Demetra Application: An integrated genotype analysis web server for clinical genomics in endometriosis

Louis Papageorgiou, Maria I Zervou, Dimitrios Vlachakis, Dimitriοs Vlachakis, Michail Matalliotakis, Ioannis Matalliotakis, Demetrios A Spandidos, Demetrios�� Spandidos, George N Goulielmos, Elias Eliopoulos
International journal of molecular medicine · 2021 · vol. 47(6) · doi:10.3892/ijmm.2021.4948 · PMID:33907838 · PMC8083807 · W3158536552
article OA: gold CC0 ⤵ 3 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

The Demetra Application is a web server that integrates genomic data, literature mining, and gene networks to identify endometriosis-associated variants and genes for clinical genomics.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 ⓘ

This paper describes the development and evaluation of Demetra Application, a web-based genotype analysis toolkit intended to support clinical genomics in endometriosis by mining and integrating >28,000 endometriosis-related publications to build a curated database of genes and SNPs and an updated gene regulatory network. Using patient whole-exome sequencing data from seven individuals in a related three-generation family, the tool classified and annotated known endometriosis-associated variants from GWAS/WGS/WES/targeted data into consolidated patient profiles with visualization outputs such as chromosome ideograms and regulatory-network graphs. The authors report that known endometriosis-associated gene variants were correctly identified while novel findings emerged from comparing outputs across patients, with functionality centered on curated literature and database concordance. This paper is centrally about endometriosis — it presents the Demetra Application webserver that identifies and visualizes endometriosis-related gene variants and SNPs from genomic sequencing data.

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Abstract

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/.

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Condition tags

endometriosis

MeSH descriptors

Endometriosis Genomics Software Databases, Genetic Data Mining Endometriosis Female Genotype Humans Polymorphism, Single Nucleotide Polymorphism, Single Nucleotide Reproducibility of Results Semantics Transcription Factors Transcription Factors User-Computer Interface

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (27)

Cited by (3)

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License: CC0 · commercial use OK