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Review
. 2020 Mar 29;9(2):8.
doi: 10.3390/ht9020008.

DMETTM Genotyping: Tools for Biomarkers Discovery in the Era of Precision Medicine

Giuseppe Agapito  1 , Marzia Settino  1 , Francesca Scionti  2 , Emanuela Altomare  2 , Pietro Hiram Guzzi  1 , Pierfrancesco Tassone  2 , Pierosandro Tagliaferri  2 , Mario Cannataro  1 , Mariamena Arbitrio  3 , Maria Teresa Di Martino  2
Affiliations
Review

DMETTM Genotyping: Tools for Biomarkers Discovery in the Era of Precision Medicine

Giuseppe Agapito et al. High Throughput. .

Abstract

The knowledge of genetic variants in genes involved in drug metabolism may be translated into reduction of adverse drug reactions, increase of efficacy, healthcare outcomes improvement and economic benefits. Many high-throughput tools are available for the genotyping of Single Nucleotide Polymorphisms (SNPs) known to be related to drugs and xenobiotics metabolism. DMETTM platform represents an example of SNPs panel to discover biomarkers correlated to efficacy or toxicity in common and rare diseases. The difficulty in analyzing the mole of information generated by DMETTM platform led to the development and implementation of algorithms and tools for statistical and data mining analysis. These softwares allow efficient handling of the omics data to validate the explorative SNPs identified by DMET assay and to correlate them with drug efficacy, toxicity and/or cancer susceptibility. In this review we present a suite of bioinformatic frameworks for the preprocessing and analysis of DMET-SNPs data. In particular, we introduce a workflow that uses the GenoMetric Query Language, a high-level query language specifically designed for genomics, able to query public datasets (such as ENCODE, TCGA, GENCODE annotation dataset, etc.) as well as to combine them with private datasets (e.g., output from Affymetrix® DMETTM Platform).

Keywords: DMETTM platform; GMQL; SNPs; bioinformatic tools; pharmacogenomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
DMET analysis workflow.
Figure 2
Figure 2
Phases of the methodology for integrating private and public datasets using GMQL-Web interface data preprocessing stage.
Figure 3
Figure 3
Workflow involving DMET platform, GMQL system and public genomic database (e.g., GENCODE).
Figure 4
Figure 4
Example of application of the methodology: integration of Affymetrix DMET platform outcome with GENCODE annotation database.
Figure 5
Figure 5
Listing 1—query for integrating DMET dataset with GENCODE dataset.
Figure 6
Figure 6
Results of Listing 1.
See this image and copyright information in PMC

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