Information Package / Course Catalogue
Statistical Methods For Bioinformatics
Course Code: BİS626
Course Type: Area Elective
Couse Group: Third Cycle (Doctorate Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 3
Objectives of the Course

To introduce the topics in probability and statistics useful in computational molecular biology and bioinformatics.

Course Content

Brief history of Bioinformatics, Basic concepts of Molecular Biology, Markov Chain, The Analysis of one DNA sequence, Modeling DNA and signals in DNA, Alignments and simple tests for significant similarity in an Alignment(BLAST technique), Alignment algorithms for two sequences and dynamic programming, Entropy and related concepts, Relative entropy and binding energy, Finding instances of known and unknown Sites, Correlation of positions in sequences, Gene expression, microarrays, and multiple testing, Evolutionary models/ Phylogenetic tree estimation.

Name of Lecturer(s)
Learning Outcomes
1.Be able to identify topics in probability and statistics used in computational biology and bioinformatics
2.Be able to explain and interpret selected statistical models used in bioinformatics.
3.Be able to relate the topics in probability and statistics to bioinformatics computer packages
4.Be able to develop statistical techniques useful for analyzing data in the field of computational biology
5.To provide approaches to the analysis of large amounts of data in data banks
Recommended or Required Reading
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Bioinformatics
Week 2 - Theoretical
Introduction to microarray technology, basic term and concepts
Week 3 - Theoretical
Introduction to microarray technology, basic term and concepts
Week 4 - Theoretical
Descriptive Statistics
Week 5 - Theoretical
Post-Hoc Tests
Week 6 - Theoretical
Bioinformatics databases (NCBI, EMBL, Refseq, Genbank, PDB, SwissProt)
Week 7 - Theoretical
Medical data and its usage
Week 8 - Theoretical
Literature review and discussion (Midterm exam)
Week 9 - Theoretical
Cells using multiple data generator systems
Week 10 - Theoretical
data types and analysis
Week 11 - Theoretical
Data types and analysis
Week 12 - Theoretical
Gene clustering
Week 13 - Theoretical
Discriminant analysis
Week 14 - Theoretical
Discriminant analysis
Week 15 - Final Exam
Final Exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%5
Assignment1%5
Midterm Examination1%20
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140342
Midterm Examination110111
Final Examination120121
TOTAL WORKLOAD (hours)74
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
OÇ-1
4
5
5
4
3
2
3
OÇ-2
5
4
5
5
3
2
2
OÇ-3
4
4
4
4
3
3
3
OÇ-4
5
5
5
4
3
4
4
OÇ-5
Adnan Menderes University - Information Package / Course Catalogue
2026