Automated long-read metagenomics workflow, using either PacBio HiFi or Nanopore sequencing reads as input to generate characterized MAGs.
The mmlong2 workflow is a continuation of mmlong.
- Snakemake workflow running dependencies from a Singularity container for enhanced reproducibility
- Bioinformatics tool and parameter optimizations for high complexity metagenomics samples
- Circular prokaryotic genome extraction as separate genome bins
- Eukaryotic contig removal for reduced prokaryotic genome contamination
- Differential coverage support for improved prokaryotic genome recovery
- Iterative ensemble binning strategy for improved prokaryotic genome recovery
- Genome quality classification according to MIMAG guidelines
- Expanded prokaryotic genome quality assessment, including microdiversity approximation and chimerism checks
- Taxonomic classification at prokaryotic genome, contig and 16S rRNA levels
- Generation of analysis-ready dataframes at genome bin and contig levels
The recommended way of installing mmlong2 is by setting up a Conda environment through Bioconda:
mamba install -c bioconda mmlong2
A local Conda environment with the latest workflow code can also be created by using the following code:
mamba create --prefix mmlong2 -c conda-forge -c bioconda snakemake=8.2.3 singularity=3.8.6 zenodo_get pv pigz tar yq ncbi-amrfinderplus -y
mamba activate ./mmlong2 || source activate ./mmlong2
git clone https://github.com/Serka-M/mmlong2 mmlong2/repo
cp -r mmlong2/repo/src/* mmlong2/bin
chmod +x mmlong2/bin/mmlong2
mmlong2 -h
Bioinformatics tools and other software dependencies will be automatically installed when running the workflow for the first time.
By default, a pre-built Singularity container will be downloaded and set up, although pre-defined Conda environments can also be used by running the workflow with the --conda_envs_only setting.
To acquire prokaryotic genome taxonomy and annotation results, databases are necessary and can be automatically installed by running the following command:
mmlong2 --install_databases
If some of the databases are already installed, they can also be used by the workflow without downloading (e.g. --database_gtdb option). Alternatively, a guide for manual database installation is also provided.
mmlong2-lite -np nanopore_reads.fastq.gz -o output_dir -p 100
MAIN INPUTS:
-np --nanopore_reads Path to Nanopore reads
-pb --pacbio_reads Path to PacBio HiFi reads
-o --output_dir Output directory name (default: mmlong2)
-p --processes Number of processes/multi-threading (default: 3)
OPTIONAL SETTINGS:
-db --install_databases Install missing databases used by the workflow
-dbd --database_dir Output directory for database installation (default: current working directory)
-cov --coverage CSV dataframe for differential coverage binning (e.g. NP/PB/IL,/path/to/reads.fastq)
-run --run_until Run pipeline until a specified stage completes (e.g. assembly polishing filtering singletons coverage binning taxonomy annotation extraqc stats)
-tmp --temporary_dir Directory for temporary files (default: current working directory)
-dbg --use_metamdbg Use metaMDBG for assembly of PacBio reads (default: use metaFlye)
-med --medaka_model Medaka polishing model (default: r1041_e82_400bps_sup_v5.0.0)
-mo --medaka_off Do not run Medaka polishing with Nanopore assemblies (default: use Medaka)
-vmb --use_vamb Use VAMB for binning (default: use GraphMB)
-sem --semibin_model Binning model for SemiBin (default: global)
-mlc --min_len_contig Minimum assembly contig length (default: 3000)
-mlb --min_len_bin Minimum genomic bin size (default: 250000)
-rna --database_rrna 16S rRNA database to use
-gunc --database_gunc Gunc database to use
-bkt --database_bakta Bakta database to use
-kj --database_kaiju Kaiju database to use
-gtdb --database_gtdb GTDB-tk database to use
-h --help Print help information
-v --version Print workflow version number
ADVANCED SETTINGS:
-fmo --flye_min_ovlp Minimum overlap between reads used by Flye assembler (default: auto)
-fmc --flye_min_cov Minimum initial contig coverage used by Flye assembler (default: 3)
-env --conda_envs_only Use conda environments instead of container (default: use container)
-n --dryrun Print summary of jobs for the Snakemake workflow
-t --touch Touch Snakemake output files
-r1 --rule1 Run specified Snakemake rule for the MAG production part of the workflow
-r2 --rule2 Run specified Snakemake rule for the MAG processing part of the workflow
-x1 --extra_inputs1 Extra inputs for the MAG production part of the Snakemake workflow
-x2 --extra_inputs2 Extra inputs for the MAG processing part of the Snakemake workflow
-xb --extra_inputs_bakta Extra inputs (comma-separated) for MAG annotation using Bakta
To perform genome recovery with differential coverage, prepare a 2-column comma-separated dataframe, indicating the additional read datatype (NP for Nanopore, PB for PacBio, IL for short reads) and read file location.
Dataframe example:
PB,/path/to/your/reads/file1.fastq
NP,/path/to/your/reads/file2.fastq
IL,/path/to/your/reads/file3.fastq.gz
The prepared dataframe can be provided to the workflow through the -cov option.
<output_name>_assembly.fasta- assembled and polished metagenome<output_name>_16S.fa- 16S rRNA sequences, recovered from the polished metagenome<output_name>_bins.tsv- per-bin results dataframe<output_name>_contigs.tsv- per-contig results dataframe<output_name>_general.tsv- workflow result summary as a single row dataframedependencies.csv- list of dependencies used and their versionsbins- directory for metagenome assembled genomesbakta- directory, containing bin annotation results from bakta
Suggestions on improving the workflow or fixing bugs are always welcome.
Please use the GitHub Issues section or e-mail to mase@bio.aau.dk for providing feedback.