2022 Computational Biology

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Academic unit or major
Graduate major in Life Science and Technology
Itoh Takehiko  Yamada Takuji  Kitao Akio 
Class Format
Lecture    (Livestream)
Media-enhanced courses
Day/Period(Room No.)
Mon1-2()  Thr1-2()  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

How deep knowledge or useful information can we retrieve from diverse and enormous data obtain from multi-omics analysis? This course forcuses on Bioinformatics. Topics includes molecular evolution, sequence analysis, comparative genomics, multi-omics analysis, algorithms for bioinformatics, molecular or metabolic network analysis, and data mining methods. By combining lectures and exercises, the course enables students to understand and acquire the fundamentals of bioinformatics widely applicable to biological research. Bioinformatic approaches taught in this course are not only useful in analyzing multi-omics data, but are applicable to various other types of biological problems. Group work will also be conducted for better understanding.

Student learning outcomes

By the end of this course, students will be able to:
1) Understand principles and methods of sequence analysis based on molecular evolution
2) Understand the knowledge obtained by comparing the gene sequences and genomic sequences
3) Understand computer algorithms in bioinformatic analyses
4) Understand the fundamentals and applications of multi- omics analysis
5) Understanding of basics and applications of molecular dynamics simulation



Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Required learning should be completed outside of the classroom for preparation and review purposes.
This class will be conducted by using Zoom system to reduce the burden caused by travel for students enrolled at both Ookayama and Suzukakedai campuses when taking classes and doing group work.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Overview of classical biomolecular simulation Understanding of overview of classical biomolecular simulation
Class 2 Model building of biomolecules (molecular mechanics, etc) Understanding of molecular mechanics
Class 3 Classical biomolecular simulation Understanding of molecular dynamics simulation
Class 4 Applications of simulation and analysis Understanding of applications of simulation and analysis of the obtained results.
Class 5 Computer modeling of biomolecules Understanding of computer modeling of biomolecules using molecular simulation
Class 6 Overview of genome information analysis using NGS and principles of NGS Understanding the background of genomic information analysis using NGS
Class 7 Mapping-based NGS analysis and its algorithms (1) Understanding the basic algorithms used in mapping-based NGS analysis (1)
Class 8 Mapping-based NGS analysis and its algorithms (2) Understanding the basic algorithms used in mapping-based NGS analysis (2)
Class 9 Algorithms in genome assembly, RNA-seq analysis, and ChIP-seq analysis Understanding the basic algorithms used in genome assembly, RNA-seq analysis, and ChIP-seq analysis
Class 10 Overview of fundamental bioinformatics Understanding of overview of fundamental bioinformatics
Class 11 Basics of omics data analysis Understanding of omics data analysis
Class 12 Metagenomics for microbiome Understanding of metagenomics
Class 13 Applications of metagenomics for human gut microbiome Understanding of applications of metagenomics
Class 14 Basics of machine learning for omics data Understanding of machine learning for omics data

Out-of-Class Study Time (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.



Reference books, course materials, etc.

Neil C. Jones and Pavel A. Pevzner. An Introduction to Bioinformatics Algorithms. ISBN-13: 978-0262101066
Masatoshi Nei and Sudhir Kumar. Molecular Evolution and Phylogenetics. ISBN-13: 978-0195135855

Assessment criteria and methods

By written reports for each class.

Related courses

  • None

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Basic level of physical chemistry (quantum chemistry and classical mechanics)
Basic level of mathematics (calculus and linear algebra)
Basic level of statistical physics
Basic level of genomics

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