GitHub repository for the manuscript "Effective binning of metagenomic contigs using COntrastive Multi-viEw representation learning".
- Overview
- System Requirements
- Install COMEBin via bioconda
- Install COMEBin via source code
- A test dataset to demo COMEBin
- Preprocessing
- How to run COMEBin
- References
- Contacts and bug reports
The framework of COMEBin is shown in the following figure, which is mainly divided into the following steps: 1) Data augmentation: construct five sets of augmented data by randomly extracting subsequences from the original contigs, resulting in six views for each original contig; 2) Construct feature vector: construct nucleotide frequency feature and coverage feature for each contig (including the original sequences and augmented sequences); 3) Contrastive learning: obtain low-dimensional embedding representations suitable for binning with heterogeneous information based on multi-view contrastive learning, and 4) Clustering: generate binning results based on community division algorithm Leiden.
Among them, the network structure used in contrastive learning includes two parts: 1) "Coverage network": process coverage features and 2) "Combine network": integrate the k-mer features and the "Coverage network" to obtain representations containing heterogeneous information.
COMEBin requires only a standard computer with enough RAM to support the in-memory operations.
COMEBin v1.0.0 is supported and tested in Linux systems.
COMEBin can be installed as a Bioconda's package.
To run COMEBin with CPU only:
conda create -n comebin_env
conda activate comebin_env
conda install -c conda-forge -c bioconda comebinTo run COMEBin with GPU (which provides faster performance when a GPU is available), you should also install PyTorch with GPU support:
conda create -n comebin_env
conda activate comebin_env
conda install -c conda-forge -c bioconda comebin
conda install pytorch pytorch-cuda=11.8 -c pytorch -c nvidia -c conda-forgeYou can also install COMEBin from the source code. After installing Anaconda (or miniconda), first, obtain COMEBin:
git clone https://github.com/ziyewang/COMEBin.gitThen, create an environment to run COMEBin.
cd path_to_COMEBin
conda env create -f comebin_env.yaml
conda activate comebin_envWe provide a small dataset to demo and test the software. Test data is available at https://drive.google.com/file/d/1xWpN2z8JTaAzWW4TcOl0Lr4Y_x--Fs5s/view?usp=sharing. The inputs for COMEBin include contigs and BAM files (reads mapping to the contigs).
Contig file: comebin_test_data/BATS_SAMN07137077_METAG.scaffolds.min500.fasta.f1k.fasta
BAM files: comebin_test_data/bamfiles/SRR5720343.bamRun COMEBin on the test dataset:
cd path_to_COMEBin/COMEBin
CUDA_VISIBLE_DEVICES=0 bash run_comebin.sh -a path_to_comebin_test_data/BATS_SAMN07137077_METAG.scaffolds.min500.fasta.f1k.fasta \
-p path_to_comebin_test_data/bamfiles \
-o path_to_comebin_test_data/run_comebin_test \
-n 6 \
-t 40Excepted output is given in path_to_comebin_test_data/run_comebin_test
Final result (bins): path_to_comebin_test_data/run_comebin_test/comebin_res/comebin_res_bins
Final result in tsv format: path_to_comebin_test_data/run_comebin_test/comebin_res/comebin_res.tsvThe preprocessing steps aim to generate bam files as input to our program.
Several binning methods can generate bam files by aligning reads to contigs (such as MetaWRAP), and we provide one way to generate the input files as follows.
To generate bam files from sequencing reads directly, run the script slightly modified from the "binning.sh" of MetaWRAP. The script supports different types of sequencing reads, and the default type is "paired" ([readsX_1.fastq readsX_2.fastq ...]).
cd path_to_COMEBin
cd COMEBin/scripts
bash gen_cov_file.sh -a contig_file \
-o output_dir_of_bamfiles \
path_to_sequencing_reads/*fastq
Options:
-a STR metagenomic assembly file
-o STR output directory (to save the coverage files)
-b STR directory for the bam files (optional)
-t INT number of threads (default=1)
-m INT amount of RAM available (default=4)
-l INT minimum contig length to the bin (default=1000bp).
--single-end non-paired reads mode (provide *.fastq files)
--interleaved input read files contain interleaved paired-end reads
-f Forward read suffix for paired reads (default="_1.fastq")
-r Reverse read suffix for paired reads (default="_2.fastq")
And the users can run the following command to keep the contigs longer than 1000bp for binning.
cd path_to_COMEBin
cd COMEBin/scripts
python Filter_tooshort.py final.contigs.fa 1000conda activate comebin_env
run_comebin.sh -a ${contig_file} \
-o ${output_path} \
-p ${path_to_bamfiles} \
-t 40conda activate comebin_env
cd path_to_COMEBin/COMEBin
bash run_comebin.sh -a ${contig_file} \
-o ${output_path} \
-p ${path_to_bamfiles} \
-t 40Usage: run_comebin.sh [options] -a contig_file -o output_dir -p bam_file_path
Options:
-a STR metagenomic assembly file
-o STR output directory
-p STR path to access to the bam files
-n INT number of views for contrastive multiple-view learning (default=6)
-t INT number of threads (default=5)
-l FLOAT temperature in loss function (default=0.07 for assemblies with an N50 > 10000, default=0.15 for others)
-e INT embedding size for combine network (default=2048)
-c INT embedding size for coverage network (default=2048)
-b INT batch size for training process (default=1024)[1] Meyer F, Fritz A, Deng Z L, et al. Critical assessment of metagenome interpretation: the second round of challenges[J]. Nature methods, 2022, 19(4): 429-440.
[2] Parks D H, Imelfort M, Skennerton C T, et al. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes[J]. Genome research, 2015, 25(7): 1043-1055.
[3] https://github.com/dparks1134/UniteM.
[4] Pan S, Zhu C, Zhao X M, et al. A deep siamese neural network improves metagenome-assembled genomes in microbiome datasets across different environments[J]. Nature communications, 2022, 13(1): 2326.
Please feel free to send bug reports or questions to Ziye Wang: zwang17@fudan.edu.cn and Prof. Shanfeng Zhu: zhusf@fudan.edu.cn
Wang, Z., You, R., Han, H. et al. Effective binning of metagenomic contigs using contrastive multi-view representation learning. Nat Commun 15, 585 (2024). https://doi.org/10.1038/s41467-023-44290-z