
OpenCell on AWS
Provided by: Chan Zuckerberg Biohub , part of the AWS Open Data Sponsorship Program
Provided by: Chan Zuckerberg Biohub , part of the AWS Open Data Sponsorship Program

OpenCell on AWS
Provided by: Chan Zuckerberg Biohub , part of the AWS Open Data Sponsorship Program
Provided by: Chan Zuckerberg Biohub , part of the AWS Open Data Sponsorship Program
This product is part of the AWS Open Data Sponsorship Program and contains data sets that are publicly available for anyone to access and use. No subscription is required. Unless specifically stated in the applicable data set documentation, data sets available through the AWS Open Data Sponsorship Program are not provided and maintained by AWS.
Description
The OpenCell project is a proteome-scale effort to measure the localization and interactions of human proteins using high-throughput genome engineering to endogenously tag thousands of proteins in the human proteome. This dataset consists of the raw confocal fluorescence microscopy images for all tagged cell lines in the OpenCell library. These images can be interpreted both individually, to determine the localization of particular proteins of interest, and in aggregate, by training machine learning models to classify or quantify subcellular localization patterns.
Documentation
How to cite
OpenCell on AWS was accessed on DATE
from https://registry.opendata.aws/czb-opencell .
Update frequency
This is the final version of the dataset.
Support information
Managed by: Chan Zuckerberg Biohub
Contact: opencell@czbiohub.org
General AWS Data Exchange support
Resources on AWS
Description
Live-cell confocal fluorescence microscopy images of the OpenCell library of fluorescently tagged cell lines
Resource type
S3 Bucket
Amazon Resource Name (ARN)
arn:aws:s3:::czb-opencell
AWS Region
us-west-2
AWS CLI Access (No AWS account required)
aws s3 ls --no-sign-request s3://czb-opencell/
Usage examples
Publications
- OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization by Nathan H. Cho, Keith C. Cheveralls, Andreas-David Brunner, Kibeom Kim, André C. Michaelis, Preethi Raghavan, et al.
- Self-Supervised Deep-Learning Encodes High-Resolution Features of Protein Subcellular Localization by Hirofumi Kobayashi, Keith C. Cheveralls, Manuel D. Leonetti, Loic A. Royer