PhosNetVis：一个用于快速激酶-底物富集分析及磷酸化蛋白质组学数据交互式2D/3D网络可视化的基于Web的工具
PhosNetVis: a web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data
AI总结：
针对磷酸化蛋白质组学数据激酶-底物网络分析工具缺乏交互式探索的问题，提出基于Web的PhosNetVis工具，简化分析步骤，支持2D/3D网络可视化，助力各技能水平研究者快速获取生物学见解。
摘要：
蛋白质磷酸化涉及由另一种蛋白质（激酶）对一种蛋白质（底物）残基的可逆修饰。液相色谱-质谱研究正快速生成跨多种条件的大量蛋白质磷酸化数据集。研究人员随后必须推断负责每个底物磷酸化位点变化的激酶。然而，推断激酶-底物相互作用（KSIs）的工具并未优化以交互式地探索由此产生的大型复杂网络、显著的磷酸化位点和状态。因此，目前存在一种未满足的需求，即需要一种工具来促进对磷酸化蛋白质组学数据集的用户友好分析、交互式探索、可视化和交流。我们提出了PhosNetVis，这是一个基于Web的工具，适用于各种计算技能水平的研究人员，通过在单一工具内简化磷酸化蛋白质组学数据分析步骤，轻松推断、生成并交互式地探索2D或3D的KSI网络。PhosNetVis降低了研究人员快速生成高质量可视化以从其磷酸化蛋白质组学数据集中获取生物学见解的门槛。该工具可在以下网址获取：https://gumuslab.github.io/PhosNetVis/
英文摘要：
Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers in rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at: https://gumuslab.github.io/PhosNetVis/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
