OpenCell on AWS

Stream data with DDA:

from dagshub.streaming import DagsHubFilesystem

fs = DagsHubFilesystem(".", repo_url="https://dagshub.com/DagsHub-Datasets/czb-opencell-dataset")

fs.listdir("s3://czb-opencell")

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.

Contact:

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.

Update Frequency:

This is the final version of the dataset.

Managed By:

https://www.czbiohub.org/

Resources:

1. resource:
   • Description: Live-cell confocal fluorescence microscopy images of the OpenCell library of fluorescently tagged cell lines
   • ARN: arn:aws:s3:::czb-opencell
   • Region: us-west-2
   • Type: S3 Bucket

Tags:

aws-pds, biology, cell biology, life sciences, imaging, cell imaging, fluorescence imaging, microscopy, computer vision, machine learning

Tools & Applications:

1. tools & applications:

   • Title: OpenCell web portal
   • URL: https://opencell.czbiohub.org
   • AuthorName: OpenCell team
   • AuthorURL: https://www.czbiohub.org/manuel-leonetti-intracellular-architecture

2. tools & applications:

   • Title: cytoself (an unsupervised ML model to quantify localization patterns)
   • URL: https://github.com/royerlab/cytoself
   • AuthorName: Hirofumi Kobayashi, Keith C. Cheveralls, Manuel D. Leonetti, Loic A. Royer
   • AuthorURL: https://www.royerlab.org

Publication:

1. publication:

   • Title: OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization
   • URL: https://www.biorxiv.org/content/10.1101/2021.03.29.437450v1
   • AuthorName: Nathan H. Cho, Keith C. Cheveralls, Andreas-David Brunner, Kibeom Kim, André C. Michaelis, Preethi Raghavan, et al.
   • AuthorURL: https://www.czbiohub.org/manuel-leonetti-intracellular-architecture

2. publication:

   • Title: Self-Supervised Deep-Learning Encodes High-Resolution Features of Protein Subcellular Localization
   • URL: https://www.biorxiv.org/content/10.1101/2021.03.29.437595v1
   • AuthorName: Hirofumi Kobayashi, Keith C. Cheveralls, Manuel D. Leonetti, Loic A. Royer
   • AuthorURL: https://www.royerlab.org