Agricultural Bioinformatics
Research Unit's
Educational Program
Application Guidelines FY2024
Notice! All lectures will be conducted in Japanese.

1. Introduction
The importance of bioinformatics in the fields of agricultural and life sciences
has significantly increased. Serving as a vital methodology, bioinformatics
plays an indispensable role in addressing today's critical social issues of food,the environment, and life. Moreover, it serves as a means of integrating
diversedisciplines. Hence, thereis apressing need for

educationencompassing both the fundamental and applied aspects of this discipline.
Our program provides hands-on education in bioinformatics and conducts
cutting-edge

research

inagricultural

and

life

sciences

relatedtobioinformatics. Over the past 20 years, more than 8,300 individuals haveparticipated in courses and earned credits through our program. Through our
educational and research endeavors, we strive to establish a platform forsocialcollaboration

andinternational

cooperationinagriculturalbioinformatics.
2. Application ProceduresThis program offers graduate-level classes based on an undergraduate-levelknowledge of life sciences. Additionally, as many classes require computer
usage, students are expected to possess some proficiency in basic computer
operations. Therefore, we anticipate that our courses will be attended by
graduate students in the natural sciences as well as by working professionals
engaged in research and development in agricultural and life sciences,including medical and pharmaceutical sciences.
If you are a student at the University of Tokyo, please register through UTAS.
If you are not a student at the University of Tokyo, please apply using theapplication form available on the website from May 7 to June 18. Only
applicants who have successfully passed the selection process will be eligible
to participate in the program. Successful applicants will be notified via email
by June 28.
Program guidance
We will post the guidance materials on our website starting Wednesday, April 3.

1

3. Classes (see page 6 and after for details)
Generally, classes offered in this program have three categories.
Fundamentals:
Introduction to Biological Sequence Analysis, Introduction to Genome
Informatics, Introduction to Biostatistics, and Introduction to Structural
Bioinformatics are mainly for those who have no research experience using
bioinformatics. You will learn how to use various life science databases andbioinformatics tools and will also be taught the basic elements of statistics.
Methodologies:
Based on the Fundamentals classes. These lectures focus on various big data
generation methods, observation devices, and computer methods foranalyzing them (pattern recognition, machine learning, statistical model andmodel selection, molecular modeling).
Advanced Topics:
You can learn about programming, which is becoming increasingly important
as a means of utilizing IoT, analyzing large amounts of diverse experimental
and observational data, and improving the efficiency and development ofresearch. We aim to provide feedback on individual research issues bytouching on actual use cases of bioinformatics.

2

4. List of lecture courses
Course CodeCourse Namesemester*/credit

3912135

Introduction to Biological Sequence Analysis

S1・13912136

Introduction to Genome Informatics

S1・1

3912103

Introduction to Biostatistics

S1・1

3912139

Introduction to Structural Bioinformatics

S1・1

3912137

Knowledge Information Processing

SP・13912105

Sequence Statistics and Mathematical Biology

3912106

Molecular Modeling and Simulation

S1・1

3912138

Omics Analysis

A1・13912108

Functional Genomics

S1・13912109

Introduction to Systems Biology

S1・13912157

Field Informatics

S1・1

3912111Special Lectures on Agricultural Bioinformatics I

A1・1

3912112Special Lectures on Agricultural Bioinformatics II

S1・1

3912140

Special Lectures on Agricultural Bioinformatics III

S1・1

3912141

Special Lectures on Agricultural Bioinformatics IV―

3912142

Research Exercise on Agricultural Bioinformatics

―

*

―

Semester for registering courses using UTAS

The four fundamental subjects are Intermediate Subjects. University of Tokyo
undergraduate students can add these to their course credits.
060700130 Introduction to Biological Sequence Analysis
060700140 Introduction to Genome Informatics
060700150 Introduction to Biostatistics
060700160 Introduction to Structural Bioinformatics
University of Tokyo graduate students who complete and pass the following
courses may count them toward the credits required for completion of thisprogram.
3901159

Biometrics

S1・2

3908101

Biotechnology informatics

S1・1

4917490

Advanced Lectures in Applied Computer Science XVII

S1・1

4917491

Advanced Lectures in Applied Computer Science XVIII

S1・1

4917891

Advanced Lectures in Applied Computer Science 13A

S1・1

3

5. Notes
For University of Tokyo students⚫

Most lectures are online (Zoom) only.

