Available courses

This workshop is for Biotechnology students at the National University of Ireland, Galway. 

The program has the following schedule:

Week 1, Feb 8 to 12

  • Mon, Feb 8: Course introduction; Uniprot protein knowledgebase
  • Tue, Feb 9: NCBI databases; tools at NCBI and Uniprot
  • Wed, Feb 10: Blast search at NCBI servers
    • Using result filters; Blast quiz plus practical exercises
  • Thu, Feb 11: Multiple sequence alignment and MSA visualization 
    • SeaView, sequence logos;
    • MSA workshop
  • Fri, Feb 12: Introduction to work with protein structures, PDB database
    • First UCSF Chimera demos

Week 2, Feb 15 to 19

  • Mon, Feb 15: Protein visualizations with UCSF Chimera
  • Tue, Feb 16: Ensembl genome browser and tools
  • Wed, Feb 17: Genomic studies of DNA variation in humans, using Ensembl and linked databases
  • Thu, Feb 18: Basics of transcriptome analysis with high- and low-throughput technologies
  • Fri, Feb 19: Gene expression and protein analysis (with a focus on cancer)

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.

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

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

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

Broad course topic: Basics of search, manipulation and analyses of 3D structures of large biological molecules, especially proteins. 

  • Basics of protein structures and structure determination.
  • Principles in molecular graphics
  • Molecular graphics in practice: illustrating and highlighting molecular details within software (UCSF Chimera) and in exported images; generating molecular surfaces 
  • Comparison of structures: overlaying molecules and measuring their structural similarity 
  • Molecular animations 
  • Theory of protein modeling; introduction to protein dynamics 
  • Validation and analysis of models, including Ramachandran plots.

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

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

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