Paper --> Deepurify: a multi-modal deep language model to remove contamination from metagenome-assembled genomes
Deepurify elevates metagenome-assembled genomes' (MAGs) quality by utilizing a multi-modal deep language model to filter contaminated contigs, and it can leverage GPU acceleration.
Dependencies (All of the dependencies can be installed via conda with the commands in the INSTALLATION section):
Please independently install the following tools and ensure their proper functionality.
- prodigal v 2.6.3 (ORF/CDS-prediction)
- hmmer v.3.3.2 (Detecting conserved single-copy marker genes)
- CheckM2 v 1.0.1 (Evaluate the quality of MAGs)
- dRep v3.5.0 (Filter replicated MAGs)
- CONCOCT v1.1.0 (Binner)
- MetaBAT2 v2.15 (Binner)
- Semibin2 v2.1.0 (Binner)
- PyTorch v2.1.0
Note: Ensure that all the listed dependencies above are installed and functioning without any errors.
Create deepurify's conda environment by using this command:
conda env create -n deepurify -f deepurify-conda-env.yml
Do not forget to download the database files for CheckM2 !!! and set environmental variable CHECKM2DB using: export CHECKM2DB="/path/to/database/uniref100.KO.1.dmnd".
and Please download PyTorch v2.1.0 -cu121 (or higher version) from http://pytorch.org/ if you want to use GPUs (We highly recommend to use GPUs).
conda activate deepurify
conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia
After prepared the environment, the code of Deepurify can be installed via pip simply.
conda activate deepurify
pip install Deepurify==2.3.4
Download the model weight and other files (Deepurify-DB.zip) for running Deepurify from this LINK.
- Unzip the downloaded file (Deepurify-DB.zip) and set an environmental variable called "DeepurifyInfoFiles" by adding the following line to the last line of .bashrc file (The path of the file: ~/.bashrc):
export DeepurifyInfoFiles=/path/of/this/Deepurify-DB/
For example: 'export DeepurifyInfoFiles=/home/csbhzou/software/Deepurify-DB/'.
- Save the .bashrc file, and then execute:
source .bashrc
- You can set the '--db_folder_path' in CLI to the path of the 'Deepurify-DB' folder if you do not want to set the environmental variable.
1. You can run Deepurify with 'clean' mode through the following command:
deepurify clean -i ./input_folder/ -o ./output_folder/ --bin_suffix fasta --gpu_num 1 --each_gpu_threads 1 --db_folder_path /path/of/this/Deepurify-DB/
usage: deepurify clean [-h] -i INPUT_PATH -o OUTPUT_PATH --bin_suffix BIN_SUFFIX [--gpu_num GPU_NUM] [--batch_size_per_gpu BATCH_SIZE_PER_GPU] [--each_gpu_threads EACH_GPU_THREADS]
[--overlapping_ratio OVERLAPPING_RATIO] [--cut_seq_length CUT_SEQ_LENGTH] [--mag_length_threshold MAG_LENGTH_THRESHOLD] [--num_process NUM_PROCESS]
[--topk_or_greedy_search {topk,greedy}] [--topK_num TOPK_NUM] [--temp_output_folder TEMP_OUTPUT_FOLDER] [--db_folder_path DB_FOLDER_PATH]
[--model_weight_path MODEL_WEIGHT_PATH] [--taxo_vocab_path TAXO_VOCAB_PATH] [--taxo_tree_path TAXO_TREE_PATH]
optional arguments:
-h, --help show this help message and exit
-i INPUT_PATH, --input_path INPUT_PATH
The folder of input MAGs.
-o OUTPUT_PATH, --output_path OUTPUT_PATH
The folder used to output cleaned MAGs.
--bin_suffix BIN_SUFFIX
The bin suffix of MAG files.
--gpu_num GPU_NUM The number of GPUs to be used can be specified. Defaults to 1. If you set it to 0, the code will utilize the CPU. However, please note that using the CPU can
result in significantly slower processing speed. It is recommended to provide at least one GPU (>= GTX-1060-6GB) for accelerating the speed.