⚫

All lectures will be conducted in Japanese. Please refer to the current
year’s Graduate School Handbook for the availability of materials in
English.

⚫

Lectures for University of Tokyo students (students who can register
through UTAS) will generally take place 17:15-18:45 and 19:00-20:30.
Changes to the course schedule will be announced on UTAS, UTOL, or X
(Formerly Twitter) as soon as possible.

⚫

Reflection from UTAS to UTOL takes one day. Even if you register for thecourse just before the lecture, you cannot view the UTOL, get the lecturematerials, or register for attendance. Please complete the course
registration through UTAS at least two days before the class starts.

⚫

Please prepare online classes, referencing utelecon (Online Class and Web
Conference Portal Site).

⚫

The Zoom URL and information about the lectures will be posted on UTASor UTOL.

⚫

If you receive an error message when you register at UTAS even though
the lecture schedules do not overlap, please register one of the courses
at UTAS and submit an additional registration form to the Educational
Section of the graduate school to which you belong.

⚫

In the Graduate School of Agricultural and Life Sciences of the Universityof Tokyo, and in many other graduate schools of the University of Tokyo,
credits for some courses taken in this program can be added to the credits
required to complete a master's degree. For details, please refer to thecurrent year's Graduate School Handbook.

⚫

Please install the necessary software for each lecture beforehand.

For students outside of the University of Tokyo⚫

If you are not a student at the University of Tokyo, please apply during
the application period from May 7 to June 18. Applications submitted
outside this period will not be accepted.

⚫

Only those who have passed the selection process will be allowed toattend. Successful applicants will be notified by email by June 28.

⚫

Students who pass the selection process will be required to watch lectures4

that will be distributed on-demand from July 4.
⚫

Outside students will not be able to attend course lectures on the samedays and times as University of Tokyo students. Please refer to the course
details for the lectures that will be delivered on-demand.

⚫

The information about the lectures will be posted on dedicated slack.

⚫

Before attending a lecture, please install the necessary software on yourPC by referring to the software installation page on our website.

⚫

Details of the on-demand streaming will be sent to you via email. You canwatch a lecture as many times as you like during the on-demand
streaming period, and you may also ask questions to the teacher.

⚫

Credits earned by taking this program cannot be used to complete a
master's degree at another university or to apply for a degree from the
National Institution for NIAD-QE Evaluation.

⚫

Please install the necessary software for each lecture beforehand.

⚫

Even after registration is accepted, on-demand distribution may becanceled due to circumstances.

For all students⚫

Those who have acquired at least 8 credits will be recognized ashaving completed the specialized education course and will beawarded a "Certificate of Completion."56. Course details
CourseInstructors

Introduction to Biological Sequence Analysis(Course code:3912135/060700130)
Kentaro Shimizu, Kenro Oshima, Koji Kadota

Lecture4/10 (Wed), 4/17 (Wed), 4/24 (Wed), 5/1 (Wed)dates*University of Tokyo students only

Outsidestudents

On-demand distribution available(Distribution period from 7/4 to 10/31,
Assignment submission until 8/31)

Course objectives / Overview
This course focuses on the use of databases and basic analysis methods
for life sciences. The use of sequence and functional databases will be
introduced, and basic methods such as homology search, motif analysis,
Python programming, and phylogenetic analysis will be explained in a
hands-on format.
Schedule
1) Sequence database and homology search
2) Genome Database and Programming3) Various methods for predicting functions from sequences
4) Molecular evolution and phylogenetic tree construction
Teaching Methods
This course is conducted using Zoom, and students can watch recorded
lectures.
Grade Evaluation
Students are evaluated based on the completion of homework assignments.

