Appyters: Turning Jupyter Notebooks into data-driven web apps

Daniel J.B. Clarke; Minji Jeon; Daniel J. Stein; Nicole Moiseyev; Eryk Kropiwnicki; Charles Dai; Zhuorui Xie; Megan L. Wojciechowicz; Skylar Litz; Jason Hom; John Erol Evangelista; 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

doi:10.1016/j.patter.2021.100213

Appyters: Turning Jupyter Notebooks into data-driven web apps

Daniel J.B. Clarke, Minji Jeon, Daniel J. Stein, Nicole Moiseyev, Eryk Kropiwnicki, Charles Dai, Zhuorui Xie, Megan L. Wojciechowicz, Skylar Litz, Jason Hom, John Erol Evangelista, Lucas Goldman, Serena Zhang, Christine Yoon, Tahmid Ahamed, Samantha Bhuiyan, Minxuan Cheng, Julie Karam, Kathleen M. Jagodnik, Ingrid ShuAlexander Lachmann, Sam Ayling, Sherry L. Jenkins, Avi Ma'ayan

Research output: Contribution to journal › Article › peer-review

68 Scopus citations

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.

Original language	English
Article number	100213
Journal	Patterns
Volume	2
Issue number	3
DOIs	https://doi.org/10.1016/j.patter.2021.100213
State	Published - 12 Mar 2021

Keywords

DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems
RNA-seq
TCGA
big data
data analysis
data visualization
gene set enrichment analysis
machine learning
notebooks
scRNA-seq
workflow

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10.1016/j.patter.2021.100213

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Clarke, D. J. B., Jeon, M., Stein, D. J., Moiseyev, N., Kropiwnicki, E., Dai, C., Xie, Z., Wojciechowicz, M. L., Litz, S., Hom, J., Evangelista, J. E., Goldman, L., Zhang, S., Yoon, C., Ahamed, T., Bhuiyan, S., Cheng, M., Karam, J., Jagodnik, K. M., ... Ma'ayan, A. (2021). Appyters: Turning Jupyter Notebooks into data-driven web apps. Patterns, 2(3), Article 100213. https://doi.org/10.1016/j.patter.2021.100213

@article{a98278e64660445dae5730d0aa246862,

title = "Appyters: Turning Jupyter Notebooks into data-driven web apps",

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.",

keywords = "DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems, RNA-seq, TCGA, big data, data analysis, data visualization, gene set enrichment analysis, machine learning, notebooks, scRNA-seq, workflow",

author = "Clarke, {Daniel J.B.} and Minji Jeon and Stein, {Daniel J.} and Nicole Moiseyev and Eryk Kropiwnicki and Charles Dai and Zhuorui Xie and Wojciechowicz, {Megan L.} and Skylar Litz and Jason Hom and Evangelista, {John Erol} and Lucas Goldman and Serena Zhang and Christine Yoon and Tahmid Ahamed and Samantha Bhuiyan and Minxuan Cheng and Julie Karam and Jagodnik, {Kathleen M.} and Ingrid Shu and Alexander Lachmann and Sam Ayling and Jenkins, {Sherry L.} and Avi Ma'ayan",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors",

year = "2021",

month = mar,

day = "12",

doi = "10.1016/j.patter.2021.100213",

language = "English",

volume = "2",

journal = "Patterns",

issn = "2666-3899",

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Clarke, DJB, Jeon, M, Stein, DJ, Moiseyev, N, Kropiwnicki, E, Dai, C, Xie, Z, Wojciechowicz, ML, Litz, S, Hom, J, Evangelista, JE, Goldman, L, Zhang, S, Yoon, C, Ahamed, T, Bhuiyan, S, Cheng, M, Karam, J, Jagodnik, KM, Shu, I, Lachmann, A, Ayling, S, Jenkins, SL & Ma'ayan, A 2021, 'Appyters: Turning Jupyter Notebooks into data-driven web apps', Patterns, vol. 2, no. 3, 100213. https://doi.org/10.1016/j.patter.2021.100213

TY - JOUR

T1 - Appyters

T2 - Turning Jupyter Notebooks into data-driven web apps

AU - Clarke, Daniel J.B.

AU - Jeon, Minji

AU - Stein, Daniel J.

AU - Moiseyev, Nicole

AU - Kropiwnicki, Eryk

AU - Dai, Charles

AU - Xie, Zhuorui

AU - Wojciechowicz, Megan L.

AU - Litz, Skylar

AU - Hom, Jason

AU - Evangelista, John Erol

AU - Goldman, Lucas

AU - Zhang, Serena

AU - Yoon, Christine

AU - Ahamed, Tahmid

AU - Bhuiyan, Samantha

AU - Cheng, Minxuan

AU - Karam, Julie

AU - Jagodnik, Kathleen M.

AU - Shu, Ingrid

AU - Lachmann, Alexander

AU - Ayling, Sam

AU - Jenkins, Sherry L.

AU - Ma'ayan, Avi

PY - 2021/3/12

Y1 - 2021/3/12

N2 - 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.

AB - 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.

KW - DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems

KW - RNA-seq

KW - TCGA

KW - big data

KW - data analysis

KW - data visualization

KW - gene set enrichment analysis

KW - machine learning

KW - notebooks

KW - scRNA-seq

KW - workflow

UR - http://www.scopus.com/inward/record.url?scp=85102264861&partnerID=8YFLogxK

U2 - 10.1016/j.patter.2021.100213

DO - 10.1016/j.patter.2021.100213

M3 - Article

AN - SCOPUS:85102264861

SN - 2666-3899

VL - 2

JO - Patterns

JF - Patterns

IS - 3

M1 - 100213

ER -

Appyters: Turning Jupyter Notebooks into data-driven web apps

Abstract

Keywords

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