Evo 2 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.
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ESMC
ESMC represents the newest advancement in the ESM series of protein language models, pushing the boundaries of representation learning within the field of protein biology. With training on billions of evolutionary sequences, it adeptly captures representations that encapsulate a mechanistic understanding of protein structure and function. The model utilizes a transformer architecture, focusing on sequences as its primary modality, and is trained on a vast dataset comprising up to 6 billion proteins. ESMC is tailored for various protein science applications, such as predicting structures, annotating functions, designing proteins, and exploring evolutionary connections among proteins. Additionally, it possesses the capability to create novel proteins based on partial sequences, structures, or functional constraints, thereby enabling researchers to investigate innovative avenues in protein design and biological discovery. Accessible through the Biohub Platform, ESMC can be utilized via an API and the ESM Python package, which includes quickstart resources for installation, API key generation, and platform connectivity, ensuring a seamless experience for users. This comprehensive accessibility encourages a broader engagement with protein research and enhances collaborative efforts in the scientific community.
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Evo Designer
Evo Designer is a cutting-edge tool created by the Arc Institute, harnessing the power of the Evo 2 genomic foundation model to aid in the generation and analysis of DNA sequences. Users can enter nucleotide sequences or select specific organisms, prompting the model to produce relevant DNA sequences tailored to their needs. This platform also offers detailed annotations of coding regions and provides 3D protein visualizations for prokaryotic sequences through ESMFold, enhancing the understanding of protein structures. In addition to these features, Evo Designer evaluates sequences by calculating their perplexity and per-nucleotide entropy, which helps researchers gauge the complexity and variability of the sequences they are working with. The Evo 2 model at the core of this tool has been trained on an impressive dataset of over 9 trillion nucleotides sourced from a wide variety of prokaryotic and eukaryotic genomes. Utilizing a sophisticated deep learning architecture, it models biological sequences with single-nucleotide precision and boasts a context window that can extend up to 1 million tokens, thereby ensuring high accuracy in sequence representation and analysis. This combination of features makes Evo Designer an invaluable resource for genetic research and exploration.
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Company Details
Company:
Arc Institute
Headquarters:
United States
Website:
arcinstitute.org/tools/evo
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