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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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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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. |
format | Online Article Text |
id | pubmed-7961182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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