Reference Books
門田幸二、清水謙多郎、岸野洋久、寺田透

共編著、Web 連携テキスト バイオ

インフォマティクス 基礎から応用、培風館、2022
Hiroyuki Toh (ed.), Understanding Bioinformatics, Kodansha, 2018.
H. Bono, Life Science Data Analysis, MEDSi、2021
Notes on taking the course
We use BLAST、MEGA、and Python. Please install them before attending
the class.
Other
If you have never accessed various databases related to bioinformatics,
please take this course.
6

Introduction to Genome Informatics

Course(Course code:3912136/060700140)

Instructors

Koji Kadota, Hiroshi Mori

Lecture4/9(Tue), 4/16 (Tue), 4/23 (Tue), 4/30 (Tue)dates*University of Tokyo students only
On-demand distribution available

Outside(Distribution period from 7/4 to 10/31,

students

Assignment submission until 8/31)

Course objectives / Overview
In this course, we will discuss the basics of the fields of genome
information analysis and metagenomic analysis.
Includedarelectureson k-meranalysisfrequently

used inthebioinformatics field.
Schedule
1) General introduction to genome information analysis system and
RStudio (4/9: Kadota)
2) Basics of Metagenomic Analysis (4/16: Mori)
3) Applications of Metagenomic Analysis (4/23: Mori)
4) Basics and applications of k-mer Analysis (4/30: Kadota)
Teaching Methods
This course is conducted using Zoom, and students can watch recorded
lectures.
Grade Evaluation
Students are evaluated based on the completion of homework assignments.

Reference Books
門田幸二、清水謙多郎、岸野洋久、寺田透

共編著、Web 連携テキスト バイオ

インフォマティクス 基礎から応用、培風館、2022
H. Bono, Life Science Data Analysis, 2nd Edition, MEDSi, 2021
Nagata, N., Introduction to Bioinformatics Reading by Evolution, Morikita
Publishing, 2019.
Hiroyuki Toh (ed.), Understanding Bioinformatics, Kodansha, 2018.
Notes on taking the course
We use RStudio. Please install R and RStudio. Please install various R
packages by referring to UTOL (for University of Tokyo students) or slack(for outside students).

7

CourseIntroduction to Biostatistics(Course code:3912103/060700150)

Instructor

Hiroyoshi Iwata

Lecture4/12 (Fri), 4/19 (Fri), 4/26 (Fri), 5/10 (Fri)

dates*University of Tokyo students only

Outsidestudents

On-demand distribution available(Distribution period from 7/4 to 10/31,
Assignment submission until 8/31)

Course objectives / Overview
This course teaches introductory biostatistics using the statistical analysis
software R. This course is designed for students who are using R for the
first time and will focus on practical training using laptop computers.
Schedule
1) Visualizing data in R
2) Regression analysis, analysis of variance3) Principal component analysis, multidimensional scaling
4) Hierarchical cluster analysis, non-hierarchical cluster analysis
Teaching Methods
This course is conducted using Zoom, and students can watch recorded
lectures.
Grade Evaluation
Students are evaluated based on the completion of homework assignments.

Reference Books
Reference books for further study will be introduced during the course.
Notes on taking the course
We use RStudio. Please install R and RStudio. Please install them before
attending the class.

8

CourseIntroduction to Structural Bioinformatics(Course code：3912139/060700160)

Instructors

Tohru Terada, Koji Nagata, Takeshi Kawabata

Lecture4/11 (Thu), 4/18 (Thu), 4/25 (Thu), 5/2 (Thu)

dates*University of Tokyo students only

Outsidestudents

On-demand distribution available(Distribution period from 7/4 to 10/31,
Assignment submission until 8/31)

Course objectives / Overview
This course instructs students on how to use a protein 3D structure
database and illustrates its applications. This course also explains
information processing methods used in 3D structure determination.
Schedule
1) Use of 3D structure database and visualization of 3D structure data
2) Information extraction from 3D structure database3) Informatics of 3D structure determination by X-ray crystallography4) Informatics of 3D structure determination by cryo-electron microscopy
Teaching Methods
This course is conducted using Zoom, and students can watch recorded
lectures.
Grade Evaluation
Students are evaluated based on the completion of homework assignments.

