Course Code: CUBS2542
Course Title: BIOINFORMATICS
Credits: 4
Type: (3+1+0)
Course Title: BIOINFORMATICS
Credits: 4
Type: (3+1+0)
Course Overview:
This course provides an in-depth introduction to bioinformatics, focusing on the use of biological databases, sequence alignment techniques, and molecular phylogenetics. Through a combination of theoretical knowledge and practical exercises, students will learn to critically assess biological data and perform various bioinformatics analyses to infer evolutionary relationships.
Prerequisites:
- Basic understanding of molecular biology and genetics.
- Basic proficiency in computer usage and familiarity with bioinformatics tools.
Course Objectives:
- Comprehend the fundamental principles of biological databases, including their types, significance, and potential pitfalls. (L2)
- Understand sequence alignment techniques, including the evolutionary basis, scoring matrices, and the statistical significance of sequence alignments. (L3)
- Apply multiple sequence alignment methods and molecular phylogenetics concepts to analyze biological data and infer evolutionary relationships. (L4)
Course Learning Outcomes:
- CO1: Critically assess the relevance and limitations of biological databases and effectively retrieve information from them. (L2)
- CO2: Perform database similarity searches using tools like BLAST and FASTA and evaluate the significance of search results. (L3)
- CO3: Independently conduct multiple sequence alignments and molecular phylogenetic analyses, including the application of distance-based and character-based methods, and evaluate the quality of phylogenetic trees, demonstrating their ability to analyze and interpret biological data in an evolutionary context. (L4)
CO-PO Mapping Matrix:
CO\PO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
CO1 | 3 | 2 | – | – | 2 | 1 | – | – | – | – | – | – |
CO2 | 3 | 3 | 2 | 2 | 3 | – | – | – | 2 | – | – | – |
CO3 | 2 | 3 | 3 | 3 | 3 | 2 | 2 | 2 | 3 | – | – | – |
L1 – Remember
L2 – Understand
L3 – Apply
L4 – Analyze
L5 – Evaluate
L6 – Create
L2 – Understand
L3 – Apply
L4 – Analyze
L5 – Evaluate
L6 – Create
SYLLABUS
Module 1: Introduction to Biological Databases
- What Is a Database?
- Types of Databases
- Biological Databases
- Pitfalls of Biological Databases
- Information Retrieval from Biological Databases
Module 2: Pairwise Sequence Alignment
- Evolutionary Basis
- Sequence Homology versus Sequence Similarity
- Sequence Similarity versus Sequence Identity
- Methods
- Scoring Matrices
- Statistical Significance of Sequence Alignment
Module 3: Database Similarity Searching
- Unique Requirements of Database Searching
- Heuristic Database Searching
- Basic Local Alignment Search Tool (BLAST)
- FASTA
- Comparison of FASTA and BLAST
- Database Searching with the Smith-Waterman Method
- Exercise:
- Hypothesis: Heuristic search methods (BLAST, FASTA) always yield results comparable to exact methods (Smith-Waterman).
- Exercise: Compare the results and computational efficiency of the same dataset’s heuristic and exact search methods.
Module 4: Multiple Sequence Alignment
- Scoring Function
- Exhaustive Algorithms
- Heuristic Algorithms
- Practical Issues
- Exercise:
- Hypothesis: Heuristic algorithms for multiple sequence alignment are as accurate as exhaustive algorithms.
- Exercise: Compare alignments generated by heuristic and exhaustive algorithms and assess their accuracy against known reference alignments.
Module 5: Molecular Phylogenetics
- Molecular Evolution and Molecular Phylogenetics
- Terminology
- Gene Phylogeny versus Species Phylogeny
- Forms of Tree Representation
- Why Finding a True Tree Is Difficult
- Procedure
- Distance-Based Methods
- Character-Based Methods
- Phylogenetic Tree Evaluation
- Phylogenetic Programs
- Exercise:
- Hypothesis: All phylogenetic methods produce similar evolutionary trees.
- Exercise: Generate phylogenetic trees using distance-based and character-based methods and compare their topologies and accuracy with known evolutionary relationships.
Text Book:
- Xiong, Jin. Essential Bioinformatics. Cambridge University Press, 2006.
