There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 7082 | - |
dc.citation.number | 13 | - |
dc.citation.startPage | 7071 | - |
dc.citation.title | NUCLEIC ACIDS RESEARCH | - |
dc.citation.volume | 51 | - |
dc.contributor.author | Seo, Euijin | - |
dc.contributor.author | Choi, Yun-Nam | - |
dc.contributor.author | Shin, Ye Rim | - |
dc.contributor.author | Kim, Donghyuk | - |
dc.contributor.author | Lee, Jeong Wook | - |
dc.date.accessioned | 2023-12-21T12:38:46Z | - |
dc.date.available | 2023-12-21T12:38:46Z | - |
dc.date.created | 2023-06-27 | - |
dc.date.issued | 2023-07 | - |
dc.description.abstract | Deep generative models, which can approximate complex data distribution from large datasets, are widely used in biological dataset analysis. In particular, they can identify and unravel hidden traits encoded within a complicated nucleotide sequence, allowing us to design genetic parts with accuracy. Here, we provide a deep-learning based generic framework to design and evaluate synthetic promoters for cyanobacteria using generative models, which was in turn validated with cell-free transcription assay. We developed a deep generative model and a predictive model using a variational autoencoder and convolutional neural network, respectively. Using native promoter sequences of the model unicellular cyanobacterium Synechocystis sp. PCC 6803 as a training dataset, we generated 10 000 synthetic promoter sequences and predicted their strengths. By position weight matrix and k-mer analyses, we confirmed that our model captured a valid feature of cyanobacteria promoters from the dataset. Furthermore, critical subregion identification analysis consistently revealed the importance of the -10 box sequence motif in cyanobacteria promoters. Moreover, we validated that the generated promoter sequence can efficiently drive transcription via cell-free transcription assay. This approach, combining in silico and in vitro studies, will provide a foundation for the rapid design and validation of synthetic promoters, especially for non-model organisms. | - |
dc.identifier.bibliographicCitation | NUCLEIC ACIDS RESEARCH, v.51, no.13, pp.7071 - 7082 | - |
dc.identifier.doi | 10.1093/nar/gkad451 | - |
dc.identifier.issn | 0305-1048 | - |
dc.identifier.scopusid | 2-s2.0-85165521638 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/64717 | - |
dc.identifier.wosid | 000996622300001 | - |
dc.language | 영어 | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.title | Design of synthetic promoters for cyanobacteria with generative deep-learning model | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.type.docType | Article; Early Access | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | CONVOLUTIONAL NEURAL-NETWORKS | - |
dc.subject.keywordPlus | GENE-EXPRESSION | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.