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.
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
Bioinformatics
✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | Practical and/or problem-solving skills |
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 | |
---|---|---|
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 | Dynamics of gene regulatory networks (1): simple transcriptional regulation and negative feedback loop | Understanding of basic mathematical modeling of gene regulation |
Class 5 | Dynamics of gene regulatory networks (2): positive feedback and feedforward loops | Understanding of basic dynamical systems theory |
Class 6 | Mathematical analysis of gene expression rhythms (1): circadian rhythms | Understanding of computer modeling of biomolecules using molecular simulation |
Class 7 | Mathematical analysis of gene expression rhythms (2): ultradian rhythms | Understanding of mechanisms for gene expression rhythms |
Class 8 | Overview of genome information analysis using NGS and principles of NGS | Understanding the background of genomic information analysis using NGS |
Class 9 | Mapping-based NGS analysis and its algorithms | Understanding the basic algorithms used in mapping-based NGS analysis |
Class 10 | 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 11 | Overview of fundamental bioinformatics | Understanding of overview of fundamental bioinformatics |
Class 12 | Basics of omics data analysis | Understanding of omics data analysis |
Class 13 | Metagenomics for microbiome | Understanding of metagenomics |
Class 14 | Applications of metagenomics for human gut microbiome | Understanding of applications of metagenomics |
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.
None
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
By written reports for each class.
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