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Syllabus ( BENG 415 )


   Basic information
Course title: Bioinformatic Data Analysis
Course code: BENG 415
Lecturer: Prof. Dr. Tunahan ÇAKIR
ECTS credits: 5
GTU credits: 3 ()
Year, Semester: 4, Fall and Spring
Level of course: First Cycle (Undergraduate)
Type of course: Departmental Elective
Language of instruction: English
Mode of delivery: Face to face
Pre- and co-requisites: BENG 215 (at least DD)
Professional practice: No
Purpose of the course: The focus of the course is the use of programming languages for advanced bioinformatic applications
   Learning outcomes Up

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 Up
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

Quiz
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

Quiz
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 Up
Method of assessment Week number Weight (%)
Mid-terms: 8 20
Other in-term studies: - 0
Project: - 0
Homework: 2,5,8,11,13 40
Quiz: 4,7,11,14 15
Final exam: 16 25
  Total weight:
(%)
   Workload Up
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 4
Own study for mid-term exam: 9 1
Mid-term: 2 1
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)
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