Appyters: Turning Jupyter Notebooks into data-driven web apps

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 Notebo...

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Vydané v:Patterns (New York, N.Y.) Ročník 2; číslo 3; s. 100213
Hlavní autori: 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
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Inc 12.03.2021
Elsevier
Predmet:
big data
data analysis
data visualization
gene set enrichment analysis
machine learning
notebooks
RNA-seq
scRNA-seq
TCGA
workflow
scRNA-seq
data analysis
workflow
RNA-seq
notebooks
gene set enrichment analysis
DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems
data visualization
big data
machine learning
TCGA
ISSN:2666-3899, 2666-3899
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Shrnutí: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. •Appyters turn Jupyter Notebooks into full-stack web-based applications•The Appyters Catalog serves over 75 Appyters•Appyters provide a way to parameterize and generalize Jupyter Notebooks•Appyter reports have permanent URLs that can be shared and published Appyters facilitate bioinformaticians to convert their Jupyter Notebook workflows into lightweight, interactive, open-source, reproducible web-based bioinformatics applications. The Appyters Catalog is a software platform that enables biomedical researchers to analyze and visualize their data in many ways. Appyters were developed to create many bioinformatics web-based reusable workflows, including applications to build customized machine learning pipelines, analyze omics data, and produce publishable figures. Appyters turn Jupyter Notebooks into fully functional standalone web-based bioinformatics applications. 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 an Appyters Catalog. In summary, Appyters enable the rapid development of lightweight, interactive, open-source, reproducible web-based bioinformatics applications.
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ISSN:2666-3899
2666-3899
DOI:10.1016/j.patter.2021.100213