vitax-rag 0.2.0

ViTax-RAG: Alignment-augmented language model for viral taxonomy classification

ViTax-RAG: Retrieval-Augmented Language Model for Viral Taxonomy Classification

Introduction

ViTax-RAG is a hybrid viral taxonomy classification framework that integrates BLAST-based retrieval with a Bi-Hyena genomic language model to achieve adaptive, accurate, and robust taxonomic assignment from metagenomic sequences.

Installation

ViTax-RAG can be installed either via pip (recommended) or from source.

External Dependency: NCBI BLAST+ (Optional but Recommended)

ViTax-RAG optionally uses NCBI BLAST+ for retrieval-augmented inference.

Install via conda (recommended)

conda install -c bioconda blast

Verify installation:

which blastn
blastn -version

Option 1️⃣ (Recommended): Install via pip

pip install vitax-rag

This installs the ViTax-RAG command-line tool:

vitax-rag --help

Required External Data

ViTax-RAG requires external BLAST database files, which are not packaged inside the Python wheel.

git clone https://github.com/Ying-Lab/ViTax-Rag.git
cd ViTax-Rag

Quick Start

Basic run (automatic device selection)

vitax-rag \
  --contigs test_contigs.fasta \
  --data_dir ./data \
  --out result.txt

Option 2️⃣ : Install from Source (Development Mode)

Install dependencies:

git clone https://github.com/Ying-Lab/ViTax-Rag.git
cd ViTax-Rag
pip install -r requirements.txt

Install NCBI BLAST+:

# 1) Download the latest BLAST+ tarball (adjust version if needed)
mkdir -p ~/downloads && cd ~/downloads
wget https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/ncbi-blast-2.12.0+-x64-linux.tar.gz

# 2) Extract and move to a permanent location
tar -zxvf ncbi-blast-2.12.0+-x64-linux.tar.gz
mv ncbi-blast-2.12.0+ ~/blast+

# 3) Persist PATH (so BLAST commands are available in every shell)
echo 'export PATH="$HOME/blast+/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

# 4) Verify installation
which blastn
blastn -version

Quick Start

Basic run (auto device selection):

python predict.py --contigs test_contigs.fasta --out output.txt --device auto

Adjust chunking and batch size for long contigs:

python predict.py --contigs test_contigs.fasta --chunk_size 2000 --window_size 400 

Command-Line Options

• --contigs FASTA input path, default test_contigs.fasta
• --out output predictions file, default output.txt
• --confidence confidence threshold, default 0.6
• --window_size sliding step size, default 400
• --chunk_size chunk length, default 2000
• --batch_size batch size, default 64
• --rc Use bidirectional prediction, default true
• --augment use BLAST augmentation, default true
• --augment_len target augmented length, default 4000
• --device auto/cpu/cuda, default auto

Output

• One line per input sequence; fields are space-separated: sequence_id label confidence

• sequence_id: taken from the FASTA header (text after > up to the first space)

• label: either unclassified or TaxonName_TaxonLevel

• TaxonLevel: one of Genus, Family, Order, Class; if genus confidence is below --confidence, the classifier backs off to higher levels

• confidence: normalized score in [0, 1], formatted to two decimals; higher means stronger support for the predicted taxon

• Ordering: lines follow the order of sequences in the input FASTA

• Device and augmentation: enabling --rc (reverse complement) and --augment (BLAST) can change predictions and confidence

• Output file: written to the path specified by --out and printed as Done, please check the output file: <path>