cell and Molecular Biology – P. K. Gupta5. Bioinformatics, Author- Baxevanis.

6. Mathematics by R. D. Sharma

7. Mathematics By R. S. Aggrawal

I year

SEMESTER-II

Paper I – Statistics


Unit I

Introduction to datatypes and Source.

Population and sample, Classification and presentation of data

Unit II

Measure of central tendency and dispersion: Mean, median, mode, range,

standard deviation, variance

Unit III

Correlation and Regression: types, Methods of calculation Karl-pearson’s,

Spearman’s Regression equation and fitting

Unit IV

Probability Distribution: Basics of Binomial, Poisson and Normal distributions

and their application in biology

Unit V

Random Variable; Discrete and Continuous Probability Distribution, Probability

mass function, probability Density function, Mathematical Expectation.

I year

SEMESTER-IIPaper II- Introduction to Bioinformatics II


Unit I Visualization

• Methods for presenting large quantities of biological data: sequence

viewers (Artemis, SeqVISTA), 3D structure viewers (Rasmol,

SPDBv, Chime, Cn3D, PyMol), anatomical visualization

Unit II Sequence Alignments

• Local and Global alignment, pairwise and multiple sequence

alignment (BLAST, Clustal W)

• Introduction to Dynamic Programming

Unit III Interoperability

• The challenges of data exchange and integration

• Ontologies, interchange languages and standardization efforts.

• XML, UMLS, CORBA, PYTHON and OMG/LIFESCIENCE

Unit IV PERL

• Introduction to PERL and its application

• Programming: Program to store a DNA and Protein Sequence,

Concatenating DNA fragments, Transcription: DNA to RNA,

Calculating the reverse complement, Reading protein sequence in

files, array and loops.

Unit V Gene Expression

• Gene expression in prokaryotes and eukaryotes, transcription

factors binding sites.

• SNP, EST, STS

Practical Course

1. Visualize protein molecule using RASMOL

2. Visualize protein molecule using SPDBv

3. To perform sequence similarity search using BLAST.

4. To perform multiple sequence alignment using Clustal W.

5. Perl Programming1

6. Perl programming2

References

1. Bioinformatics, Author- Baxevanis.

2. Principles of Biostatistics, Authors- Pagano et al.

3. Introduction to Biostatistics, Authors- Forthoter and Lec.

4. Beginning Perl for Bioinformatics by James Tisdall


6. P. S. Verma and Aggrawal

7. Introduction to Biostatistics, Authors- Forthoter and Lec.

8. Introduction to Bioinformatics Arthur M. Lesk University of Cambridge

9. Fundamental of Mathematics and Statisticas; Kapoor Gupta

10. Fundamental of Statistics; S. C. Gupta

II year

SEMESTER-III

Paper I –Coordinate Geometry, Vectors


Unit I

Coordinate geometry: Distance between two points, section formula, Locus of

points, equation of lines

Unit II

Circle, Elipse, Parabola, Hyperbola

Unit III

Vector: Addition, subtraction, dots, cross, scalar triple product

Unit IV

Vector differentiation and vector integration

Unit V

Gradient, divergence, curl of a vector, equation of normal

II year

SEMESTER IIIPaper II –Structural Bioinformatics I


Unit I

Fundamentals of X-ray diffraction

NMR spectroscopy of macromolecules

Unit II

Protein Structure: Primary, Secondary, Super Secondary, Domains, Tertiary,

Quaternary

Ramachandran plot

Unit III

Protein secondary structure classification databases: HSSP, FSSP, CATH, SCOP

Unit IV

Protein secondary structure prediction methods: GOR, Chou-Fasman, PHD, PSI-

PRED, JPred

Unit V

Motif and Domain: Motif databases and analysis tools

Domain databases (CDD, SMART, ProDom) and analysis tools

Structural features of RNA: Primary, Secondary, Tertiary

References



10. Molecular Modeling- Principles and application- Andrew R. Leach11. Principles of Biochemistry- Lehninger

12. Biochemistry- Stryer

13. Cell and Molecular Biology – P. K. Gupta14. Bioinformatics, Author- Baxevanis.

II year

SEMESTER-IV

Paper I- Matrices, Transforms


Unit I

Matrices, Types of matrices, Addition of matrices, Substraction of matrices and

Product of matrices, Properties of matrix multiplication.

Unit II

Transpose of matrix, Symmetric and Skew-symmetric matrices, Hermitian and

skew Hermitian matrices, Inverse of matrix

Unit III

Fourier transform, Laplace transform and other standard transform.

Unit IV

Boolean logic, addition, subtraction, multiplication and division using binary,

octal and hexadecimal system.

