图蛋白:突变可视化。

Tychele Turner
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引用次数: 16

摘要

背景:下一代测序已经能够在DNA序列水平上检查变异,并且可以通过在蛋白质水平上评估变异来进一步加强。一种强大的方法是可视化这些数据,通常会揭示在同一数据的文本版本中无法立即显现的模式。许多研究人员都想知道他们的氨基酸变化在蛋白质中的位置。蛋白质内变异的聚类与非聚类可以显示疾病中发生的生物学变化的有趣方面。发现:我们描述了一个免费的工具,Plot Protein,可以从命令行执行,也可以通过web浏览器作为图形界面使用,以实现蛋白质水平氨基酸变化的可视化。这使得研究人员能够以一种快速而统一的方式绘制出测序研究的变异图。可用的功能包括绘制氨基酸变化,结构域,翻译后修饰,参考序列,保护,保护评分,以及缩放功能。在此,我们提供了一个使用该工具检查RET蛋白的案例,并展示了多发性内分泌瘤2A (MEN2A)中蛋白质突变的聚类如何揭示疾病机制的重要信息。结论:Plot Protein是研究蛋白质中氨基酸变化及其定位的有效工具。描述了该软件的命令行和web服务器版本,使用户能够获得有关其突变的可视化知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Plot protein: visualization of mutations.

Background: Next-generation sequencing has enabled examination of variation at the DNA sequence level and can be further enhanced by evaluation of the variants at the protein level. One powerful method is to visualize these data often revealing patterns not immediately apparent in a text version of the same data. Many investigators are interested in knowing where their amino acid changes reside within a protein. Clustering of variation within a protein versus non-clustering can show interesting aspects of the biological changes happening in disease.

Finding: We describe a freely available tool, Plot Protein, executable from the command line or utilized as a graphical interface through a web browser, to enable visualization of amino acid changes at the protein level. This allows researchers to plot variation from their sequencing studies in a quick and uniform way. The features available include plotting amino acid changes, domains, post-translational modifications, reference sequence, conservation, conservation score, and also zoom capabilities. Herein we provide a case example using this tool to examine the RET protein and we demonstrate how clustering of mutations within the protein in Multiple Endocrine Neoplasia 2A (MEN2A) reveals important information about disease mechanism.

Conclusions: Plot Protein is a useful tool for investigating amino acid changes and their localization within proteins. Command line and web server versions of this software are described that enable users to derive visual knowledge about their mutations.

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