--batch_size_per_gpu BATCH_SIZE_PER_GPU
The batch size per GPU determines the number of sequences that will be loaded onto each GPU. This parameter is only applicable if the --gpu_num option is set to a
value greater than 0. The default value is 4, meaning that one sequence will be loaded per GPU batch. The batch size for the CPU is 4.
--each_gpu_threads EACH_GPU_THREADS
The number of threads per GPU (or CPU) determines the parallelism level during the contigs' inference stage. If the value of --gpu_num is greater than 0, each GPU will
have a set number of threads to do inference. Similarly, if --gpu_num is set to 0 and the code will run on the CPU, the specified number of threads will be used. By
default, the number of threads per GPU or CPU is set to 1. The --batch_size_per_gpu value will be divided by the number of threads to determine the batch size per
thread.
--overlapping_ratio OVERLAPPING_RATIO
The --overlapping_ratio is a parameter used when the length of a contig exceeds the specified --cut_seq_length. By default, the overlapping ratio is set to 0.5.
This means that when a contig is longer than the --cut_seq_length, it will be split into overlapping subsequences with 0.5 overlaps between consecutive
subsequences.
--cut_seq_length CUT_SEQ_LENGTH
The --cut_seq_length parameter determines the length at which a contig will be cut if its length exceeds this value. The default setting is 8192, which is also the
maximum length allowed during training. If a contig's length surpasses this threshold, it will be divided into smaller subsequences with lengths equal to or less
than the cut_seq_length.
--mag_length_threshold MAG_LENGTH_THRESHOLD
The threshold for the total length of a MAG's contigs is used to filter generated MAGs after applying single-copy genes (SCGs). The default threshold is set to
200,000, which represents the total length of the contigs in base pairs (bp). MAGs with a total contig length equal to or greater than this threshold will be
considered for further analysis or inclusion, while MAGs with a total contig length below the threshold may be filtered out.
--num_process NUM_PROCESS
The maximum number of threads will be used. All CPUs will be used if it is None. Defaults to None
--topk_or_greedy_search {topk,greedy}
Topk searching or greedy searching to label a contig. Defaults to "topk".
--topK_num TOPK_NUM During the top-k searching approach, the default behavior is to search for the top-k nodes that exhibit the highest cosine similarity with the contig's encoded
vector. By default, the value of k is set to 3, meaning that the three most similar nodes in terms of cosine similarity will be considered for labeling the contig.
Please note that this parameter does not have any effect when using the greedy search approach (topK_num=1). Defaults to 3.
--temp_output_folder TEMP_OUTPUT_FOLDER
The temporary files generated during the process can be stored in a specified folder path. By default, if no path is provided (i.e., set to None), the temporary
files will be stored in the parent folder of the '--input_path' location. However, you have the option to specify a different folder path to store these temporary
files if needed.
--db_folder_path DB_FOLDER_PATH
The path of the database folder. Defaults to None. It is expected that the environmental variable 'DeepurifyInfoFiles' has been set
to point to the appropriate folder if no path is provided (i.e., set to None). Please ensure the 'DeepurifyInfoFiles' environmental variable is correctly configured if the path is not explicitly
provided.
--model_weight_path MODEL_WEIGHT_PATH
The path of model weight. (In database folder) Defaults to None.
--taxo_vocab_path TAXO_VOCAB_PATH
The path of taxon vocabulary. (In database folder) Defaults to None.
--taxo_tree_path TAXO_TREE_PATH
The path of taxonomic tree. (In database folder) Defaults to None.
Please run 'deepurify clean -h' for more details.
