THEORY EXAMINATION (SEM–VI) 2016-17 BIOINFORMATICS

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

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.