Notes on taking the course
We use UCSF Chimera, Modeller, and CCP4 Software Suite. Please installthem before attending the class.

9

CourseInstructor

Knowledge Information Processing(Course code:3912137)
Minoru Asogawa
9/20(Fri), 9/27 (Fri), 10/4 (Fri), 10/11 (Fri), 10/18(Fri),

Lecture10/25 (Fri), 11/1 (Fri)

dates*University of Tokyo students only
*Lecture time is 17:15-19:00, unlike other lectures.

Outsidestudents

On-demand distribution available(Distribution period from 10/31 to 1/23,
Assignment submission until 12/26)

Course objectives / Overview
This course provides an introduction to pattern recognition and machinelearning using bioinformatics data.
Schedule
1) Neural network basic, 2) Discrimination analysis and application
examples, 3) Analysis method for trained neural network, deep learningand correlation analysis, 4) Clustering analysis and principal component
analysis, 5) Kernel based learning (SVM), 6) Decision tree, and 7) Hidden
Markov model.
Teaching Methods
This course is conducted using Zoom, and students can watch recorded
lectures.
Grade Evaluation
In the previous year, the course grade was determined on the basis of fivereport assignments. This evaluation method may be altered depending on
the student's proficiency level.
Notes on taking the course
We use R. Please install R and RStudio. It's assumed that you have alreadymastered the primary usage of R (or RStudio).
Please refer to https://www.iu.a.u-tokyo.ac.jp/textbook/appendix.html
and understand the basics.

10

CourseMolecular Modeling and Simulation(Course code:3912106)

Instructor

Tohru Terada

Lecture5/9 (Thu), 5/16 (Thu), 5/23 (Thu), 6/6 (Thu)

dates*University of Tokyo students only

Outsidestudents

On-demand distribution available(Distribution period from 8/1 to 12/26,
Assignment submission until 10/31)

Course objectives / Overview
This course teaches students about the molecular orbital, molecularmechanics, molecular dynamics, Monte Carlo, and complex modelingmethods. This course also provides training in each of these methods.
Schedule
1) Molecular orbital method
2) Molecular mechanics method
3) Molecular dynamics method
4) Monte Carlo method
5) Complex modeling method
Teaching Methods
This course is conducted using Zoom, and students can watch recorded
lectures.
Grade Evaluation
Students are evaluated based on the completion of homework assignments.

Reference Books
Andrew R. Leach, Molecular Modeling: Principles and Applications, Prentice
Hall, 2001.
Notes on taking the course
We use UCSF Chimera, NAMD, and Python. Please install them before
attending the class.

11

CourseInstructors

Omics Analysis(Course code:3912138)

Koji Kadota

Lecture10/2 (Wed), 10/9 (Wed), 10/16 (Wed)dates*University of Tokyo students only
*Lecture time is 13:00-17:00, unlike other lectures.

Outsidestudents

On-demand distribution available(Distribution period from 11/2 to 1/23,
Assignment submission until 12/26)

Course objectives / Overview
Many students struggle with mathematical formulas and algorithms. This
course focuses on the problem of identifying regions with specificcharacteristics in the genome sequence and carefully explains the thoughtprocess of algorithm development researchers, including basic concepts
and performance evaluation metrics.
Schedule
1) Basics of epigenome analysis and CGI region prediction (section 2.4 in
the textbook)2) PSO algorithm (first half of section 2.6 in the textbook)3) Elongation of the CGI domain using reinforcement learning (second
half of section 2.6 in the textbook)
Teaching Methods
This course is conducted using Zoom, and students can watch recorded
lectures.
Grade Evaluation
Students are evaluated based on the completion of homework assignments.

