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README.md
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- RNA
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- genomic
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- metagenomic
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language:
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- en
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## **Model Overview**
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**METAGENE-1** is a 7B parameter metagenomic foundation model designed for pandemic monitoring, trained on over 1.5T base pairs of DNA and RNA sequenced from wastewater.
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**METAGENE-1** is a 7-billion-parameter autoregressive transformer language model, which we refer to as a *metagenomic foundation model*, that was trained on a novel corpus of diverse metagenomic DNA and RNA sequences comprising over 1.5 trillion base pairs. This dataset is sourced from a large collection of human wastewater samples, processed and sequenced using deep metagenomic (next-generation) sequencing methods. Unlike genomic models that focus on individual genomes or curated sets of specific species, the aim of METAGENE-1 is to capture the full distribution of genomic information present across the human microbiome. After pretraining, this model is designed to aid in tasks in the areas of biosurveillance, pandemic monitoring, and pathogen detection.
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- RNA
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- genomic
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- metagenomic
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- metagene
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- metagene-1
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language:
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- en
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## **Model Overview**
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**METAGENE-1** is a 7B parameter metagenomic foundation model designed for pandemic monitoring, trained on over 1.5T base pairs of DNA and RNA sequenced from wastewater.
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https://metagene.ai
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**METAGENE-1** is a 7-billion-parameter autoregressive transformer language model, which we refer to as a *metagenomic foundation model*, that was trained on a novel corpus of diverse metagenomic DNA and RNA sequences comprising over 1.5 trillion base pairs. This dataset is sourced from a large collection of human wastewater samples, processed and sequenced using deep metagenomic (next-generation) sequencing methods. Unlike genomic models that focus on individual genomes or curated sets of specific species, the aim of METAGENE-1 is to capture the full distribution of genomic information present across the human microbiome. After pretraining, this model is designed to aid in tasks in the areas of biosurveillance, pandemic monitoring, and pathogen detection.
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