插件
计算机科学
工作流程
软件
可用性
Python(编程语言)
数据挖掘
蛋白质组学
数据科学
生物信息学
程序设计语言
人机交互
生物
数据库
生物化学
基因
作者
Stefka Tyanova,Tikira Temu,Pavel Sinitcyn,Arthur Carlson,Marco Y. Hein,Tamar Geiger,Matthias Mann,Jürgen Cox
出处
期刊:Nature Methods
[Springer Nature]
日期:2016-06-27
卷期号:13 (9): 731-740
被引量:5955
摘要
Perseus is a comprehensive, user-friendly software platform for the biological analysis of quantitative proteomics data. It is intended to help biologists with little bioinformatics training to interpret protein expression, post-translational modification and interaction data. Also in this issue, see the Perspective by Röst et al. A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform ( http://www.perseus-framework.org ) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
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