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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
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School of Environment and Society
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- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Undergraduate major in Life Science and Technology
- Instructor(s)
- Itoh Takehiko Yamada Takuji
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue7-8(南4号館3階第二演習室) Fri7-8(南4号館3階第二演習室)
- Group
- -
- Course number
- LST.A351
- Credits
- 2
- Academic year
- 2021
- Offered quarter
- 2Q
- Syllabus updated
- 2021/6/14
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course focuses on the genome informatics which is a branch of bioinformatics. Genome informatics is a field which has achieved rapid development in recent years. Students will be acquired the knowledge of both genome biology and genome informatics fields.
Student learning outcomes
By the end of this course, students will be able to:
1) Understand the basic experimental methods in genome sequence analysis.
2) Understand the computational analysis methods in genome informatics fields.
3) Understand the latest topics in genome informatics.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
|
In this lecture, faculty members who have practical experience in genome/gene information analysis in a private company will utilize their practical experience. Classes will be provided to show that genome informatics can be applied not only to basic research at universities but also to useful genome/gene analysis in private companies. |
Keywords
Genome informatics, sequence analysis, Next Generation Sequnecer
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Before coming to class, students should read the course schedule and check what topics will be
covered. Required learning should be completed outside of the classroom for preparation and review
purposes.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction to genome informatics | Understand the outline of genome informatics. |
| Class 2 | Genome sequence determination -- DNA sequencing -- | Understand the fundamental experimental methods of DNA sequencing and the outline of genetic sequence analysis. |
| Class 3 | Exercise A-1 using next-generation sequencer data | Understand the outline of computational analysis methods of Next Generation Sequencing data. |
| Class 4 | Exercise A-2 using next-generation sequencer data | Understand the outline of computational analysis methods of Next Generation Sequencing data. |
| Class 5 | Exercise A-3 using next-generation sequencer data | Understand the outline of computational analysis methods of Next Generation Sequencing data. |
| Class 6 | Exercise B-1 using next-generation sequencer data | Understand the outline of computational analysis methods of Next Generation Sequencing data. |
| Class 7 | Exercise B-2 using next-generation sequencer data | Understand the outline of computational analysis methods of Next Generation Sequencing data. |
| Class 8 | Exercise B-3 using next-generation sequencer data | Understand the outline of computational analysis methods of Next Generation Sequencing data. |
| Class 9 | Exercise 1 using metagenome sequence data | Understand the outline of computational analysis methods of metagenome sequence data |
| Class 10 | Exercise 2 using metagenome sequence data | Understand the outline of computational analysis methods of metagenome sequence data |
| Class 11 | Exercise 3 using metagenome sequence data | Understand the outline of computational analysis methods of metagenome sequence data |
| Class 12 | Exercise 4 using metagenome sequence data | Understand the outline of computational analysis methods of metagenome sequence data |
| Class 13 | Exercise 5 using metagenome sequence data | Understand the outline of computational analysis methods of metagenome sequence data |
| Class 14 | Exercise 6 using metagenome sequence data | Understand the outline of computational analysis methods of metagenome sequence data |
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)
None
Reference books, course materials, etc.
T.A. Brown Genomes (3rd edition)
David Mount. Bioinformatics: Sequence and Genome Analysis 2nd Edition
Assessment criteria and methods
Exercise 100%
Related courses
- LST.A246 ： Bioinformatics
Prerequisites (i.e., required knowledge, skills, courses, etc.)
No prerequisites.
