Guest Speaker: Giuseppe Agapito, Ph.D. Adjunct Professor at the University of Catanzaro School of Computers Science Engineering and Biomedical and visiting scientist at the Princess Margaret Cancer Centre

Date and Time

Location

Food Science lecture room 128

Details

Abstract

Clinical Bioinformatics is currently growing and is based on the integration of clinical and omics data aiming at the development of personalized medicine. Thus, the introduction of novel technologies able to investigate the relationship among clinical states and biological machineries may help the development of this field. For instance, the Affymetrix DMET platform (drug metabolism enzymes and transporters) is able to study the relationship between the variation of the genome of patients and drug metabolism and detection of SNPs (Single Nucleotide Polymorphism) of genes related to drug metabolism. This may allow for the discovery of genetic variants in patients who present different drug responses in pharmacogenomic and clinical studies.
Despite this, there is currently insufficient development of open-source algorithms and tools for the analysis of DMET data. Existing software tools for DMET data generally allow only the preprocessing of binary data (e.g. the DMET-Console provided by Affymetrix) and simple data analysis operations but do not allow for testing the association between the presence of SNPs and response to drugs.
We developed DMET-Analyzer, a tool for automatic association analysis between the variation of patient genomes and the clinical conditions of patients, i.e. the different response to drugs. The main advantage of DMET-Analyzer is its ease of use, as it requires no programming effort. DMET-Analyzer allows: (i) for automated workflow of analysis of DMET-SNP data, avoiding the use of multiple tools; (ii) automatic annotation of DMETSNP data and searching in existing databases of SNPs (e.g. dbSNP) and(iii) association of SNPs with pathways through searching in PharmaGKB, a major knowledge base for pharmacogenomic studies. This talk will focus on the innovative platform of the DMET-Analyzer and how it can improve your research. 

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