| Literature DB >> 33748796 |
Daniel J B Clarke1, Minji Jeon1, Daniel J Stein1, Nicole Moiseyev1, Eryk Kropiwnicki1, Charles Dai1, Zhuorui Xie1, Megan L Wojciechowicz1, Skylar Litz1, Jason Hom1, John Erol Evangelista1, Lucas Goldman1, Serena Zhang1, Christine Yoon1, Tahmid Ahamed1, Samantha Bhuiyan1, Minxuan Cheng1, Julie Karam1, Kathleen M Jagodnik1, Ingrid Shu1, Alexander Lachmann1, Sam Ayling2, Sherry L Jenkins1, Avi Ma'ayan1.
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.Entities:
Keywords: RNA-seq; TCGA; big data; data analysis; data visualization; gene set enrichment analysis; machine learning; notebooks; scRNA-seq; workflow
Year: 2021 PMID： 33748796 PMCID： PMC7961182 DOI： 10.1016/j.patter.2021.100213
Source DB: PubMed Journal: Patterns (N Y) ISSN： 2666-3899