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Archived teaching schedules 2018–2019
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BIO4450 High-throughput data analysis 5 ECTS
Period I Period II Period III Period IV
Language of instruction
Type or level of studies
Advanced studies
Course unit descriptions in the curriculum
Master's Degree Programme in Bioinformatics
Faculty of Medicine and Life Sciences

Learning outcomes

After the course, the student can:
- compare sequencing and microarray technologies used in high-throughput analysis and choose suitable ones for the analysis required.
- explain the principles of measurement technologies covered and how various inherent errors and biases of the measurement techniques affect the analysis.
- take raw data from high-throughput experiments and preprocess and normalize the data for analysis if needed using standard tools.
- apply common methods and algorithms, including state-of-the-art, to extract information from high-throughput measurement data, particularly in the context of RNA-seq and ChIP-seq data.
- discuss the statistical principles underlying the data analysis methods above and identify the benefits and weaknesses of each method.
- select suitable algorithms for the analysis and justify the choice.
- build data analysis pipelines for microarray and sequencing data analysis.

Enrolment for University Studies

Enrolment time has expired


Juha Kesseli, Teacher responsible


4-Mar-2019 – 26-May-2019
Lectures 28 hours
Tue 5-Mar-2019 at 13.15-15.00, Arvo, F211
Wed 6-Mar-2019 - 24-Apr-2019 weekly at 9.15-11.00, Arvo F213
10-Apr-2019 at 9.15 –11.00 , Arvo F211
17-Apr-2019 at 9.15 –11.00 , Arvo F211
Tue 12-Mar-2019 at 14.15-16.00, Arvo F217, NOTE Time
Tue 19-Mar-2019 at 13.15-15.00, Arvo B241
Tue 26-Mar-2019 at 13.15-15.00, Arvo A308
Tue 2-Apr-2019 at 13.15-15.00, Arvo F211
Tue 9-Apr-2019 at 13.15-15.00, Arvo A308
Tue 16-Apr-2019 at 13.15-15.00, Arvo F211
Tue 23-Apr-2019 at 13.15-15.00, Arvo F211
Tue 7-May-2019 at 13.00-16.00, Arvo F211
Exercises 16 hours
Fri 8-Mar-2019 - 26-Apr-2019 weekly at 14.15-16.00, Arvo ML72


Numeric 1-5.

Evaluation criteria

Written exam, exercises and project work.