• Overview
• Clinical and biological databases, Statistics and high throughput technologies

• Omics-based biomarkers detection
• Tool: Babelomics (http://babelomics.bioinfo.cipf.es/ from Firefox browser)
• Exercises
  1. Metabolomics, Proteomics, Transcriptomics from arrays,…
  2. RNA-Seq, microRNA-Seq,…

How can we detect groups of patients with similar expression profile? What microRNAs or genes have a common intensity pattern for an experimental group? Could we explore our data before continuing the analysis?

• Methods
• Exercises

How do we predict or classify patients with different stages of a disease according to their level of expression?

• Methods
• Exercises

• Presentation
• Tools: BoxPlotR (http://boxplot.tyerslab.com), Heatmapper (http://heatmapper.ca)
• Exercises

1. General overview of the sessions. Studies of genomic variation. Prioritization of variants and genes. Presentation.
2. CSVS (CIBERER Spanish Variant Server). This database provides information about the variability of the Spanish population. It is very useful for filtering polymorphisms and local variations when prioritizing candidate disease genes. CSVS exercises.
3. BiERapp. Interactive web application for assisting in gene prioritization in Whole-Exome Sequencing studies. BiERapp exercises.
4. TEAM (Targeted Enrichment Analysis and Management). Management of panels of genes for targeted enrichment and massive sequencing for diagnostic applications. How to design panels of genes. TEAM exercises.
5. Prioritization of genes. How to prioritize from Endeavour?. Exercises

• Presentation
• Tools:
  • PANTHER: Classification System
  • g:Profiler
  • Babelomics (http://courses.babelomics.org/ from Firefox browser)
  • Functional profiling for microRNAs: omiRas, miRNet, mirPath v.3.
• Exercises

• Presentation
• Tool: STRING: functional protein association networks
• Exercises

• hiPathia. High-throughput pathway inference analysis. hipathia_jan2019.pdf
• PathAct. Actionable pathway workshop. pathact_2018jan.pdf

Fortunately there are many studies in public respositories where is possible to select, download and process its omics data. The combination of information for all selected studies could be an interesting idea to detect a global effect along of similar studies (same topic, experimental design…). These sessions will help us to get familiar with several resources really useful to analyze these data.

1. GEO (Gene Expression Omnibus)
2. ImaGEO: Integrative Gene Expression Meta-Analysis from GEO database
3. NetworkAnalyst
4. MIO: miRNA target analysis system for Immuno-Oncology. A web tool where the he integration of several machine learning methods enable the selection of prognostic and predictive microRNAs and gene interaction network biomarkers.

• Single cell RNA-seq data analysis with ASAP.
• Tool: ASAP: Automated Single-cell Analysis Pipeline

• Approach 1: Processing & Differential expression from Babelomics + Functional profiling from PANTHER & STRING.
• Approach 2: Processing & Differential expression from GEO2R + Functional profiling from PANTHER & STRING.

• Main references
• More publications.
• Software list