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Description
Choose two tailored cell groups by utilizing metadata to uncover their most significantly differentially expressed genes. Utilize the extensive collection of millions of cells from the integrated CZ CELLxGENE corpus for in-depth analysis. Conduct interactive examinations of datasets to investigate how gene expression patterns are influenced by spatial, environmental, and genetic variables through an intuitive no-code user interface. Gain insights into existing datasets or leverage them as a foundation to discover new cell subtypes and states. Census offers the capability to access any customized segment of standardized cell data available within CZ CELLxGENE, with opportunities for exploration in both R and Python. Delve into an interactive encyclopedia containing over 700 cell types that includes comprehensive definitions, marker genes, lineage information, and associated datasets all in one location. Additionally, you can browse and obtain hundreds of standardized data collections along with more than 1,000 datasets that detail the functionality of both healthy mouse and human tissues, enriching your research and understanding of cellular biology. This resource provides a valuable tool for researchers aiming to enhance their exploration of cellular dynamics and gene expression.
Description
Evo 2 represents a cutting-edge genomic foundation model that excels in making predictions and designing tasks related to DNA, RNA, and proteins. It employs an advanced deep learning architecture that allows for the modeling of biological sequences with single-nucleotide accuracy, achieving impressive scaling of both compute and memory resources as the context length increases. With a robust training of 40 billion parameters and a context length of 1 megabase, Evo 2 has analyzed over 9 trillion nucleotides sourced from a variety of eukaryotic and prokaryotic genomes. This extensive dataset facilitates Evo 2's ability to conduct zero-shot function predictions across various biological types, including DNA, RNA, and proteins, while also being capable of generating innovative sequences that maintain a plausible genomic structure. The model's versatility has been showcased through its effectiveness in designing operational CRISPR systems and in the identification of mutations that could lead to diseases in human genes. Furthermore, Evo 2 is available to the public on Arc's GitHub repository, and it is also incorporated into the NVIDIA BioNeMo framework, enhancing its accessibility for researchers and developers alike. Its integration into existing platforms signifies a major step forward for genomic modeling and analysis.
API Access
Has API
API Access
Has API
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
CZ CELLxGENE
Country
United States
Website
cellxgene.cziscience.com
Vendor Details
Company Name
Arc Institute
Country
United States
Website
arcinstitute.org/tools/evo