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What is Illumina Connected Analytics?

Effectively manage, store, and collaborate on multi-omic datasets with ease. The Illumina Connected Analytics platform provides a secure environment for genomic data, enabling the operationalization of informatics and the extraction of valuable scientific insights. Users have the convenience of importing, creating, and modifying workflows using tools like CWL and Nextflow. The platform integrates DRAGEN bioinformatics pipelines, which enhance data processing capabilities. You can securely organize your data within a protected workspace that allows for global sharing while adhering to compliance standards. Retain your data on your own cloud infrastructure while taking advantage of our powerful platform. A versatile analysis environment, including JupyterLab Notebooks, is available for visualizing and interpreting your data effectively. With a scalable data warehouse, you can aggregate, query, and analyze both sample and population data, adapting to your growing needs. By constructing, validating, automating, and deploying informatics pipelines efficiently, you can significantly reduce the time required for genomic data analysis, which is crucial when rapid results are necessary. Additionally, the platform offers extensive profiling capabilities to discover novel drug targets and pinpoint biomarkers for drug response. Lastly, you can seamlessly integrate data from Illumina sequencing systems, ensuring a smooth and efficient workflow experience while optimizing your research outcomes. This comprehensive approach not only enhances productivity but also fosters collaboration among researchers.

What is Evo 2?

Evo 2 is an advanced genomic foundation model that excels in predicting and creating tasks associated with DNA, RNA, and proteins. Utilizing a sophisticated deep learning architecture, it models biological sequences with precision down to single-nucleotide accuracy, demonstrating remarkable scalability in both computational and memory resources as context length expands. The model has been trained on an impressive 40 billion parameters and can handle a context length of 1 megabase, analyzing an immense dataset of over 9 trillion nucleotides derived from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function predictions across a range of biological types, including DNA, RNA, and proteins, while also generating novel sequences that adhere to plausible genomic frameworks. Its robust capabilities have been highlighted in applications such as the design of efficient CRISPR systems and the identification of potentially disease-causing mutations in human genes. Additionally, Evo 2 is accessible to the public via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework, which significantly enhances its availability to researchers and developers. This integration not only broadens the model's reach but also represents a pivotal advancement in the fields of genomic modeling and analysis, paving the way for future innovations in biotechnology.

Media

Media

Integrations Supported

Amazon S3
Docker
Illumina DRAGEN Secondary Analysis
JupyterLab
Nextflow

Integrations Supported

Evo Designer
GitHub
Hugging Face
NVIDIA BioNeMo

API Availability

Has API

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Illumina

Company Location

United States

Company Website

www.illumina.com/products/by-type/informatics-products/connected-analytics.html

Company Facts

Organization Name

Arc Institute

Company Location

United States

Company Website

arcinstitute.org/tools/evo

Categories and Features

Bioinformatics

Not specified

Categories and Features

AI Models

Not specified

AI Science

Not specified

Bioinformatics

Not specified

Foundation Models

Not specified

Molecular Modeling

Not specified

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