eDNABERT-S_12S

Model Card for eDNABERT-S_12S

Model Details

Model Description

This model is our first step towards ecosystem-level modeling.

We finetuned DNABERT-S using all of our eDNA ASVs from the Australian Marine Parks project. We used 28,122 Miya 12S ASVs collected from more than 6,000 samples for finetuning.

A partner model for Berry 16S data is also available.

• Developed by: OceanOmics, Philipp Bayer
• Funded by Minderoo Foundation
• Model type: BERT
• Language(s) (NLP): DNA
• License: Apache 2.0
• Finetuned from model: DNABERT-S

Uses

Installation

There's a conda environment in this repository in DNABERT_S.yml.

conda env create -f DNABERT_S.yml

Direct Use

I've been using this model to visualise ecosystem embeddings.

import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig
from Bio import SeqIO
from sklearn.manifold import TSNE
import numpy as np
from tqdm import tqdm  # For progress tracking

# Device configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

config = AutoConfig.from_pretrained('zhihan1996/DNABERT-S', trust_remote_code = True)

# Load model and tokenizer
tokenizer_12S = AutoTokenizer.from_pretrained('OceanOmics/eDNABERT-S_12S', trust_remote_code=True)
model_12S = AutoModel.from_pretrained('OceanOmics/eDNABERT-S_12S', trust_remote_code=True, config=config)
model_12S.to(device)
model_12S.eval()
names_12, seqs_12 = [], []
for seq in SeqIO.parse('12S_all_ASVs.fasta', 'fasta'):
    if 165 <= len(str(seq.seq)) <= 180:  # More efficient condition check
        seqs_12.append(str(seq.seq))
        names_12.append(str(seq.id))

print(f"Processing {len(seqs_12)} sequences")

batch_size = 32 # tested on an A100
num_sequences = len(seqs_12)
all_e_12 = np.zeros((num_sequences, 768))

with torch.no_grad():  # no gradient calculation for inference
    for i in tqdm(range(0, num_sequences, batch_size)):
        batch_seqs = seqs_12[i:i+batch_size]

        inputs = tokenizer_12S(batch_seqs, return_tensors='pt', padding=True)
        inputs = {k: v.to(device) for k, v in inputs.items()}  # Move inputs to device

        hidden_states = model_12S(**inputs)[0]

        for j, hidden_state in enumerate(hidden_states):
            embedding_mean = torch.mean(hidden_state, dim=0)
            all_e_12[i+j] = embedding_mean.cpu().numpy()  # Store directly in pre-allocated array

print("Running TSNE...")
X_embedded = TSNE(
    n_components=2,
    learning_rate='auto',
    init='random',
    perplexity=50,  # Reasonable value
    n_jobs=-1  # Use all available cores
).fit_transform(all_e_12)

print("Saving results...")
with open('odr_all_tsne_12S.optimized.tsv', 'w') as out:
    for a, name in zip(X_embedded, names_12):
        out.write('\t'.join(map(str, list(a) + name.split("XXX"))) + '\n')

You can see results visualised in action here: https://marine-parks.minderoo.org/#!/unknown

Downstream Use

I'm hoping you'll come up with these! It would be great if we could plug this or similar models into ecosystem-level models such as BioAnalyst

Risks and out-of-Scope Use

This model is trained using Miya et al. 12S metabarcoding results based on Australian marine samples. The 12S assay is fairly fish-specific so you might not have the best time applying this model to other organisms or ecosystems.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

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Training Procedure

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Preprocessing

We had many primer dimers in our ASV data, so I got rid of too-short and too-long ASVs. Cehck what your ASVs look like.

Training Hyperparameters

Using the original DNABERT-S training script:

python main.py [.. skipping data flags..]
  --seed 1
  --max_length 2000
  --train_batch_size 8 
  --val_batch_size 8
  --lr 3e-06
  --lr_scale 100
  --epochs 3
  --feat_dim 128
  --temperature 0.05
  --con_method same_species
  --mix
  --mix_alpha 1.0
  --mix_layer_num -1
  --curriculum

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Testing Data, Factors & Metrics

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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