Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets.
Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets.
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DOI：
10.1016/j.xpro.2022.101880
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发表时间：
2022-12-16
期刊：
影响因子：
--
通讯作者：
Wagner, Gerhard
中科院分区：
文献类型：
--
作者：
Wu, Su;Wagner, Gerhard
了解肿瘤类型中真核起始因子4F（eIF 4F）复合物的失调对癌症治疗开发至关重要。我们提出了一个协议和附带的R包“eIF4F.analysis”。我们描述了分析拷贝数状态，基因丰度和化学计量，生存概率，表达协变，相关基因，mRNA/蛋白质相关性，蛋白质共表达。使用公开的大型多组学数据，eIF 4F。分析允许计算推导和统计上强大的推论，在人类癌症的起始因子调控和临床相关性的蛋白质相互作用内的eIF 4F复合物。有关本方案使用和执行的完整详细信息，请参见Wu和瓦格纳（2021）。使用大型多组学数据集分析eIF 4F失调的R软件包软件安装、数据下载和库初始化的详细步骤从多个分析中得出生物学和临床推断的指南图解代码结构解释了生物信息学管道组装技术出版商说明：进行任何实验方案都需要遵守当地机构的实验室安全和伦理指南。了解肿瘤类型中真核起始因子4F（eIF 4F）复合物的失调对癌症治疗开发至关重要。我们提出了一个协议和附带的R包“eIF4F.analysis”。我们描述了分析拷贝数状态，基因丰度和化学计量，生存概率，表达协变，相关基因，mRNA/蛋白质相关性，蛋白质共表达。使用公开的大型多组学数据，eIF 4F。分析允许计算推导和统计上强大的推论，在人类癌症的起始因子调控和临床相关性的蛋白质相互作用内的eIF 4F复合物。
Understanding dysregulation of the eukaryotic initiation factor 4F (eIF4F) complex across tumor types is critical to cancer treatment development. We present a protocol and accompanying R package “eIF4F.analysis”. We describe analysis of copy number status, gene abundance and stoichiometry, survival probability, expression covariation, correlating genes, mRNA/protein correlation, and protein co-expression. Using publicly available large multi-omics data, eIF4F.analysis permits computationally derived and statistically powerful inferences regarding initiation factor regulation in human cancers and clinical relevance of protein interactions within the eIF4F complex. For complete details on the use and execution of this protocol, please refer to Wu and Wagner (2021). An R package to analyze eIF4F dysregulation, using large multi-omics datasets Detailed steps for software installation, data download, and library initialization Guidelines for deriving biological and clinical inferences from multiple analyses Illustrated code structure explains bioinformatics pipeline assembly technique Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Understanding dysregulation of the eukaryotic initiation factor 4F (eIF4F) complex across tumor types is critical to cancer treatment development. We present a protocol and accompanying R package “eIF4F.analysis”. We describe analysis of copy number status, gene abundance and stoichiometry, survival probability, expression covariation, correlating genes, mRNA/protein correlation, and protein co-expression. Using publicly available large multi-omics data, eIF4F.analysis permits computationally derived and statistically powerful inferences regarding initiation factor regulation in human cancers and clinical relevance of protein interactions within the eIF4F complex.
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DOI：
10.1186/s13637-017-0059-z
发表时间：
2017-12
期刊：
EURASIP journal on bioinformatics & systems biology
影响因子：
--
作者：
Vella D;Zoppis I;Mauri G;Mauri P;Di Silvestre D
通讯作者：
Di Silvestre D
影响因子：
64.5
作者：
Gillette, Michael A.;Satpathy, Shankha;Carr, Steven A.
通讯作者：
Carr, Steven A.
影响因子：
14.9
作者：
Grigoriev, A
通讯作者：
Grigoriev, A
影响因子：
9.3
作者：
Wu S;Wagner G
通讯作者：
Wagner G
影响因子：
64.5
作者：
Nusinow, David P.;Szpyt, John;Gygil, Steven P.
通讯作者：
Gygil, Steven P.