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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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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961182/
https://www.ncbi.nlm.nih.gov/pubmed/33748796
http://dx.doi.org/10.1016/j.patter.2021.100213
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author 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
author_facet 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
author_sort Clarke, Daniel J.B.
collection PubMed
description 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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spelling pubmed-79611822021-03-19 Appyters: Turning Jupyter Notebooks into data-driven web apps 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 Patterns (N Y) 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. Elsevier 2021-03-04 /pmc/articles/PMC7961182/ /pubmed/33748796 http://dx.doi.org/10.1016/j.patter.2021.100213 Text en © 2021 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
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
Appyters: Turning Jupyter Notebooks into data-driven web apps
title Appyters: Turning Jupyter Notebooks into data-driven web apps
title_full Appyters: Turning Jupyter Notebooks into data-driven web apps
title_fullStr Appyters: Turning Jupyter Notebooks into data-driven web apps
title_full_unstemmed Appyters: Turning Jupyter Notebooks into data-driven web apps
title_short Appyters: Turning Jupyter Notebooks into data-driven web apps
title_sort appyters: turning jupyter notebooks into data-driven web apps
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961182/
https://www.ncbi.nlm.nih.gov/pubmed/33748796
http://dx.doi.org/10.1016/j.patter.2021.100213
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