flash:交互式数据分析和可视化自上而下的蛋白质组学。

IF 3.9 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Proteomics Pub Date : 2025-09-21 DOI:10.1002/pmic.70042
Tom David Müller, Jihyung Kim, Andrew Almaguer, Ayesha Feroz, Jaekwan Kim, Axel Walter, Wonhyeuk Jung, Oliver Kohlbacher, Kyowon Jeong
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引用次数: 0

摘要

自顶向下蛋白质组学(TDP)越来越多地应用于蛋白质形态的生物医学和临床研究。然而,TDP数据的复杂性需要灵活的可视化工具与分析工作流程集成,以简化解释和验证。现有的工具缺乏适应性和交互性,通常需要研究人员在额外的手工处理和分析上投入大量资源,以生成可发表的结果和数据。这一额外的人工干预层影响了可重复性,对一致的科学结果构成了重大挑战。FLASHApp通过提供基于web的、独立于平台的TDP数据分析和可视化应用程序来解决这些挑战。它集成了FLASHDeconv等关键工具,具有自动处理、交互式出版可视化以及通过可共享的url直接进行团队协作的特点。flashhapp是OpenMS的一部分,是开源软件,可从https://www.openms.org/FLASHApp/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。
FLASHApp: Interactive Data Analysis and Visualization for Top-Down Proteomics.

Top-down proteomics (TDP) is increasingly being applied in proteoform-resolved biomedical and clinical research. However, the complexity of TDP data demands flexible visualization tools integrated with analysis workflows to streamline interpretation and validation. Existing tools lack adaptability and interactivity, often requiring researchers to invest considerable resources on additional manual processing and analysis to generate publication-ready results and figures. This added layer of manual intervention impacts reproducibility, posing a significant challenge to consistent scientific outcomes. FLASHApp addresses these challenges by offering a web-based, platform-independent application for TDP data analysis and visualization. It integrates key tools like FLASHDeconv, featuring automated processing, interactive publication-ready visualizations, and direct team collaboration via shareable URLs. FLASHApp is open-source software as part of OpenMS and available at https://www.openms.org/FLASHApp/.

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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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