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DRomics: A Turnkey Tool to Support the Use of the Dose-Response Framework for Omics Data in Ecological Risk Assessment

机译:DRomics:支持在生态风险评估中使用剂量响应框架用于组学数据的交钥匙工具

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摘要

Omics approaches (e.g., transcriptomics, metabolomics) are promising for ecological risk assessment (ERA) since they provide mechanistic information and early warning signals. A crucial step in the analysis of omics data is the modeling of concentration-dependency which may have different trends including monotonic (e.g., linear, exponential) or biphasic (e.g., U shape, bell shape) forms. The diversity of responses raises challenges concerning detection and modeling of significant responses and effect concentration (EC) derivation. Furthermore, handling high-throughput data sets is time-consuming and requires effective and automated processing routines. Thus, we developed an open source tool (DRomics, available as an R-package and as a web-based service) which, after elimination of molecular responses (e.g., gene expressions from microarrays) with no concentration-dependency and/or high variability, identifies the best model for concentration-response curve description. Subsequently, an EC (e.g., a benchmark dose) is estimated from each curve, and curves are classified based on their model parameters. This tool is especially dedicated to manage data obtained from an experimental design favoring a great number of tested doses rather than a great number of replicates and also to handle properly monotonic and biphasic trends. The tool finally provides restitution for a table of results that can be directly used to perform ERA approaches.
机译:组学方法(例如转录组学,代谢组学)有望用于生态风险评估(ERA),因为它们提供了机械信息和预警信号。在组学数据分析中的关键步骤是对浓度依赖性建模,该依赖性可能具有不同的趋势,包括单调(例如线性,指数)或双相(例如U形,钟形)形式。响应的多样性给重大响应和效应集中(EC)派生的检测和建模提出了挑战。此外,处理高通量数据集非常耗时,并且需要有效且自动化的处理例程。因此,我们开发了一种开源工具(DRomics,可作为R-package和基于Web的服务使用),在消除分子反应(例如,微阵列的基因表达)后,该工具无浓度依赖性和/或高可变性确定最佳的浓度响应曲线描述模型。随后,从每条曲线估计EC(例如基准剂量),并根据其模型参数对曲线进行分类。该工具专门用于管理从实验设计中获得的数据,这些数据偏向大量的测试剂量,而不是大量的重复,并且还能够正确处理单调和双相趋势。该工具最终为结果表提供了恢复,该结果表可直接用于执行ERA方法。

著录项

  • 来源
    《Environmental Science & Technology》 |2018年第24期|14461-14468|共8页
  • 作者单位

    UFZ Helmholtz Ctr Environm Res, Dept Bioanalyt Ecotoxicol, Permoserstr 15, D-04318 Leipzig, Germany;

    Univ Lorraine, CNRS, UMR 7360, LIEC, F-57070 Metz, France;

    Univ Lorraine, CNRS, UMR 7360, LIEC, F-57070 Metz, France;

    Univ Lyon 1, Univ Lyon, CNRS, UMR 5558,VetAgro Sup,Lab Biometrie & Biol Evolut, F-69622 Villeurbanne, France;

    UFZ Helmholtz Ctr Environm Res, Dept Bioanalyt Ecotoxicol, Permoserstr 15, D-04318 Leipzig, Germany;

    UFZ Helmholtz Ctr Environm Res, Dept Community Ecol, Theodor Lieser Str 4, D-06120 Halle, Germany|German Ctr Integrat Biodivers Res iDiv, Deutsch Pl 5e, D-04103 Leipzig, Germany;

    German Ctr Integrat Biodivers Res iDiv, Deutsch Pl 5e, D-04103 Leipzig, Germany|UFZ Helmholtz Ctr Environm Res, Dept Soil Ecol, Theodor Lieser Str 4, D-06120 Halle, Germany;

    UFZ Helmholtz Ctr Environm Res, Dept Bioanalyt Ecotoxicol, Permoserstr 15, D-04318 Leipzig, Germany;

    Univ Lyon 1, Univ Lyon, CNRS, UMR 5558,VetAgro Sup,Lab Biometrie & Biol Evolut, F-69622 Villeurbanne, France;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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  • 正文语种 eng
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  • 入库时间 2022-08-18 03:58:39

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