TDEase: An Open-Source Data Visualization Software Framework for Targeted Proteoform Characterization by Top-Down Proteomics.

IF 3.9 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Proteomics Pub Date : 2025-08-29 DOI:10.1002/pmic.70031
Yucheng Liao, Rui Qian, Mengting Zhang, Chenghao Sun, Han Wen, Weinan E, Weijie Zhang, Mowei Zhou
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引用次数: 0

Abstract

Top-down proteomics (TDP) is a powerful approach for characterizing intact protein molecules and their diverse proteoforms. Despite recent advances, current TDP software tools often suffer from fragmented workflows, steep learning curves for non-experts, or limited interactive visualization capabilities. To address these challenges, we introduce TDEase, an integrated analytical framework designed to streamline and enhance TDP data interpretation, with a current focus on integration with the TopPIC suite package for targeted proteoform characterization. TDEase features a modular architecture comprising TDPipe, a multi-process data processing engine, and TDVis, an interactive web-based visualization module. TDPipe automates the execution of mainstream TDP analysis algorithms through a user-configurable pipeline, ensuring seamless and reproducible data processing. The TDVis module then transforms these results into dynamic, interactive dashboards, enabling multidimensional data exploration, including feature maps and PTM analysis. An alternative version, TDVisWeb, is also available for visualizing the results on an internet server or intranet workstation at institutional core facilities. We demonstrated the software capabilities in proteoform identification and comparative analysis using published histone datasets. TDEase is built with Python and open-source, allowing future improvements and incorporation of more data types as the TDP community develops new software. Source code is available at https://github.com/Computational-TDMS/TDEase.

TDEase:一个开源的数据可视化软件框架,用于自上而下的蛋白质组学靶向蛋白质形态表征。
自顶向下蛋白质组学(TDP)是表征完整蛋白质分子及其多种蛋白质形态的有力方法。尽管最近取得了进步,但当前的TDP软件工具经常受到工作流程碎片化、非专家学习曲线陡峭或交互可视化能力有限的困扰。为了应对这些挑战,我们引入了TDEase,这是一个集成的分析框架,旨在简化和增强TDP数据解释,目前的重点是与TopPIC套件包集成,用于靶向蛋白质形态表征。TDEase采用模块化架构,包括多进程数据处理引擎TDPipe和基于web的交互式可视化模块TDVis。TDPipe通过用户可配置的管道自动执行主流TDP分析算法,确保无缝和可重复的数据处理。然后,TDVis模块将这些结果转换为动态的交互式仪表板,支持多维数据探索,包括特征图和PTM分析。另一个可供选择的版本,即TDVisWeb,可在机构核心设施的互联网服务器或内部网工作站上将结果可视化。我们展示了软件在蛋白质形态鉴定和使用已发表的组蛋白数据集进行比较分析方面的能力。TDEase是用Python和开源构建的,随着TDP社区开发新软件,它允许未来的改进和合并更多的数据类型。源代码可从https://github.com/Computational-TDMS/TDEase获得。
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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来源期刊
Proteomics
Proteomics 生物-生化研究方法
CiteScore
6.30
自引率
5.90%
发文量
193
审稿时长
3 months
期刊介绍: PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.
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