Introduction to Bioinformatics for Genome Analysis
| Numbering Code | U-AGR01 2A260 LB66 | Year/Term | 2022 ・ Second semester | |
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| Number of Credits | 2 | Course Type | Lecture | |
| Target Year | 2nd year students | Target Student | ||
| Language | Japanese | Day/Period | Tue.4 | |
| Instructor name | RYOHEI TERAUCHI (Graduate School of Agriculture Professor) | |||
| Outline and Purpose of the Course | Hitoshi Kihara, a renowned plant geneticist, noted in 1946 that “the history of the earth is recorded in the layers of its crust; The history of all organisms is inscribed in the chromosomes.” Because of recent advances in DNA sequencing technologies, it became possible to obtain the genetic information coded in the whole chromosomes of various organisms, leading to the development of the discipline of Genomics. Comparison of genome sequences allows us to better understand the history and evolution of various organisms that share a common ancestor from around 3.5 billion years ago. By analyzing the genomes of these various species, numerous insights about the history and evolution of living things can be obtained. Furthermore, genome sequencing of agriculture, forestry, and fishery products enables rapid and efficient breeding. Basic genomics technology will be essential to future study in biology. However, it is not easy to handle the large amount of information written in the genome. In this course, each student will use their own laptop to obtain useful information from the genome sequence by learning the basics of information processing using PYTHON, machine learning, and the Bayesian estimation method. Furthermore, using informatics (bioinformatics) technology, we will understand biodiversity and evolution and aim to introduce technology that will help improve agricultural, forestry, and fishery products. | |||
| Course Goals |
By attending the course, students will: - Understand basic Python programming - Acquire the skills to handle large DNA sequence data - Understand the principles of next-generation DNA sequencing - Understand basic bioinformatics - Understand Bayesian inference - Understand basic machine learning - Acquire basic knowledge of genome evolution - Acquire knowledge of using genomic information for breeding |
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| Schedule and Contents |
Day 1: Introduction to Genome Analysis Day 2: Introduction to Next-Generation DNA sequencing Day 3: Introduction to Python 1 Day 4: Introduction to Python 2 Day 5: Introduction to Python 3 Day 6: Introduction to Big Data Handling 1 Day 7: Introduction to Big Data Handling 2 Day 8: Introduction to Big Data Handling 3 Day 9: Introduction to Bayesian Inference 1 Day 10: Introduction to Bayesian Inference 2 Day 11: Introduction to Machine Learning 1 Day 12: Introduction to Machine Learning 2 Day 13: Examples of Genome Analysis 1 Day 14: Examples of Genome Analysis 2 Feedback: We accept questions by e-mail. |
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| Evaluation Methods and Policy |
Class performance (weightage 80%) and reports (weightage 20%) . The evaluation criteria and achievement level are in accordance with the "Evaluation Criteria and Achievement Level" described in the Student Handbook of the Faculty of Agriculture for the relevant year. |
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| Course Requirements | Nothing in particular | |||
| Study outside of Class (preparation and review) | Homework will be given after every class. | |||
| Textbooks | Textbooks/References | 使用しない | ||
| Related URL | ||||
