Course - Bioinformatics and Experimental design and data analysis, Specialization Course - TBT4507
Bioinformatics and Experimental design and data analysis, Specialization Course
About
About the course
Course content
Experimental design and data analysis:-Uncertainty analysis,-Hypothesis testing,-Simple and Multiple linear regression,-Experimental design with two-level factorial experiments,-Analysis of variance (ANOVA),-Nonparametric methods,-IBM SPSS for statistical analysis.
Bioinformatics: introduction to the field of bioinformatics, covering genome sequencing methods, genome assembly, sequence alignment, evolution of genes sequences, protein structures, annotation of protein functions, and relevant tools and databases. Development of python programming skills using the Jupyter environment.
Learning outcome
Experimental design and data analysis: The student has knowledge of the basic statistical models and methods used in science and technology. The student can interpret results of hypothesis testing, regression analysis, experimental design, analysis of variance and nonparametric methods. The student can use software for statistical calculations and analyzes.
Bioinformatics: The student understands genome sequencing methods and their limitations. The student can apply computational tools for genome assembly and annotation. The student can explain the principles and applications of sequence alignment. The student can extract information from bioinformatics databases and write python scripts to process and analyze multiple types of data. The student can combine data from different bioinformatics databases to analyze the function of genes and proteins.
Learning methods and activities
Experimental design and data analysis: Lectures (24 h), exercises (8 h) and self-study (64 h).
Bioinformatics: Weekly lectures (8 x 3h) divided into theory (1h) and computer exercises (2h). Group project and home study (self-paced).
Compulsory assignments
- Exercises statistics
Further on evaluation
Experimental design and data analysis: The number of exercises is 4, all must be approved to take the exam. Written exam (3h).
Bioinformatics: Group project with group presentation and individual report.
Each graded part (the written exam in Experimental design and data analysis, the project in Bioinformatics) counts 50% of the total grade in the course.
Both the project and the written exam must be passed to pass the course.
If there is a re-sit examination, the examination form may be changed from written to oral. In case of retake after a failed exam or to improve grade, students can choose to retake only one part of the assessment.
Recommended previous knowledge
Experimental design and data analysis: Basic course in Statistics from BSc.
Bioinformatics: Introduction to Python (TDT4110 or equivalent), Cell and Molecular biology (BI1001 or equivalent), Biochemistry (TBT4102 or equivalent).
Course materials
Probability and Statistics for Engineers and Scientists (9th Edition) by Ronald E. Walpole.
Bioinformatics: lecture slides and documentation for relevant tools and databases (given during the lectures).
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| TBT4506 | 3.7 sp | Autumn 2020 |
| TBT4508 | 3.7 sp | Autumn 2020 |
| MATV4008 | 3.7 sp | Autumn 2020 |
| BT2100 | 3.7 sp | Autumn 2026 |
Subject areas
- Technological subjects