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ProteomeExpert: a Docker image-based web server for exploring, modeling, visualizing and mining quantitative proteomic datasets.


ABSTRACT:

Summary

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.

Availability and implementation

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 information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Zhu T 

PROVIDER: S-EPMC8055226 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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ProteomeExpert: a Docker image-based web server for exploring, modeling, visualizing and mining quantitative proteomic datasets.

Zhu Tiansheng T   Chen Hao H   Yan Xishan X   Wu Zhicheng Z   Zhou Xiaoxu X   Xiao Qi Q   Ge Weigang W   Zhang Qiushi Q   Xu Chao C   Xu Luang L   Ruan Guan G   Xue Zhangzhi Z   Yuan Chunhui C   Chen Guo-Bo GB   Guo Tiannan T  

Bioinformatics (Oxford, England) 20210401 2


<h4>Summary</h4>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  ...[more]

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