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2020　Biological Data Analysis

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Academic unit or major
Undergraduate major in Computer Science
Instructor(s)
Akiyama Yutaka  Yamamura Masayuki 
Class Format
Lecture    (ZOOM)
Media-enhanced courses
Day/Period(Room No.)
Tue7-8(H121)  Fri7-8(H121)  
Group
-
Course number
CSC.T353
Credits
2
Academic year
2020
Offered quarter
2Q
Syllabus updated
2020/9/18
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course focuses on data representation methods and comparative and knowledge extraction algorithms for massive biological data. Topics include pairwise sequence alignment, dynamic programming, multiple sequence alignment, phylogenetic tree estimation, approximated methods, sequential motif representation, rapid homology search techniques versus large-scale database, protein structure modeling and structure prediction, and so on. No biological nor biochemical knowledge are prerequisite. The basic biology notions are introduced within the course, and students are required to consider the topics in the view of computational algorithms and its complexity.
Biological information analysis is significantly important for our society in the 21st century in order to improve our quality of life, environment, and safety. Thus this course is aiming at providing the fundamental understanding to the nature of biological data and typical algorithms, like as dynamic programming, system simulation based on differential equations, repeatedly used in this area. On the other hand, most of the methods explained in the course are also applicable to wide range of engineering subjects. We aim to provide this course to students as an illustrative example how computer science techniques are applied in a specific real-world problem.

Student learning outcomes

By the successful completion of this course, students will be able to:
1) Explain several data representation for sequence analysis (e.g. regular expression, profile matrix, HMM),
2) Explain the notion and implementation of dynamic programing, as well as its several applications in bioinfomatics,
3) Explain the important role of approximated methods in multiple sequence alignment and phylogenetic tree estimation, in terms of computational complexity,
4) Explain the notion of e-value and p-value in homology search against a large database, and compute the values,
5) Explain several algorithmic techniques to make faster homology search against a large database, and
6) Explain analog and digital simulation approaches for behaviors of living cells.

Keywords

biological information, sequence analysis, dynamic programming, hidden Markov model, analog simulation, digital simulation

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills ✔ Critical thinking skills Practical and/or problem-solving skills

Class flow

Each class starts from explanation of new topic (through notion, example, systems, applicational importance, etc.). At the end of class, students are given exercise problems related to the lecture given that day to solve.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Global sequence alignment 　- Optimal path search, dynamic programming, global sequence alignment, local sequence alignment Calculate global/local sequence alignment based on dynamic programming
Class 2 Multiple sequence alignment 　- Complexity of multiple alignment, and heuristic methods Calculate multiple sequence alignment based on star method or tree-based method
Class 3 Phylogenetic tree estimation 　－Distance matrix method，character state method，and bootstrap evaluation Calculate phylogenetic tree based on UPGMA method or NJ method
Class 4 Homology search against database 　－Amino acid mutation matrix，hit significance，e-value，bit score, and p-value Calculate e-value and p-value for a hit in homology search
Class 5 Faster methods for sequence homology search 　－FASTA，BLAST，and PSI-BLAST Build a k-mer index table for faster similarity search
Class 6 Motif representation and extraction 　－Regular expression, profile matrix, and hidden Markov model Understanding several mathematical models for representing sequence motifs
Class 7 Protein structure analysis 　－Secondary structure, tertiary structure, and molecular simulation Understand protein secondary/tertiary structures and analysis methods
Class 8 Analog simulation of living cells (1) To be announced on the class
Class 9 Analog simulation of living cells (2) To be announced on the class
Class 10 Analog simulation of living cells (3) To be announced on the class
Class 11 Digital simulation of living cells (1) To be announced on the class
Class 12 Digital simulation of living cells (2) To be announced on the class
Class 13 Digital simulation of living cells (3) To be announced on the class
Class 14 Advanced topics on life simulations To be announced on the class

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

Original slides by Akiyama and Yamamura are provided.

Reference books, course materials, etc.

(Ed. Japanese Society of Bioinformatics). Introduction to Bioinformatics. Tokyo: Keio University Press; ISBN：978-4-7664-2251-1. (Japanese)

Assessment criteria and methods

Students' knowledge of data representations, algorithms, and applications in biological information analysis, and their ability to apply them to problems will be assessed.
Final exams 80%, exercise problems 20%.

Related courses

  • none

Prerequisites (i.e., required knowledge, skills, courses, etc.)

none

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