测定化合物的最小转录特征用于靶标预测。

Florian Nigsch, Janna Hutz, Ben Cornett, Douglas W Selinger, Gregory McAllister, Somnath Bandyopadhyay, Joseph Loureiro, Jeremy L Jenkins
{"title":"测定化合物的最小转录特征用于靶标预测。","authors":"Florian Nigsch,&nbsp;Janna Hutz,&nbsp;Ben Cornett,&nbsp;Douglas W Selinger,&nbsp;Gregory McAllister,&nbsp;Somnath Bandyopadhyay,&nbsp;Joseph Loureiro,&nbsp;Jeremy L Jenkins","doi":"10.1186/1687-4153-2012-2","DOIUrl":null,"url":null,"abstract":"<p><p> The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation.</p>","PeriodicalId":72957,"journal":{"name":"EURASIP journal on bioinformatics & systems biology","volume":"2012 1","pages":"2"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1687-4153-2012-2","citationCount":"10","resultStr":"{\"title\":\"Determination of minimal transcriptional signatures of compounds for target prediction.\",\"authors\":\"Florian Nigsch,&nbsp;Janna Hutz,&nbsp;Ben Cornett,&nbsp;Douglas W Selinger,&nbsp;Gregory McAllister,&nbsp;Somnath Bandyopadhyay,&nbsp;Joseph Loureiro,&nbsp;Jeremy L Jenkins\",\"doi\":\"10.1186/1687-4153-2012-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p> The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation.</p>\",\"PeriodicalId\":72957,\"journal\":{\"name\":\"EURASIP journal on bioinformatics & systems biology\",\"volume\":\"2012 1\",\"pages\":\"2\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1186/1687-4153-2012-2\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EURASIP journal on bioinformatics & systems biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/1687-4153-2012-2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EURASIP journal on bioinformatics & systems biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/1687-4153-2012-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

摘要

化合物的分子靶点和作用机制的确定是药物开发的关键环节。基于头部表达谱的多路复用技术允许在高通量模式下测量化合物处理细胞的转录特征。这样的轮廓可以用来深入了解化合物的作用模式和它们调节的蛋白质目标。通过这些基因特征的靶预测代理,我们探索了使用转录谱来捕获受干扰细胞测定的生物学变异性的重要方面。我们发现来自表达数据的签名和来自生物相互作用网络的签名表现同样好,并且我们表明基因签名可以使用遗传算法进行优化。大约128个基因的基因特征似乎是最通用的,捕获了通过复合处理对细胞施加的最大扰动。此外,我们发现氧化磷酸化是捕获化合物扰动的最一般方法之一的证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Determination of minimal transcriptional signatures of compounds for target prediction.

The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data. On biometric systems: electrocardiogram Gaussianity and data synthesis. BCC-NER: bidirectional, contextual clues named entity tagger for gene/protein mention recognition. Review of stochastic hybrid systems with applications in biological systems modeling and analysis. Bayesian inference for biomarker discovery in proteomics: an analytic solution.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1