Operon identifier: Identification of operon structures in the whole genome

Simple item page
but.event.date26.04.2022cs
but.event.titleSTUDENT EEICT 2022cs
dc.contributor.authorNejezchlebová, Julie
dc.contributor.authorSchwarzerová, Jana
dc.date.accessioned2022-12-06T13:21:58Z
dc.date.available2022-12-06T13:21:58Z
dc.date.issued2022cs
dc.description.abstractCurrently, operon prediction is based on the distance of neighboring genes on the functional relationships of their products that encode proteins in a given nucleotide sequence, or on ORF distances. This study deals with the design of a new function that detects operon structures based on information from gene expression or alternatively in combination with previous information from current online available tools. The function was implemented in Python language and tested on Clostridium beijerinckii NRRL B-598. This bacterium has huge potential in biotechnology and research due to its fermentation product, butanol.en
dc.formattextcs
dc.format.extent80-83cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 28st Conference STUDENT EEICT 2022: Selected papers. s. 80-83. ISBN 978-80-214-6030-0cs
dc.identifier.doi10.13164/eeict.2022.80
dc.identifier.isbn978-80-214-6030-0
dc.identifier.urihttp://hdl.handle.net/11012/208607
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 28st Conference STUDENT EEICT 2022: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectGene expression information, Transcription unit, Correlation coefficient, Clostridium beijerinckiien
dc.titleOperon identifier: Identification of operon structures in the whole genomeen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
080_eeict_2022.pdf
Size:
1.55 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Student EEICT 2022: Selected Papers