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: Upload; Codebase

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 to broaden their accessibility with Appyters. Appyters turn into fully functional standalone web-based bioinformatics applications. present users an entry form enabling them upload set various parameters for multitude workflows. Once is filled, Appyter executes corresponding notebook in cloud, producing output without requiring user interact directly code. were used create many reusable workflows, including applications build customized machine learning pipelines, analyze omics data, produce publishable figures. These are served Catalog at https://appyters.maayanlab.cloud. In summary, enable rapid development