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Topic outline
- Welcome to Bioinformatics
Welcome to Bioinformatics
Welcome to the Bio-informatics world.....
"If you want to compete in Bioinformatics ,first you need to compete for really smart people.You need really smart people who understand how to manipulate nanomolecule..."
So enjoy learning.....................
Course Rationale:
Bio-Informatics has been the most used methods of
incorporating intelligence of biological world to computer science. It is
therefore necessary to develop a good understanding of their operation and how
they can be used as building blocks for computerized application of biology.
This course explores the inner workings of a biological world from the
programmer’s perspective by implementing different algorithms of Computer
Science.
Course Objectives
- To gather knowledge about biological world and relation with Computer Science and Engineering
- To analyze some existing methods
and algorithms for specific biological problems
- To grow the research interest in the field of Bioinformatics
- To know the scope of bioinformatics research area and motivate them for research work.
Course Outcomes (CO’s):
- CO1: Understand the basics of Bioinformatics such as molecular and cellular biology, DNA sequencing, Gene
duplication, Paralog, Ortholog, Homolog, Selectivity, Sensitivity, Phylogenetic
Tree.
- CO2: Analyze algorithms for various existing methods for specific topic such as Global and Local Alignment, FASTA,
HMM, Parsimony, Distance Approach, Maximum Likelihood Estimation.
- CO3: Ability to design and evaluate algorithms for specific biological problems.
Book-1 Book-2
- Week-1: Introduction to Bioinformatics
Week-1: Introduction to Bioinformatics
1. Basics of
Bioinformatics- Cell, Gene, Genome
2. Scope of Bioinformatics
Expected Learning Outcome
1. Able to understand the relation between
computer Scientists with Biologists
2. Able to grow research interest in this field
Lecture 1:
- Week-2: Molecular and Cellular Biology
Week-2: Molecular and Cellular Biology
1. Basics of Molecular Biology-DNA, RNA
2. Structure of DNA and RNA, DNA Replication
3. Cell Division- Mitosis and Meiosis
Expected Learning Outcome
1. Able to understand the structural and functional difference between DNA and RNA
2. Able to know how the cell division is occurred in a cell and DNA Replication
Additional Materials
▹DNA Replication -
- Week-3: Gene Structure and Splicing
Week-3: Gene Structure and Splicing
1. Gene Structure, Gene regulation and Splicing
2. DNA --> RNA --> Protein
Expected Learning Outcome
1. Able to understand of Functional and non-functional parts of Gene
2. Realize the process of Protein synthesis through translation and transcription process
- Week-4: DNA Sequencing
Week-4: DNA Sequencing
1. Basic terminologies to perform DNA sequencing
2. DNA Sequencing Process- Sanger Method
3. Different Generations of Sequencing
Expected Learning Outcome
1. Able to understand the DNA Sequencing Process
2. How Gnome Sequencing is performed in different generations
Lecture Content
- Week-5: Sequence Alignment
Week-5: Sequence Alignment
1. Sequence Alignment- why it is needed
2. Sequence Alignment Method
3. Global and Local Alignment for pairwise sequence
Expected Learning Outcome
1. Able to know Sequence Alignment, different methods of pairwise sequence
2. Able to perform Global and Local alignment for pairwise sequence
- Week-6: Multiple Sequence Alignment
Week-6: Multiple Sequence Alignment
1. Multiple Sequence Alignment- importance, motivation, challenge
2. MSA methods-Dynamic Programming, Greedy Approach, Progressive, Iterative method
Expected Learning Outcome
1. Able to know the importance, challenges of MSA
2. Able to analyze different methods of MSA
Lecture Content
- Week-7: Mid-Term Exam
- Week-8: Gene Duplication and Read Mapping
Week-8: Gene Duplication and Read Mapping
Topics to Discussion
1. Different types of
mutations
2. Gene Duplication- Homolog, Ortholog, Paralog and Speciation
3. Read Mapping- Keyword
tree, suffix tree, suffix array, Burrows Wheeler Transform
Expected Learning
Outcome
1. Able to know different
types of mutations
2. Analyze and
differentiate clearly ablout Homolog, Paralog, Ortholog and Speciation
3. Able to understand
suffix tree, suffix array, Burrows wheeler transform
- Week-9: Database Searching
Week-9: Database Searching
1. TP, TN, FP, FN
2. Selectivity, Sensitivity
3. Hash Table used in FASTAExpected Learning Outcome
1. Able to understand TP, TN, FP, FN, Sensitivity and Selectivity
2. Able to create hash table in FASTA.
Lecture ContenT
- Week-10: HMM- Forward Algorithm
Week-10: HMM- Forward Algorithm
1. Markov Chain Model, Notation
2. Probability of a Sequence for a Given Markov Chain Model
3. CpG Island
4. Hidden Markov
Model- Forward Algorithm
Expected Learning Outcome
1. Able to clear understand about Markov Chain Model, CpG Island
2. Able to implement Forward Algorithm using HMM
Forward Algorithm:
Lecture Content
- Week 11: HMM-Viterbi Algorithm
Week 11: HMM-Viterbi Algorithm
1. Hidden Markov Model, Notations
2. Implement of Hidden Markov Model - Viterbi Algorithm
Expected Learning Outcome
1. Able to understand HMM
2. Able to apply Viterbi algorithm by using HMM
Lecture Content
Quiz 2 Assignment
Restricted Not available unless: You belong to PC B
Quiz 2 Assignment
Restricted Not available unless: You belong to PC A
- Week-12: Phylogenetic Tree
Week-12: Phylogenetic Tree
1. Phylogenetic Analysis
and MSA, Evolution, Phylogenetic Tree Basics
2. Types of Phylogenetic Tree -
Rooted and Unrooted Tree
3. Different Approaches of Phylogenetic Tree - Parsimony, Distance, Maximum
Likelihood
Expected Learning Outcome
1. Able to understand evolution of different species, Phylogenetic tree
2. Understanding of Rooted and Unrooted tree
3. Able to generate Phylogenetic tree by Parsimony and Distance approaches
Lecture Content
- Week-13: Maximum Likelihood Estimation
Week-13: Maximum Likelihood Estimation
1. Maximum Likelihood Estimation
2. Calculate Maximum Likelihood of a Phylogenetic Tree with known history
Expected Learning Outcome
1. Able to understand Maximum Likelihood estimation
2. Able to calculate Maximum Likelihood of a Phylogenetic Tree with known history
Reference Video:
Lecture Content
Quiz 3 Assignment
Restricted Not available unless: You belong to PC A
Quiz 3 Assignment
Restricted Not available unless: You belong to PC B
- Week-14: Final Examination
Week-14: Final Examination
Final Examinatin (PC-A) Assignment
Restricted Not available unless: You belong to PC A
Final Examination(PC-B) Assignment
Restricted Not available unless: You belong to PC B