MoS-TEC:基于时间表达曲线模型选择的毒物基因组学数据库

IF 3.1 Q2 TOXICOLOGY
Franziska Kappenberg, Benedikt Küthe, Jörg Rahnenführer
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引用次数: 0

摘要

MoS-TEC是一个新开发的毒物基因组学数据库,采用统计模型选择方法拟合时间表达曲线。毒物基因组学数据提供了基因组对化合物反应的信息,通常以基因表达值来衡量。如果有不同暴露时间的此类实验数据,那么暴露时间与基因表达值之间的功能关系可能会引起人们的兴趣。TG-GATEs(开放毒物基因组学项目-基因组学辅助毒性评估系统)数据库提供了 170 种化合物的全基因组基因表达数据信息。我们使用 MCP-Mod 对这些数据进行了广泛的模型选择。具体来说,我们考虑了 120 种具有完整数据集的化合物在大鼠肝脏体内实验中八个时间点的基因表达数据。MCP-Mod 是一种两步法,包括多重比较程序 (MCP) 和建模 (Mod) 方法。结果是估计的时间-表达曲线,该曲线模拟了所有基因和化合物组合的暴露时间与基因表达值之间的关系。我们介绍了一种适当的数据归一化方法,并报告了每种化合物和所有化合物选择的模型。对于解释方差值较大的高质量模型拟合,sigEmax 模型最常被选中。通过新的 R Shiny 应用程序 MoS-TEC,研究人员可以轻松获取所有化合物的所有基因的最佳曲线拟合结果。它可以在线使用,无需安装其他软件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MoS-TEC: A toxicogenomics database based on model selection for time-expression curves

MoS-TEC is a newly developed toxicogenomics database for time-expression curves fitted with a statistical model selection approach. Toxicogenomic data provide information on the response of the genome to compounds, often measured in terms of gene expression values. When such experimental data are available for different exposure times, the functional relationships between the exposure time and the expression values of genes might be of interest. The TG-GATEs (Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) database provides such information for genomewide gene expression data for 170 compounds. We performed extensive model selection using MCP-Mod on these data. Specifically, gene expression data measured for eight time points from in vivo experiments on rat liver for 120 compounds with complete datasets were considered. MCP-Mod is a two-step approach, including a multiple comparison procedure (MCP) and a modelling (Mod) approach. The results are estimated time-expression curves that model the relationship between exposure time and gene expression values for all combinations of genes and compounds. We present an appropriate data normalization approach and report which models were selected per compound and in total. For high-quality model fits with a large value for the explained variance, the sigEmax model was most frequently selected. The new R Shiny application MoS-TEC provides easy access for researchers to the best curve fit for all genes individually for all compounds. It can be used online without installing additional software.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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