ProteoArk:生物学家的一锅蛋白质组学数据分析和可视化工具

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Mahammad Nisar, Sreelakshmi Pathappillil Soman, Sourav Sreelan, Levin John, Sneha M. Pinto, Richard Kumaran Kandasamy, Yashwanth Subbannayya, Thottethodi Subrahmanya Keshava Prasad, Saptami Kanekar, Rajesh Raju and Rex Devasahayam Arokia Balaya*, 
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引用次数: 0

摘要

ProteoArk是一个基于网络的工具,提供了一系列计算管道,用于基于质谱的蛋白质组学数据的综合分析和可视化。该应用程序包括四个主要部分,旨在解决单个平台上质谱数据分析的各个方面,包括无标签和标记样品(SILAC/iTRAQ/TMT),差异表达分析和数据可视化。ProteoArk支持Proteome Discoverer, MaxQuant和MSFragger搜索结果的后处理。该工具还包括功能富集分析,如基因本体,蛋白质蛋白质相互作用,途径分析和差异表达分析,其中包括各种统计测试。通过简化工作流程和开发用户友好的界面,我们为具有蛋白质组学数据分析基本生物信息学技能的用户创建了一个强大且可访问的解决方案。用户只需单击一下即可轻松创建手稿就绪的图形,包括主成分分析、热图(k均值和分层)、MA图、火山图和圆形条形图。ProteoArk是使用Django框架开发的,用户可以免费使用[https://ciods.in/proteoark/]]。用户还可以使用Docker下载并运行ProteoArk的独立版本,说明参见[https://ciods.in/proteoark/dockerpage]]。应用程序代码、输入数据和文档可在https://github.com/ArokiaRex/proteoark上在线获得。教程视频可以在YouTube上找到:https://www.youtube.com/watch?v=WFMKAZ9Slq4&ab_channel=RexD.A.B。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

ProteoArk: A One-Pot Proteomics Data Analysis and Visualization Tool for Biologists

ProteoArk: A One-Pot Proteomics Data Analysis and Visualization Tool for Biologists

ProteoArk is a web-based tool that offers a range of computational pipelines for comprehensive analysis and visualization of mass spectrometry-based proteomics data. The application comprises four primary sections designed to address various aspects of mass spectrometry data analysis in a single platform, including label-free and labeled samples (SILAC/iTRAQ/TMT), differential expression analysis, and data visualization. ProteoArk supports postprocessing of Proteome Discoverer, MaxQuant, and MSFragger search results. The tool also includes functional enrichment analyses such as gene ontology, protein–protein interactions, pathway analysis, and differential expression analysis, which incorporate various statistical tests. By streamlining workflows and developing user-friendly interfaces, we created a robust and accessible solution for users with basic bioinformatics skills in proteomic data analysis. Users can easily create manuscript-ready figures with a single click, including principal component analysis, heatmaps (K-means and hierarchical), MA plots, volcano plots, and circular bar plots. ProteoArk is developed using the Django framework and is freely available for users [https://ciods.in/proteoark/]. Users can also download and run the standalone version of ProteoArk using Docker as described in the instructions [https://ciods.in/proteoark/dockerpage]. The application code, input data, and documentation are available online at https://github.com/ArokiaRex/proteoark. A tutorial video is available on YouTube: https://www.youtube.com/watch?v=WFMKAZ9Slq4&ab_channel=RexD.A.B.

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来源期刊
Journal of Proteome Research
Journal of Proteome Research 生物-生化研究方法
CiteScore
9.00
自引率
4.50%
发文量
251
审稿时长
3 months
期刊介绍: Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".
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