2. You can run Deepurify with 'clean' mode through the cleanMAGs function in Python.
from Deepurify.clean_func import cleanMAGs
if __name__ == "__main__":
input_folder = "./input_folder/"
bin_suffix = "fasta"
output_folder = "./output_folder/"
cleanMAGs(
output_bin_folder_path=output_folder,
batch_size_per_gpu=4,
each_gpu_threads=1,
input_bins_folder=input_folder,
bin_suffix=bin_suffix,
gpu_work_ratio=[0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125], # enable 8 GPUs with equal work ratios.
db_files_path="./Deepurify-DB/"
)
1. You can run Deepurify with 're-bin' mode through the following command:
deepurify re-bin -c ./contigs.fasta -o ./output_folder/ -s ./sorted.bam --gpu_num 1 --each_gpu_threads 1 --db_folder_path /path/of/this/Deepurify-DB/
usage: deepurify re-bin [-h] -c CONTIGS PATH -s SORTED_BAM_PATH -o OUTPUT_PATH [--binning_mode BINNING_MODE] [--gpu_num GPU_NUM] [--batch_size_per_gpu BATCH_SIZE_PER_GPU]
[--each_gpu_threads EACH_GPU_THREADS] [--overlapping_ratio OVERLAPPING_RATIO] [--cut_seq_length CUT_SEQ_LENGTH] [--mag_length_threshold MAG_LENGTH_THRESHOLD]
[--num_process NUM_PROCESS] [--topk_or_greedy_search {topk,greedy}] [--topK_num TOPK_NUM] [--temp_output_folder TEMP_OUTPUT_FOLDER]
[--db_folder_path DB_FOLDER_PATH] [--model_weight_path MODEL_WEIGHT_PATH] [--taxo_vocab_path TAXO_VOCAB_PATH] [--taxo_tree_path TAXO_TREE_PATH]
optional arguments:
-h, --help show this help message and exit
-c CONTIGS PATH, --contigs_path CONTIGS PATH
The contigs fasta path.
-s SORTED_BAM_PATH, --sorted_bam_path SORTED_BAM_PATH
The sorted bam path.
-o OUTPUT_PATH, --output_path OUTPUT_PATH
The folder used to output cleaned MAGs.
--binning_mode BINNING_MODE
If this parameter is None, then the semibin2, concoct, and metabat2 will all run. Defaults to None. The other modes are: 'semibin2', 'concoct', and 'metabat2'.
--gpu_num GPU_NUM The number of GPUs to be used can be specified. Defaults to 1. If you set it to 0, the code will utilize the CPU. However, please note that using the CPU can
result in significantly slower processing speed. It is recommended to provide at least one GPU (>= GTX-1060-6GB) for accelerating the speed.
--batch_size_per_gpu BATCH_SIZE_PER_GPU
The batch size per GPU determines the number of sequences that will be loaded onto each GPU. This parameter is only applicable if the --gpu_num option is set to a
value greater than 0. The default value is 4, meaning that one sequence will be loaded per GPU batch. The batch size for the CPU is 4.
--each_gpu_threads EACH_GPU_THREADS
The number of threads per GPU (or CPU) determines the parallelism level during the contigs' inference stage. If the value of --gpu_num is greater than 0, each GPU will
have a set number of threads to do inference. Similarly, if --gpu_num is set to 0 and the code will run on the CPU, the specified number of threads will be used. By
default, the number of threads per GPU or CPU is set to 1. The --batch_size_per_gpu value will be divided by the number of threads to determine the batch size per
thread.
--overlapping_ratio OVERLAPPING_RATIO
The --overlapping_ratio is a parameter used when the length of a contig exceeds the specified --cut_seq_length. By default, the overlapping ratio is set to 0.5.
This means that when a contig is longer than the --cut_seq_length, it will be split into overlapping subsequences with 0.5 overlaps between consecutive
subsequences.
--cut_seq_length CUT_SEQ_LENGTH
The --cut_seq_length parameter determines the length at which a contig will be cut if its length exceeds this value. The default setting is 8192, which is also the
maximum length allowed during training. If a contig's length surpasses this threshold, it will be divided into smaller subsequences with lengths equal to or less
than the cut_seq_length.