Unit V

Computer Networking: LAN, WAN, Modem, Optical Vs Electronic Networking,

security of the network, Firewalls, Network Goals, Application Network

II year

SEMESTER-IV

Paper II –Structural Bioinformatics II


Unit I

Introduction to RNA Secondary structure prediction

Methods for RNA Secondary structure prediction

Limitation of RNA Secondary structure prediction

Unit II

Protein Tertiary structure prediction methods: Homology Modeling, Fold

Recognition, Abintio Method. Comparison between and tertiary structure

Unit III

Molecular Dynamics of Protein

Molecular Docking of Protein, Small molecule and Nucleotide

Unit IV

Protein folding, Protein Engineering, Concepts of Force Field

Unit V

HMM (Hidden Marcov Model): Introduction to HMM, its application in

Sequence alignment and Structure prediction, based Softwares (HMMER and

HMMSTR)

References


2. Molecular Modeling- Principles and application- Andrew R. Leach3. Principles of Biochemistry- Lehninger


5. Bioinformatics from Genomes to Drugs- Thomas Lengauer

III year

SEMESTER-V

Paper I - Genomics


Unit I

Genome: Anatomy of Prokaryotic and Eukaryotic Genomes.

Genome Sequencing, Introduction to Genome Analysis

Unit II

Genome Databases: Human Genome database, Mouse Genome database, Plasma

Genome database, E. coli Genome database, Arabidopsis Genome database

Unit III

Gene identification: In prokaryotes and eukaryotes

Genome annotation

Unit IV

Genomics: Fundamentals of Structural, Comparative and Functional Genomics

and it’s applications

Unit V

Proteomics: Fundamentals concepts, Two dimensional electrophoresis, Mass

Spectroscopy

References


2. Bioinformatics and Functional Genomics- Jonathan Pevsner

3. Genomes- T. A. Brown

III year

SEMESTER-V

Paper II - Metabolomics


Unit I

Metabolic Pathways: Glycolysis, Kreb Cycle, oxidative and non oxidative

phosphorylation, cyclic and non-cyclic photophosphorylation, Calvin’s cycle

Unit II

Enzyme chains, multi-enzyme complex, multi functional enzyme, Regulation

enzyme, Feed back control of metabolic pathways.

Unit III

Metabolic Pathway database (KEGG Pathway database), Concept of metabolome

and metabolomics

Unit IV

Biodiversity Informatics: Fundamental Concept, Species diversity.

Datasets in Biodiversity informatics: Tree of life, Species 2000, NBII, ATCC

Unit V

Phylogeny, Introduction to Phlogenetic analysis, Concept of Phylogenetic Tree

References

1. Principles of Biochemistry- Lehninger



III year

SEMESTER-VI

Paper I: Drug Designing and Cheminformatics


Unit I

Microarray: Techniques, Design, Analysis, Drug target identification

Unit II

Drug Discovery and Design: Target identification, Target Validation, Lead

Identification, Lead Optimization, Preclinical Pharmacology & Toxicology

Unit III

Cheminformatics: Cheminformatics tools for drug discovery.

Chemical structure representation (SMILE and SMART).

Chemical databases: CSD, ACD, WDI, Chem bank, hazardous chemical database,

PUBCHEM

Unit IV

Quantitative structure activity relationship (QSAR, 2D and 3D-QSAR)

Combinatorial libraries and their design

Unit V

High throughput screening, Virtual screening, Lipinski’s rule of five

References



6. Molecular Modeling- Principles and application- Andrew R. Leach

7. Structural Bioinformatics – Philip E. Bourne and Helge Weissig8. Introduction to Bioinformatics Arthur M. Lesk University of Cambridge

9. Bioinformatics, Author- Baxevanis.

III year

SEMESTER-VI

Paper II: Computer Graphics and Machine Learning


Unit I

Graphics display devices, Raster and Random scan devices, color CRT monitors.

Unit II

Color models: CMY, HSV, RGB, Visualization technique.

Unit III

Generation of lines, circle, polygons and color filling using standard functions in

C.

Geometric Transformations: Rotation, scaling, translation (2D & 3D).

Unit IV

Artificial Neural Networks, Genetic algorithm, Bayesian modeling and their

application

Unit V

Monte Carlo Simulation Method, Markov Models, Dynamic Programming and

their application

References

4. Bioinformatics: The Machine Learning Approach, Pierre Baldi and Søren Brunak

5. Genetic Programming On the Programming of Computers by Means of Natural

Selection John R. Koza

6. An Introduction to Genetic Algorithms Mitchell Melanie

7. Neural Network by Siman Haykins

8. Bioinformatics and Functional Genomics- Jonathan Pevsner

9. Computer Graphics by Hern and Backer

syllabus of b.sc. (bioinformatics) · · 2012-02-04syllabus of b.sc. (bioinformatics) subject-...

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