Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddings

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dc.citation.journalTitleIEEE Engineering in Medicine and Biology Society Conference Proceedingseng
dc.contributor.authorAriza-Jimenez L.
dc.contributor.authorQuintero O.L.
dc.contributor.authorPinel N.
dc.contributor.departmentUniversidad EAFIT. Escuela de Cienciasspa
dc.contributor.researchgroupModelado Matemáticospa
dc.date.accessioned2021-04-12T14:07:16Z
dc.date.available2021-04-12T14:07:16Z
dc.date.issued2018-01-01
dc.description.abstractShotgun metagenomic studies attempt to reconstruct population genome sequences from complex microbial communities. In some traditional genome demarcation approaches, high-dimensional sequence data are embedded into two-dimensional spaces and subsequently binned into candidate genomic populations. One such approach uses a combination of the Barnes-Hut approximation and the t -Stochastic Neighbor Embedding (BH-SNE) algorithm for dimensionality reduction of DNA sequence data pentamer profiles; and demarcation of groups based on Gaussian mixture models within humanimposed boundaries. We found that genome demarcation from three-dimensional BH-SNE embeddings consistently results in more accurate binnings than 2-D embeddings. We further addressed the lack of a priori population number information by developing an unsupervised binning approach based on the Subtractive and Fuzzy c-means (FCM) clustering algorithms combined with internal clustering validity indices. Lastly, we addressed the subject of shared membership of individual data objects in a mixed community by assigning a degree of membership to individual objects using the FCM algorithm, and discriminated between confidently binned and uncertain sequence data objects from the community for subsequent biological interpretation. The binning of metagenome sequence fragments according to thresholds in the degree of membership opens the door for the identification of horizontally transferred elements and other genomic regions of uncertain assignment in which biologically meaningful information resides. The reported approach improves the unsupervised genome demarcation of populations within complex communities, increases the confidence in the coherence of the binned elements, and enables the identification of evolutionary processes ignored in hard-binning approaches in shotgun metagenomic studies. © 2018 IEEE.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8480
dc.identifier.doi10.1109/EMBC.2018.8512529
dc.identifier.issn05891019
dc.identifier.issn1557170X
dc.identifier.otherPUBMED;30440633
dc.identifier.otherSCOPUS;2-s2.0-85056638520
dc.identifier.urihttp://hdl.handle.net/10784/27802
dc.language.isoengeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85056638520&doi=10.1109%2fEMBC.2018.8512529&partnerID=40&md5=9e7b2d279646efeef082154f24b16240
dc.rightsInstitute of Electrical and Electronics Engineers Inc.
dc.sourceIEEE Engineering in Medicine and Biology Society Conference Proceedings
dc.subject.keywordalgorithmeng
dc.subject.keywordcluster analysiseng
dc.subject.keywordDNA sequenceeng
dc.subject.keywordgenomicseng
dc.subject.keywordmetagenomeeng
dc.subject.keywordmetagenomicseng
dc.subject.keywordAlgorithmseng
dc.subject.keywordCluster Analysiseng
dc.subject.keywordGenomicseng
dc.subject.keywordMetagenomeeng
dc.subject.keywordMetagenomicseng
dc.subject.keywordSequence Analysiseng
dc.subject.keywordDNAeng
dc.titleUnsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddingseng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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