Discovering genomic islands in unannotated bacterial genomes using sequence embedding.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2024-06-17 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae089
Priyanka Banerjee, Oliver Eulenstein, Iddo Friedberg
{"title":"Discovering genomic islands in unannotated bacterial genomes using sequence embedding.","authors":"Priyanka Banerjee, Oliver Eulenstein, Iddo Friedberg","doi":"10.1093/bioadv/vbae089","DOIUrl":null,"url":null,"abstract":"<p><strong>Motivation: </strong>Genomic islands (GEIs) are clusters of genes in bacterial genomes that are typically acquired by horizontal gene transfer. GEIs play a crucial role in the evolution of bacteria by rapidly introducing genetic diversity and thus helping them adapt to changing environments. Specifically of interest to human health, many GEIs contain pathogenicity and antimicrobial resistance genes. Detecting GEIs is, therefore, an important problem in biomedical and environmental research. There have been many previous studies for computationally identifying GEIs. Still, most of these studies rely on detecting anomalies in the unannotated nucleotide sequences or on a fixed set of known features on annotated nucleotide sequences.</p><p><strong>Results: </strong>Here, we present TreasureIsland, which uses a new unsupervised representation of DNA sequences to predict GEIs. We developed a high-precision boundary detection method featuring an incremental fine-tuning of GEI borders, and we evaluated the accuracy of this framework using a new comprehensive reference dataset, Benbow. We show that TreasureIsland's accuracy rivals other GEI predictors, enabling efficient and faster identification of GEIs in unannotated bacterial genomes.</p><p><strong>Availability and implementation: </strong>TreasureIsland is available under an MIT license at: https://github.com/FriedbergLab/GenomicIslandPrediction.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11193100/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioadv/vbae089","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Motivation: Genomic islands (GEIs) are clusters of genes in bacterial genomes that are typically acquired by horizontal gene transfer. GEIs play a crucial role in the evolution of bacteria by rapidly introducing genetic diversity and thus helping them adapt to changing environments. Specifically of interest to human health, many GEIs contain pathogenicity and antimicrobial resistance genes. Detecting GEIs is, therefore, an important problem in biomedical and environmental research. There have been many previous studies for computationally identifying GEIs. Still, most of these studies rely on detecting anomalies in the unannotated nucleotide sequences or on a fixed set of known features on annotated nucleotide sequences.

Results: Here, we present TreasureIsland, which uses a new unsupervised representation of DNA sequences to predict GEIs. We developed a high-precision boundary detection method featuring an incremental fine-tuning of GEI borders, and we evaluated the accuracy of this framework using a new comprehensive reference dataset, Benbow. We show that TreasureIsland's accuracy rivals other GEI predictors, enabling efficient and faster identification of GEIs in unannotated bacterial genomes.

Availability and implementation: TreasureIsland is available under an MIT license at: https://github.com/FriedbergLab/GenomicIslandPrediction.

利用序列嵌入发现未注释细菌基因组中的基因组岛
动因:基因组岛(GEIs)是细菌基因组中的基因簇,通常通过水平基因转移获得。基因组岛在细菌进化过程中发挥着至关重要的作用,它能迅速引入遗传多样性,从而帮助细菌适应不断变化的环境。与人类健康特别相关的是,许多 GEI 包含致病性和抗菌性基因。因此,检测 GEIs 是生物医学和环境研究中的一个重要问题。此前已有许多通过计算识别 GEI 的研究。不过,这些研究大多依赖于检测未注释核苷酸序列中的异常或注释核苷酸序列上的固定已知特征集:在这里,我们介绍了 TreasureIsland,它使用一种新的 DNA 序列无监督表示法来预测 GEI。我们开发了一种高精度边界检测方法,其特点是对 GEI 边界进行增量微调,并使用新的综合参考数据集 Benbow 评估了这一框架的准确性。我们使用新的综合参考数据集 Benbow 对这一框架的准确性进行了评估。我们发现 TreasureIsland 的准确性可与其他 GEI 预测器媲美,能在未注释的细菌基因组中高效、快速地识别 GEI:TreasureIsland 在 MIT 许可下可用:https://github.com/FriedbergLab/GenomicIslandPrediction。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
1.60
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信