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DGH-GO

2023-12Artigo científico Acesso aberto
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dc.contributor.authorAsif, Muhammad
dc.contributor.authorMartiniano, Hugo F. M. C.
dc.contributor.authorLamúrias, André
dc.contributor.authorKausar, Samina
dc.contributor.authorCouto, Francisco M.
dc.contributor.institutionNOVALincs
dc.contributor.pblBioMed Central (BMC)
dc.date.accessioned2023-07-14T22:21:20Z
dc.date.available2023-07-14T22:21:20Z
dc.date.issued2023-12
dc.description
dc.description.abstractBackground: Complex diseases such as neurodevelopmental disorders (NDDs) exhibit multiple etiologies. The multi-etiological nature of complex-diseases emerges from distinct but functionally similar group of genes. Different diseases sharing genes of such groups show related clinical outcomes that further restrict our understanding of disease mechanisms, thus, limiting the applications of personalized medicine approaches to complex genetic disorders. Results: Here, we present an interactive and user-friendly application, called DGH-GO. DGH-GO allows biologists to dissect the genetic heterogeneity of complex diseases by stratifying the putative disease-causing genes into clusters that may contribute to distinct disease outcome development. It can also be used to study the shared etiology of complex-diseases. DGH-GO creates a semantic similarity matrix for the input genes by using Gene Ontology (GO). The resultant matrix can be visualized in 2D plots using different dimension reduction methods (T-SNE, Principal component analysis, umap and Principal coordinate analysis). In the next step, clusters of functionally similar genes are identified from genes functional similarities assessed through GO. This is achieved by employing four different clustering methods (K-means, Hierarchical, Fuzzy and PAM). The user may change the clustering parameters and explore their effect on stratification immediately. DGH-GO was applied to genes disrupted by rare genetic variants in Autism Spectrum Disorder (ASD) patients. The analysis confirmed the multi-etiological nature of ASD by identifying four clusters of genes that were enriched for distinct biological mechanisms and clinical outcome. In the second case study, the analysis of genes shared by different NDDs showed that genes causing multiple disorders tend to aggregate in similar clusters, indicating a possible shared etiology. Conclusion: DGH-GO is a user-friendly application that allows biologists to study the multi-etiological nature of complex diseases by dissecting their genetic heterogeneity. In summary, functional similarities, dimension reduction and clustering methods, coupled with interactive visualization and control over analysis allows biologists to explore and analyze their datasets without requiring expert knowledge on these methods. The source code of proposed application is available at https://github.com/Muh-Asif/DGH-GOen
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent20
dc.format.extent1999730
dc.identifier.doi10.1186/s12859-023-05290-4
dc.identifier.issn1471-2105
dc.identifier.otherPURE: 66125696
dc.identifier.otherPURE UUID: 27645777-96a7-456f-a5b7-fd61efc70898
dc.identifier.otherScopus: 85153918433
dc.identifier.otherWOS: 000979900000004
dc.identifier.otherPubMed: 37101154
dc.identifier.otherPubMedCentral: PMC10134522
dc.identifier.urihttp://hdl.handle.net/10362/155310
dc.identifier.urlhttps://www.scopus.com/pages/publications/85153918433
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso para Financiamento de Projetos de Investigação Científica e Desenvolvimento Tecnológico em Todos os Domínios Científicos - 2017/PTDC%2FCCI-BIO%2F28685%2F2017/PT
dc.relationDeep Semantic Tagger
dc.relationLASIGE - Extreme Computing
dc.relationLASIGE - Extreme Computing
dc.subjectDimension reduction
dc.subjectFunctionally similarities
dc.subjectGene ontology
dc.subjectGenetic heterogeneity
dc.subjectNeurodevelopmental disorders
dc.subjectSemantic similarity
dc.subjectUnsupervised learning
dc.subjectStructural Biology
dc.subjectBiochemistry
dc.subjectMolecular Biology
dc.subjectComputer Science Applications
dc.subjectApplied Mathematics
dc.titleDGH-GOen
dc.title.subtitledissecting the genetic heterogeneity of complex diseases using gene ontologyen
dc.typejournal article
degois.publication.issue1
degois.publication.titleBMC Bioinformatics
degois.publication.volume24
dspace.entity.typePublication
oaire.awardNumberPTDC/CCI-BIO/28685/2017
oaire.awardNumberUIDB/00408/2020
oaire.awardNumberUIDP/00408/2020
oaire.awardTitleDeep Semantic Tagger
oaire.awardTitleLASIGE - Extreme Computing
oaire.awardTitleLASIGE - Extreme Computing
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso para Financiamento de Projetos de Investigação Científica e Desenvolvimento Tecnológico em Todos os Domínios Científicos - 2017/PTDC%2FCCI-BIO%2F28685%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00408%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00408%2F2020/PT
oaire.fundingStreamConcurso para Financiamento de Projetos de Investigação Científica e Desenvolvimento Tecnológico em Todos os Domínios Científicos - 2017
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication5105dcce-8c4c-4623-ba42-c3f7b16bac54
relation.isProjectOfPublication3eb06293-18be-490b-a68c-2da04a879a11
relation.isProjectOfPublicationd73f71a1-1b91-43fa-a3b8-df544c07753a
relation.isProjectOfPublication.latestForDiscovery3eb06293-18be-490b-a68c-2da04a879a11

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