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.
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.