2020 Genome Informatics

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Academic unit or major
Undergraduate major in Life Science and Technology
Instructor(s)
Itoh Takehiko  Yamada Takuji 
Course component(s)
Lecture
Mode of instruction
 
Day/Period(Room No.)
Intensive (南4号館3階第二演習室)  
Group
-
Course number
LST.A351
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 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 1 using next-generation sequencer data Understand the outline of computational analysis methods of Next Generation Sequencing data.
Class 4 Exercise 2 using next-generation sequencer data Understand the outline of computational analysis methods of Next Generation Sequencing data.
Class 5 Exercise 3 using next-generation sequencer data Understand the outline of computational analysis methods of Next Generation Sequencing data.
Class 6 Exercise 4 using next-generation sequencer data Understand the outline of computational analysis methods of Next Generation Sequencing data.
Class 7 Exercise 5 using next-generation sequencer data Understand the outline of computational analysis methods of Next Generation Sequencing data.
Class 8 Exercise 6 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

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.

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