学部・大学院区分 Undergraduate / Graduate		農・博前

時間割コード Registration Code		2930010

科目区分 Course Category		Ｃ類 Category C

科目名　【日本語】 Course Title		データサイエンス特別講義４

科目名　【英語】 Course Title		Special Lecture on Data Science 4

コースナンバリングコード Course Numbering Code

担当教員　【日本語】 Instructor		白武　勝裕 ○ 太田垣　駿吾兒島　孝明

担当教員　【英語】 Instructor		SHIRATAKE Katsuhiro ○ OTAGAKI Shungo KOJIMA Takaaki

単位数 Credits		1

開講期・開講時間帯 Term / Day / Period		春集中その他その他 Intensive(Spring) Other Other

対象学年 Year		１年 1

授業形態 Course style

授業の目的　【日本語】
Goals of the Course(JPN)

データサイエンス特別講義１，２，３を基礎とし，トランスクリプトミクスの実際，プロテオミクスやメタボロミクスを含めたマルチオミクスの概要を学ぶ．

授業の目的　【英語】
Goals of the Course

Based on Data Sciences 1, 2 and 3, this course introduces practical approaches of transcriptomics and outline of multi-omics including proteomics and metabolomics.

到達目標　【日本語】
Objectives of the Course(JPN)

Based on Data Sciences 1, 2 and 3, in this course, students get knowledge of practical approaches of transcriptomics and outline of multi-omics including proteomics and metabolomics.

到達目標【英語】
Objectives of the Course

授業の内容や構成
Course Content / Plan

1. Preparing a computing environment for NGS analysis in Windows/Mac (Instructions for Windows Subsystem for Linux 2; Instructions for homebrew/Bioconda to install popular bioinformatics tools; Instructions for downloading NGS data from Sequence Read Archive)
2. Guidance for RNA-Seq data analysis pipelines (Read mapping, calculation of the expression values and statistical analysis)
3. Genome-wide analysis of binding sites of a DNA-binding transcription factor (Mapping, Peak detection and Identification of promoter regulated by the target transcription factor)
4. Integrated data mining with biological big data using R (Correlation analysis and Clustering)
5. 16S rDNA metagenome analysis using bioinformatics tools (LocalBLAST and QIIME2)
6. Outline of proteomics and metabolomics
7. Application of multi-omics

1. Preparing a computing environment for NGS analysis in Mac (Instructions for homebrew/Bioconda to install popular bioinformatics tools; Instructions for downloading NGS data from Sequence Read Archive)
2. Guidance for RNA-Seq data analysis pipelines (Read mapping, calculation of the expression values and statistical analysis)
3. Genome-wide analysis of binding sites of a DNA-binding transcription factor (Mapping, Peak detection and Identification of promoter regulated by the target transcription factor)
4. Integrated data mining with biological big data using R (Correlation analysis and Clustering)
5. 16S rDNA metagenome analysis using bioinformatics tools (LocalBLAST and QIIME2)
6. Outline of proteomics and metabolomics
7. Application of multi-omics

履修条件・関連する科目
Course Prerequisites and Related Courses

Data Sciences 1, 2 and 3

成績評価の方法と基準
Course Evaluation Method and Criteria

Evaluate each lesson by attendance and short report.

教科書・テキスト
Textbook

None

参考書
Reference Book

課外学習等（授業時間外学習の指示）
Study Load(Self-directed Learning Outside Course Hours)

After lecture, students should learn about practice of omics study by reading scientific papers and by accessing to webtools for omics study.

使用言語
Language Used in the Course

授業開講形態等
Lecture format, etc.

遠隔授業（オンデマンド型）で行う場合の追加措置
Additional measures for remote class (on-demand class)