--mag_length_threshold MAG_LENGTH_THRESHOLD
The threshold for the total length of a MAG's contigs is used to filter generated MAGs after applying single-copy genes (SCGs). The default threshold is set to
200,000, which represents the total length of the contigs in base pairs (bp). MAGs with a total contig length equal to or greater than this threshold will be
considered for further analysis or inclusion, while MAGs with a total contig length below the threshold may be filtered out.
--num_process NUM_PROCESS
The maximum number of threads will be used. All CPUs will be used if it is None. Defaults to None
--topk_or_greedy_search {topk,greedy}
Topk searching or greedy searching to label a contig. Defaults to "topk".
--topK_num TOPK_NUM During the top-k searching approach, the default behavior is to search for the top-k nodes that exhibit the highest cosine similarity with the contig's encoded
vector. By default, the value of k is set to 3, meaning that the three most similar nodes in terms of cosine similarity will be considered for labeling the contig.
Please note that this parameter does not have any effect when using the greedy search approach (topK_num=1). Defaults to 3.
--temp_output_folder TEMP_OUTPUT_FOLDER
The temporary files generated during the process can be stored in a specified folder path. By default, if no path is provided (i.e., set to None), the temporary
files will be stored in the parent folder of the '--input_path' location. However, you have the option to specify a different folder path to store these temporary
files if needed.
--db_folder_path DB_FOLDER_PATH
The path of the database folder. Defaults to None. It is expected that the environmental variable 'DeepurifyInfoFiles' has been set
to point to the appropriate folder if no path is provided (i.e., set to None). Please ensure the 'DeepurifyInfoFiles' environmental variable is correctly configured if the path is not explicitly
provided.
--model_weight_path MODEL_WEIGHT_PATH
The path of model weight. (In database folder) Defaults to None.
--taxo_vocab_path TAXO_VOCAB_PATH
The path of taxon vocabulary. (In database folder) Defaults to None.
--taxo_tree_path TAXO_TREE_PATH
The path of taxonomic tree. (In database folder) Defaults to None.
Please run 'deepurify re-bin -h' for more details.
2. You can run the Deepurify with 're-bin' mode through the cleanMAGs function in Python.
from Deepurify.clean_func import cleanMAGs
if __name__ == "__main__":
contig_fasta_path = "./contigs.fasta"
bam_file_path = "./sorted.bam"
output_folder = "./output_folder/"
cleanMAGs(
output_bin_folder_path=output_folder,
batch_size_per_gpu=4,
each_gpu_threads=1,
contig_fasta_path=contig_fasta_path,
sorted_bam_file=bam_file_path,
gpu_work_ratio=[0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.25], # enable 8 GPUs with equal work ratios.
db_files_path="./Deepurify-DB/"
)
Please refer to the documentation of this function for more details.
This file contains the following columns:
- MAG name (first column),
- completeness of MAG (second column),
- contamination of MAG (third column),
- MAG quality (fourth column),
- System: Linux
- CPU: No restriction.
- RAM: >= 32 GB
- GPU: The GPU memory must be equal to or greater than 6GB. (5273MB)
This system can run the deepurify commands in the "Running Deepurify" section.
- System: NVIDIA DGX Server Version 5.5.1 (GNU/Linux 5.4.0-131-generic x86_64)
- CPU: AMD EPYC 7742 64-Core Processor (2 Sockets)
- RAM: 1TB
- GPU: 8 GPUs (A100-40GB)
This system can run the configuration in the main.py file.
cleanMAGs(
output_bin_folder_path=cur_output_folder,
batch_size_per_gpu=48,
each_gpu_threads=4,
# setting of contig inference stage
contig_fasta_path=cur_input_contigs,
sorted_bam_file=cur_bam,
gpu_work_ratio=[0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125],
num_process=256,
db_files_path="./GTDB_Taxa_Info/"
)