NCHU Course Outline
Course Name (中) 生物資訊於植物病理應用實務(6835)
(Eng.) Bioinformatic Applications in Plant Pathology
Offering Dept Master Program in Plant Health Care
Course Type Elective Credits 2 Teacher Tao-Ho Chang
Department Master Program in Plant Health Care/Graduate Language English Semester 2025-FALL
Course Description Current biology studies have entered into the omic era. The mass amount of sequencing and high throughput results have raised the attention for programming data analysis. This course aims at the topic of bioinformatic processes related to plant pathology. The case studies with bioinformatics will help students to practice and understand how to use the computational tools to design the experiments and analyse the results.
Prerequisites
self-directed learning in the course Y
Relevance of Course Objectives and Core Learning Outcomes(%) Teaching and Assessment Methods for Course Objectives
Course Objectives Competency Indicators Ratio(%) Teaching Methods Assessment Methods
1. Introduction of the concepts of bioinformatics
2. Understanding the program we use in class (Online resource and R program)
3. Cases studies in bioinformatics
topic Discussion/Production
Exercises
Attendance
Assignment
Course Content and Homework/Schedule/Tests Schedule
Week Course Content
Week 1 Introduction and course brief
Week 2 R program: Introduction and basic function
Week 3 R program: Data analysis and graphics
Week 4 Web tools: Galaxy bioinformatic
Week 5 Transcriptome analysis: RNAseq raw data, cleaning, and transcriptome assemble
Week 6 Transcriptome analysis: differentially expressed genes (DEGs)
Week 7 Transcriptome analysis: weighted gene correlation network analysis
Week 8 Transcriptome analysis: GO and KEGG pathway analysis
Week 9 Midterm
Week 10 Image analysis: ImageJ introduction, counting colonies
Week 11 Image analysis: Disease symptom analysis, machine learning concept
Week 12 Web tools: NCBI, ENSEMBL, TAIR
Week 13 Web tools: PlantGDB, Phytozome
Week 14 Web tools: benchling
Week 15 Genome analysis: Genome annotation and genome comparisons
Week 16 Web tools: Protein data bank Protein visulisation: PyMOL Final remarks
self-directed
learning
Evaluation
Group discussion participation (30%)
Presentation (20%)
Assignment of practices (50%)
Textbook & other References
Khan, A., Singh, S., and Singh, V. K. (2021). “Bioinformatics in Plant Pathology,” in Emerging Trends in Plant Pathology, eds. K. P. Singh, S. Jahagirdar, and B. K. Sarma (Singapore: Springer), 725–844. doi: 10.1007/978-981-15-6275-4_32.
Kabacoff, R.I. (2015). R in action: data analysis and graphics 2nd Ed. (USA: Manning publication).
Teaching Aids & Teacher's Website
1. A comprehensive flowchart showing the step-by-step process from raw sequencing data to analysed results helps students visualise the entire bioinformatics workflow.
2. A pre-loaded virtual machine image with all required bioinformatics tools and sample datasets allows students to start practising immediately without software installation issues.
3. A collection of real plant pathology case studies with accompanying datasets gives students hands-on experience with research problems and their solutions.
4. An interactive command-line cheat sheet listing essential bioinformatics commands and their usage, serving as a quick reference during practical sessions.
Office Hours
Friday from 13:00 to 15:00
Sustainable Development Goals, SDGs(Link URL)
09.Industry, Innovation and Infrastructureinclude experience courses：N
Please respect the intellectual property rights and use the materials legally.Please respect gender equality.
Update Date, year/month/day：None Printed Date, year/month/day：2025 / 6 / 18
The second-hand book website：http://www.myub.com.tw/