• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

一个用于通用模块化反应分析的 R 包及其在雌激素和视黄酸受体相互作用中的应用。

An R package for generic modular response analysis and its application to estrogen and retinoic acid receptor crosstalk.

机构信息

Inserm U1194, Institut de Recherche en Cancérologie de Montpellier, Montpellier, France.

University of Montpellier, Montpellier, France.

出版信息

Sci Rep. 2021 Mar 31;11(1):7272. doi: 10.1038/s41598-021-86544-0.

DOI:10.1038/s41598-021-86544-0
PMID:33790340
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8012374/
Abstract

Modular response analysis (MRA) is a widely used inference technique developed to uncover directions and strengths of connections in molecular networks under a steady-state condition by means of perturbation experiments. We devised several extensions of this methodology to search genomic data for new associations with a biological network inferred by MRA, to improve the predictive accuracy of MRA-inferred networks, and to estimate confidence intervals of MRA parameters from datasets with low numbers of replicates. The classical MRA computations and their extensions were implemented in a freely available R package called aiMeRA ( https://github.com/bioinfo-ircm/aiMeRA/ ). We illustrated the application of our package by assessing the crosstalk between estrogen and retinoic acid receptors, two nuclear receptors implicated in several hormone-driven cancers, such as breast cancer. Based on new data generated for this study, our analysis revealed potential cross-inhibition mediated by the shared corepressors NRIP1 and LCoR. We designed aiMeRA for non-specialists and to allow biologists to perform their own analyses.

摘要

模块响应分析（MRA）是一种广泛使用的推断技术，旨在通过扰动实验揭示在稳态条件下分子网络中连接的方向和强度。我们设计了几种这种方法的扩展，以便在基因组数据中搜索与 MRA 推断的生物网络的新关联，提高 MRA 推断网络的预测准确性，并从复制次数较少的数据集估计 MRA 参数的置信区间。经典的 MRA 计算及其扩展在一个名为 aiMeRA（https://github.com/bioinfo-ircm/aiMeRA/）的免费 R 包中实现。我们通过评估雌激素和视黄酸受体之间的串扰来说明我们的包的应用，这两种核受体参与了几种激素驱动的癌症，如乳腺癌。基于为此研究生成的新数据，我们的分析揭示了由共享的共抑制剂 NRIP1 和 LCoR 介导的潜在的交叉抑制。我们设计了 aiMeRA 供非专业人士使用，并允许生物学家进行自己的分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a97b/8012374/63ab08ce67a3/41598_2021_86544_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a97b/8012374/7dea3767858c/41598_2021_86544_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a97b/8012374/6cb5457a010c/41598_2021_86544_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a97b/8012374/6dde6632ed27/41598_2021_86544_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a97b/8012374/3f760c6bda2e/41598_2021_86544_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a97b/8012374/63ab08ce67a3/41598_2021_86544_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a97b/8012374/7dea3767858c/41598_2021_86544_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a97b/8012374/6cb5457a010c/41598_2021_86544_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a97b/8012374/6dde6632ed27/41598_2021_86544_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a97b/8012374/3f760c6bda2e/41598_2021_86544_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a97b/8012374/63ab08ce67a3/41598_2021_86544_Fig5_HTML.jpg

相似文献

1
An R package for generic modular response analysis and its application to estrogen and retinoic acid receptor crosstalk.一个用于通用模块化反应分析的 R 包及其在雌激素和视黄酸受体相互作用中的应用。
Sci Rep. 2021 Mar 31;11(1):7272. doi: 10.1038/s41598-021-86544-0.
2
corto: a lightweight R package for gene network inference and master regulator analysis.corto：一个用于基因网络推断和主调控因子分析的轻量级 R 包。
Bioinformatics. 2020 Jun 1;36(12):3916-3917. doi: 10.1093/bioinformatics/btaa223.
3
NPA: an R package for computing network perturbation amplitudes using gene expression data and two-layer networks.NPA：一个使用基因表达数据和两层网络计算网络干扰幅度的 R 包。
BMC Bioinformatics. 2019 Sep 3;20(1):451. doi: 10.1186/s12859-019-3016-x.
4
Dominant-negative nuclear receptor corepressor relieves transcriptional inhibition of retinoic acid receptor but does not alter the agonist/antagonist activities of the tamoxifen-bound estrogen receptor.显性负性核受体共抑制因子可解除视黄酸受体的转录抑制作用，但不改变他莫昔芬结合的雌激素受体的激动剂/拮抗剂活性。
Mol Endocrinol. 2003 Aug;17(8):1543-54. doi: 10.1210/me.2001-0144. Epub 2003 May 1.
5
Modelling signalling networks from perturbation data.从扰动数据建立信号网络模型。
Bioinformatics. 2018 Dec 1;34(23):4079-4086. doi: 10.1093/bioinformatics/bty473.
6
A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer.一种用于推断多尺度调控网络的多属性高斯图形模型：在乳腺癌中的应用
Methods Mol Biol. 2019;1883:143-160. doi: 10.1007/978-1-4939-8882-2_6.
7
Biological Network Inference and analysis using SEBINI and CABIN.使用SEBINI和CABIN进行生物网络推断与分析。
Methods Mol Biol. 2009;541:551-76. doi: 10.1007/978-1-59745-243-4_24.
8
Genomic antagonism between retinoic acid and estrogen signaling in breast cancer.乳腺癌中视黄酸与雌激素信号传导之间的基因组拮抗作用。
Cell. 2009 Jun 26;137(7):1259-71. doi: 10.1016/j.cell.2009.04.043.
9
Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'.使用 R 包 'ddepn' 中的基于抽样的方法从纵向数据中推断信号网络。
BMC Bioinformatics. 2011 Jul 19;12:291. doi: 10.1186/1471-2105-12-291.
10
Application of modular response analysis to medium- to large-size biological systems.模块化响应分析在中大型生物系统中的应用。
PLoS Comput Biol. 2022 Apr 20;18(4):e1009312. doi: 10.1371/journal.pcbi.1009312. eCollection 2022 Apr.

