Zhu Tiansheng, Chen Hao, Yan Xishan, Wu Zhicheng, Zhou Xiaoxu, Xiao Qi, Ge Weigang, Zhang Qiushi, Xu Chao, Xu Luang, Ruan Guan, Xue Zhangzhi, Yuan Chunhui, Chen Guo-Bo, Guo Tiannan
Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
Bioinformatics. 2021 Apr 19;37(2):273-275. doi: 10.1093/bioinformatics/btaa1088.
The rapid progresses of high-throughput sequencing technology-based omics and mass spectrometry-based proteomics, such as data-independent acquisition and its penetration to clinical studies have generated increasing number of proteomic datasets containing hundreds to thousands of samples. To analyze these quantitative proteomic datasets and other omics (e.g. transcriptomics and metabolomics) datasets more efficiently and conveniently, we present a web server-based software tool ProteomeExpert implemented in Docker, which offers various analysis tools for experimental design, data mining, interpretation and visualization of quantitative proteomic datasets. ProteomeExpert can be deployed on an operating system with Docker installed or with R language environment.
The Docker image of ProteomeExpert is freely available from https://hub.docker.com/r/lifeinfo/proteomeexpert. The source code of ProteomeExpert is also openly accessible at http://www.github.com/guomics-lab/ProteomeExpert/. In addition, a demo server is provided at https://proteomic.shinyapps.io/peserver/.
Supplementary data are available at Bioinformatics online.
基于高通量测序技术的组学和基于质谱的蛋白质组学取得了快速进展，例如数据非依赖采集及其在临床研究中的渗透，产生了越来越多包含数百到数千个样本的蛋白质组学数据集。为了更高效、便捷地分析这些定量蛋白质组学数据集和其他组学（如转录组学和代谢组学）数据集，我们展示了一种基于网络服务器的软件工具ProteomeExpert，它以Docker实现，为定量蛋白质组学数据集的实验设计、数据挖掘、解释和可视化提供了各种分析工具。ProteomeExpert可以部署在安装了Docker的操作系统上或具有R语言环境的系统上。
ProteomeExpert的Docker镜像可从https://hub.docker.com/r/lifeinfo/proteomeexpert免费获取。ProteomeExpert的源代码也可在http://www.github.com/guomics-lab/ProteomeExpert/上公开访问。此外，在https://proteomic.shinyapps.io/peserver/提供了一个演示服务器。
补充数据可在《生物信息学》在线获取。
