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. 2012 Oct 5:13:258.
doi: 10.1186/1471-2105-13-258.

DMET-analyzer: automatic analysis of Affymetrix DMET data

Pietro Hiram Guzzi  1 , Giuseppe Agapito, Maria Teresa Di Martino, Mariamena Arbitrio, Pierfrancesco Tassone, Pierosandro Tagliaferri, Mario Cannataro
Affiliations

DMET-analyzer: automatic analysis of Affymetrix DMET data

Pietro Hiram Guzzi et al. BMC Bioinformatics. .

Abstract

Background: 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 among the variation of the genome of patients and drug metabolism, detecting SNPs (Single Nucleotide Polymorphism) on genes related to drug metabolism. This may allow for instance to find genetic variants in patients which present different drug responses, in pharmacogenomics and clinical studies. Despite this, there is currently a lack in the 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 to test the association of the presence of SNPs with the response to drugs.

Results: We developed DMET-Analyzer a tool for the automatic association analysis among the variation of the patient genomes and the clinical conditions of patients, i.e. the different response to drugs. The proposed system allows: (i) to automatize the workflow of analysis of DMET-SNP data avoiding the use of multiple tools; (ii) the automatic annotation of DMET-SNP data and the search in existing databases of SNPs (e.g. dbSNP), (iii) the association of SNP with pathway through the search in PharmaGKB, a major knowledge base for pharmacogenomic studies. DMET-Analyzer has a simple graphical user interface that allows users (doctors/biologists) to upload and analyse DMET files produced by Affymetrix DMET-Console in an interactive way. The effectiveness and easy use of DMET Analyzer is demonstrated through different case studies regarding the analysis of clinical datasets produced in the University Hospital of Catanzaro, Italy.

Conclusion: DMET Analyzer is a novel tool able to automatically analyse data produced by the DMET-platform in case-control association studies. Using such tool user may avoid wasting time in the manual execution of multiple statistical tests avoiding possible errors and reducing the amount of time needed for a whole experiment. Moreover annotations and the direct link to external databases may increase the biological knowledge extracted. The system is freely available for academic purposes at: https://sourceforge.net/projects/dmetanalyzer/files/

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Figures

Figure 1
Figure 1
Workflow of a clinical bioinformatics experiment from the sample collection to the data analysis. Workflow of data in a typical experiment
Figure 2
Figure 2
Workflow of an experiment of analysis through the software. Figure shows the workflow of execution of a typical analysis. Initially user loads data into the software as depicted in the upper left corner of Figure 2(a). Then user has to attribute the right class to each sample (Figure 2b) and to choose the analysis method Figure 2(c). The software calculates the allele frequencies for each allele and for each probe. At this point DMET Analyzer calculates the Fisher’s-tests and finally it shows the results in a new window in which probes may be sorted alphabetically or by p-value as depicted in Figure 2(d). User can select a SNP in this table and may visualize annotation data by just clicking on the SNP identifier as depicted in Figure 2(e). Analogously, user may visualize the distribution of variants using the embedded visualizer as evidenced in Figure 2(f)
Figure 3
Figure 3
Memory Occupancy and Execution Times. Figure shows the execution time and the total amount of requested memory for a growing dimension of dataset. We performed these measures for different datasets considering ten datasets from 100 to 1000 patients increased by 100. Results show that the implementation of DMET Analyzer and the algorithmic choice enable the processing of this dataset requesting approximately the same time and the same memory for the execution (except for the initial loading of files)
Figure 4
Figure 4
Comparison with existing Tools. Comparison of DMET Analyzer with respect to existing software tools considering a typical workflow of analysis. Data produced by the DMET platform may be preprocessed using apt-dmet-genotype. Then this data may be given as input to DMET-Console to be transformed into a format readable by other softwares. Diversely DMET console may perform these two steps. Then this data may be processed by statistical tools after some manual steps. Conversely our software is able to perform automatically all final steps
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