Bioinformatics Academy

This is a dedicated place for common issues regarding the Biodata Analysis PgCert Program jpintly organized by HiDucator and Pázmány Péter Catholic University.General information, general forum, and non-course specific video sessions are available here.

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.

This course teaches you the use of biological databases, mainly sequence databases. In addition to finding and retrieving protein or DNA sequences, we learn how to align sequences to discover their similarities, and how to use the alignments to create phylogenetic trees to reveal the most likely patterns of descendence among them.  

Programming for beginners, using the Python language: 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 NumPy, Pandas modules 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