Textbook
門田幸二、清水謙多郎、岸野洋久、寺田透

共編著、Web 連携テキスト バイオ

インフォマティクス 基礎から応用、培風館、2022
Notes on taking the course
We use RStudio. Please install R and RStudio before attending the class.

12

CourseFunctional Genomics (Course code:3912108)

Instructors

Koji Kadota, Yasuhiro Tanizawa, Koichi Higashi

Lecture5/14 (Tue), 5/21 (Tue), 5/28 (Tue), 6/4 (Tue)dates*University of Tokyo students only
On-demand distribution available

Outsidestudents(Distribution period from 8/1 to 12/26,
Assignment submission until 10/31)

Course objectives / Overview
This lecture focuses on methods used in the fields of genome analysis andtranscriptome analysis.
We will cover de novo assembly and annotation of bacterial genomes, Linuxbasics, comparative genomic analysis, chromosome structure analysis,
and single cell analysis.
Schedule
1) Genome analysis: Bacterial genome analysis and its surroundings(5/14: Tanizawa)
2) Linux basics: Comparative genomic analysis on Linux (5/21: Kadota)
3) Genome Analysis: Chromosome structure analysis centered on Hi-C
data (5/28: Higashi)
4) Transcriptome Analysis: (6/4: Higashi)
Teaching Methods
This course is conducted using Zoom, and students can watch recorded
lectures.
Grade Evaluation
Students are evaluated based on the completion of homework assignments.

Reference Books
H. Bono (ed.), RNA-Seq Data Analysis Iron Plate Recipes for Wet Labs,
Yodosha, 2019.
Hiroyuki Toh (ed.), Understanding Bioinformatics, Kodansha, 2018.
H. Bono, Life Science Data Analysis, MEDSi, 2021
Platformfor

Advanced

GenomeScience(ed.),

Self-study

PythonBioinformatics Analysis, Yodosha, 2021
Notes on taking the course
We use RStudio. Please install R and RStudio before attending the class.
side students).

13

CourseInstructor
Lecturedates
Outsidestudents

Introduction to Systems Biology (Course code:3912109）
Masanori Arita
7/5(Fri), 7/12 (Fri), 7/19 (Fri)*University of Tokyo students only
*Lecture time is 13:00-17:00, unlike other lectures.
On-demand distribution available(Distribution period from 8/1 to 12/26,
Assignment submission until 10/31)

Course objectives / Overview
Introductionofstatisticalandgraph-theoretic

analyses

for

omics

information to understand life as a system.
Schedule
Seven lectures in 3 days. The course explains the underlying ideas andmethods of dimensional reduction and sparse modeling in big- and omics
data, and network analysis. This course also covers recent research trends
with concrete examples.
Teaching Methods
This course is conducted using Zoom, and students can watch recorded
lectures.
Grade Evaluation
Students are evaluated based on the completion of homework assignments.

Reference Books
門田幸二、清水謙多郎、岸野洋久、寺田透

共編著、Web 連携テキスト バイオ

インフォマティクス 基礎から応用、培風館、2022
Uri Alon, An Introduction to Systems Biology: Design Principles of Biological
Circuits, Chapman & Hall/CRC, 2019

Notes on taking the course
CellDesigner and R software programs are necessary. Please install before
attending the class.

