| Autor: |
Papageorgiou L; Laboratory of Genetics, Department of Biotechnology, Agricultural University of Athens, 11855 Athens, Greece., Zervou MI; Section of Molecular Pathology and Human Genetics, Department of Internal Medicine, School of Medicine, University of Crete, 71003 Heraklion, Greece., Vlachakis D; Laboratory of Genetics, Department of Biotechnology, Agricultural University of Athens, 11855 Athens, Greece., Matalliotakis M; Section of Molecular Pathology and Human Genetics, Department of Internal Medicine, School of Medicine, University of Crete, 71003 Heraklion, Greece., Matalliotakis I; Department of Obstetrics and Gynecology, 'Venizeleio and Pananio' General Hospital of Heraklion, 71409 Heraklion, Greece., Spandidos DA; Laboratory of Clinical Virology, School of Medicine, University of Crete, 71003 Heraklion, Greece., Goulielmos GN; Section of Molecular Pathology and Human Genetics, Department of Internal Medicine, School of Medicine, University of Crete, 71003 Heraklion, Greece., Eliopoulos E; Laboratory of Genetics, Department of Biotechnology, Agricultural University of Athens, 11855 Athens, Greece. |
| Abstrakt: |
Demetra Application is a holistic integrated and scalable bioinformatics web‑based tool designed to assist medical experts and researchers in the process of diagnosing endometriosis. The application identifies the most prominent gene variants and single nucleotide polymorphisms (SNPs) causing endometriosis using the genomic data provided for the patient by a medical expert. The present study analyzed >28.000 endometriosis‑related publications using data mining and semantic techniques aimed towards extracting the endometriosis‑related genes and SNPs. The extracted knowledge was filtered, evaluated, annotated, classified, and stored in the Demetra Application Database (DAD). Moreover, an updated gene regulatory network with the genes implements in endometriosis was established. This was followed by the design and development of the Demetra Application, in which the generated datasets and results were included. The application was tested and presented herein with whole‑exome sequencing data from seven related patients with endometriosis. Endometriosis‑related SNPs and variants identified in genome‑wide association studies (GWAS), whole‑genome (WGS), whole‑exome (WES), or targeted sequencing information were classified, annotated and analyzed in a consolidated patient profile with clinical significance information. Probable genes associated with the patient's genomic profile were visualized using several graphs, including chromosome ideograms, statistic bars and regulatory networks through data mining studies with relative publications, in an effort to obtain a representative number of the most credible candidate genes and biological pathways associated with endometriosis. An evaluation analysis was performed on seven patients from a three‑generation family with endometriosis. All the recognized gene variants that were previously considered to be associated with endometriosis were properly identified in the output profile per patient, and by comparing the results, novel findings emerged. This novel and accessible webserver tool of endometriosis to assist medical experts in the clinical genomics and precision medicine procedure is available at http://geneticslab.aua.gr/. |