引用本文的文献

1
The transcription factor RIP140 regulates interferon γ signaling in breast cancer.转录因子RIP140调节乳腺癌中的干扰素γ信号传导。
Int J Cancer. 2025 Jul 1;157(1):170-182. doi: 10.1002/ijc.35405. Epub 2025 Mar 10.
2
Testing and overcoming the limitations of modular response analysis.测试并克服模块化响应分析的局限性。
Brief Bioinform. 2025 Mar 4;26(2). doi: 10.1093/bib/bbaf098.
3
Gene regulatory network inference during cell fate decisions by perturbation strategies.通过扰动策略推断细胞命运决定过程中的基因调控网络

本文引用的文献

1
Mapping connections in signaling networks with ambiguous modularity.利用具有模糊模块性的信号网络进行连接映射。
NPJ Syst Biol Appl. 2019 May 23;5:19. doi: 10.1038/s41540-019-0096-1. eCollection 2019.
2
Comparative Network Reconstruction using mixed integer programming.基于混合整数规划的对比网络重建。
Bioinformatics. 2018 Sep 1;34(17):i997-i1004. doi: 10.1093/bioinformatics/bty616.
3
Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction.基于模块化响应分析的网络重构中测量噪声、实验设计和估计方法的影响。
NPJ Syst Biol Appl. 2025 Mar 4;11(1):23. doi: 10.1038/s41540-025-00504-2.
4
Design and selection of optimal ErbB-targeting bispecific antibodies in pancreatic cancer.胰腺癌中最优的 ErbB 靶向双特异性抗体的设计与选择。
Front Immunol. 2023 Apr 20;14:1168444. doi: 10.3389/fimmu.2023.1168444. eCollection 2023.
5
Modular response analysis reformulated as a multilinear regression problem.模块响应分析重新表述为多线性回归问题。
Bioinformatics. 2023 Apr 3;39(4). doi: 10.1093/bioinformatics/btad166.
6
Application of modular response analysis to medium- to large-size biological systems.模块化响应分析在中大型生物系统中的应用。
PLoS Comput Biol. 2022 Apr 20;18(4):e1009312. doi: 10.1371/journal.pcbi.1009312. eCollection 2022 Apr.
Sci Rep. 2018 Nov 1;8(1):16217. doi: 10.1038/s41598-018-34353-3.
4
Importance of RIP140 and LCoR Sub-Cellular Localization for Their Association With Breast Cancer Aggressiveness and Patient Survival.RIP140和LCoR亚细胞定位对于其与乳腺癌侵袭性及患者生存相关性的重要性
Transl Oncol. 2018 Oct;11(5):1090-1096. doi: 10.1016/j.tranon.2018.06.006. Epub 2018 Jul 11.
5
Modelling signalling networks from perturbation data.从扰动数据建立信号网络模型。
Bioinformatics. 2018 Dec 1;34(23):4079-4086. doi: 10.1093/bioinformatics/bty473.
6
Complex regulation of LCoR signaling in breast cancer cells.乳腺癌细胞中LCoR信号的复杂调控。
Oncogene. 2017 Aug 17;36(33):4790-4801. doi: 10.1038/onc.2017.97. Epub 2017 Apr 17.
7
HTSeq--a Python framework to work with high-throughput sequencing data.HTSeq——一个用于处理高通量测序数据的Python框架。
Bioinformatics. 2015 Jan 15;31(2):166-9. doi: 10.1093/bioinformatics/btu638. Epub 2014 Sep 25.
8
Negative regulation of estrogen signaling by ERβ and RIP140 in ovarian cancer cells.雌激素受体β（ERβ）和受体相互作用蛋白140（RIP140）对卵巢癌细胞中雌激素信号的负调控
Mol Endocrinol. 2013 Sep;27(9):1429-41. doi: 10.1210/me.2012-1351. Epub 2013 Jul 24.
9
Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology.将贝叶斯变量选择与模块化响应分析相结合以推断生化网络拓扑结构。
BMC Syst Biol. 2013 Jul 6;7:57. doi: 10.1186/1752-0509-7-57.
10
Integrative genomics of gene and metabolic regulation by estrogen receptors α and β, and their coregulators.雌激素受体 α 和 β 及其共调节因子的基因和代谢调控的综合基因组学。
Mol Syst Biol. 2013 Jun 18;9:676. doi: 10.1038/msb.2013.28.