TB002103 Hands-on Bioinformatics for Multi-Omics Data Analysis and Machine Learning

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School Graduate School of Biomedical and Health Sciences (Master’s Course)

Lecture Code TB002103 Subject Classification Specialized Education

Subject Name マルチオミクスデータ解析と機械学習のための実践的バイオインフォマティクス

Subject Name
（Katakana）マルチオミクスデータカイセキトキカイガクシュウノタメノジッセンテキバイオインフォマティクス

Subject Name in
English Hands-on Bioinformatics for Multi-Omics Data Analysis and Machine Learning

Instructor HAYES CLAIR NELSON,NAKAHARA HIKARU

Instructor
(Katakana) ヘイズ　クレアーネルソン,ナカハラ　ヒカル

Campus Kasumi Semester/Term 1st-Year, Second Semester, 3Term

Days, Periods, and Classrooms (3T) Thur11-12

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Lecture-based, exercise-based, discussion-based

Credits 1.0 Class Hours/Week 2 Language of Instruction B : Japanese／English

Course Level 7 : Graduate Special Studies

Course Area（Area） 26 : Biological and Life Sciences

Course Area（Discipline） 04 : Life Sciences

Eligible Students Students with basic familiarity with bioinformatics and Linux,

Keywords genomics, transcriptomics, pipelines, proteomics, protein structure prediction, metabolomics, machine learning, deep learning, biomarkers, Python, Snakemake, multi-omics

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline Basic familiarity with bioinformatics tools may not be enough to fully leverage data from large-scale multi-omics studies. This advanced course focuses on how to create scalable, reproducible, and automated data analysis pipelines using Bash scripting and workflow engines. The course covers advanced genomics techniques, including long-read sequencing, de novo assembly, and metagenomics. We will also cover cutting-edge techniques in single-cell and spatially resolved transcriptomics. With this foundation, we will then explore multi-omics methods to jointly analyze data from transcriptomics, proteomics, and metabolomics experiments. Finally, we will explore how machine learning can be used for biomarker discovery and predictive modeling from bioinformatics data. Each session will include a lecture taught in English and Japanese to introduce core theoretical concepts, followed by a hands-on workshop where students will apply what they have learned through guided exercises using real biological data. By the end of the course, students will be able to chain bioinformatics tools together into robust cloud-ready multi-omics analysis pipelines.

Class Schedule 1) Advanced bioinformatics pipelines
2) Advanced genomics methods
3) Advanced transcriptomics methods
4) Proteomics and protein structure analysis
5) Metabolomics and lipidomics
6) Multi-omics integration
7) Machine learning for biomarker discovery
8) Deep learning for medical image analysis

Text/Reference
Books,etc. Learning materials will be provided during the course.

PC or AV used in
Class,etc. Text, Handouts, Audio Materials, Visual Materials

(More Details)

Learning techniques to be incorporated Discussions, Quizzes/ Quiz format, Project Learning

Suggestions on
Preparation and
Review The course project is intended to help guide students in learning the specific skills needed for their current or planned research.

Requirements A PC capable of connecting to the Internet.

Grading Method Attendance, proactive learning attitude, assignments in class

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2026Year	School/Graduate School	Graduate School of Biomedical and Health Sciences (Master’s Course)
Lecture Code	TB002103	Subject Classification	Specialized Education
Subject Name	マルチオミクスデータ解析と機械学習のための実践的バイオインフォマティクス
Subject Name （Katakana）	マルチオミクスデータカイセキトキカイガクシュウノタメノジッセンテキバイオインフォマティクス
Subject Name in English	Hands-on Bioinformatics for Multi-Omics Data Analysis and Machine Learning
Instructor	HAYES CLAIR NELSON,NAKAHARA HIKARU
Instructor (Katakana)	ヘイズ　クレアーネルソン,ナカハラ　ヒカル
Campus	Kasumi	Semester/Term	1st-Year, Second Semester, 3Term
Days, Periods, and Classrooms	(3T) Thur11-12
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face
Lecture-based, exercise-based, discussion-based
Credits	1.0	Class Hours/Week	2	Language of Instruction	B : Japanese／English
Course Level	7 : Graduate Special Studies
Course Area（Area）	26 : Biological and Life Sciences
Course Area（Discipline）	04 : Life Sciences
Eligible Students	Students with basic familiarity with bioinformatics and Linux,
Keywords	genomics, transcriptomics, pipelines, proteomics, protein structure prediction, metabolomics, machine learning, deep learning, biomarkers, Python, Snakemake, multi-omics
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	Basic familiarity with bioinformatics tools may not be enough to fully leverage data from large-scale multi-omics studies. This advanced course focuses on how to create scalable, reproducible, and automated data analysis pipelines using Bash scripting and workflow engines. The course covers advanced genomics techniques, including long-read sequencing, de novo assembly, and metagenomics. We will also cover cutting-edge techniques in single-cell and spatially resolved transcriptomics. With this foundation, we will then explore multi-omics methods to jointly analyze data from transcriptomics, proteomics, and metabolomics experiments. Finally, we will explore how machine learning can be used for biomarker discovery and predictive modeling from bioinformatics data. Each session will include a lecture taught in English and Japanese to introduce core theoretical concepts, followed by a hands-on workshop where students will apply what they have learned through guided exercises using real biological data. By the end of the course, students will be able to chain bioinformatics tools together into robust cloud-ready multi-omics analysis pipelines.
Class Schedule	1) Advanced bioinformatics pipelines 2) Advanced genomics methods 3) Advanced transcriptomics methods 4) Proteomics and protein structure analysis 5) Metabolomics and lipidomics 6) Multi-omics integration 7) Machine learning for biomarker discovery 8) Deep learning for medical image analysis
Text/Reference Books,etc.	Learning materials will be provided during the course.
PC or AV used in Class,etc.	Text, Handouts, Audio Materials, Visual Materials
(More Details)
Learning techniques to be incorporated	Discussions, Quizzes/ Quiz format, Project Learning
Suggestions on Preparation and Review	The course project is intended to help guide students in learning the specific skills needed for their current or planned research.
Requirements	A PC capable of connecting to the Internet.
Grading Method	Attendance, proactive learning attitude, assignments in class
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

Back to syllabus main page