14

CourseInstructors

Field Informatics(Course code:3912157）
Yoshihiro Ohmori, Takeshi Izawa, Hiroyoshi Iwata, Wei Guo,
Naoyuki Sotta

Lecture6/5 (Wed), 6/12 (Wed), 6/19 (Wed), 6/26 (Wed)dates*University of Tokyo students only

Outsidestudents

On-demand distribution available(Distribution period from 8/1 to 12/26,
Assignment submission until 10/31)

Course objectives / Overview
The quantity and quality of crop production are not determined solely bygenomic information but are determined as a result of interactions withenvironmental conditions of field, such as weather, soil, nutrient, andmicroorganisms, etc. that change spatially and temporally. In this lecture,
we will introduce various research methods using images, genome,transcriptome, and ionome information obtained from the field.
Schedule (This course provides omnibus lectures covering topics below)
6/5: Image analysis in agriculture
6/12: Genome Wide Association Study and Genomic Prediction
6/19: Field-transcriptome analysis
6/26: Measurement of plant traits from image (The first half)
Machine learning using ionome -classification- (The second half)
Teaching Methods
This course is conducted online using Zoom, and students can watch
recorded lectures. We will use R and RStudio for the hands-on practice.
Grade Evaluation
Evaluation is based on the submission of report on any topic of interest
treated in the course.
Textbook
門田幸二、清水謙多郎、岸野洋久、寺田透

共編著、Web 連携テキスト バイオ

インフォマティクス 基礎から応用、培風館、2022
Notes on taking the course
We use Rstudio. Please install R and RStudio before attending the class.
Field informatics relates to lectures, Introduction to Biostatistics, Special
Lectures on Agricultural Bioinformatics II and III.

15

CourseSpecial Lectures on Agricultural Bioinformatics I(Course code:3912111)

Instructor

Jianqiang Sun

Lecture10/8 (Tue), 10/15 (Tue), 10/22 (Tue), 10/29 (Tue)dates*University of Tokyo students only
*Lecture time is 13:15-16:30, unlike other lectures.

Outsidestudents

On-demand distribution available(Distribution period from 10/31 to 1/23,
Assignment submission until 12/26)

Course objectives / Overview
Smart agriculture enables high quality and large crop production by
utilizing advanced technologies such as ICT and IoT, which are currently
being promoted worldwide. One of the technologies that supports thefoundation of smart agriculture is programming languages. Python is oneof the easiest programming languages to learn and has a wide range ofapplications. In this course, we will cover the basics of Python andintroduce examples of the latest practical applications of Python inagriculture as well as in molecular biology fields.
Schedule
1) Python basics (variable, data type, control flow)2) Text data processing3) Data analysis (CSV file processing, data visualization)4) Machine learning in agriculture (deep learning, Web API, etc)
Teaching Methods
This course is conducted using Zoom, and students can watch recorded
lectures.
Grade Evaluation
Students are evaluated based on the completion of homework assignments.

Reference Web Sites
http://diveintopython3-ja.rdy.jp/
https://utokyo-ipp.github.io/
Notes on taking the course
Miniconda3 (https://docs.conda.io/) will be used for the lecture, but
students may also use Google Colab (https://colab.research.google.com/).

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CourseSpecial Lectures on Agricultural Bioinformatics II(Course code:3912112)

Instructor

Gen Sakurai, Yoshihiro Ohmori

Lecture6/7 (Fri), 6/14 (Fri), 6/21 (Fri), 6/28 (Fri)

dates*University of Tokyo students only

Outsidestudents

On-demand distribution available(Distribution period from 8/1 to 12/26,
Assignment submission until 10/31)

Course objectives / Overview
In this lecture, to learn the basics of building a mathematical model in eachcrop cultivation system, we will learn the method of mathematicallymodeling and simulating the growth of plants, the relationship between
plants and micro-meteorology, and plant-physiological phenomena. We
aim to use this lecture as a starting point for constructing mathematicalmodels of biological phenomena such as plant growth and the dynamics ofminerals in plants. Lectures will include instruction on importantmathematical models relating to agrometeorology, plant growth, and the
dynamics of minerals and coding exercises on the formulas. In this lecture,
we will learn the basic knowledge of them using R, so even those without
experience with R can take this course.
Schedule
1.Modelling of plant light reception and transpiration
2.Modelling of photosynthesis and plant growth
3.Basics of water potential, diffusion equation, and the dynamics of waterin soil
4.Modeling of water flow in plants
Teaching Methods
This course is conducted using Zoom, and students can watch recorded
lectures.
Grade Evaluation
Students are evaluated based on the completion of homework assignments.

