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GaetanBenoitDev / metaMDBG

MetaMDBG: a lightweight assembler for long and accurate metagenomics reads.
MIT License
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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] 31/07/2024: MetaMDBG is in version 1.0: it can now handle nanopore R10.4+ data. Is it available on bioconda!

  • Added minimizer-space correction step
  • MetaMDBG parameters changed (options now use explicit names)
  • Contig information format changed in final assemblty fasta file (see Readme - "Contig information")
  • Removed rescued circular step
  • Removed dependencies to samtools and wfmash
  • Fixed missing "time" dependencies in conda recipe
  • Fixed bug in gfa command

Installation

Conda

conda install -c conda-forge -c bioconda metamdbg

Building from source (using conda)

See details

Choose an installation directory, then copy-paste the following commands. ```sh # 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 metamdbg conda env config vars set CPATH=${CONDA_PREFIX}/include:${CPATH} conda deactivate # Activate metaMDBG environment conda activate metamdbg # Compile the software mkdir build cd build cmake .. make -j 3 ``` After successful installation, an executable named metaMDBG will appear in ./build/bin.

Building from source

See details

**Prerequisites** - gcc 9.4+ - cmake 3.10+ - zlib - openmp - minimap2 2.24+

```sh git clone https://github.com/GaetanBenoitDev/metaMDBG.git cd metaMDBG mkdir build cd build cmake .. make -j 3 ```

Usage

Usage:  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-hihi 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 4

MetaMDBG will generate polished contigs in outputDir ("contigs.fasta.gz").

Contig information

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.

Advanced usage

Generating an assembly graph

After 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 4

Assembly 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 0

Then, choose a k value and produce the graph (optionnaly add parameters --contigpath and/or --readpath).

metaMDBG gfa --assembly-dir ./assemblyDir/ --k 21

MetaMDBG 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.

Low-memory contig polisher

Results

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.

License

metaMDBG is freely available under the MIT License.

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