• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. process transcriptomic and genomic data in R programming language

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts in the field of Bioinformatics and Systems Biology
   2. Process and analyze genome-scale biological data and map them on genome-scale cellular networks by using statistical methods and data mining methods.

   Method of assessment

   1. Written exam
   2. Homework assignment
2. Use and apply BioConductor packages to analyze biological data

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts in the field of Bioinformatics and Systems Biology
   2. Process and analyze genome-scale biological data and map them on genome-scale cellular networks by using statistical methods and data mining methods.
   3. Work effectively in multi-disciplinary research teams

   Method of assessment

   1. Written exam
   2. Homework assignment
3. present their bioinformatics analysis results by using high quality visual aids

   Contribution to Program Outcomes

   1. COMPETENCIES
   2. Communication and Social Competence
   3. Write progress reports clearly on the basis of published documents, thesis, etc

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Seminar/presentation

Contents
Week 1:	Defining transcriptomic data in R: matrices, lists and dataframes
Week 2:	Manipulating transcriptomic datasets in R: Loops (if, for), File input and output, Plotting, Apply family function Homework
Week 3:	Generating reports in R markdown Faster and Easier manipulation of transcriptomic datasets with TidyVerse-dplyr package
Week 4:	Visualization of transcriptomic data: TidyVerse-tidyr and TidyVerse-ggplot2 packages Quiz Homework
Week 5:	R: Bioconductor Data Types I- Biostrings, IRanges, GRanges, BSGenome packages for Genomic Data
Week 6:	R: Bioconductor Data Types II- GenomicFeatures, AnnotationHub, ExpressionSet, GeoQuery packages for Genomic and Transcriptomic Data Homework
Week 7:	R: Annotating Genetic Information with BiomaRt Regular Expressions-Basics Quiz
Week 8:	Midterm 1 Regular Expressions-Advanced Patterns and application to character-based genomic dataframes
Week 9:	Developing web interfaces with Shiny : Basics
Week 10:	Developing web interfaces with Shiny: Advanced Features Designing a Shiny web-application for statistical analysis and visualization of trancriptomic and genomic data Homework
Week 11:	Review of scientific articles on bioinformatic applications of Shiny
Week 12:	Advanced and scientific-quality vizualization of omic data with genvizR and plotly packages Text mining of bioinformatic sources (web-based articles) via tidytext package Quiz Homework
Week 13:	Speeding up R codes for processing large omic datasets: Profiling and memory, Parallelization
Week 14:	Bioinformatic Databases, SQL-based search of databases and dataframes in R Quiz Homework
Week 15*:	-
Week 16*:	Final Exam
Textbooks and materials:	1. D. Maclean, "R Bioinformatics Cookbook", Packt Publishing, 2019 2. R.A. Irizzary, "Introduction to Data Science: Data Analysis and Prediction Algorithms with R", Chapman & Hall, 2019
Recommended readings:	3. R. Gentleman, "R Programming for Bioinformatics", Chapman & Hall, 2008
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	8	20
Other in-term studies:		0
Project:		0
Homework:	2,4,6,10,12,14	35
Quiz:	4,7,12,14	15
Final exam:	16	30
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	4	14
Practice, Recitation:	0	0
Homework:	6	9
Term project:	0	0
Term project presentation:	0	0
Quiz:	1	4
Own study for mid-term exam:	10	1
Mid-term:	2	1
Personal studies for final exam:	12	1
Final exam:	3	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)