Demetra Application: An Integrated Genotype Analysis Web Server for Clinical Genomics in Endometriosis

Overview

Journal Int J Mol Med

Specialty Genetics

Date 2021 Apr 28

PMID 33907838

Citations 3

Authors

Louis Papageorgiou

Maria I Zervou

Dimitrios Vlachakis

Michail Matalliotakis

Ioannis Matalliotakis

Demetrios A Spandidos

George N Goulielmos

Elias Eliopoulos

Affiliations

Soon will be listed here.

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/.

Citing Articles

Semantic and Population Analysis of the Genetic Targets Related to COVID-19 and Its Association with Genes and Diseases.

Papageorgiou L, Papakonstantinou E, Diakou I, Pierouli K, Dragoumani K, Bacopoulou F Adv Exp Med Biol. 2023; 1423:59-78.

PMID: 37525033 DOI: 10.1007/978-3-031-31978-5_6.

Genetic factors involved in the co‑occurrence of endometriosis with ankylosing spondylitis (Review).

Zervou M, Papageorgiou L, Vlachakis D, Spandidos D, Eliopoulos E, Goulielmos G Mol Med Rep. 2023; 27(5).

PMID: 36960867 PMC: 10071292. DOI: 10.3892/mmr.2023.12983.

Epione application: An integrated web‑toolkit of clinical genomics and personalized medicine in systemic lupus erythematosus.

Papageorgiou L, Alkenaris H, Zervou M, Vlachakis D, Matalliotakis I, Spandidos D Int J Mol Med. 2021; 49(1).

PMID: 34791504 PMC: 8612305. DOI: 10.3892/ijmm.2021.5063.

References

Goulielmos G, Matalliotakis M, Matalliotaki C, Eliopoulos E, Matalliotakis I, Zervou M . Endometriosis research in the -omics era. Gene. 2020; 741:144545. DOI: 10.1016/j.gene.2020.144545. View

Liu J, Zhao M . A PubMed-wide study of endometriosis. Genomics. 2016; 108(3-4):151-157. DOI: 10.1016/j.ygeno.2016.10.003. View

De Sanctis V, Matalliotakis M, Soliman A, Elsefdy H, Di Maio S, Fiscina B . A focus on the distinctions and current evidence of endometriosis in adolescents. Best Pract Res Clin Obstet Gynaecol. 2018; 51:138-150. DOI: 10.1016/j.bpobgyn.2018.01.023. View

Joseph S, Mahale S . Endometriosis Knowledgebase: a gene-based resource on endometriosis. Database (Oxford). 2019; 2019. PMC: 6551373. DOI: 10.1093/database/baz062. View

Jurca G, Addam O, Aksac A, Gao S, Ozyer T, Demetrick D . Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends. BMC Res Notes. 2016; 9:236. PMC: 4845430. DOI: 10.1186/s13104-016-2023-5. View

Xu J, Kim S, Song M, Jeong M, Kim D, Kang J . Building a PubMed knowledge graph. Sci Data. 2020; 7(1):205. PMC: 7320186. DOI: 10.1038/s41597-020-0543-2. View

Palmer S, Barnhart K . Biomarkers in reproductive medicine: the promise, and can it be fulfilled?. Fertil Steril. 2012; 99(4):954-62. PMC: 3602311. DOI: 10.1016/j.fertnstert.2012.11.019. View

Khan R, Mittelman D . Consumer genomics will change your life, whether you get tested or not. Genome Biol. 2018; 19(1):120. PMC: 6100720. DOI: 10.1186/s13059-018-1506-1. View

Albertsen H, Matalliotaki C, Matalliotakis M, Zervou M, Matalliotakis I, Spandidos D . Whole exome sequencing identifies hemizygous deletions in the UGT2B28 and USP17L2 genes in a three‑generation family with endometriosis. Mol Med Rep. 2019; 19(3):1716-1720. PMC: 6390005. DOI: 10.3892/mmr.2019.9818. View

10.

Perakakis N, Yazdani A, Karniadakis G, Mantzoros C . Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics. Metabolism. 2018; 87:A1-A9. PMC: 6325641. DOI: 10.1016/j.metabol.2018.08.002. View

11.

Xiao H, Yang L, Liu J, Jiao Y, Lu L, Zhao H . Protein-protein interaction analysis to identify biomarker networks for endometriosis. Exp Ther Med. 2017; 14(5):4647-4654. PMC: 5704338. DOI: 10.3892/etm.2017.5185. View

12.

Allot A, Peng Y, Wei C, Lee K, Phan L, Lu Z . LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC. Nucleic Acids Res. 2018; 46(W1):W530-W536. PMC: 6030971. DOI: 10.1093/nar/gky355. View

13.

Zondervan K, Becker C, Koga K, Missmer S, Taylor R, Vigano P . Endometriosis. Nat Rev Dis Primers. 2018; 4(1):9. DOI: 10.1038/s41572-018-0008-5. View

14.

Yang J, Li Y, Liu Q, Li L, Feng A, Wang T . Brief introduction of medical database and data mining technology in big data era. J Evid Based Med. 2020; 13(1):57-69. PMC: 7065247. DOI: 10.1111/jebm.12373. View

15.

Sherry S, Ward M, Kholodov M, Baker J, Phan L, Smigielski E . dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2000; 29(1):308-11. PMC: 29783. DOI: 10.1093/nar/29.1.308. View

16.

Vassilopoulou L, Matalliotakis M, Zervou M, Matalliotaki C, Krithinakis K, Matalliotakis I . Defining the genetic profile of endometriosis. Exp Ther Med. 2019; 17(5):3267-3281. PMC: 6447774. DOI: 10.3892/etm.2019.7346. View

17.

Agarwal S, Chapron C, Giudice L, Laufer M, Leyland N, Missmer S . Clinical diagnosis of endometriosis: a call to action. Am J Obstet Gynecol. 2019; 220(4):354.e1-354.e12. DOI: 10.1016/j.ajog.2018.12.039. View

18.

Anastasiu C, Moga M, Neculau A, Balan A, Scarneciu I, Dragomir R . Biomarkers for the Noninvasive Diagnosis of Endometriosis: State of the Art and Future Perspectives. Int J Mol Sci. 2020; 21(5). PMC: 7084761. DOI: 10.3390/ijms21051750. View

19.

Roberts J, Middleton A . Genetics in the 21st Century: Implications for patients, consumers and citizens. F1000Res. 2018; 6:2020. PMC: 5721930. DOI: 10.12688/f1000research.12850.2. View

20.

Brown G, Hem V, Katz K, Ovetsky M, Wallin C, Ermolaeva O . Gene: a gene-centered information resource at NCBI. Nucleic Acids Res. 2014; 43(Database issue):D36-42. PMC: 4383897. DOI: 10.1093/nar/gku1055. View