Bioinformatics Academy

Introduction to structural bioinformatics 2020

Understanding of biological processes can be taken to a new level by studying the 3-dimensional molecular structures of DNA, proteins, and various smaller biomolecules. “Structural bioinformatics” will make you familiar with search, manipulation and analyses of structures of large biological molecules, especially proteins.

You will learn basic and advanced techniques of protein visualization, and basic analyses of structural models of proteins. After this course you will be able to describe major methods of acquiring protein structural data, make publication-quality images of protein structures, compare proteins by superimposing (overlaying) their structures, make simple molecular animations, find or construct 3D models of structurally unknown proteins, and describe the principles and limitations of protein modeling.

Course duration: 2 weeks, expected workload 10 to 15 hours per week.

Suggested background knowledge: Basic protein biochemistry, understanding the concepts of primary, secondary, and tertiary structures of proteins, and basic use of Internet and web browsers.

Teacher: Martti Tolvanen

Transcriptome analysis with R 2020

The purpose of this course is to teach R statistical environment to be applied to transcriptome data analysis. After this course, the students will be able to use R for analyzing diverse data types from very different biological experiments focused on gene expression. The topics will introduce the theoretical aspects of the covered methodologies, and after that, assignments and activities will provide opportunities to explore the practical ways of performing the analyses.

You will learn how to use R for performing statistical analysis relevant for molecular biologists. You will learn how to perform simple sequence analysis with R. You will get an essential overview of biological network analysis and the highly popular enrichment analysis of gene lists. Additionally, the most prominent transcriptome analysis methods will be covered with medium to advanced level code examples and practices.

Teacher: Csaba Ortutay

Transcriptome analysis with R 2018

The purpose of this course is to teach R statistical environment to be applied to transcriptome data analysis. After this course, the students will be able to use R for analyzing diverse data types from very different biological experiments focused on gene expression. The topics will introduce the theoretical aspects of the covered methodologies, and after that, assignments and activities will provide opportunities to explore the practical ways of performing the analyses.

You will learn how to use R for performing statistical analysis relevant for molecular biologists. You will learn how to perform simple sequence analysis with R. You will get an essential overview of biological network analysis and the highly popular enrichment analysis of gene lists. Additionally, the most prominent transcriptome analysis methods will be covered with medium to advanced level code examples and practices.

Teacher: Csaba Ortutay

High throughput data analysis with R 2017

The purpose of this course to teach R statistical environment to be applied to high-throughput biological data. After this course, the students will be able to use R for analyzing diverse data types from high-throughput biological experiments with a strong focus on different transcriptomics methods. Half of the course is dedicated to the analysis of data from next-generation sequencing experiments. All the topics will introduce the theoretical aspects of the introduced methodologies, and after that, assignments and activities will provide opportunities to explore the practical ways of performing the analyses.

Biological data analysis with R - 2017 1

The purpose of this course to teach R statistical environment to be applied to biological data analysis. After this course, the students will be able to use R for analyzing diverse data types from very different biological experiments. The topics will introduce the theoretical aspects of the introduced methodologies, and after that, assignments and activities will provide opportunities to explore the practical ways of performing the analyses.