2025 (Current Year) Faculty Courses School of Life Science and Technology Undergraduate major in Life Science and Technology
Bioinformatics2 (LST)
- Academic unit or major
- Undergraduate major in Life Science and Technology
- Instructor(s)
- Kengo Sato / Takuji Yamada / Akio Kitao / Takehiko Itoh / Koichiro Uriu
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue (W2-401（W241）) / 5-6 Fri (W2-401（W241）)
- Class
- -
- Course Code
- LST.A350
- Number of credits
- 200
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Jun 5, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course focuses on the basics of bioinformatics, which is an integrated field of life science and information science.
Course description and aims
Understanding advanced topics of bioinformatics
Keywords
Bioinformatics, database, sequence analysis, phylogenetic analysis, biophysics, machine learning, analysis and prediction of protein structure
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Lecture is given for each topic, followed by some practices when necessary.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Review of sequence data analysis, including alignment, homology search, and phylogenetic inference. | Understand the fundamental methods of sequence comparison used in sequence data analysis. |
| Class 2 | Basics of NGS and its analysis, including SNP detection and RNA-seq analysis. | Understand the basics of next-generation sequencing (NGS) data and its applications. |
| Class 3 | In silico drug discovery based on machine learning | Understand the application of informatics, especially machine learning, in drug discovery. |
| Class 4 | RNA structure prediction | Understand the principles of RNA 2D/3D structure prediction. |
| Class 5 | RNA sequence analysis | Understand advanced algorithms for RNA sequence analysis. |
| Class 6 | Stochastic processes in life science: carcinogenesis as an evolutionary process | Understand stochastic processes such as Moran and branching processes. |
| Class 7 | Basic systems biology: mathematical analysis of gene regulation (ordinary differential equations) | Understand ordinary differential equations describing gene regulation. |
| Class 8 | Self-organization of spatial patterns: fish skin pattern formation (partial differential equations) | Understand the mechanism of spontaneous pattern formation in reaction-diffusion systems. |
| Class 9 | Introduction to protein structure information | Understand basics of protein strcture information |
| Class 10 | Data analysis of protein structure information | Understand ｄata analysis of protein structure informatioｄ |
| Class 11 | Prediction of protein structure information | Understand prediction methods of protein structure information |
| Class 12 | Metagenomic analysis: taxonomic and functional profiling | Understand the relationship between microbial taxonomy and function, and methods for their inference |
| Class 13 | Statistical analysis and visualization of metagenomic data | Understand statistical analyses of metagenomic diversity and the visualisation method |
| Class 14 | Application of Metagenomic Analysis to Human Gut Data | Understand applications of metagenomic analysis to the human gut environment and their interpretation |
Study advice (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)
None.
Reference books, course materials, etc.
Japanese Society of Bioinformatics. Bioinformatics Nyumon (Japanese), ISBN-13: 978-4766422511
David Mount. Bioinformatics: Sequence and Genome Analysis 2nd Edition, ISBN-13: 978-0879697129
Evaluation methods and criteria
Evaluation will be based on a final examination tailored to the content covered by each instructor.
Related courses
- LST.A246 ： Bioinformatics(LST)
- LST.A351 ： Bioinformatics3 (LST)
- LST.A241 ： Biostatistics
Prerequisites
Recommend to be taken concurrently with LST.A351 ： Bioinformatics3 (LST).