About this previous year paper
BIOINFORMATICS (NIT063)
Time: 3 Hours Max Marks: 100
SECTION – A (Short Answer Questions)
(10 × 2 = 20 Marks)
(a) What is bioinformatics?
Bioinformatics is an interdisciplinary field that uses computer science, mathematics, and statistics to store, analyze, and interpret biological data such as DNA, RNA, and protein sequences.
(b) Expand NCBI, EMBJ, DDBJ, and PIR
NCBI: National Center for Biotechnology Information
EMBJ (EMBL): European Molecular Biology Laboratory
DDBJ: DNA Data Bank of Japan PIR: Protein Information Resource
(c) Interfaces
Interfaces are graphical or command-based interaction layers that allow users to access bioinformatics databases and tools easily, such as web interfaces for sequence search tools.
(d) Informative site
An informative site is a biological database or web resource that provides curated and meaningful biological information, such as gene function or protein structure.
(e) Role of microarray in bioinformatics
Microarrays are used to analyze gene expression levels of thousands of genes simultaneously, helping in disease diagnosis, drug discovery, and functional genomics.
(f) Data mining tools – description and classification
Common data mining tools include: Classification tools – assign data to predefined classes
Clustering tools – group similar data Association rule tools – find relationships
Prediction tools – forecast outcomes
(g) Macromolecular structure data
Examples include protein structures and nucleic acid structures.
Information present: 3D atomic coordinates
Bond lengths and angles Secondary and tertiary structures
(h) Three methods for pairwise sequence alignment
Dot matrix method
Dynamic programming (Needleman–Wunsch, Smith–Waterman)
Heuristic methods
(i) Genetic distance and phylogenetic methods
Genetic distance measures evolutionary divergence between sequences.
Distance-based methods: UPGMA Neighbor-Joining
(j) Information from cluster analysis of microarray data
Cluster analysis groups genes with similar expression patterns, helping identify co-regulated genes.
Example: Genes activated together during cancer progression.
SECTION – B (Long Answer Questions)
(Attempt any FIVE – 5 × 10 = 50 Marks)
2(a) Classification of major bioinformatics databases
Bioinformatics databases are classified as:
Primary databases – Raw data
Examples: GenBank, EMBL, DDBJ
Secondary databases – Analyzed data
Examples: PROSITE, Pfam
Composite databases – Combined sources
Examples: NR database
These databases store sequences, structures, and annotations.
2(b) Central dogma of molecular biology
The central dogma explains information flow in biological systems:
DNA→RNA→ProteinDNA \rightarrow RNA \rightarrow ProteinDNA→RNA→Protein
DNA stores genetic information
RNA transfers information
Proteins perform cellular functions
It is an information science because genetic data is encoded, transmitted, and decoded.
2(c) Data retrieval tools and data mining steps
Data retrieval tools:
BLAST
FASTA
Entrez
Steps in data mining: Data collection Data preprocessing
Pattern discovery Knowledge evaluation
2(d) Structure visualization and rendering tools
Structure visualization allows viewing 3D molecular structures.
Rendering tools: Wireframe Space-filling (CPK)
Ribbon and cartoon models
These help in understanding protein folding and interactions.
2(e) Microarray spotting and result analysis
Spotting process: DNA extraction Probe preparation
Spotting on glass slide
Hybridization Scanning
Result analysis: Image processing
Normalization Statistical analysis
Clustering
2(f) Data mining methods for pattern extraction
Methods include: Classification Clustering
Association rule mining Neural networks Decision trees
These help extract hidden biological patterns.
2(g) Clustering vs classification
| Clustering | Classification |
|---|---|
| Unsupervised | Supervised |
| No predefined classes | Predefined classes |
Hierarchical clustering: builds tree-like structure
K-means clustering: partitions data into k groups
2(h) Text mining and NLP approach
Text mining extracts useful information from biological literature.
NLP stages: Tokenization Parsing
Entity recognition Relationship extraction
Used in gene–disease association discovery.
SECTION – C (Very Long Answer Questions)
(Attempt any TWO – 2 × 15 = 30 Marks)
3. Bioinformatics applications
(a) Phylogenetic analysis
Studies evolutionary relationships using sequence alignment and tree construction methods.
(b) Genome annotation
Identifies genes, coding regions, and regulatory elements in genome sequences.
(c) Proteomics
Study of complete protein set, their structure, function, and interactions.
(d) Drug discovery
Uses bioinformatics tools to identify drug targets, predict binding, and reduce development time.
4. Machine learning process in bioinformatics
Basic process: Data collection Feature extraction
Model training Validation
Prediction
(a) Neural networks
Used for pattern recognition and gene expression analysis.
(b) Decision trees
Tree-based models used for classification and decision-making in biological data.
5. Collaboration–communication model
This model explains information exchange between researchers, databases, and tools.
Hierarchy:
Data providers
Analysis tools
Knowledge bases
End users
Examples include collaborative genome projects and shared biological repositories.