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引用次数: 0
摘要
基于质谱技术的系统蛋白质组学可以识别调控失调的蛋白质中心和相关的疾病特征。获取差异表达蛋白(DEPs)是下游生物信息学分析最重要的一步。然而,通过现有工具从具有多种实验条件或疾病类型的数据集中提取具有统计学意义的差异表达蛋白仍然是一项艰巨的任务。这种分析往往需要大量的生物信息学专业知识,这使得计算分析经验有限的研究人员无法胜任。 为了以用户友好的方式揭示数据中多种条件之间的差异,我们在此介绍 FlexStat,这是一种基于网络的界面,可通过组合分析提取 DEPs。该工具接受蛋白质表达矩阵作为输入,并为各种实验条件或疾病类型的每一种可想象的组合系统地生成 DEP 结果。FlexStat 包括一套强大的统计工具,用于数据预处理、DEP 提取和可发布的可视化,这些工具都是以自动化方式建立在成熟的 R 科学库上。该分析套件已在各种公共蛋白质组数据集中进行了验证,以展示其对综合数据集进行快速、同步配对比较的高性能。 FlexStat 使用 R 语言实现,可在 https://jglab.shinyapps.io/flexstatv1-pipeline-only/ 免费获取。源代码可从 https://github.com/kts-desilva/FlexStat/tree/main 获取。 补充数据可在 Bioinformatics Advances 在线查阅。
FlexStat: Combinatory differentially expressed protein extraction
Mass spectrometry-based system proteomics allows identification of dysregulated protein hubs and associated disease-related features. Obtaining differentially expressed proteins (DEPs) is the most important step of downstream bioinformatics analysis. However, the extraction of statistically significant DEPs from datasets with multiple experimental conditions or disease types through currently available tools remains a laborious task. More often such an analysis requires considerable bioinformatics expertise, making it inaccessible to researchers with limited computational analytics experience.
To uncover the differences among the many conditions within the data in a user-friendly manner, here we introduce FlexStat, a web-based interface that extracts DEPs through combinatory analysis. This tool accepts a protein expression matrix as input and systematically generates DEP results for every conceivable combination of various experimental conditions or disease types. FlexStat includes a suite of robust statistical tools for data preprocessing, in addition to DEP extraction, and publication-ready visualization, which are built on established R scientific libraries in an automated manner. This analytics suite was validated in diverse public proteomic datasets to showcase its high performance of rapid and simultaneous pairwise comparisons of comprehensive data sets.
FlexStat is implemented in R and is freely available at https://jglab.shinyapps.io/flexstatv1-pipeline-only/. The source code is accessible at https://github.com/kts-desilva/FlexStat/tree/main.
Supplementary data are available at Bioinformatics Advances online.
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.