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--- |
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license: apache-2.0 |
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tags: |
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- DNA |
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- RNA |
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- genomic |
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- metagenomic |
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--- |
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# METAGENE-1 |
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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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We carry out byte-pair encoding (BPE) tokenization on our dataset, tailored for metagenomic sequences, and then pretrain our model. We detail the pretraining data, tokenization strategy, and model architecture, highlighting the considerations and design choices that enable the effective modeling of metagenomic data, in our technical report. |
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## **Usage** |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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# Load the tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("metagene-ai/METAGENE-1") |
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model = AutoModelForCausalLM.from_pretrained("metagene-ai/METAGENE-1", torch_dtype=torch.bfloat16) |
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# Example input: Hexamita inflata 5.8S ribosomal RNA gene sequence |
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input_sequence = ( |
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"TCACCGTTCTACAATCCCAAGCTGGAGTCAAGCTCAACAGGGTCTTCTTGCCCCGCTGAGGGTTACACTCGCCCGTTCCCGAGTCTGTGGTTTCGCGAAGATATGACCAGGGACAGTAAGAACC" |
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) |
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# Tokenize the input sequence and truncate to the first 12 tokens |
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input_tokens = tokenizer.encode(input_sequence, return_tensors="pt", add_special_tokens=False)[..., :12] |
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# Generate output from the model with a max sequence length of 32 tokens |
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generated_tokens = model.generate(input_tokens, max_length=32) |
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# Decode the generated output and clean up the result |
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generated_sequence = tokenizer.decode(generated_tokens[0], skip_special_tokens=True) |
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generated_sequence = generated_sequence.replace(" ", "").replace("_", "") |
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# Print the original input and the model's output |
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print(f"馃搫 Input Sequence:\n{input_sequence}") |
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print(f"馃敩 Generated Sequence:\n{generated_sequence}") |
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``` |
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## **Benchmark Performance** |
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We evaluate METAGENE-1 across three tasks: pathogen detection, zero-shot embedding benchmarks (**Gene-MTEB**), and genome understanding (**GUE**), achieving state-of-the-art performance on most benchmarks. For more details, check out our [paper](TODO). |
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### **Pathogen Detection** |
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The pathogen detection benchmark evaluates **METAGENE-1**鈥檚 ability to classify sequencing reads as human pathogens or non-pathogens across four distinct datasets, each derived from different sequencing deliveries and designed to mimic real-world conditions with limited training data. |
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| | **DNABERT-2** | **DNABERT-S** | **NT-2.5b-Multi** | **NT-2.5b-1000g** | **METAGENE-1** | |
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|-------------------------------|---------------|---------------|-------------------|-------------------|----------------| |
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| **Pathogen-Detect (avg.)** | 87.92 | 87.02 | 82.43 | 79.02 | **92.96** | |
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| **Pathogen-Detect-1** | 86.73 | 85.43 | 83.80 | 77.52 | **92.14** | |
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| **Pathogen-Detect-2** | 86.90 | 85.23 | 83.53 | 80.38 | **90.91** | |
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| **Pathogen-Detect-3** | 88.30 | 89.01 | 82.48 | 79.83 | **93.70** | |
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| **Pathogen-Detect-4** | 89.77 | 88.41 | 79.91 | 78.37 | **95.10** | |
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### **Gene-MTEB** |
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The Gene-MTEB benchmark evaluates **METAGENE-1**鈥檚 ability to produce high-quality, zero-shot genomic representations through eight classification and eight clustering tasks. |
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| | **DNABERT-2** | **DNABERT-S** | **NT-2.5b-Multi** | **NT-2.5b-1000g** | **METAGENE-1** | |
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|--------------------------------|---------------|---------------|-------------------|-------------------|----------------| |
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| **Human-Virus (avg.)** | 0.564 | 0.570 | 0.675 | 0.710 | **0.775** | |
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| Human-Virus-1 | 0.594 | 0.605 | 0.671 | 0.721 | **0.828** | |
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| Human-Virus-2 | 0.507 | 0.510 | 0.652 | 0.624 | **0.742** | |
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| Human-Virus-3 | 0.606 | 0.612 | 0.758 | 0.740 | **0.835** | |
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| Human-Virus-4 | 0.550 | 0.551 | 0.620 | **0.755** | 0.697 | |
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| **HMPD (avg.)** | 0.397 | 0.403 | 0.449 | 0.451 | **0.465** | |
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| HMPD-single | 0.292 | 0.293 | 0.285 | 0.292 | **0.297** | |
