Appyters: Turning Jupyter Notebooks into data-driven web apps

• DOI: 10.1016/j.patter.2021.100213
• Publication Date: 2021-03-07T20:49:43Z
• Authors: Daniel J.B. Clarke; Minji Jeon; Daniel J. Stein; Nicole Moiseyev; Eryk Kropiwnicki; Charles Dai; Zhuorui Xie; Megan L. Wojciech...; Skylar Litz; Jason Hom; John Erol Evangel...; Lucas Goldman; Serena Zhang; Christine Yoon; Tahmid Ahamed; Samantha Bhuiyan; Minxuan Cheng; Julie Karam; Kathleen M. Jagodnik; Ingrid Shu; Alexander Lachmann; Sam Ayling; Sherry L. Jenkins; Avi Ma'ayan
• Keywords: 0206 medical engineering; 02 engineering and technology; Article

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.