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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.