MCSCS105-1 BioComputing M.Tech Model Question Paper : mgu.ac.in
Name of the College : Mahatma Gandhi University
Department : Computer Science and Engineering
Subject Code/Name : MCSCS 105-1/BioComputing
Sem : I
Website : mgu.ac.in
Document Type : Model Question Paper
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BioComputing Model Question Paper :
M.TECH. DEGREE EXAMINATION :
Branch: Computer Science and Engineering
Specialization : Computer Science and Engineering
Related : MGU MCSCS104 Object Oriented Software Engineering M.Tech Model Question Paper : www.pdfquestion.in/5012.html
Model Question Paper – I
First Semester :
MCSCS 105-1 BioComputing
(Elective I)
(Regular – 2013 Admission onwards)
Time: 3hrs
Maximum:100 marks
Answer the following Questions. :
1. a) Explain the different Molecular Biology Tools (12).
b) Discuss the Gene Structure(7)
c) What is Base Pairing(6)
or
2. a) Which are the major Databases in Bioinformatics (9)
b) Discuss the different Data Retrieval Tools (9)
c) How Data Mining can be done in Biological Databases (7)
3. a) Explain the different methods of Sequence Alignments (10)
b) What is Phylogenetic Analysis? (8 )
c)How is Scoring Matrices used to compare two sequences (7)
or
4. a) Explain the FASTA Algorithm(9)
b)Explain the BLAST Algorithm(9)
c) Compare the FASTA and BLAST Algorithms(7)
5. a) Explain the Hidden Markov Models (15)
b) What are DNA Microarrays (10)
or
6. a)Explain the different Gene prediction methods(13)
b) Discuss the different clustering techniques used to identify the pattern in gene-expression (12)
7. a) How is Protein Structure Visualized? (9)
b) How Protein is classified based on Structures? (8)
c) Discuss the different Protein Classification Approaches (8)
or
8. a) Discuss the various tools used for protein identification and characterization (9)
b) Explain Sequence based Protein Prediction (8)
c) Discuss the AB initio Approach for Protein Prediction (8)
MCSCS 105-1
BioComputing :
(Elective I)
(Regular – 2013 Admission onwards)
Time: 3hrs
Maximum:100 marks
Answer the following Questions. :
1. a) Explain the different Molecular Biology Tools (12).
b) Discuss the Gene Structure(7)
c) What is Base Pairing(6)
or
2. a) Which are the major Databases in Bioinformatics (9)
b) Discuss the different Data Retrieval Tools (9)
c) How Data Mining can be done in Biological Databases (7)
3. a) Explain the different methods of Sequence Alignments (10)
b) What is Phylogenetic Analysis? (8 )
c)How is Scoring Matrices used to compare two sequences (7)
or
4. a) Explain the FASTA Algorithm(9)
b)Explain the BLAST Algorithm(9)
c) Compare the FASTA and BLAST Algorithms(7)
5. a) Explain the Hidden Markov Models (15)
b) What are DNA Microarrays (10)
or
6. a)Explain the different Gene prediction methods(13)
b) Discuss the different clustering techniques used to identify the pattern in gene-expression (12)
7. a) How is Protein Structure Visualized? (9)
b) How Protein is classified based on Structures? (8)
c) Discuss the different Protein Classification Approaches (8)
or
8. a) Discuss the various tools used for protein identification and characterization (9)
b) Explain Sequence based Protein Prediction (8)
c) Discuss the AB initio Approach for Protein Prediction (8)
Syllabus :
Module 1 :
Molecular Biology and Biological Chemistry – The Genetic Material, Gene Structure and Information Content, Protein Structure and Function, The Nature of Chemical Bonds, Molecular Biology Tools, Genomic Information Content, Major Databases in Bioinformatics
Information Search and Data Retrieval- Tools for Web Search, Data Retrieval Tools, Data Mining of Biological Databases
Gene Analysis and Gene Mapping- Genome Analysis, Genome Mapping, Physical Maps, Cloning The Entire Genome, Genome Sequencing,The Human Genome Project (HGP)
Module 2 :
Alignment of Pairs of Sequences – Methods of Sequence Alignments, Using Scoring Matrices, Measuring Sequence Detection Efficiency, Methods of Multiple Sequence Alignment, Evaluating Multiple Alignments, Phylogenetic Analysis, Tree Evaluation
Tools for Similarity Search and Sequence Alignment – Working with FASTA, BLAST, FASTA and BLAST Algorithms Comparison
Module 3 :
Profiles and Hidden Markov Models – Using Profiles, Hidden Markov Models Gene Identification and Prediction – Basis of Gene Prediction, Pattern Recognition, Gene Prediction Methods
Gene Expression and Microarrays – Working with DNA Microarrays, Clustering Gene Expression Profiles, Data Sources and Tools for Microarray Analysis, Applications of Microarray Technology
Module 4 :
Protein Classification and Structure Visualization – Protein Structure Visualization, Protein Structure Databases, Protein Structure Alignment, Domain Architecture Databases, Protein Classification Approaches, Protein Identification and Characterization, Primary and Secondary Structure Analysis and Prediction, Patterns and Fingerprints Search, Methods of 2D Structure Prediction, Protein Prediction from a DNA Sequence
Proteomics – Tools and Techniques in Proteomics, Protein-Protein Interactions, Methods of Gene Family Identification
Computational Methods for Pathways and Systems Biology – Analysis of Pathways, Metabolic Control Analysis, Simulation of Cellular Activities, Biological Markup Languages