Bioinformatics Academy

Heatmaps are very handy tools for the analysis and visualization of large multi-dimensional datasets. They are often used with high-throughput gene expression data. If you want to locate hidden groups among analyzed genes, or association between experimental conditions and gene expression patterns, heatmaps are the way to go because they bring together the statistical rigor of clustering with the visual cognitive excellence of your mind. In this crash course you will learn the important tricks how to apply this tool successfully in your projects. Besides you will get an opportunity to practice creating heatmaps with online services, or if you are at a more advanced level, with R statistical environment.

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 
  
• Similarity-based searches in databases (Blast)
  
• Comparison and alignment of sequences
• Discovery of protein sequence motifs and sequence features; metabolic pathway 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

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