The VMGC is a large-scale reference genome resource of the human vaginal microbiome, including over 33,000 genomes derived from 786 prokaryotes, 11 fungi, and 4,263 viruses associated with the human vagina. In terms of representation, the VMGC demonstrates high efficiency in capturing microbial sequences, with a median mapping rate of 91.7% across 4,472 vaginal metagenomic samples obtained from 14 countries.
Huang L, Guo R, Li S, Wu X, Zhang Y, Guo S, Lv Y, Xiao Z, Kang J, Meng J, Zhou P, Ma J, You W, Zhang Y, Yu H, Zhao J, Huang G, Duan Z, Yan Q, Sun W. 2024. A multi-kingdom collection of 33,804 reference genomes for the human vaginal microbiome. Nature Microbiology doi:10.1038/s41564-024-01751-5.
The microbial sequence data for VMGC is stored at https://zenodo.org/records/10457006, including the data presented in the table below.
| Description | Size | Filename |
|---|---|---|
| Full prokaryotic genomes (n=19,542) | 8.1 GB | VMGC_prokaryote_MAG.tar.gz |
| Annotations of prokaryotic genomes (Source/Quality/Clustering...) | 2.3 MB | VMGC_prokaryote_MAG.info |
| Nonredundant prokaryotic genomes (n=786) | 440 MB | VMGC_prokaryote_SGB.tar.gz |
| Annotations of nonredundant prokaryotic genomes (Taxonomy/...) | 120 KB | VMGC_prokaryote_SGB.info |
| Full eukaryotic genomes (n=42, including 4 from parasites) | 192 MB | VMGC_eukaryocyte.tar.gz |
| Annotations of eukaryotic genomes (Taxonomy/Quality/Clusting/...) | 11 KB | VMGC_eukaryocyte.info |
| Full viral genomes (n=14,224) and vOTUs (n=4,263) | 197 MB | VMGC_virus.tar.gz |
| Annotations of eukaryotic genomes (Taxonomy/Quality/Clusting/Host...) | 969 KB | VMGC_virus.info |
| Kraken & Bracken database | 2.1 GB | VMGC_prokaryote_SGB_KrakenDB.tar.gz |
Metagenome-assembled genomes in the VMGC were obtained through a process that integrated both single-coverage binning and Mash-based multiple-coverage binning methods. The specific operational steps are outlined below.
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Required dependencies
Mash 2.3
GNU parallel 20201122
bwa-mem2 2.2.1
MetaBAT v2
perl 5.16.3
combine.pl -
Step 1: Calculate the Mash distances between the assembled files, and obtain the top 20 closest samples for each assembled sample.
mash sketch -p 50 -s 100000 -k 32 -o mash.sketch contigs/*.fasta
find contigs/*.fasta | parallel -k -j 10 mash dist mash.sketch.msh {} \| sort -nk3 \| head -n 20 \| sed -e "s/contigs\\\///g" -e "s/.fasta//g" > mash.sketch.msh.top20
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Step 2: For a assembly file, clean reads from the top 20 closest samples (including its own reads) by Mash distance were used to calculate the sequencing depth of contigs.
find contigs/*.fasta | parallel --colsep '\t' -j 20 bwa-mem2 index -p {} {}
cat mash.sketch.msh.top20 | parallel --colsep '\t' -j 10 mkdir -p depth/{2} \; bwa-mem2 mem -t 10 contigs/{2} clean_reads/{1}.1.fq.gz clean_reads/{1}.2.fq.gz \| samtools view -bS - -@ 10 \| samtools sort -@ 10 -o depth/{2}/{1}.sort.bam \&\& jgi_summarize_bam_contig_depths --outputDepth depth/{2}/{1}.sort.bam.depth depth/{2}/{1}.sort.bam
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Step 3: Integrate depth calculation files using the public script combine.pl, and perform multiple-coverage binning.
find depth/* -type d | parallel -j 10 combine.pl {}/*.sort.bam.depth \> {}.depth
find depth/*.depth | parallel -j 10 mkdir -p bins/{/.}/ \; metabat2 -i contigs/{/.}.fasta -a {} -o bins/{/.}/{/.}.mbin -m 2000 -s 200000 --saveCls --unbinned --seed 2020
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Step 4: Perform single-coverage binning.
find depth/* -type d | parallel -j 10 metabat2 -i contigs/{/.}.fasta -a depth/{/.}/{/.}.depth -o bins/{/.}/{/.}.sbin -m 2000 -s 200000 --saveCls --unbinned --seed 2020
Based on the 786 species-level genome bins (SGBs) in the VMGC, we reconstructed the prokaryotic composition of the vagina using Kraken2 and Bracken tools.
