VirRep: A hybrid language representation learning framework for identifying viruses from human gut metagenomes
VirRep is a fully automated tool for identifying viruses from human gut metagenomes, which comnbines a context-aware encoder and an evolution-aware encoder to leverage the strength of both DNA language rules and sequence homology to improve classification performance.
Make sure you have the dependencies below installed and accessible in your $PATH.
- python >= 3.8
- biopython
- numpy
- pandas
- pytorch >= 2.1
- tqdm
- scipy
- scikit-learn
- packaging
- Create a virtual environment
conda create -n vr python
conda activate vr
- Install python modules: biopython, numpy, pandas, tqdm, scipy, scikit-learn, packaging
conda install -c bioconda biopython numpy, pandas, tqdm, scipy, scikit-learn, packaging
or
pip install biopython numpy, pandas, tqdm, scipy, scikit-learn, packaging
- Install pytorch following the instructions in https://pytorch.org/get-started/locally/. For example, if your machine has NVIDIA GPU and supprots CUDA 11.8, you can run the following command:
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
git clone https://github.com/Dongyq815/VirRep.git
The input of VirRep is a fasta file containing the sequences to predict, and the output consists of a .tsv file recording
the predicted score and a fasta file containing the predicted viral sequences. The higher score indicate higher
likelihood of being a viral sequence.
- Run VirRep on a test dataset with GPU acceleration:
python VirRep.py -i test/test.fasta -o vr_out -w 2 --use-amp --min-score 1000-5000:0.9,5001-10000:0.8,10001-Inf:0.7
ls vr_out
In the output directory (vr_out), there are two files:
test_viruses.fna: identified viral sequencestest_score.tsv: table with score of each viral sequence and a few more features
Note
Note that suffix ||full or ||partial is appended to the original sequence identifier,
indicating whether the viral sequence is extracted from a larger scaffold.
- Run VirRep skipping over the iterative segment extension mechanism:
python VirRep.py -i test/test.fasta -o vr_out --use-amp --provirus-off -w 2
This is useful when comparing VirRep with other methods on a benchmark dataset,
as all input sequences will report a score in the output .tsv file.
- Run VirRep on bulk metagenomes:
python VirRep.py -i test/toy.fasta -o vr_out --use-amp --conservative -w 2
In this mode, VirRep will use conservative settings to reduce false positives and only output high-confidence viral sequences.
You can run python VirRep.py -h to see all options.
-h, --help show this help message and exit
-i INPUT_FILE, --input-file INPUT_FILE
Input file in fasta format. (default: None)
-o OUTPUT_DIR, --output-dir OUTPUT_DIR
Output directory for saving the prediction. (default: None)
--label LABEL Prefix to append to the output filename. (default: )
-m MODEL, --model MODEL
Model path. (default: model/VirRep.pth)
--use-amp Use automatic mixed precision to accelerate computational effeciency. (default: False)
--conservative Apply conservative settings. This is equivalent to the following criteria:
baseline 0.8; min_score 1000-8000:0.9,8001-10000:0.85,10001-Inf:0.8. (default: False)
-l MINLEN, --minlen MINLEN
Minimun length (should be >= 1000) of a sequence to predict.
Sequences shorter than this length will be ignored. (default:1000)
--provirus-off Skip the iterative segment extension procedure (default: False)
-b BASELINE, --baseline BASELINE
The baseline score for starting segment extension (default: 0.5)
--max-gap MAX_GAP Maximum distance for two adjacent extended regions to be merged.
Two regions are merged if the distance between them meet this
condition or the 'max-frac' condition. A value < 1000 means to
skip the extension procedure. (default: 5000)
--max-frac MAX_FRAC Maximum ratio of the distance between the two regions to be merged to the sum of
the lengths of the two regions. Two regions are merged if the distance between
them meet this condition or the 'max-gap' condition. A value <= 0 means to
disable this option. (default: 0.1)
--provirus-minlen PROVIRUS_MINLEN
Minimun length of a region within the prokaryotic genome to be considered as a provirus.
A value <= 1000 means all significant regions will be considered as proviruses. Smaller
values will result in more viral sequences in the expense of higher false positives.
(default: 5000)
--provirus-minfrac PROVIRUS_MINFRAC
Minimum ratio of the length of the region to be considered as a provirus to the length
of the whole sequence. A value <= 0 means all significant regions will be considered as
proviruses. Smaller values will result in more viral sequences in the expense of higher
false positives. (default: 1)
-c MIN_SCORE, --min-score MIN_SCORE
Minimum score of a sequence to be finally retained as a viral candidate.
Users can set different cutoffs for different sequence lengths
(e.g., 1000-5000:0.95,5001-10000:0.9,10001-Inf:0.8). Length range must
start from 1000 and end with Inf without any gap. Different cutoffs are
seperated by comma in desending order with correspoding length intervals
in asending order. (default: 1000-Inf:0.5)
-k MINLEN_KEEP, --minlen-keep MINLEN_KEEP
Minimum length of a viral hit to be finally kept. (default: 1500)
-n BATCH_SIZE, --batch-size BATCH_SIZE
How many 1 kb-long segments to score at one time. Larger batch size may reducce the
running time, but will require more GPU memory. (default: 256)
-w NUM_WORKERS, --num-workers NUM_WORKERS
How many subprocesses to use for data loading.
0 means that the data will be loaded in the main process. (default: 0)
--cpu Run VirRep on CPU. By default, VirRep will be run on GPU. (default: False)
--cpu-threads CPU_THREADS
Number of threads used to run VirRep on CPU.
0 means to use all available threads. (default: 0)
--gpu-device GPU_DEVICE
Device number of the GPU to run VirRep. (default: 0)