MetaMDBG is a fast and low-memory assembler for long and accurate metagenomics reads (e.g. PacBio HiFi, Nanopore r10.4). It is based on the minimizer de-Brujin graph (MDBG), which have been reimplemetend specifically for metagenomics assembly. MetaMDBG combines an efficient multi-k approach in minimizer-space for dealing with uneven species coverages, and a novel abundance-based filtering method for simplifying strain complexity.
The method nanoMDBG for assembling simplex Nanopore reads (R10.4+) is integrated in metaMDBG.
Developper: Gaëtan Benoit
Contact: gaetanbenoitdev at gmail dot com
Important
15/09/2025: MetaMDBG version 1.2 is out:
- Improved assembly quality (clipping events, dereplication, polishing, etc)
- Improved memory performances
- Added checkpoint system
conda install -c conda-forge -c bioconda metamdbgSee details
Choose an installation directory, then copy-paste the following commands.
# Download metaMDBG repository
git clone https://github.com/GaetanBenoitDev/metaMDBG.git
# Create metaMDBG conda environment
cd metaMDBG
conda env create -f conda_env.yml
conda activate metamdbg1.2
conda env config vars set CPATH=${CONDA_PREFIX}/include:${CPATH}
conda deactivate
# Activate metaMDBG environment
conda activate metamdbg1.2
# Compile the software
mkdir build
cd build
cmake ..
make -j 3After successful installation, an executable named metaMDBG will appear in ./build/bin.
See details
Prerequisites
- gcc 9.4+
- cmake 3.10+
- zlib
- openmp
- minimap2 2.28+
- GNU time
git clone https://github.com/GaetanBenoitDev/metaMDBG.git
cd metaMDBG
mkdir build
cd build
cmake ..
make -j 3Usage: metaMDBG asm {OPTIONS}
Basic options:
--out-dir Output dir for contigs and temporary files
--in-hifi PacBio HiFi read filename(s) (separated by space)
--in-ont Nanopore R10.4+ read filename(s) (separated by space)
--threads Number of cores [1]
# Nanopore assembly
metaMDBG asm --out-dir ./outputDir/ --in-ont reads.fastq.gz --threads 4
# Hifi assembly
metaMDBG asm --out-dir ./outputDir/ --in-hifi reads.fastq.gz --threads 4
# Multiple sample co-assembly
metaMDBG asm --out-dir ./outputDir/ --in-ont reads_A.fastq.gz reads_B.fastq.gz reads_C.fastq.gz --threads 4MetaMDBG will generate polished contigs in outputDir ("contigs.fasta.gz").
Contig information, such as whether it is circular or not, are contained in contig headers in the resulting assembly file. Examples:
>ctg112 length=7013 coverage=6 circular=yes
ACGTAGCTTATAGCGAGTATCG...
>ctg37 length=1988 coverage=3 circular=no
ATTATTGATTAGGGCTATGCAT...
>ctg82 length=3824 coverage=13 circular=no
AATTCCGGCGGCGTATTATTAC...Headers are composed of several fields seperated by space.
- ctgID: the name of the contig
- length: the length of the contig in bps
- coverage: an estimated read coverage for the contig
- circular: whether the contig is circular or no
If an assembly run stops for any reason, simply resubmit the same command. MetaMDBG will automatically skip completed steps and resume from the last checkpoint.
# Set minimizer length to 16 and use only 0.2% of total k-mers for assembly.
metaMDBG asm --out-dir ./outputDir/ --in-ont reads.fastq.gz --kmer-size 16 --density-assembly 0.002
# Stop assembly after reaching k-th iteration.
metaMDBG asm --out-dir ./outputDir/ --in-ont reads.fastq.gz --max-k 11
# Filter out unique k-min-mers to improve performances.
# Useful for scaling to very large datasets, but may reduce assembly quality and completeness.
# By default, metaMDBG attempts to rescue low-abundance genomic k-min-mers.
metaMDBG asm --out-dir ./outputDir/ --in-ont reads.fastq.gz --min-abundance 2
# Filter out reads with low average per-base quality (using phred score)
metaMDBG asm --out-dir ./outputDir/ --in-ont reads.fastq.gz --min-read-quality 10
# Skip correction step (for ONT data)
metaMDBG asm --out-dir ./outputDir/ --in-ont reads.fastq.gz --skip-correction
# Tune correction step (for ONT data).
# In this example, we recruit similar reads used for correcting a target read with minimum read
# overlap of 2000 bp and min identity of 97%, and we use 5% of k-mers for correction.
metaMDBG asm --out-dir ./outputDir/ --in-ont reads.fastq.gz --density-correction 0.05 --min-read-identity 0.97 --min-read-overlap 2000After a successful run of metaMDBG, assembly graph (.gfa) can be generated with the following command.
metaMDBG gfa --assembly-dir ./assemblyDir/ --k 21 --contigpath --readpath --threads 4Assembly dir must be a metaMDBG output dir (the one containing the contig file "contigs.fasta.gz"). The --k parameter correspond to the level of resolution of the graph: lower k values will produce graph with high connectivity but shorter unitigs, while higher k graphs will be more fragmented but with longer unitigs. The two optional parameters --contigpath and --readpath allow to generate the path of contigs and reads in the graph respectivelly.
First, display the available k values and their corresponding sequence length in bps (those sequence length in bps are equivalent to the k-mer size that would be used in a traditional de-Brujin graph).
metaMDBG gfa --assembly-dir ./assemblyDir/ --k 0Then, choose a k value and produce the graph (optionnaly add parameters --contigpath and/or --readpath).
metaMDBG gfa --assembly-dir ./assemblyDir/ --k 21MetaMDBG will generate the assembly graph in the GFA format in assemblyDir (e.g. "assemblyGraph_k21_4013bps.gfa").
Note 1) Unitig sequences in the gfa file are not polished, they have the same error rate as in the original reads. Note 2) To generate the unitig sequences, a pass on the original reads that generated the assembly is required, if you have moved the original readsets, you will need to edit the file ./assemblyDir/tmp/input.txt with the new paths. Note 3) In nanopore mode, the read-path are not very accurate because of the high error rate, we recommend using actual aligner instead, such as graphAligner.
Assembly quality and performances on three HiFi PacBio metagenomics samples (using 16 cores).
| Sample | Accession | # bases (Gb) | Wall clock time (h) | Peak memory (GB) | >1Mb near-complete circular contigs | Near-complete MAGs |
|---|---|---|---|---|---|---|
| Human Gut | SRR15275213 | 18.5 | 7 | 6 | 34 | 70 |
| Anaerobic Digester | ERR10905742 | 64.7 | 13 | 7 | 62 | 130 |
| Sheep rumen | SRR14289618 | 206.4 | 108 | 22 | 266 | 447 |
Near-complete: ≥90% completeness and ≤5% contamination (assessed by checkM). Binning was performed with metabat2.
metaMDBG is freely available under the MIT License.
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Gaetan Benoit, Sebastien Raguideau, Robert James, Adam M. Phillippy, Rayan Chikhi and Christopher Quince High-quality metagenome assembly from long accurate reads with metaMDBG, Nature Biotechnology (2023).
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Gaetan Benoit, Robert James, Sebastien Raguideau, Georgina Alabone, Tim Goodall, Rayan Chikhi and Christopher Quince High-quality metagenome assembly from nanopore reads with nanoMDBG, Biorxiv (2025).
