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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
- Graduate major in Life Science and Technology
- Instructor(s)
- Itoh Takehiko Yamada Takuji Kitao Akio
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Mon1-2() Thr1-2()
- Group
- -
- Course number
- LST.A408
- Credits
- 2
- Academic year
- 2022
- Offered quarter
- 3Q
- Syllabus updated
- 2022/9/1
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
How deep knowledge or useful information can we retrieve from diverse and enormous data obtain from multi-omics analysis? This course forcuses on Bioinformatics. Topics includes molecular evolution, sequence analysis, comparative genomics, multi-omics analysis, algorithms for bioinformatics, molecular or metabolic network analysis, and data mining methods. By combining lectures and exercises, the course enables students to understand and acquire the fundamentals of bioinformatics widely applicable to biological research. Bioinformatic approaches taught in this course are not only useful in analyzing multi-omics data, but are applicable to various other types of biological problems. Group work will also be conducted for better understanding.
Student learning outcomes
By the end of this course, students will be able to:
1) Understand principles and methods of sequence analysis based on molecular evolution
2) Understand the knowledge obtained by comparing the gene sequences and genomic sequences
3) Understand computer algorithms in bioinformatic analyses
4) Understand the fundamentals and applications of multi- omics analysis
5) Understanding of basics and applications of molecular dynamics simulation
Keywords
Bioinformatics
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | Practical and/or problem-solving skills |
Class flow
Required learning should be completed outside of the classroom for preparation and review purposes.
This class will be conducted by using Zoom system to reduce the burden caused by travel for students enrolled at both Ookayama and Suzukakedai campuses when taking classes and doing group work.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Overview of classical biomolecular simulation | Understanding of overview of classical biomolecular simulation |
| Class 2 | Model building of biomolecules (molecular mechanics, etc) | Understanding of molecular mechanics |
| Class 3 | Classical biomolecular simulation | Understanding of molecular dynamics simulation |
| Class 4 | Applications of simulation and analysis | Understanding of applications of simulation and analysis of the obtained results. |
| Class 5 | Computer modeling of biomolecules | Understanding of computer modeling of biomolecules using molecular simulation |
| Class 6 | Overview of genome information analysis using NGS and principles of NGS | Understanding the background of genomic information analysis using NGS |
| Class 7 | Mapping-based NGS analysis and its algorithms (1) | Understanding the basic algorithms used in mapping-based NGS analysis (1) |
| Class 8 | Mapping-based NGS analysis and its algorithms (2) | Understanding the basic algorithms used in mapping-based NGS analysis (2) |
| Class 9 | Algorithms in genome assembly, RNA-seq analysis, and ChIP-seq analysis | Understanding the basic algorithms used in genome assembly, RNA-seq analysis, and ChIP-seq analysis |
| Class 10 | Overview of fundamental bioinformatics | Understanding of overview of fundamental bioinformatics |
| Class 11 | Basics of omics data analysis | Understanding of omics data analysis |
| Class 12 | Metagenomics for microbiome | Understanding of metagenomics |
| Class 13 | Applications of metagenomics for human gut microbiome | Understanding of applications of metagenomics |
| Class 14 | Basics of machine learning for omics data | Understanding of machine learning for omics 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.
Neil C. Jones and Pavel A. Pevzner. An Introduction to Bioinformatics Algorithms. ISBN-13: 978-0262101066
Masatoshi Nei and Sudhir Kumar. Molecular Evolution and Phylogenetics. ISBN-13: 978-0195135855
Assessment criteria and methods
By written reports for each class.
Related courses
- None
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Basic level of physical chemistry (quantum chemistry and classical mechanics)
Basic level of mathematics (calculus and linear algebra)
Basic level of statistical physics
Basic level of genomics
