Available courses

Biodata Analysis certification program

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 for analyzing gene expression data

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.


High-throughput data analysis with R

Advanced structural bioinformatics

Phylogenetics

Biopython

Biological Data Analysis with R 2020

Genomics, Transcriptomics, and Proteomics 2021

Biological databases and online analysis tools

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

Biological data analysis with R

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

Introduction to bio-statistics

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

Introduction to programming in Python

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 NumPy, Pandas modules
Recommended reading

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

Assignment tests for e-learning teachers


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