DRomics, a workflow to exploit dose-response omics data in ecotoxicology

Marie Laure Delignette-Muller, Aurélie Siberchicot, Floriane Larras, Elise Billoir
{"title":"DRomics, a workflow to exploit dose-response omics data in ecotoxicology","authors":"Marie Laure Delignette-Muller, Aurélie Siberchicot, Floriane Larras, Elise Billoir","doi":"10.24072/pcjournal.325","DOIUrl":null,"url":null,"abstract":"Omics technologies has opened new possibilities to assess environmental risks and to understand the mode(s) of action of pollutants. Coupled to dose-response experimental designs, they allow a non-targeted assessment of organism responses at the molecular level along an exposure gradient. However, describing the dose-response relationships on such high-throughput data is no easy task. In a first part, we review the software available for this purpose, and their main features. We set out arguments on some statistical and modeling choices we have made while developing the R package DRomics and its positioning compared to others tools. The DRomics main analysis workflow is made available through a web interface, namely a shiny app named DRomics-shiny. Next, we present the new functionalities recently implemented. DRomics has been augmented especially to be able to handle varied omics data considering the nature of the measured signal (e.g. counts of reads in RNAseq) and the way data were collected (e.g. batch effect, situation with no experimental replicates). Another important upgrade is the development of tools to ease the biological interpretation of results. Various functions are proposed to visualize, summarize and compare the responses, for different biological groups (defined from biological annotation), optionally at different experimental levels (e.g. measurements at several omics level or in different experimental conditions). A new shiny app named DRomicsInterpreter-shiny is dedicated to the biological interpretation of results. The institutional web page https://lbbe.univ-lyon1.fr/fr/dromics gathers links to all resources related to DRomics, including the two shiny applications.","PeriodicalId":74413,"journal":{"name":"Peer community journal","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Peer community journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24072/pcjournal.325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Omics technologies has opened new possibilities to assess environmental risks and to understand the mode(s) of action of pollutants. Coupled to dose-response experimental designs, they allow a non-targeted assessment of organism responses at the molecular level along an exposure gradient. However, describing the dose-response relationships on such high-throughput data is no easy task. In a first part, we review the software available for this purpose, and their main features. We set out arguments on some statistical and modeling choices we have made while developing the R package DRomics and its positioning compared to others tools. The DRomics main analysis workflow is made available through a web interface, namely a shiny app named DRomics-shiny. Next, we present the new functionalities recently implemented. DRomics has been augmented especially to be able to handle varied omics data considering the nature of the measured signal (e.g. counts of reads in RNAseq) and the way data were collected (e.g. batch effect, situation with no experimental replicates). Another important upgrade is the development of tools to ease the biological interpretation of results. Various functions are proposed to visualize, summarize and compare the responses, for different biological groups (defined from biological annotation), optionally at different experimental levels (e.g. measurements at several omics level or in different experimental conditions). A new shiny app named DRomicsInterpreter-shiny is dedicated to the biological interpretation of results. The institutional web page https://lbbe.univ-lyon1.fr/fr/dromics gathers links to all resources related to DRomics, including the two shiny applications.
在生态毒理学中利用剂量-反应组学数据的工作流
组学技术为评估环境风险和了解污染物的作用模式开辟了新的可能性。与剂量-反应实验设计相结合,它们允许沿着暴露梯度在分子水平上对生物体反应进行非靶向评估。然而,在如此高通量的数据上描述剂量-反应关系并非易事。在第一部分中,我们回顾了可用于此目的的软件及其主要功能。我们列出了在开发R包DRomics时所做的一些统计和建模选择,以及它与其他工具相比的定位。DRomics的主要分析工作流程是通过web界面提供的,即一个名为DRomics-shiny的闪亮应用程序。接下来,我们介绍最近实现的新功能。考虑到测量信号的性质(例如RNAseq中读取的计数)和数据收集的方式(例如批处理效应,没有实验重复的情况),DRomics已经得到了增强,特别是能够处理各种组学数据。另一个重要的升级是开发工具来简化对结果的生物学解释。提出了各种功能来可视化,总结和比较不同生物群体(从生物学注释定义)的响应,可选地在不同的实验水平(例如在几个组学水平或在不同的实验条件下进行测量)。一款名为DRomicsInterpreter-shiny的新闪亮应用程序致力于对结果进行生物学解释。该机构的网页https://lbbe.univ-lyon1.fr/fr/dromics收集了与DRomics相关的所有资源的链接,包括这两个闪亮的应用程序。
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
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