Institute for Biocomputation and Physics of Complex Systems of the University of Zaragoza

  1. 1. Introduction to statistical methods and programming in the R language.
  2. 2.- Review of some distributions of probability.
  3. 3.- Classical inference and Bayesian methods.
  4. 4.- Classification and prediction.
  5. 5.- Markov chains and hidden models.
  6. 6.- Model validation, false positives and negatives. Receiver Operating Characteristic curves.
  7. 7.- Multiple testing and error control. Limitations of Bonferroni, alternatives.tures
  8. 8.- Introduction to processing large data files in the terminal with Perl one-liners.
  9. 9.- Bioinformatics file formats: FASTA, FASTQ, PDB/PDBML, Newick.
  10. 10.- Dynamic programming algorithms for local and global alignments.
  11. 11.- Sequence similarity searches in local databases.
  12. 12.- Multiple sequence alignment of nucleotide and protein sequences.
  13. 13.- Alignment and superposition of protein structures. Scores.
  14. 14.- Phylogenetic trees from distance matrices and evolution models.