Reference Books
Reference books for further study will be introduced during the course.
Notes on taking the course
We use RStudio. Please install it before attending the class.

17

CourseInstructors
Lecturedates
Outsidestudents

Special Lectures on Agricultural Bioinformatics III(Course code:3912140)
Noboru Koshizuka, Shinsuke Kobayashi, Yoshihiro Ohmori
5/15 (Fri), 5/22 (Fri), 5/29 (Fri)*University of Tokyo students only
*Lecture time is 13:00-17:00, unlike other lectures.
On-demand distribution available(Distribution period from 8/1 to 12/26,
Assignment submission until 10/31)

Course objectives / Overview
In this course, students will learn to write a simple data processing programfor environmental monitoring using data from environmental sensors
installed in the greenhouse of the Institute for Sustainable Agro-ecosystem
Services (UTokyo ISAS). The goal is to be able to write data processing
programs in Python, handle data in JSON and CSV formats, and performstatistical processing and graphing.
Schedule
Lecture / PracticeDay 1: Overview of IoT Environment / Basic programming using Python
Day 2: Fundamentals of IoT Technology1 / Data processing programming
Day 3: Fundamentals of IoT Technology2 / Presentation of assignments
Teaching Methods
This course is conducted using Zoom, and students can watch recorded
lectures.
Grade Evaluation
Students are evaluated based on the completion of homework assignments.

Notes on taking the course
Use Google Colaboratory (Colab: Python execution environment in thecloud). In this lecture, the content will be understandable even for students
who are new to programming, but it is desirable to have some programming
experience regardless of language.
This course is related to the Methodology "Field Informatics" and theAdvanced Topics "Special Lectures on Agricultural Bioinformatics II."
Therefore, we recommend that you also take them.

18

7.Program faculty
Program faculty includes full-time faculty members of the Agricultural
Bioinformatics Research Unit, which is the main organizer of the program, as
well as adjunct faculty and lecturers belonging to other universities andresearch institutes.
Unit Representative
Dean, Graduate School of
Agricultural and Life Sciences

Yasuhiro Nakashima

Unit Faculty
Assoc. Prof.

Koji Kadotakoji.kadota@gmail.com

Assoc. Prof.

Yoshihiro Ohmori ayohmori@g.ecc.u-tokyo.ac.jp

Proj.Prof.

Kentaro Shimizu

shimizu@bi.a.u-tokyo.ac.jp

Adjunct faculty (in charge of administration and lectures)
Professor

Tohru Terada

Department of Biotechnology

Professor

Masanori Arita

National Institute of Genetics

Professor

Hiroyoshi Iwata

Professor

Hideaki Nojiri

Department of Biotechnology

Professor

Koji Nagata

Department of Biological Chemistry

Department of Agricultural andEnvironmental Biology

Cooperating lecturers
Professor
Visiting
Lecturer
SeniorResearcher

Kenro Oshima

Hosei University

Minoru Asogawa

Tokai University School of Medicine

Jianqiang Sun

National Agriculture and Food Research
Organization

Program office staff
Academic Support Staff

Aya Miura

Project Academic Specialist

Tomoko Terada

19

8. Contact information, program secretariat
In addition to questions about courses, we also accept inquiries about how to
use bioinformatics in your own research. Please feel free to contact us by email.
Agricultural Bioinformatics Research Unit, Graduate School of Agricultural andLife Sciences, The University of Tokyo
1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
Email: info@iu.a.u-tokyo.ac.jp
URL: https://www.iu.a.u-tokyo.ac.jp
X(Formerly Twitter): @Agribio_utokyo
Yayoi Campus Map: The program office is located on the first basement floorof the Faculty of Agriculture Building 2 (Room 14-2).

20