### Course Outline

Credits: 4
Open to: MTech in Computational Biology (& others who are interested)

Post-Conditions:
The aim of this course is to understand the algorithms frequently used in Computational Biology. Upon successful completion, a student should:
1. Understand the following in detail – Needleman-Wunsch algorithm, Smith-Waterman algorithm, dynamic programming, progressive alignment techniques, iterative alignment techniques, hidden Markov models for sequence search and alignment
2. Understand the basics of protein sequence-, structure- and homology- searches, sequence motifs, alignments and phylogeny
3. Be able to efficiently implement algorithms to generate protein sequence alignments, understand the complexity of existing approaches to protein sequence alignments, understand and implement different techniques to generate 3D structure alignments of protein structures.
Brief Description: This course focuses on the algorithmic aspect of commonly used techniques in bioinformatics. Algorithms for both sequence and structure-based analysis are considered. The first half of the course will be devoted to introduction to algorithms, programming in Python and R for bioinformatics to motivate students towards algorithms in computational biology. The second half will deal with sequence and structure-based algorithms.

### Logistics

 Part-1 (before mid-sem) Part - 2 (after mid-sem) Instructor Debajyoti Bera Dhirendra Kumar (guest, IGIB) Time W 10-11:30, F 11:30-1 M 10-11:30, Th 10-11:30 Venue C11 C23 ReferenceResource Online Lecture Notes by Jeff Erickson Handbook of Computational Molecular Biology by Srinivas Aluru

#### Grading (tentative):

• Lecture Quizzes (Pre-midsem - best 6) - 6 x 1% = 6%
• Tests - 1 x 7% = 7%
• Written homework - 2 x 4% = 8%
• Programming assignment - 4 x 3.5% = 14%
• Mid-sem exam - 25%
• Final exam - 40%

### Lecture Schedule

• Lec 1-13: Algorithm design and analysis (by Debajyoti Bera)
• NP-hardness and reduction
• Divide and conquer
• Dynamic programming
• Backtracking and heuristics search
• String matching
• Lec 14: Computational Biology (by Dhirendra Kumar)
• Lec 14: Introduction to bioinformatics using python
• Lec 15: Workshop on use of python for bioinformatics (Guest lecture by Sikander Hayat)
• Lec 16-17: Sequence alignment (including MSA)
• Lec 18-19: Proteomics
• Lec 20-22: Sequence alignment, Intro. Phylogenetic tree, UPGMA clustering, Neighbor joining, Distance matrices
• Lec 23-24: HMM, PFAM
• Lec 25-26: Hands-on on BLAST

1. HW1
2. HW2
3. HW3

### Programming Homeworks

1. ProgHW1
2. ProgHW2
3. ProgHW3
4. ProgHW4
5. ProgHW5

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