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

Papageorgiou,Louis; Zervou,Maria I.; Vlachakis,Dimitrios; Matalliotakis,Michail; Matalliotakis,Ioannis; Spandidos,Demetrios A.; Goulielmos,George N.; Eliopoulos,Elias

doi:10.3892/ijmm.2021.4948

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

Authors:
- Louis Papageorgiou
- Maria I. Zervou
- Dimitrios Vlachakis
- Michail Matalliotakis
- Ioannis Matalliotakis
- Demetrios A. Spandidos
- George N. Goulielmos
- Elias Eliopoulos
View Affiliations

Affiliations: Laboratory of Genetics, Department of Biotechnology, Agricultural University of Athens, 11855 Athens, Greece, Section of Molecular Pathology and Human Genetics, Department of Internal Medicine, School of Medicine, University of Crete, 71003 Heraklion, Greece, Department of Obstetrics and Gynecology, ‘Venizeleio and Pananio’ General Hospital of Heraklion, 71409 Heraklion, Greece, Laboratory of Clinical Virology, School of Medicine, University of Crete, 71003 Heraklion, Greece
Published online on: April 27, 2021 https://doi.org/10.3892/ijmm.2021.4948
Article Number: 115
Copyright: © Papageorgiou et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

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/.

View Figures

View References

1	Halis G and Arici A: Endometriosis and inflammation in infertility. Ann N Y Acad Sci. 1034:300–315. 2004. View Article : Google Scholar
2	Zondervan KT, Becker CM, Koga K, Missmer SA, Taylor RN and Viganò P: Endometriosis. Nat Rev Dis Primers. 4:92018. View Article : Google Scholar : PubMed/NCBI
3	Sapkota Y, Steinthorsdottir V, Morris AP, Fassbender A, Rahmioglu N, De Vivo I, Buring JE, Zhang F, Edwards TL, Jones S, et al: iPSYCH-SSI-Broad Group: Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism. Nat Commun. 8:155392017. View Article : Google Scholar
4	Vassilopoulou L, Matalliotakis M, Zervou MI, Matalliotaki C, Krithinakis K, Matalliotakis I, Spandidos DA and Goulielmos GN: Defining the genetic profile of endometriosis. Exp Ther Med. 17:3267–3281. 2019.PubMed/NCBI
5	Alborzi S, Hosseini-Nohadani A, Poordast T and Shomali Z: Surgical outcomes of laparoscopic endometriosis surgery: A 6 year experience. Curr Med Res Opin. 33:2229–2234. 2017. View Article : Google Scholar : PubMed/NCBI
6	Anastasiu CV, Moga MA, Elena Neculau A, Bălan A, Scârneciu I, Dragomir RM, Dull AM and Chicea LM: Biomarkers for the noninvasive diagnosis of endometriosis: state of the art and future perspectives. Int J Mol Sci. 21:212020. View Article : Google Scholar
7	Goulielmos GN, Matalliotakis M, Matalliotaki C, Eliopoulos E, Matalliotakis I and Zervou MI: Endometriosis research in the -omics era. Gene. 741:1445452020. View Article : Google Scholar : PubMed/NCBI
8	Palmer SS and Barnhart KT: Biomarkers in reproductive medicine: The promise, and can it be fulfilled? Fertil Steril. 99:954–962. 2013. View Article : Google Scholar :
9	de Sanctis V, Matalliotakis M, Soliman AT, Elsefdy H, Di Maio S and Fiscina B: A focus on the distinctions and current evidence of endometriosis in adolescents. Best Pract Res Clin Obstet Gynaecol. 51:138–150. 2018. View Article : Google Scholar : PubMed/NCBI
10	Agarwal SK, Chapron C, Giudice LC, Laufer MR, Leyland N, Missmer SA, Singh SS and Taylor HS: Clinical diagnosis of endometriosis: a call to action. Am J Obstet Gynecol. 220:354.e1–354.e12. 2019. View Article : Google Scholar
11	Tam V, Patel N, Turcotte M, Bossé Y, Paré G and Meyre D: Benefits and limitations of genome-wide association studies. Nat Rev Genet. 20:467–484. 2019. View Article : Google Scholar : PubMed/NCBI
