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dc.contributor.authorHobbs, Emma Elizabeth Mary
dc.contributor.authorGloster, Tracey
dc.contributor.authorChapman, Sean
dc.contributor.authorPritchard, Leighton
dc.date.accessioned2022-01-06T14:30:01Z
dc.date.available2022-01-06T14:30:01Z
dc.date.issued2021-04-26
dc.identifier.citationHobbs , E E M , Gloster , T , Chapman , S & Pritchard , L 2021 , ' Comprehensive evaluation of CAZyme prediction tools in fungal and bacterial species ' , Microbiology Society Annual Conference , 26/04/21 - 30/04/21 .en
dc.identifier.citationconferenceen
dc.identifier.otherPURE: 274924020
dc.identifier.otherPURE UUID: 6f0c07ec-886b-41d8-96ba-8d850228e313
dc.identifier.otherORCID: /0000-0002-8470-4739/work/96817696
dc.identifier.urihttp://hdl.handle.net/10023/24612
dc.description.abstractCarbohydrate Active enZymes (CAZymes) are pivotal in pathogen recognition, signalling, structure and energy metabolism. CAZy is the most comprehensive CAZyme database, cataloguing CAZymes into sequence-based CAZy families. The CAZyme prediction tools dbCAN, CUPP and eCAMI annotate CAZymes with CAZy families. However, these tools have not been independently evaluated on a common high-quality dataset. Additionally, previous evaluations did not evaluate the binary classification of CAZymes/non-CAZymes, and the multilabel classification of CAZymes to multiple CAZy families.
dc.language.isoeng
dc.rightsCopyright © 2021 The Authors.en
dc.subjectCAZymesen
dc.subjectdbCANen
dc.subjectCUPPen
dc.subjecteCAMIen
dc.subjectCAZyme predictionen
dc.subjectCAZyen
dc.subjectQR Microbiologyen
dc.subject.lccQRen
dc.titleComprehensive evaluation of CAZyme prediction tools in fungal and bacterial speciesen
dc.typeConference posteren
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Biomedical Sciences Research Complexen
dc.description.statusNon peer revieweden
dc.identifier.urlhttps://doi.org/10.6084/m9.figshare.14370836.v3en


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