Appyters: Turning Jupyter Notebooks into data-driven web apps

Daniel Clarke(Icahn School of Medicine at Mount Sinai), Minji Jeon(Icahn School of Medicine at Mount Sinai), Daniel Stein(Icahn School of Medicine at Mount Sinai), Nicole Moiseyev(Icahn School of Medicine at Mount Sinai), Eryk Kropiwnicki(Icahn School of Medicine at Mount Sinai), Charles S. Dai(Icahn School of Medicine at Mount Sinai), Zhuorui Xie(Icahn School of Medicine at Mount Sinai), Megan L. Wojciechowicz(Icahn School of Medicine at Mount Sinai), Skylar Litz(Icahn School of Medicine at Mount Sinai), Jason Hom(Icahn School of Medicine at Mount Sinai), John Erol Evangelista(Icahn School of Medicine at Mount Sinai), Lucas Goldman(Icahn School of Medicine at Mount Sinai), Serena Zhang(Icahn School of Medicine at Mount Sinai), Christine Yoon(Icahn School of Medicine at Mount Sinai), Tahmid Ahamed(Icahn School of Medicine at Mount Sinai), Samantha Bhuiyan(Icahn School of Medicine at Mount Sinai), Minxuan Cheng(Icahn School of Medicine at Mount Sinai), Julie Karam(Icahn School of Medicine at Mount Sinai), Kathleen M. Jagodnik(Icahn School of Medicine at Mount Sinai), Ingrid Shu(Icahn School of Medicine at Mount Sinai), Alexander Lachmann(Icahn School of Medicine at Mount Sinai), Sam Ayling, Sherry L. Jenkins(Icahn School of Medicine at Mount Sinai), Avi Ma’ayan(Icahn School of Medicine at Mount Sinai)
Patterns
March 1, 2021
10.1016/j.patter.2021.100213
Cited by 127Open Access
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Abstract

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.

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