• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS MBG 5036 TRANSCRIPTOME DATA ANALYSIS ELECTIVE 3 0 0 9

Offered By

Molecular Biology and Genetics

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR GÖKHAN KARAKÜLAH

Offered to

Molecular Biology and Genetics Molecular Biology and Genetics

Course Objective

To gain a thorough understanding of the bioinformatics methods and tools for retrieving, manipulating, analysing and interpreting transcriptome data.

Learning Outcomes of the Course Unit

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

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

MBG 5035 - R Programming for Bioinformatics

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Overview of sequencing technologies 2 Applications of transcriptome sequencing 3 Computational infrastructure and basic data analysis for transcriptome sequencing 4 Base-calling for bioinformaticians 5 Basics of read alignment 6 Quantification tools 7 Expression profiling - I 8 Expression profiling - II 9 MIDTERM 10 Differential expression for RNA sequencing - I 11 Differential expression for RNA sequencing - II 12 Enrichment and pathway analysis 13 Visualization techniques 14 MicroRNA expression profiling and discovery 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

Oral presentation, literature search and discussion

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 * 0.20 + MTE * 0.30 + FIN * 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.20 + MTE * 0.30 + RST * 0.50 *** 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 1 15 Preparation before/after weekly lectures 15 6 90 Preparation for Mid-term Exam 1 15 15 Preparation for Final Exam 1 25 25 Preparing Individual Assignments 1 15 15 Preparing Group Assignments 1 25 25 Final 1 2 2 Mid-term 1 2 2 TOTAL WORKLOAD (hours) 219

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