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luanbei/
PACMT

metagenomics

viral-identification

hierarchical-classification

taxonomic-classification

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PACMT

PACMT （Pathogen Automated Classification Model base on Transformer） is a pretrained sequence model-based framework for viral identification and hierarchical taxonomic classification of metagenomic sequences.

This repository contains the trained PACMT model files and taxonomy resources. The source code, example files and detailed usage instructions are available at:

https://github.com/luanbei/PACMT

Use the current scripts from the GitHub repository's script/ directory. The three prediction scripts require pacmt_retrained_common.py in that same directory.

Model description

PACMT uses a two-stage serial workflow:

Binary viral screening: a binary classifier predicts whether an input sequence is viral or non-viral.
Hierarchical viral classification: sequences predicted as viral are further classified at the order, family, genus and species levels.

For hierarchical classification, PACMT uses taxonomy-consistent path decoding to select a biologically valid order-family-genus-species prediction path.

Repository contents

The recommended file structure of this Hugging Face repository is:

text

PACMT/
├── README.md
├── backbone/
│   ├── config.json
│   ├── pytorch_model.bin
│   ├── tokenizer.json
│   ├── tokenizer_config.json
│   ├── configuration_bert.py
│   ├── bert_layers.py
│   ├── bert_padding.py
│   └── flash_attn_triton.py
├── binary_model/
│   ├── config.json
│   ├── pytorch_model.bin
│   ├── head_config.json
│   ├── tokenizer.json
│   ├── tokenizer_config.json
│   └── special_tokens_map.json
├── hierarchy_model/
│   ├── pytorch_model.bin
│   ├── head_config.json
│   ├── tokenizer.json
│   ├── tokenizer_config.json
│   ├── special_tokens_map.json
│   ├── label_taxonomy_mapping.csv
│   ├── taxonomy_paths.csv
│   ├── taxonomy_paths_with_names.csv
│   └── label_sizes.json
└── taxonomy/
    ├── label_taxonomy_mapping.csv
    └── taxonomy_paths.csv

Required files

To run the complete PACMT prediction workflow, the following files or directories are required:

text

backbone/
binary_model/
hierarchy_model/
taxonomy/label_taxonomy_mapping.csv
taxonomy/taxonomy_paths.csv

The label_taxonomy_mapping.csv file maps internal label IDs to taxonomy names and should contain at least:

rank,label_id,taxonomy_name

The taxonomy_paths.csv file defines valid hierarchical taxonomy paths and should contain at least:

order_id,family_id,genus_id,species_id

Installation and usage

Please install PACMT from the GitHub repository:

bash

git clone https://github.com/luanbei/PACMT.git
cd PACMT
conda create -n pacmt python=3.8 -y
conda activate pacmt
pip install -r requirements.txt

Download this Hugging Face model repository and place the files under the models/ directory:

bash

pip install -U huggingface_hub
hf download luanbei/PACMT --local-dir models

After downloading, the local model directory should look like:

text

models/
├── backbone/
├── binary_model/
├── hierarchy_model/
└── taxonomy/

Complete prediction workflow

The complete two-stage PACMT workflow first performs binary viral screening and then applies hierarchical taxonomic classification to sequences predicted as viral.

bash

python script/predict_bina_hiera.py \
  --backbone_dir models/backbone \
  --binary_ckpt_dir models/binary_model \
  --hierarchy_ckpt_dir models/hierarchy_model \
  --mapping_csv models/taxonomy/label_taxonomy_mapping.csv \
  --taxonomy_path_csv models/taxonomy/taxonomy_paths.csv \
  --input_csv examples/example.csv \
  --seq_col seq \
  --id_col id \
  --seg_len 500 \
  --stride 250 \
  --max_length 512 \
  --batch_size 32 \
  --device cuda \
  --virus_threshold 0.5 \
  --tau 0.2 \
  --out_csv pacmt_predictions.csv

For FASTA input, replace the CSV input arguments with:

--input_fasta examples/example.fasta

Binary viral screening only

bash

python script/predict_binary.py \
  --backbone_dir models/backbone \
  --ckpt_dir models/binary_model \
  --input_csv examples/example.csv \
  --seq_col seq \
  --id_col id \
  --seg_len 500 \
  --stride 250 \
  --max_length 512 \
  --batch_size 32 \
  --device cuda \
  --tau 0.2 \
  --threshold 0.5 \
  --out_csv binary_predictions.csv

Hierarchical classification only

bash

python script/predict_hierarchy.py \
  --backbone_dir models/backbone \
  --ckpt_dir models/hierarchy_model \
  --mapping_csv models/taxonomy/label_taxonomy_mapping.csv \
  --taxonomy_path_csv models/taxonomy/taxonomy_paths.csv \
  --input_csv examples/example.csv \
  --seq_col seq \
  --id_col id \
  --seg_len 500 \
  --stride 250 \
  --max_length 512 \
  --batch_size 32 \
  --device cuda \
  --tau 0.2 \
  --out_csv hierarchy_predictions.csv

Output

The complete workflow outputs a CSV file containing:

text

id
seq_len
n_segments
is_virus
virus_confidence
order_id, order_name, order_conf
family_id, family_name, family_conf
genus_id, genus_name, genus_conf
species_id, species_name, species_conf
joint_score
log_joint_score

is_virus=1 indicates that the input sequence is predicted as viral. If is_virus=0, the hierarchical taxonomic fields are left empty.

Intended use

PACMT is intended for research use in viral sequence screening and hierarchical taxonomic annotation of metagenomic sequences.

Limitations

Species-level prediction is generally more difficult than higher-rank prediction.
Predictions for short, divergent or underrepresented viral sequences should be interpreted carefully.
The hierarchical classifier relies on the released taxonomy mapping files and valid taxonomy paths.
PACMT should be used as a research tool and should not be used as the sole basis for clinical decision-making.

Citation

If you use PACMT, please cite:

Luan B, Li P, et al. PACMT: a pretrained language model-based framework for viral identification and hierarchical taxonomic classification of metagenomic data.