Module 1: Introduction to Biological Databases
Hours | Session Type | Topic Name | Learning Outcome(s) |
---|---|---|---|
1 | Theory | What Is a Database? | Understanding basic database concepts. |
1 | Theory | Types of Databases | Differentiating between various types of databases. |
1 | Theory | Biological Databases | Identifying specific biological databases. |
1 | Theory | Pitfalls of Biological Databases | Recognizing limitations and potential errors. |
1 | Theory | Information Retrieval from Biological Databases | Learning effective information retrieval techniques. |
2 | Theory | Falsification Exercise Introduction | Hypothesis: Biological databases contain comprehensive and error-free data. Examining potential errors. |
3 | Practice | Using Biological Databases | Hands-on practice retrieving data from various databases. |
2 | Project | Project Planning and Setup | Initial project setup and planning. |
1 | Assignment | Presentation | |
Total Hours: 14 Hours |
Module 2: Pairwise Sequence Alignment
Hours | Session Type | Topic Name | Learning Outcome(s) |
---|---|---|---|
1 | Theory | Evolutionary Basis of Sequence Alignment | Understand how sequence alignment is grounded in evolutionary principles. |
1 | Theory | Sequence Homology vs Similarity | Differentiate between homologous and similar sequences and their biological implications. |
1 | Theory | Sequence Similarity vs Sequence Identity | Interpret the difference between similarity and identity and their roles in alignment scoring. |
2 | Theory | Methods of Pairwise Sequence Alignment (Global & Local) | Gain knowledge of alignment strategies and algorithms like Needleman-Wunsch and Smith-Waterman. |
1 | Theory | Scoring Matrices (PAM, BLOSUM) | Understand how substitution matrices influence alignment results. |
2 | Theory | Statistical Significance of Sequence Alignment | Evaluate alignment results using statistical tools like E-value, bit score, and Z-score. |
3 | Practice | Performing Pairwise Sequence Alignments | Hands-on session using tools such as EMBOSS, BLAST, or Clustal Omega for real alignments. |
2 | Project | Project Planning and Setup | Designing and initializing a project using pairwise alignment for gene or protein comparison. |
1 | Assignment | Presentation | |
Total Hours: 14 Hours |
Module 3: Nucleotide Sequence Databases
Hours | Session Type | Topic Name | Learning Outcome(s) |
---|---|---|---|
1 | Theory | Introduction to Nucleotide Sequence Databases | Understand the structure, content, and functions of nucleotide sequence databases. |
2 | Theory | GenBank, EMBL, and DDBJ | Explore major public nucleotide databases and their data submission and retrieval formats. |
2 | Theory | Structure of a GenBank Record | Analyze fields such as locus, features, annotations, and references in a GenBank flat file. |
2 | Practice | Searching and Retrieving Sequences from NCBI | Hands-on experience using Entrez and NCBI tools to search, retrieve, and interpret nucleotide sequences. |
2 | Practice | Using EBI and DDBJ Portals | Familiarize with alternative nucleotide database interfaces and search tools provided by EBI and DDBJ. |
2 | Project | Comparative Database Analysis | Conduct comparative evaluation of GenBank, EMBL, and DDBJ based on structure, features, and accessibility. |
1 | Assignment | Presentation | |
Total Hours: 12 Hours |
Module 4: Protein Sequence Databases
Hours | Session Type | Topic Name | Learning Outcome(s) |
---|---|---|---|
1 | Theory | Introduction to Protein Sequence Databases | Explain the role and importance of protein sequence databases in bioinformatics. |
2 | Theory | UniProtKB, Swiss-Prot, TrEMBL | Distinguish between curated and automatically annotated protein databases and their features. |
2 | Theory | PIR, PDB, and RefSeq Protein | Explore additional protein resources, including structural and reference protein data repositories. |
2 | Practice | Searching Protein Databases | Perform database queries using UniProt, PIR, and NCBI protein interfaces. |
2 | Practice | Understanding a UniProt Entry | Analyze a protein record, including function, domains, GO terms, cross-references, and structure links. |
2 | Project | Comparative Study on Protein Databases | Evaluate various protein databases based on depth of annotation, accessibility, and scientific utility. |
1 | Assignment | Presentation | |
Total Hours: 12 Hours |
Module 5: Structure Databases
Hours | Session Type | Topic Name | Learning Outcome(s) |
---|---|---|---|
1 | Theory | Introduction to Molecular Structure Databases | Explain the significance of structure databases and types of molecular structures stored. |
2 | Theory | Protein Data Bank (PDB) | Describe the data model, file formats, and functional features of the PDB database. |
2 | Theory | MMDB, SCOP, and CATH | Differentiate structure classification systems and understand their hierarchical structure. |
2 | Practice | Browsing and Downloading Structures from PDB and MMDB | Retrieve and interpret structural data using online platforms such as RCSB PDB and NCBI MMDB. |
2 | Practice | Visualization of Macromolecular Structures | Visualize protein structures using molecular viewers such as PyMOL, RasMol, or Jmol. |
2 | Project | Comparative Study on Structure Classification Databases | Evaluate the SCOP and CATH classification systems in terms of coverage, depth, and utility. |
1 | Assignment | Presentation | |
Total Hours: 12 Hours |
Biological Databases – Quick Access Links
Below is a curated list of widely used biological databases categorized by type:
1. Nucleotide Sequence Databases
- NCBI (GenBank) – National Center for Biotechnology Information
- ENA – European Nucleotide Archive
- DDBJ – DNA Data Bank of Japan
2. Protein Sequence Databases
3. Structure Databases
- PDB – Protein Data Bank
- MMDB – Molecular Modeling Database (NCBI)
- SCOP – Structural Classification of Proteins
- CATH – Protein Structure Classification
4. Functional and Pathway Databases
- KEGG – Kyoto Encyclopedia of Genes and Genomes
- Reactome – Pathway Database
- BioCyc – Collection of Pathway/Genome Databases