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

Learning outcomes

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

1. Students will be able to analyse and model the NGS data via system biology approaches

   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. Link the concepts belonging to the different disciplines and interpret & analyze scientific research in these disciplines.Question and find out innovative approaches
   4. Gain original, independent and critical thinking, and develop theoretical concepts and tools
   5. Analyze critically and evaluate his/her findings and those of others
   6. COMPETENCIES
   7. Work effectively in multi-disciplinary research teams
   8. Find out new methods to improve his/her knowledge.
   9. Communication and Social Competence

   Method of assessment

   1. Laboratory exercise/exam
   2. Seminar/presentation
2. Acquiring advanced skill in computer programming and use of bioinformatic tools

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts in the field of Bioinformatics and Systems Biology
   2. Find out new methods to improve his/her knowledge.

   Method of assessment

   1. Laboratory exercise/exam
   2. Seminar/presentation
3. Students will be able to integrate NGS data analysis methods with modelling techniques.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts in the field of Bioinformatics and Systems Biology
   2. COMPETENCIES
   3. Learning Competence
   4. Find out new methods to improve his/her knowledge.

   Method of assessment

   1. Laboratory exercise/exam
   2. Seminar/presentation

Contents
Week 1:	Classification and regression methods
Week 2:	Analysis of Variance (ANOVA) methods
Week 3:	Monte Carlo Methods
Week 4:	Analysis of NGS data (RNAseq)
Week 5:	Analysis of NGS data (ChIPseq)
Week 6:	Analysis of NGS data (MethylSeq)
Week 7:	Analysis of NGS data (HiSeq)
Week 8:	Analysis of variant in genome sequence data
Week 9:	Reconstruction of variant trees in genome samples
Week 10:	Analysis of scRNA data
Week 11:	Analysis of scDNA data
Week 12:	Cell classification using scRNA data
Week 13:	Cell cycle stage identification using scRNA data
Week 14:	Identification of cell types in bulk RNA seq data via scRNA-trained models
Week 15*:	-
Week 16*:	Final sınavı
Textbooks and materials:	Literatürdeki makalelerden faydalanılacaktır
Recommended readings:	Design and Analysis Of Experiments, Douglas Montgomery, 8th ed.
	* 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:		0
Other in-term studies:	4,5,6,7,8,9,10,11,12,13,14	80
Project:		0
Homework:		0
Quiz:		0
Final exam:	16	20
	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:	7	10
Practice, Recitation:	7	10
Homework:	0	0
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	0	0
Mid-term:	0	0
Personal studies for final exam:	0	0
Final exam:	3	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)