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Archived teaching schedules 2016–2017
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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

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


14-Mar-2017 – 10-May-2017
Lectures 28 hours
Tue 14-Mar-2017 - 2-May-2017 weekly at 13-15, Arvo Rh A313, Note! The lecture on 2 May is in Arvo A308!
Wed 15-Mar-2017 - 3-May-2017 weekly at 9-11, Arvo Rh A313
Exercises 16 hours
Fri 17-Mar-2017 - 5-May-2017 weekly at 14-16, Arvo, Computer classroom ML72
Independent work 80 hours


Numeric 1-5.

Evaluation criteria

Exam, exercises and project work.

Further information

BIO2200, BIO2310 and BIO4440 or equivalent knowledge required as a prerequisite.