Uusi opinto-opas (sisältäen myös opetusohjelmat) lukuvuodelle 2018-2019 sijaitsee osoitteessa https://opas.peppi.utu.fi . Tältä sivustolta löytyvät enää vanhat opinto-oppaat ja opetusohjelmat.

The new study guide (incl. teaching schedules) for academic year 2018-2019 can be found at https://studyguide.utu.fi. This site contains only previous years' guides.

x !
Arkistoitu opetussuunnitelma 2016–2018
Selaamasi opetussuunnitelma ei ole enää voimassa. Tarkista tiedot voimassa olevasta opetussuunnitelmasta.
Opinto-oppaat » Matemaattis-luonnontieteellinen tiedekunta » Tulevaisuuden teknologioiden laitos » Optional studies
BIOI4450 High Throughput Data Analysis 5 op
Organised by
Bioinformatics
Preceding studies
Compulsory:

General description

This course is the continuation of Biological Data Analysis with R, introducing professional methods for analyzing high-throughput data, such as DNA microarray and next-generation sequencing data.

Learning outcomes

After this course the students will be able to analyze high-throughput data from very diverse experiments including microaarray and next generation sequencing data. They will be able to handle and analyze such data, and help experimental scientist in the interpretation. Lectures will introduce the theoretical aspects of the introduced methodologies, and after that, students will have opportunity to explore the practical ways of performing the analyses by obligatory homework exercises. This course will provide the students knowledge about state-of-the-art methodologies and the related data analysis practices.

Contents

1. Flow cytometry: counting and sorting stained cells
2. Next-generation sequencing: introduction and genomic applications
3. Quantitative transcriptomics: qRT-PCR
4. Advanced transcriptomics: gene expression microarrays
5. Next-generation sequencing in transcriptomics: RNA-seq experiments
6. Deciphering the regulome: from CHIP to CHIP-seq

Teaching methods

Lectures. Independent work by following the provided instructions. Work and learning must be documented by Moodle postings. Home assignments: weekly programming tasks and weekly submissions about lecture material.

Teaching language

English

Modes of study

Option 1
Available for:
  • Degree Programme Students
  • Other Students
  • Doctoral Students
  • Exchange Students
Exercise(s)
  • In English
Written exam
  • In English

Home assignments, classroom and independent coding practices, exam

Evaluation

Numeric 0-5.

Recommended year of study

1. year spring

Study materials

Lecture notes and other material provided by the teacher.
Book: Ortutay and Ortutay: Molecular Data Analysis Using R. John Wiley & Sons, Inc. 2016.

Belongs to following study modules

Tulevaisuuden teknologioiden laitos
Biokemian laitos
Biokemian laitos
Tulevaisuuden teknologioiden laitos
2018–2019
Teaching
Archived Teaching Schedule. Please refer to current Teaching Shedule.
Implementation details are unavailable.
Tulevaisuuden teknologioiden laitos
MDP in Bioinformatics
MDP in Embedded Computing
MDP in Digital Health and Life Sciences (Tech.)
Opintokokonaisuudet