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Archived teaching schedules 2015–2016
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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


16-Mar-2016 – 20-May-2016
Lectures 28 hours
Wed 16-Mar-2016 - 18-May-2016 weekly at 9-11, Histo Room FM1 (5rd Floor), Weekly meetings
Tue 22-Mar-2016 - 17-May-2016 weekly at 13-15, Histo Room FM1 (5rd Floor), Weekly meetings
Exercises 16 hours
Fri 18-Mar-2016 - 20-May-2016 weekly at 14-16, Computer classroom ML74 FM1 (5th Floor), Weekly meetings
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.