A Comparison of the TempO-Seq S1500+ Platform to RNA-Seq and Microarray Using Rat Liver Mode of Action Samples.
A Comparison of the TempO-Seq S1500+ Platform to RNA-Seq and Microarray Using Rat Liver Mode of Action Samples.
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DOI：
10.3389/fgene.2018.00485
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发表时间：
2018
影响因子：
3.7
通讯作者：
Auerbach SS
中科院分区：
文献类型：
--
作者：
Bushel PR;Paules RS;Auerbach SS
TempO-SeqTM平台允许靶向转录组学分析，目前被许多团队用于执行高通量基因表达分析。在这里，我们使用45个纯化RNA样本进行了基因表达特征的比较，这些样本来自暴露于五种作用模式（MOAs）之一的化学物质的大鼠肝脏。这些样品先前已使用AffymetrixTM大鼠基因组230 2.0微阵列和Illumina®全转录组RNA-Seq进行评估。将这些数据与使用大鼠S1500+ β基因集的TempO-Seq分析进行比较，发现与信号噪声、均方根误差和/或变异性来源相关的平台存在明显差异。微阵列和TempO-Seq在MOA和化学处理方面捕获了最大的可变性，而RNA-Seq在MOA内具有更高的噪声和更大的样品差异。然而，通过分层聚类、基因子网络连通性和moa变异基因的生物过程表征分析数据显示，样品明显是按处理分组的，而不是按基因表达平台分组的。总的来说，这些发现表明TempO-Seq平台的结果与其他更成熟的全基因组转录组测量方法的结果是一致的。
The TempO-SeqTM platform allows for targeted transcriptomic analysis and is currently used by many groups to perform high-throughput gene expression analysis. Herein we performed a comparison of gene expression characteristics measured using 45 purified RNA samples from the livers of rats exposed to chemicals that fall into one of five modes of action (MOAs). These samples have been previously evaluated using AffymetrixTM rat genome 230 2.0 microarrays and Illumina® whole transcriptome RNA-Seq. Comparison of these data with TempO-Seq analysis using the rat S1500+ beta gene set identified clear differences in the platforms related to signal to noise, root mean squared error, and/or sources of variability. Microarray and TempO-Seq captured the most variability in terms of MOA and chemical treatment whereas RNA-Seq had higher noise and larger differences between samples within a MOA. However, analysis of the data by hierarchical clustering, gene subnetwork connectivity and biological process representation of MOA-varying genes revealed that the samples clearly grouped by treatment as opposed to gene expression platform. Overall these findings demonstrate that the results from the TempO-Seq platform are consistent with findings on other more established approaches for measuring the genome-wide transcriptome.
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影响因子：
3.7
作者：
Yeakley JM;Shepard PJ;Goyena DE;VanSteenhouse HC;McComb JD;Seligmann BE
通讯作者：
Seligmann BE
使用不同微阵列平台生成的预测特征基因和分类器的一致性
DOI：
10.1038/tpj.2010.34
发表时间：
2010-08
期刊：
The pharmacogenomics journal
影响因子：
--
作者：
Fan X;Lobenhofer EK;Chen M;Shi W;Huang J;Luo J;Zhang J;Walker SJ;Chu TM;Li L;Wolfinger R;Bao W;Paules RS;Bushel PR;Li J;Shi T;Nikolskaya T;Nikolsky Y;Hong H;Deng Y;Cheng Y;Fang H;Shi L;Tong W
通讯作者：
Tong W
影响因子：
46.9
作者：
Wang, Charles;Gong, Binsheng;Bushel, Pierre R.;Thierry-Mieg, Jean;Thierry-Mieg, Danielle;Xu, Joshua;Fang, Hong;Hong, Huixiao;Shen, Jie;Su, Zhenqiang;Meehan, Joe;Li, Xiaojin;Yang, Lu;Li, Haiqing;Labaj, Pawel P.;Kreil, David P.;Megherbi, Dalila;Gaj, Stan;Caiment, Florian;van Delft, Joost;Kleinjans, Jos;Scherer, Andreas;Devanarayan, Viswanath;Wang, Jian;Yang, Yong;Qian, Hui-Rong;Lancashire, Lee J.;Bessarabova, Marina;Nikolsky, Yuri;Furlanello, Cesare;Chierici, Marco;Albanese, Davide;Jurman, Giuseppe;Riccadonna, Samantha;Filosi, Michele;Visintainer, Roberto;Zhang, Ke K.;Li, Jainying;Hsieh, Jui-Hua;Svoboda, Daniel L.;Fuscoe, James C.;Deng, Youping;Shi, Leming;Paules, Richard S.;Auerbach, Scott S.;Tong, Weida
通讯作者：
Tong, Weida
影响因子：
9.8
作者：
Gong, Binsheng;Wang, Charles;Su, Zhenqiang;Hong, Huixiao;Thierry-Mieg, Jean;Thierry-Mieg, Danielle;Shi, Leming;Auerbach, Scott S.;Tong, Weida;Xu, Joshua
通讯作者：
Xu, Joshua
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y