Available courses

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.


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.
 

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.
 

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.

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.

An overview of the major biological databases and an introduction of the basic sequence analysis methods 

  • Biological databases with the main focus on DNA and protein sequences 
  • Comparison and alignment of sequences, similarity-based searches in databases
  • Discovery of protein sequence motifs and sequence features; metabolic pathway data 
  • Genome browsers and sources of gene expression data; gene lists and the concept of enrichment 
  • Micro-RNAs and their targets; protein visualization

Recommended reading

  • Pevzner, P. (2011): Bioinformatics for biologists, Cambridge University Press
  • Lesk, A.M. (2005): Introduction to bioinformatics, Oxford University Press

Programming for beginners, using the Python langauge:

  • Concepts in programming, fundamentals of algorithms
  • Basic variable types & data structures
  • Program organization, loops and conditional statements
  • Basics of file input/output
  • Parsing text files
  • Basics of GUI programming
Recommended reading

  • Lutz, M (2011): Programming Python. O’Reilly

An application oriented course focusing on how statistical methods can be used to address common problems in the analysis of results from molecular biology experiments.

  • Comparing simple groups: hypothesis testing
  • Multiple groups: ANOVA and related concepts
  • Hypothesis testing in complex experimental settings: Randomized complete block design
  • Dose and response: regression models
  • Handling low sample sizes with General Linear Models
  • Planning optimal sample sizes: how many animal do I need?

Recommended reading

  • Aho Ken A. (2014) Foundational and applied statistics for biologists using R. CRC press

The purpose of this course is to teach how the R statistical environment can be applied for biological data analysis.

  • Introduction to R: Installation, package management, basic operations
  • Sequences and sequence analysis 
  • Annotating gene groups: Ontologies, pathways, enrichment analysis 
  • Proteomics: mass spectometry 
  • Reconstructing gene regulation networks 
  • Network analysis: iGraph

Recommended reading

  • Ortutay & Ortutay (2017): Molecular Data Analysis Using R. ISBN: 978-1-119-16502-6. , Wiley-Blackwell

Familiarity with Internet sources for genome-wide data; basic skills in using tools at these web sites; understanding how modern high-throughput methods generate sequence data and gene and protein expression data; practical skill of using genome browsers to access genome data and genome comparison data; understanding gene prediction and genome annotation pipelines; skill of performing individual gene predictions; understanding different levels of variation in human genomes; understanding basic workflows of microarray data analysis and next-generation sequencing data analysis; basic knowledge of experimental methods in proteomics and metabolomics which enables understanding data analysis in these fields; skill of identifying proteins from mass spectroscopic data.

Recommended reading

  • Campbell, A.M. & Heyer L.J. (2006) Discovering Genomics, Proteomics and Bioinformatics, 2nd ed.. Benjamin Cummings Press

Basics of search, manipulation and analyses of structures of large biological molecules, especially proteins. 

  • Basics of protein structures and structure determination. Simple validation of models by Ramachandran plots. Basic use of molecular graphics software 
  • Molecular graphics: illustrating and highlighting molecular details on screen and print; generating molecular surfaces 
  • Comparison of structures: overlaying molecules and measuring their structural similarity 
  • Molecular animations 
  • Theory of protein modeling and protein dynamics 
  • Validation and analysis of models

Recommended reading

  • Gu, J & Bourne, P.E. (eds, 2009): Structural bioinformatics, 2nd ed. Wiley-Blackwell 
  • Gáspári Z (ed. 2020): Structural bioinformatics: Methods and Protocols. Humana Press


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