• 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 programming environment

   Contribution to Program Outcomes

   1. Acquire knowledge for research methods which are required to develop novel application methods
   2. Design processes for the investigation of bioengineering problems, collect data, analyze and interpret the results.

   Method of assessment

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

   Contribution to Program Outcomes

   1. Design processes for the investigation of bioengineering problems, collect data, analyze and interpret the results.

   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. Convert biological, chemical, physical and mathematical principles into novel applications for the benefit of society,
   2. Design processes for the investigation of bioengineering problems, collect data, analyze and interpret the results.

   Method of assessment

   1. Written exam
   2. Homework assignment

Contents
Week 1:	Defining transcriptomic data in a programming language: vectors, matrices, lists
Week 2:	Defining transcriptomic data in a programming language: dataframes Manipulating transcriptomic datasets in a programming language: Loops (if, for), File input and output, Plotting Basics Homework
Week 3:	Plotting functions, writing functions, vectoral programming approach
Week 4:	Generating reports in a programming environment Quiz
Week 5:	Faster and Easier manipulation of transcriptomic datasets in a programming environment Homework
Week 6:	Visualization of transcriptomic data in a programming environment
Week 7:	Bioconductor Data Types I- packages for Genomic Data MidTerm I
Week 8:	Bioconductor Data Types II- packages for Genomic and Transcriptomic Data Homework
Week 9:	Midterm 1 Annotating Genetic Information in a programming environment
Week 10:	Regular Expressions- Basic Functions, Advanced Patterns and application to character-based genomic dataframes
Week 11:	Developing web interfaces for bioinformatic algorithms in a programming environment : Basics Quiz, Homework
Week 12:	Developing web interfaces for bioinformatic algorithms in a programming environment: Advanced Features
Week 13:	Designing web-application for statistical analysis and visualization of trancriptomic and genomic data Homework Review of scientific articles on bioinformatic applications of web interfaces Homework
Week 14:	Speeding up codes for processing large omic datasets: Profiling and memory, Parallelization MidTerm II
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:	7,14	35
Other in-term studies:	-	0
Project:	-	0
Homework:	2,5,8,11,13	25
Quiz:	4,11	10
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:	2	14
Practice, Recitation:	0	0
Homework:	6	5
Term project:	0	0
Term project presentation:	0	0
Quiz:	1	2
Own study for mid-term exam:	6	2
Mid-term:	1.5	2
Personal studies for final exam:	10	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)