2025 (Current Year) Faculty Courses School of Life Science and Technology Undergraduate major in Life Science and Technology

Bioinformatics3 (LST)

Academic unit or major: Undergraduate major in Life Science and Technology
Instructor(s): Takehiko Itoh / Takuji Yamada / Akio Kitao / Kengo Sato / Koichiro Uriu
Class Format: Lecture (Face-to-face)
Media-enhanced courses: -
Day of week/Period (Classrooms): 7-8 Tue (南4号館3階第二演習室) / 7-8 Fri (南4号館3階第二演習室)
Class: -
Course Code: LST.A351
Number of credits: 200
Course offered: 2025
Offered quarter: 2Q
Syllabus updated: Jun 5, 2025
Language: Japanese

Syllabus

Course overview and goals

In this course, students will reinforce what they learned in Bioinformatics I and II through hands-on exercises.

Course description and aims

By the end of this course, students will be able to:
1) Understand computational approaches used in the field of bioinformatics.
2) Understand current topics and recent advances in bioinformatics.

Student learning outcomes

実務経験と講義内容との関連 (又は実践的教育内容)

In this lecture, faculty members with practical experience in genome and gene information analysis from a private company will share their expertise.
Classes will be provided to show that bioinformatics can be applied not only to basic research at universities but also to various kinds of analysis in private companies.

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

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.
In FY2024, the course will be offered in a face-to-face format.

Course schedule/Objectives

	Course schedule	Objectives
Class 1	Practical training in homology search, sequence alignment, and phylogenetic tree construction	Understand the fundamentals of sequence data analysis.
Class 2	Practical training in gene expression analysis using RNA-seq data	Understand the outline of computational analysis methods of Next Generation Sequencing data.
Class 3	Machine Learning Programming (in silico drug discovery)	Programming exercises of machine learning in Google Colab environment
Class 4	Machine Learning Programming (in silico drug discovery)	Understand the handling of small molecules and implementation of machine learning in Python
Class 5	Machine Learning Programming (in silico drug discovery)	Understanding In Silico Drug Discovery through Virtual Screening
Class 6	Introduction to MATLAB: stochastic simulation of carcinogenesis	Understand how to perform stochastic simulations.
Class 7	Ordinary differential equation in MATLAB: simulation of circadian rhythms	Understand the numerical solution of ordinary differential equations.
Class 8	Partial differential equations in MATLAB: simulation of reaction-diffusion equations	Understand the numerical solution of partial differential equations.
Class 9	Basic excercise of protein structure information	Understand protein structure information through basic excercise
Class 10	Excercise of proetin structure information analysis	Understand analysis of proetin structure information through excercise
Class 11	Excersice of protein structure prediction	Understand protein structure prediction through excercise
Class 12	Practical Training: Data Acquisition and Preprocessing	Gain a practical understanding of public data acquisition and preprocessing steps
Class 13	Practical Training: Taxonomic Classification and Functional Profiling	Understand how to perform and analyse taxonomic classification and functional profiling of bacterial communities
Class 14	Practical Training: Group Comparison and Diversity Analysis	Understand how to evaluate gut microbial environments using group comparisons and diversity analysis

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.

T.A. Brown Genomes (3rd edition)
David Mount. Bioinformatics: Sequence and Genome Analysis 2nd Edition

Evaluation methods and criteria

Evaluation will be based on participation in exercises and reports assigned by each instructor.

Related courses

LST.A246 ： Bioinformatics
LST.A241 ： Biostatistics
LST.A350 ： Bioinformatics2 (LST)

Prerequisites

It is strongly recommended to take LST.A350: Bioinformatics II concurrently.