Disentangling cobionts and contamination in long-read genomic data using sequence composition.

IF 2.1 3区 生物学 Q3 GENETICS & HEREDITY
Claudia C Weber
{"title":"Disentangling cobionts and contamination in long-read genomic data using sequence composition.","authors":"Claudia C Weber","doi":"10.1093/g3journal/jkae187","DOIUrl":null,"url":null,"abstract":"<p><p>The recent acceleration in genome sequencing targeting previously unexplored parts of the tree of life presents computational challenges. Samples collected from the wild often contain sequences from several organisms, including the target, its cobionts, and contaminants. Effective methods are therefore needed to separate sequences. Though advances in sequencing technology make this task easier, it remains difficult to taxonomically assign sequences from eukaryotic taxa that are not well represented in databases. Therefore, reference-based methods alone are insufficient. Here, I examine how we can take advantage of differences in sequence composition between organisms to identify symbionts, parasites, and contaminants in samples, with minimal reliance on reference data. To this end, I explore data from the Darwin Tree of Life project, including hundreds of high-quality HiFi read sets from insects. Visualizing two-dimensional representations of read tetranucleotide composition learned by a variational autoencoder can reveal distinct components of a sample. Annotating the embeddings with additional information, such as coding density, estimated coverage, or taxonomic labels allows rapid assessment of the contents of a dataset. The approach scales to millions of sequences, making it possible to explore unassembled read sets, even for large genomes. Combined with interactive visualization tools, it allows a large fraction of cobionts reported by reference-based screening to be identified. Crucially, it also facilitates retrieving genomes for which suitable reference data are absent.</p>","PeriodicalId":12468,"journal":{"name":"G3: Genes|Genomes|Genetics","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540323/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"G3: Genes|Genomes|Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/g3journal/jkae187","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0

Abstract

The recent acceleration in genome sequencing targeting previously unexplored parts of the tree of life presents computational challenges. Samples collected from the wild often contain sequences from several organisms, including the target, its cobionts, and contaminants. Effective methods are therefore needed to separate sequences. Though advances in sequencing technology make this task easier, it remains difficult to taxonomically assign sequences from eukaryotic taxa that are not well represented in databases. Therefore, reference-based methods alone are insufficient. Here, I examine how we can take advantage of differences in sequence composition between organisms to identify symbionts, parasites, and contaminants in samples, with minimal reliance on reference data. To this end, I explore data from the Darwin Tree of Life project, including hundreds of high-quality HiFi read sets from insects. Visualizing two-dimensional representations of read tetranucleotide composition learned by a variational autoencoder can reveal distinct components of a sample. Annotating the embeddings with additional information, such as coding density, estimated coverage, or taxonomic labels allows rapid assessment of the contents of a dataset. The approach scales to millions of sequences, making it possible to explore unassembled read sets, even for large genomes. Combined with interactive visualization tools, it allows a large fraction of cobionts reported by reference-based screening to be identified. Crucially, it also facilitates retrieving genomes for which suitable reference data are absent.

利用序列组成在长读基因组数据中区分同源物和污染
最近,针对生命树中以前未探索的部分的基因组测序工作加速进行,这给计算带来了挑战。从野外采集的样本往往包含来自多种生物的序列,包括目标生物、其共生体和污染物。因此需要有效的方法来分离序列。虽然测序技术的进步使这项工作变得更加容易,但要对数据库中代表性不强的真核生物类群的序列进行分类学分配仍然十分困难。因此,仅靠基于参考文献的方法是不够的。在此,我将探讨如何利用生物间序列组成的差异来识别样本中的共生体、寄生虫和污染物,同时尽量减少对参考数据的依赖。为此,我探索了达尔文生命之树项目的数据,包括数百个来自昆虫的高质量 HiFi 读数集。将变异自动编码器学习到的读数四核苷酸组成的二维表示可视化,可以揭示样本的独特成分。用编码密度、估计覆盖率或分类标签等附加信息对嵌入进行注释,可以快速评估数据集的内容。这种方法可扩展到数百万个序列,即使是大型基因组也能探索未组装的读取集。该方法与交互式可视化工具相结合,可以鉴定出参考文献筛选报告的大部分共生菌。最重要的是,它还有助于检索缺乏合适参考数据的基因组。
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
G3: Genes|Genomes|Genetics
G3: Genes|Genomes|Genetics GENETICS & HEREDITY-
CiteScore
5.10
自引率
3.80%
发文量
305
审稿时长
3-8 weeks
期刊介绍: G3: Genes, Genomes, Genetics provides a forum for the publication of high‐quality foundational research, particularly research that generates useful genetic and genomic information such as genome maps, single gene studies, genome‐wide association and QTL studies, as well as genome reports, mutant screens, and advances in methods and technology. The Editorial Board of G3 believes that rapid dissemination of these data is the necessary foundation for analysis that leads to mechanistic insights. G3, published by the Genetics Society of America, meets the critical and growing need of the genetics community for rapid review and publication of important results in all areas of genetics. G3 offers the opportunity to publish the puzzling finding or to present unpublished results that may not have been submitted for review and publication due to a perceived lack of a potential high-impact finding. G3 has earned the DOAJ Seal, which is a mark of certification for open access journals, awarded by DOAJ to journals that achieve a high level of openness, adhere to Best Practice and high publishing standards.
×
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