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Required dependencies
Kraken 2.1.3
Bracken 2.8
Python 3.8.16
R 4.2.3
GNU parallel 20201122
kraken_MAGdb.sh -
Step 1: Create customized Kraken2 and Bracken databases.
1. The information on "sgb.info" is derived from the annotation results of GTDB-tk. 2. "sgb.seq" includes the file paths for the genomes of SGBs. 3. "KBdb" is the output folder path, and the generated data includes the following files: >tree KBdb/ KBdb/ ├── database150mers.kmer_distrib ├── database150mers.kraken ├── database.kraken ├── hash.k2d ├── library │ └── added │ ├── nFPH00iyWZ.fna │ ├── nFPH00iyWZ.fna.masked │ ├── prelim_map.txt │ └── prelim_map_ZLPtIxoxoZ.txt ├── opts.k2d ├── seqid2taxid.map ├── taxo.k2d └── taxonomy ├── db.accession2taxid ├── names.dmp ├── nodes.dmp └── prelim_map.txt -
Step 2: The clean reads from each sample are mapped to the database, generating compositions at various taxonomic levels.
find clean_reads/*.1.fq.gz | sed 's/.1.fq.gz//'| parallel -j 5 kraken2 --threads 10 --confidence 0.1 --db KBdb --report prof/{/}.report --report-minimizer-data --output prof/{/}.output {}.1.fq.gz {}.2.fq.gz
find prof/*.report | parallel -j 5 bracken -d KBdb -i {} -o {}.bracken -r 150 -l S -t 1
We utilized the aforementioned process, which integrates both single-sample binning and Mash-based multiple-sample binning methods, to generate raw bins. Subsequently, we employed EukRep and BUSCO tools to extract fungal genomes from raw bins.
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Required dependencies
EukRep 0.6.7
BUSCO 5.4.2
perl 5.16.3
GNU parallel 20201122 -
Step 1: Remove the prokaryotic sequences from the raw bins.
find bins/*/*.[ms]bin.[0-9]*.fa -size +3M | parallel -j 20 EukRep --min 2000 -i {} -o euk_bin/{/.}.fa
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Step 2: Assess the genome quality of candidate bins, and extract fungal genomes.
find euk_bin/*.fa -size +3M | parallel -j 20 busco -m genome -l fungi_odb10 -i {} -q -o busco/{/.}
grep " C:" busco/*/*txt | perl -ne '/fungi_odb10.(.*).txt.*C:(.*?)%.*D:(.*?)%/;print "ln -s euk_bin/$1.fa fungal_bins/\n" if $2>50 and $3<5' |sh
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Required dependencies
seqkit 2.1.0
checkv 0.7.0
VIBRANT 1.2.1
DeepVirFinder 1.0
BLAST 2.12.0+
HMMER 3.3.2
MinCED 0.4.2
Prodigal 2.6.3
diamond 2.0.13.151
perl 5.16.3
Python 3.8.16
GNU Awk 4.0.2
GNU parallel 20201122
virusFind_flow.sh
virusClstr.sh
virusHost.sh
virusTaxAnno.sh -
Step 1: Extract the viral sequences from the assembly files.
find contigs/*.fasta | parallel -j 20 virusFind_flow.sh {} 5000 {/.} virus/{/.}
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Step 2: Cluster viral sequences to generate Viral Operational Taxonomic Units (vOTUs).
virusClstr.sh virus/*.virus.fa votu clstr 50
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Step 3: Taxonomic annotation for vOTUs.
prodigal -q -d clstr/votu.ffn -o clstr/votu.gff -p meta -a clstr/votu.faa -f gff
virusTaxAnno.sh clstr/votu.faa clstr/votu.fa tax/votu
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Step 4: Virus-Host prediction based on the 19,542 prokaryotic MAGs in the VMGC.
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Step 4.1: Find CRISPRs in all MAGs, and build BLAST databases.
find mag_fasta/*.fa | parallel -j 20 minced -minNR 2 {} csp/{/.}.csp csp/{/.}.gff
cat csp/*.csp |grep '^Sequence\|\[' | perl -ne 'if(/^Sequence.*/){/'\''(\S+)'\''/; $a=$1;$b=1}else{/(\d+)(\s+)(\S+)(\s+)(\S+)/;print ">$a.sp$b\n$5\n";$b++}' > csp.tot.fa
makeblastdb -in csp.tot.fa -dbtype nucl -out csp.tot.fa
cat mag_fasta/*.fa > mag.fa && makeblastdb -in mag.fa -dbtype nucl -out mag.fa
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Step 4.2: Predict hosts.
virusHost.sh clstr/votu.fa csp.tot.fa mag.fa mag.tax
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Note that the associated scripts nested within the shell scripts are also available on https://github.com/RChGO/VMGC/tree/main/Pipelines.
Correspondence and requests for materials should be addressed to grchun@hotmail.com