Denecker Thomas, Durand William, Maupetit Julien, Hébert Charles, Camadro Jean-Michel, Poulain Pierre, Lelandais Gaëlle
CEA, CNRS, Univ. Paris-Sud, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France.
TailorDev SAS, Clermont-Ferrand, France.
PeerJ. 2019 Mar 27;7:e6623. doi: 10.7717/peerj.6623. eCollection 2019.
In biology, high-throughput experimental technologies, also referred as "omics" technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project.
The Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github.com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database. It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies.
The Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data.
在生物学领域,高通量实验技术,也被称为“组学”技术,在研究实验室中越来越常用。在单次实验中可以获得数千个基因表达测量值。研究人员经常面临着对他们所掌握的所有生物信息进行注释、存储、探索和挖掘的挑战。我们在此介绍Pixel网络应用程序(Pixel Web App),这是一个原创的内容管理平台,可帮助参与多组学生物项目的人员。
Pixel Web App采用开源技术构建,并托管在协作开发平台GitHub(https://github.com/Candihub/pixel)上。它使用Django框架用Python编写,并将所有数据存储在PostgreSQL数据库中。它是在开源环境下开发的,并遵循BSD 3条款许可。Pixel Web App还经过了大量的单元测试和功能测试,具有很高的代码覆盖率,并由CircleCI提供持续集成。为了简化Pixel Web App的开发和部署,使用Docker和Docker Compose来捆绑应用程序及其依赖项。
Pixel Web App为研究人员提供了一种直观的方式来注释、存储、探索和挖掘他们的多组学结果。它可以安装在个人计算机或服务器上,以满足许多用户的需求。此外,任何人都可以通过直接在GitHub上贡献(鼓励这样做)或自行扩展Pixel来增强该应用程序,以更好地满足他们的需求。Pixel Web App不提供任何用于分析数据的计算程序。尽管如此,它有助于快速探索和挖掘现有结果,并在研究数据管理中占据战略地位。