Genomics and transcriptomics data analysis

R. Sanges, 35 hours


Theoretical lectures

  • Cells, genomes, genes and the Central Dogma

  • The definition of an eukariotic gene is an evolving concept

  • Transcription, transposons, non-coding and the evolution of organismal complexity

  • Bioinformatics databases and tools

  • Simple sequences handling and mining using public web databases and tools

  • Good practices for experimental design

  • Transcriptome mining and meta analysis with gene expression data

  • Transposons and the brain


Practical sessions

  • R for dummies

  • Variables: scalars, vectors, lists, matrices, data-frames, operators

  • Data exploration and selection: operators and operations, distributions, correlations, boxplots, histograms, xyplots, subsetting, filtering, t-test, multiple hypothesis testing, p-value correction and false discovery rate

  • Input/output: text file import, data-frame export, chart export

  • Extracting and analyzing molecular data using genome browsers and public genomic databases and tools