Bioinformatics and Functional Genomics using R

   IMPORTANT DATES for this Course
   Deadline for applications: June 11th 2012
   Notification of acceptance dates:
        EARLY:  June 8th 2012
        NORMAL:  June 14th 2012
   Course date: June 18th to June 20th 2012 - 9h30 - 18h30


Javier de Las Rivas MSc, PhD, is a CSIC Research Scientist and PI of the Bioinformatics and Functional Genomics group at the Cancer Research Center (IBMCC, CSIC/USAL) in Salamanca ( Spain). He is biochemist and after postdoctoral stays in London (Imperial College, IC) and New York (Mount Sinai School of Medicine, MSSM) he started his current research group in 2002, focusing his studies on the development and application of bioinformatics and computational biology to the field of cancer genomics and proteomics.

Affiliation: Consejo Superior de Investigaciones Científicas (CSIC) and Universidad de Salamanca (USAL), Salamanca, ES

Celia Fontanillo MsEng, is a young scientist, with a degree in Computer Science, who has been working at the Bioinformatics and Functional Genomics group of the Cancer Research Center (IBMCC, CSIC/USAL) in Salamanca (Spain) for five years. Her expertise is centered in the development of algorithms and methods in bioinformatics and genomics.

Affiliation: Consejo Superior de Investigaciones Científicas (CSIC) and Universidad de Salamanca (USAL), Salamanca, ES

Course description

This is a practical course on Bioinformatics applied to analysis of Gene expression and Genomic data using mainly R and Bioconductor tools and algorithms. The course will focus in the use of robust methods to analyse microarray datasets, mainly from Affymetrix platform and from clinical-biomedical studies, integrating several strategies to achieve an optimum construction and analyses of gene expression profiles. The Course will be mostly practical using computers and prepared protocols.
It will be divided in three major sessions:

1. Gene Expression Profiling. Microarrays genome-wide expression data integration, normalization, gene-based signal calculation, differential expression (using different algorithms: SAM, Bayesian and Limma) and clustering. Databases that integrate gene signatures.

2. Classification and Prediction using genomic data. Application of machine learning techniques to the analysis of gene expression data to allow classification of different states or different diseases subtypes. Error calculations. Class-prediction using de novo samples.

3. Gene Functional Enrichment. Gene list functional annotation using tools for statistical enrichment based on multiple concurrent annotation. Application of algorithms that measure outcome and response to assign specific functions to gene expression datasets.

Target Audience

Researchers in Biology and Biomedical areas (PhD students, PostDocs or Senior) working with genomic and gene expression data or with gene lists in general, that what to learn useful and powerful tools and methods for bioinformatic analysis.

Course Pre-requisites

Basic familiarity with genomic data and bioinformatics data resources. Minimum familiarity with R (although most scripts and command lines will be provided with written tutorials).

Detailed description

Instituto Gulbenkian de Ciência,

Apartado 14, 2781-901 Oeiras, Portugal

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Last updated:  May 21st 2012