12	Khan R and Mittelman D: Consumer genomics will change your life, whether you get tested or not. Genome Biol. 19:1202018. View Article : Google Scholar : PubMed/NCBI
13	Roberts J and Middleton A: Genetics in the 21st Century: Implications for patients, consumers and citizens. F1000 Res. 6:20202017. View Article : Google Scholar
14	Perakakis N, Yazdani A, Karniadakis GE and Mantzoros C: Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics. Metabolism. 87:A1–A9. 2018. View Article : Google Scholar : PubMed/NCBI
15	Yang J, Li Y, Liu Q, Li L, Feng A, Wang T, Zheng S, Xu A and Lyu J: Brief introduction of medical database and data mining technology in big data era. J Evid Based Med. 13:57–69. 2020. View Article : Google Scholar : PubMed/NCBI
16	Xu J, Kim S, Song M, Jeong M, Kim D, Kang J, Rousseau JF, Li X, Xu W, Torvik VI, et al: Building a PubMed knowledge graph. Sci Data. 7:2052020. View Article : Google Scholar
17	Liu JL and Zhao M: A PubMed-wide study of endometriosis. Genomics. 108:151–157. 2016. View Article : Google Scholar : PubMed/NCBI
18	Allot A, Peng Y, Wei CH, Lee K, Phan L and Lu Z: LitVar: A semantic search engine for linking genomic variant data in PubMed and PMC. Nucleic Acids Res. 46(W1): W530–W536. 2018. View Article : Google Scholar : PubMed/NCBI
19	Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM and Sirotkin K: dbSNP: The NCBI database of genetic variation. Nucleic Acids Res. 29:308–311. 2001. View Article : Google Scholar :
20	Brown GR, Hem V, Katz KS, Ovetsky M, Wallin C, Ermolaeva O, Tolstoy I, Tatusova T, Pruitt KD, Maglott DR, et al: Gene: A gene-centered information resource at NCBI. Nucleic Acids Res. 43(D1): D36–D42. 2015. View Article : Google Scholar :
21	Kim S, Yeganova L, Comeau DC, Wilbur WJ and Lu Z: PubMed Phrases, an open set of coherent phrases for searching biomedical literature. Sci Data. 5:1801042018. View Article : Google Scholar : PubMed/NCBI
22	Hamosh A, Scott AF, Amberger JS, Bocchini CA and McKusick VA: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33:D514–D517. 2005. View Article : Google Scholar :
23	Joseph S and Mahale SD: Endometriosis Knowledgebase: a gene-based resource on endometriosis. Database (Oxford). 2019. pp. baz0622019, View Article : Google Scholar
24	Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, et al: ClinVar: Improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46(D1): D1062–D1067. 2018. View Article : Google Scholar :
25	Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, et al: 1000 Genomes Project Analysis Group: The variant call format and VCFtools. Bioinformatics. 27:2156–2158. 2011. View Article : Google Scholar : PubMed/NCBI
26	Banchs RE: Text Mining With MATLAB. Springer; New York, NY: 2013, View Article : Google Scholar
27	Xiao H, Yang L, Liu J, Jiao Y, Lu L and Zhao H: Protein-protein interaction analysis to identify biomarker networks for endometriosis. Exp Ther Med. 14:4647–4654. 2017.PubMed/NCBI
28	Jurca G, Addam O, Aksac A, Gao S, Özyer T, Demetrick D and Alhajj R: Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends. BMC Res Notes. 9:2362016. View Article : Google Scholar : PubMed/NCBI
29	Albertsen HM, Matalliotaki C, Matalliotakis M, Zervou MI, Matalliotakis I, Spandidos DA, Chettier R, Ward K and Goulielmos GN: Whole exome sequencing identifies hemizygous deletions in the UGT2B28 and USP17L2 genes in a three generation family with endometriosis. Mol Med Rep. 19:1716–1720. 2019.PubMed/NCBI