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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS MBG 6114 HIGH THROUGHPUT GENOMIC DATA ANALYSIS ELECTIVE 3 0 0 12

Offered By

Molecular Biology and Genetics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR GÖKHAN KARAKÜLAH

Offered to

Molecular Biology and Genetics Molecular Biology and Genetics Biomedicine and Health Technologies

Course Objective

This course aims to present high-throughput data analysis techniques for next generation sequencing data.

Learning Outcomes of the Course Unit

1   To describe high-throughput sequencing methodologies 2   To be aware of challenges in high-throughput sequencing data analysis 3   To be able to choose appropriate computatianal method for high-throughput sequencing data analysis 4   To be aware of available tools for high-throughput sequencing data analysis 5   To interpret results of high-throughput sequencing data analysis in the context of molecular biology

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

MBG 6133 - Bioinformatics Data Skills

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Overview of sequencing technology 2 Applications of high-throughput sequencing 3 Computational infrastructure and basic data analysis for high-throughput sequencing 4 Base-calling for bioinformaticians 5 De novo short-read assembly 6 Short-read mapping 7 DNA protein interaction analysis (ChIP-Seq) 8 Generation and analysis of genome-wide DNA methylation maps 9 MIDTERM 10 Differential expression for RNA sequencing 11 MicroRNA expression profiling and discovery 12 Dissecting splicing regulatory network by integrative analysis of CLIP-Seq data 13 Analysis of metagenomics data 14 High-throughput sequencing data analysis software: current state and future developments 15 Discussion 16 Assignment Presentations

Recomended or Required Reading

Textbook(s): 1. Rodríguez-Ezpeleta, Naiara, Michael Hackenberg, and Ana M. Aransay, eds. Bioinformatics for high throughput sequencing. Springer Science & Business Media, 2011. 2. Briefings in Bioinformatics, Oxford Journals, ISSN 1467-5463 3. Bioinformatics, Oxford Journals, ISSN 1367-4803 4. BMC Bioinformatics, ISSN 1471-2105

Planned Learning Activities and Teaching Methods

Face -to- Face Theoretical lectures with PowerPoint presentation and assignment presentations by students

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 ASG ASSIGNMENT 2 MTE MIDTERM EXAM 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE ASG * 0.30 + MTE * 0.30 + FIN * 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.30 + MTE * 0.30 + RST * 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

gokhan.karakulah@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 15 2 30 Tutorials 15 2 30 Preparation before/after weekly lectures 15 4 60 Preparation for Mid-term Exam 1 15 15 Preparation for Final Exam 1 25 25 Preparing Individual Assignments 1 30 30 Preparing Group Assignments 1 40 40 Preparing Presentations 15 5 75 Final 1 2 2 Mid-term 1 2 2 TOTAL WORKLOAD (hours) 309

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 LO.1 2 LO.2 2 LO.3 2 LO.4 2 LO.5 2