BIOI4450 High Throughput Data Analysis 5 ECTS
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

Department of Future Technologies
Department of Biochemistry
Department of Biochemistry
Department of Future Technologies
2016–2017
Teaching
Archived Teaching Schedule. Please refer to current Teaching Shedule.
Department of Future Technologies
DP in Computer Science
DP in Computer Science
DP Bachelor of Science in Techn.(Communication St)
DP in Information and Communication Technology
MDP in Digital Health and Life Sciences (Tech.)
Finnish Study Modules