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eXpression2Kinases (X2K) 网站：将表达谱与上游细胞信号网络联系起来。

eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks.

机构信息

DBD2K-LINCS Data Coordination and Integration Center; Knowledge Management Center for the Illuminating the Druggable Genome; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.

出版信息

Nucleic Acids Res. 2018 Jul 2;46(W1):W171-W179. doi: 10.1093/nar/gky458.

DOI:10.1093/nar/gky458
PMID:29800326
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6030863/
Abstract

While gene expression data at the mRNA level can be globally and accurately measured, profiling the activity of cell signaling pathways is currently much more difficult. eXpression2Kinases (X2K) computationally predicts involvement of upstream cell signaling pathways, given a signature of differentially expressed genes. X2K first computes enrichment for transcription factors likely to regulate the expression of the differentially expressed genes. The next step of X2K connects these enriched transcription factors through known protein-protein interactions (PPIs) to construct a subnetwork. The final step performs kinase enrichment analysis on the members of the subnetwork. X2K Web is a new implementation of the original eXpression2Kinases algorithm with important enhancements. X2K Web includes many new transcription factor and kinase libraries, and PPI networks. For demonstration, thousands of gene expression signatures induced by kinase inhibitors, applied to six breast cancer cell lines, are provided for fetching directly into X2K Web. The results are displayed as interactive downloadable vector graphic network images and bar graphs. Benchmarking various settings via random permutations enabled the identification of an optimal set of parameters to be used as the default settings in X2K Web. X2K Web is freely available from http://X2K.cloud.

摘要

虽然可以全面准确地测量 mRNA 水平的基因表达数据，但目前对细胞信号通路活性的分析要困难得多。eXpression2Kinases (X2K) 计算预测了上游细胞信号通路的参与情况，给定了差异表达基因的特征。X2K 首先计算可能调节差异表达基因表达的转录因子的富集。X2K 的下一步通过已知的蛋白质-蛋白质相互作用 (PPI) 将这些富集的转录因子连接起来，构建一个子网。最后一步对子网成员进行激酶富集分析。X2K Web 是对原始 eXpression2Kinases 算法的新实现，具有重要的增强功能。X2K Web 包含许多新的转录因子和激酶库以及 PPI 网络。为了演示，我们提供了数千个由激酶抑制剂诱导的基因表达特征，这些特征应用于六种乳腺癌细胞系，可以直接在 X2K Web 中获取。结果以交互式可下载矢量图形网络图像和条形图显示。通过随机排列进行基准测试，确定了一组最佳参数作为 X2K Web 的默认设置。X2K Web 可从 http://X2K.cloud 免费获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/77e66254f354/gky458fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/9835a56b76c4/gky458fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/3fb76840820c/gky458fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/266095c8fc9f/gky458fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/25a04c7863b5/gky458fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/25bb47d1349f/gky458fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/77e66254f354/gky458fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/9835a56b76c4/gky458fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/3fb76840820c/gky458fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/266095c8fc9f/gky458fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/25a04c7863b5/gky458fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/25bb47d1349f/gky458fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c5/6030863/77e66254f354/gky458fig6.jpg

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