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| HMPD-disease | 0.480 | 0.486 | 0.498 | 0.489 | **0.542** | |
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| HMPD-sex | 0.366 | 0.367 | 0.487 | 0.476 | **0.495** | |
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| HMPD-source | 0.451 | 0.465 | 0.523 | **0.545** | 0.526 | |
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| **HVR (avg.)** | 0.479 | 0.479 | 0.546 | 0.524 | **0.550** | |
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| HVR-p2p | 0.548 | 0.550 | 0.559 | **0.650** | 0.466 | |
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| HVR-s2s-align | 0.243 | 0.241 | 0.266 | **0.293** | 0.267 | |
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| HVR-s2s-small | 0.373 | 0.372 | 0.357 | 0.371 | **0.467** | |
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| HVR-s2s-tiny | 0.753 | 0.753 | 1.000 | 0.782 | **1.000** | |
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| **HMPR (avg.)** | 0.347 | 0.351 | 0.348 | 0.403 | **0.476** | |
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| HMPR-p2p | 0.566 | **0.580** | 0.471 | 0.543 | 0.479 | |
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| HMPR-s2s-align | 0.127 | 0.129 | 0.144 | **0.219** | 0.140 | |
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| HMPR-s2s-small | 0.419 | 0.421 | 0.443 | **0.459** | 0.432 | |
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| HMPR-s2s-tiny | 0.274 | 0.274 | 0.332 | 0.391 | **0.855** | |
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| **Global Average** | 0.475 | 0.479 | 0.525 | 0.545 | **0.590** | |
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### **GUE** |
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Next, we evaluate **METAGENE-1** on the GUE multi-species classification benchmark proposed in [DNABERT-2](https://arxiv.org/abs/2306.15006). This experiment is designed to assess the viability of **METAGENE-1** as a general-purpose genome foundation model. |
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| | **CNN** | **HyenaDNA** | **DNABERT** | **NT-2.5B-Multi** | **DNABERT-2** | **METAGENE-1** | |
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|--------------------------------|---------|--------------|-------------|-------------------|---------------|----------------| |
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| **TF-Mouse (avg.)** | 45.3 | 51.0 | 57.7 | 67.0 | 68.0 | **71.4** | |
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| 0 | 31.1 | 35.6 | 42.3 | **63.3** | 56.8 | 61.5 | |
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| 1 | 59.7 | 80.5 | 79.1 | 83.8 | **84.8** | 83.7 | |
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| 2 | 63.2 | 65.3 | 69.9 | 71.5 | 79.3 | **83.0** | |
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| 3 | 45.5 | 54.2 | 55.4 | 69.4 | 66.5 | **82.2** | |
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| 4 | 27.2 | 19.2 | 42.0 | 47.1 | **52.7** | 46.6 | |
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| **TF-Human (avg.)** | 50.7 | 56.0 | 64.4 | 62.6 | **70.1** | 68.3 | |
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| 0 | 54.0 | 62.3 | 68.0 | 66.6 | **72.0** | 68.9 | |
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| 1 | 63.2 | 67.9 | 70.9 | 66.6 | **76.1** | 70.8 | |
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| 2 | 45.2 | 46.9 | 60.5 | 58.7 | **66.5** | 65.9 | |
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| 3 | 29.8 | 41.8 | 53.0 | 51.7 | **58.5** | 58.1 | |
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| 4 | 61.5 | 61.2 | 69.8 | 69.3 | 77.4 | **77.9** | |
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| **EMP (avg.)** | 37.6 | 44.9 | 49.5 | 58.1 | 56.0 | **66.0** | |
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| H3 | 61.5 | 67.2 | 74.2 | 78.8 | 78.3 | **80.2** | |
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| H3K14ac | 29.7 | 32.0 | 42.1 | 56.2 | 52.6 | **64.9** | |
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| H3K36me3 | 38.6 | 48.3 | 48.5 | 62.0 | 56.9 | **66.7** | |
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| H3K4me1 | 26.1 | 35.8 | 43.0 | 55.3 | 50.5 | **55.3** | |
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| H3K4me2 | 25.8 | 25.8 | 31.3 | 36.5 | 31.1 | **51.2** | |
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| H3K4me3 | 20.5 | 23.1 | 28.9 | 40.3 | 36.3 | **58.5** | |
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| H3K79me3 | 46.3 | 54.1 | 60.1 | 64.7 | 67.4 | **73.0** | |
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| H3K9ac | 40.0 | 50.8 | 50.5 | 56.0 | 55.6 | **65.5** | |
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| H4 | 62.3 | 73.7 | 78.3 | 81.7 | 80.7 | **82.7** | |
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| H4ac | 25.5 | 38.4 | 38.6 | 49.1 | 50.4 | **61.7** | |
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| **PD (avg.)** | 77.1 | 35.0 | 84.6 | **88.1** | 84.2 | 82.3 | |
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| All | 75.8 | 47.4 | 90.4 | **91.0** | 86.8 | 86.0 | |
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| No-TATA | 85.1 | 52.2 | 93.6 | **94.0** | **94.3** | 93.7 | |
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| TATA | 70.3 | 5.3 | 69.8 | **79.4** | 71.6 | 67.4 | |
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| **CPD (avg.)** | 62.5 | 48.4 | **73.0** | 71.6 | 70.5 | 69.9 | |
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| All | 58.1 | 37.0 | **70.9** | 70.3 | 69.4 | 66.4 | |
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| No-TATA | 60.1 | 35.4 | 69.8 | **71.6** | 68.0 | 68.3 | |
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| TATA | 69.3 | 72.9 | **78.2** | 73.0 | 74.2 | 75.1 | |
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| **SSD** | 76.8 | 72.7 | 84.1 | **89.3** | 85.0 | 87.8 | |
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| **COVID** | 22.2 | 23.3 | 62.2 | **73.0** | 71.9 | 72.5 | |
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| **Global Win %** | 0.0 | 0.0 | 7.1 | 21.4 | 25.0 | **46.4** | |
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## **Model Details** |
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- **Release Date**: Dec XX 2024 |
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- **Model License**: Apache 2.0 |
