DEP2:用于定量蛋白质组学数据的升级综合分析工具包。

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Zhenhuan Feng, Peiyang Fang, Hui Zheng, Xiaofei Zhang
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引用次数: 0

摘要

摘要:基于质谱(MS)的蛋白质组学已经成为研究给定生物和临床样品的蛋白质组学的最有力的方法。样品制备和质谱检测技术的进步,扩大了蛋白质组学的应用范围,但也对数据分析提出了新的要求。合适的蛋白质组学数据分析工作流程主要包括质量控制、假设检验、功能挖掘和可视化。尽管每个过程都有许多工具,但仍然迫切需要一个有效和通用的串联分析工具包来快速全面地了解各种蛋白质组学数据。在这里,我们提出了DEP2,这是我们之前建立的DEP的更新版本,用于蛋白质组学数据分析。我们修改了分析工作流程,采用不同的方法来适应不同的蛋白质组学数据,引入肽-蛋白总结和偶联生物学功能探索。总之,DEP2是一个全面的工具包,用于蛋白质和肽水平的定量蛋白质组学数据。它具有更灵活的差异分析工作流程,并包括一个用户友好的Shiny应用程序,以方便数据分析。可用性和实现:DEP2可从https://github.com/mildpiggy/DEP2获得,在MIT许可下发布。欲了解更多信息和使用细节,请参阅套餐网站https://mildpiggy.github.io/DEP2/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

DEP2: an upgraded comprehensive analysis toolkit for quantitative proteomics data.

DEP2: an upgraded comprehensive analysis toolkit for quantitative proteomics data.

Summary: Mass spectrometry (MS)-based proteomics has become the most powerful approach to study the proteome of given biological and clinical samples. Advancements in sample preparation and MS detection have extended the application of proteomics but have also brought new demands on data analysis. Appropriate proteomics data analysis workflow mainly requires quality control, hypothesis testing, functional mining, and visualization. Although there are numerous tools for each process, an efficient and universal tandem analysis toolkit to obtain a quick overall view of various proteomics data is still urgently needed. Here, we present DEP2, an updated version of DEP we previously established, for proteomics data analysis. We amended the analysis workflow by incorporating alternative approaches to accommodate diverse proteomics data, introducing peptide-protein summarization and coupling biological function exploration. In summary, DEP2 is a well-rounded toolkit designed for protein- and peptide-level quantitative proteomics data. It features a more flexible differential analysis workflow and includes a user-friendly Shiny application to facilitate data analysis.

Availability and implementation: DEP2 is available at https://github.com/mildpiggy/DEP2, released under the MIT license. For further information and usage details, please refer to the package website at https://mildpiggy.github.io/DEP2/.

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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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