Clarke Daniel J B, Jeon Minji, Stein Daniel J, Moiseyev Nicole, Kropiwnicki Eryk, Dai Charles, Xie Zhuorui, Wojciechowicz Megan L, Litz Skylar, Hom Jason, Evangelista John Erol, Goldman Lucas, Zhang Serena, Yoon Christine, Ahamed Tahmid, Bhuiyan Samantha, Cheng Minxuan, Karam Julie, Jagodnik Kathleen M, Shu Ingrid, Lachmann Alexander, Ayling Sam, Jenkins Sherry L, Ma'ayan Avi
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.
Pencil Worx Design, 345 West 88th Street, New York, NY 10024, USA.
Patterns (N Y). 2021 Mar 4;2(3):100213. doi: 10.1016/j.patter.2021.100213. eCollection 2021 Mar 12.
Jupyter Notebooks have transformed the communication of data analysis pipelines by facilitating a modular structure that brings together code, markdown text, and interactive visualizations. Here, we extended Jupyter Notebooks to broaden their accessibility with Appyters. Appyters turn Jupyter Notebooks into fully functional standalone web-based bioinformatics applications. Appyters present to users an entry form enabling them to upload their data and set various parameters for a multitude of data analysis workflows. Once the form is filled, the Appyter executes the corresponding notebook in the cloud, producing the output without requiring the user to interact directly with the code. Appyters were used to create many bioinformatics web-based reusable workflows, including applications to build customized machine learning pipelines, analyze omics data, and produce publishable figures. These Appyters are served in the Appyters Catalog at https://appyters.maayanlab.cloud. In summary, Appyters enable the rapid development of interactive web-based bioinformatics applications.
Jupyter笔记本通过促进模块化结构改变了数据分析管道的交流方式，这种结构将代码、Markdown文本和交互式可视化结合在一起。在这里，我们扩展了Jupyter笔记本，以通过Appyters扩大其可访问性。Appyters将Jupyter笔记本转变为功能齐全的基于网络的独立生物信息学应用程序。Appyters向用户提供一个输入表单，使他们能够上传数据并为众多数据分析工作流程设置各种参数。一旦表单填写完毕，Appyter就在云中执行相应的笔记本，生成输出，而无需用户直接与代码进行交互。Appyters被用于创建许多基于网络的可重复使用的生物信息学工作流程，包括构建定制机器学习管道、分析组学数据以及生成可发表图形的应用程序。这些Appyters在https://appyters.maayanlab.cloud的Appyters目录中提供服务。总之，Appyters能够快速开发基于网络的交互式生物信息学应用程序。
