Bioinformatics Course 2019

Bioinformatics Course 2019

8th Bioinformatics Course Version: Intensive in R/Bioducor and Cytoscape

The overall objective of this intensive is to train its assistants in understanding the new technologies and in the different methods of analysis that this type of data represents, using free software alternatives such as "R/Bioconductor" and " Cytoscape". This will be achieved through the full understanding of the workflow involved in each technology, in order to finally obtain results whose biological interpretation is loaded meaning and allows correct decision making.

Session 1: Monday, October 14. Statistical Foundations for Bioinformatics.

  • Classical statistics: null hypothesis, alternative hypothesis, p-values.
  • Correction for multiple comparisons: adjusted p-values.
  • Bayesian statistics: a posteriori probability and decision theory.

Session 2: Thursday, October 17. R programming for Bioinformatics.

  • Introduction to programming in R and use RStudio.
  • Types of variables: vectors, matrices, factors, data frames, ready.
  • Control flow and conditionals, loops, functions, family apply.
  • Management of FASTA files, FASTQ, BAM, BED, csv, txt.

Session 3: Monday 21 October. Massive sequencing and Machine Learning in Bioinformatics.

  • Massive sequencing and single molecule technologies.
  • Machine Learning using omics data. Unsupervised methods; PCA, t-SNE, NMDS, PCoA, Clustering. Supervised methods; L/hdc, KNN, SVM, CART and Random Forest.
  • Practical in R/Bioconductor.

Session 4: Thursday, October 24. Microbiota analysis by 16/18S amplification.

  • Applications of molecular markers: 16/18S using Illumina.
  • Operational Taxonomic Unit (OTU) versus Amplicon Sequence Variant (ASV).
  • Workflow: Experimental design, trimming and filtering of files FASTQ, mapping species, phylogenetic tree construction, statistical analysis of alpha diversity, beta, range and composition of communities.
  • Practical in R/Bioconductor: workflow using public FASTQ.

Session 5: Monday 28 October. Introduction to Transcriptomics.

  • Transcriptomics: Microarrays, RNA-seq Bulk and single-cell RNA-seq.
  • Analysis of differential expression: comparison of frequentist and Bayesian statistical methods.
  • Practical in R/Bioconductor: statistical analysis of differential expression of RNA-seq and microarray data.

Session 6: Monday, November 4. Statistical analysis of differential post-expression results Part I.

  • Statistical analysis of biological pathways.
  • Use of functional annotation databases: Gene Ontology (GO), KEGG.
  • Practical in R/Bioconductor: analysis of enrichment of biological processes, molecular function, cellular components (GO) and biological pathways (KEGG). Graphic dbuilder-evente enrichments.
  • Practical in Cytoscape: introduction to Cytoscape. Analysis of enrichment. Views and customization of graphics.

Session 7: Thursday, November 7. Statistical analysis of differential post-expression results Part II.

  • Statistical analysis of biological networks.
  • Gene co-expression networks. Protein-protein interaction networks.
  • Practical in R/Bioconductor and Cytoscape. Transcriptomic will be calculated from data: networks of co-expression of interaction protein-protein.

Session 8: Friday, November 8. Statistical analysis of differential post-expression results Part III.

  • Gene regulatory networks.
  • Search in silico and microRNAs and transcription factor binding sites in vivo.
  • Immunoprecipitation of chromatin followed by sequencing: ChIP-seq.
  • Practical in R/Bioconductor and Cytoscape.


Due to the characteristics of the space, the number of seats is limited. There are only 10 seats available.

Total cost: $150,000 per person (8 sessions, 24 hours in total).

To register, write to the email:

Please note that 50% must be paid before the first session to reserve the quota.

Invoice is delivered if it is required. Registrations and more information: