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Topic outline
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Dear Students
Welcome to the Introduction to Bioinformatics (CSE446) courses, I, Israfil Mahmud Raju will be your co-pilot in this online journey of learning.
I care about your success in these courses. I'm glad you are here.
Md. Israfil Mahmud Raju
Lecturer, Department of Computer Science and Engineering
Daffodil International University
Israfil Mahmud Raju
Lecturer, Department of CSE
Daffodil International University
www.imraju.com
Office: Room: 735 AB4
Email: israfil.cse0298.c@diu.edu.bd
Phone: +8801768470001
Bioinformatics has been the most used method of incorporating intelligence of the 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.
O1
To gather knowledge about biological world and relation with Computer Science and
Engineering
O2
To analyze some existing methods and algorithms for specific biological problems
O3
To grow the research interest in the field of Bioinformatics
O4
To know the scope of bioinformatics research area and motivate them for research work.
CO1
Able to understand the basics of Bioinformatics such as molecular and cellular biology, DNA sequencing, Gene duplication, Paralog, Ortholog, Homolog, Selectivity, Sensitivity Phylogenetic Tree
CO2
Able to analyze algorithms for various existing methods for specific topic such as Global and Local Alignment, FASTA, HMM, Parsimony, Distance Approach, Maximum Likelihood Estimation
CO3
Able to design and evaluate algorithms for specific biological problems.
01
An Introduction to Bioinformatics Algorithms
by Neil C. Jones, Pavel A. Pevzner
02
Biological sequence analysis
by Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison
- Topic 1
Topic 1
Introduction to Bioinformatics
O1
Basics of Bioinformatics- Cell, Gene, Genome
O2
Scope of Bioinformatics
Expected Learning Outcome
O1
Able to understand the relation between computer Scientists with Biologists
O2
Able to grow research interest in this field
- Topic 2
Topic 2
Molecular and Cellular Biology
O1
Basics of Molecular Biology-DNA, RNA
O2
Structure of DNA and RNA, DNA Replication
O3
Cell Division- Mitosis and Meiosis
Expected Learning Outcome
O1
Able to understand the structural and functional difference between DNA and RNA
O2
Able to know how the cell division is occurred in a cell and DNA Replication
- Topic 3
Topic 3
Gene Structure and Splicing
O1
Gene Structure, Gene regulation and Splicing
O2
DNA --> RNA --> Protein
Expected Learning Outcome
O1
Able to understand of Functional and non-functional parts of Gene
O2
Realize the process of Protein synthesis through translation and transcription process
- Topic 4
Topic 4
O1
Basic terminologies to perform DNA sequencing
O2
DNA Sequencing Process- Sanger Method
O3
Different Generations of Sequencing
Expected Learning Outcome
O1
Able to understand the DNA Sequencing Process
O2
How Gnome Sequencing is performed in different generations
- Topic 5
Topic 5
O1
Sequence Alignment- why it is needed
O2
Sequence Alignment Method
O3
Global and Local Alignment for pairwise sequence
Expected Learning Outcome
O1
Able to know Sequence Alignment, different methods of pairwise sequence
O2
Able to perform Global and Local alignment for pairwise sequence
- Topic 6
Topic 6
Multiple Sequence Alignment
O1
Multiple Sequence Alignment- importance, motivation, challenge
O2
MSA methods-Dynamic Programming, Greedy Approach, Progressive, Iterative method
Expected Learning Outcome
O1
Able to know the importance, challenges of MSA
O2
Able to analyze different methods of MSA
- Topic 7
Topic 7
Gene Duplication and Read Mapping
O1
Different types of mutations
O2
Gene Duplication- Homolog, Ortholog, Paralog and Speciation
O3
Read Mapping- Keyword tree, suffix tree, suffix array, Burrows Wheeler Transform
Expected Learning Outcome
O1
Able to know different types of mutations
O2
Analyze and differentiate clearly ablout Homolog, Paralog, Ortholog and Speciation
O3
Able to understand suffix tree, suffix array, Burrows wheeler transform
- Topic 8
Topic 8
O2
Selectivity, Sensitivity
O3
Hash Table used in FASTA
Expected Learning Outcome
O1
Able to understand TP, TN, FP, FN, Sensitivity and Selectivity
O2
Able to create hash table in FASTA.
- Midterm Examination
- Topic 10
Topic 10
O1
Markov Chain Model, Notation
O2
Probability of a Sequence for a Given Markov Chain Model
O4
Hidden Markov Model- Forward Algorithm
Expected Learning Outcome
O1
Able to clear understand about Markov Chain Model, CpG Island
O2
Able to implement Forward Algorithm using HMM
- Topic 11
Topic 11
O1
Hidden Markov Model, Notations
O2
Implement of Hidden Markov Model - Viterbi Algorithm
Expected Learning Outcome
O1
Able to understand HMM
O2
Able to apply Viterbi algorithm by using HMM
- Topic 12
Topic 12
O1
Phylogenetic Analysis and MSA, Evolution, Phylogenetic Tree Basics
O2
Types of Phylogenetic Tree - Rooted and Unrooted Tree
O3
Different Approaches of Phylogenetic Tree - Parsimony, Distance, Maximum Likelihood
Expected Learning Outcome
O1
Able to understand evolution of different species, Phylogenetic tree
O2
Understanding of Rooted and Unrooted tree
O3
Able to generate Phylogenetic tree by Parsimony and Distance approaches
- Topic 13
Topic 13
Maximum Likelihood Estimation
O1
Maximum Likelihood Estimation
O2
Calculate Maximum Likelihood of a Phylogenetic Tree with known history
Expected Learning Outcome
O1
Able to understand Maximum Likelihood estimation
O2
Able to calculate Maximum Likelihood of a Phylogenetic Tree with known history
- Topic 14