nf-core/mag

Use this to specify the location of your input FastQ files. For example:

--input 'path/to/data/sample_*_{1,2}.fastq.gz'  

Alternatively, to assign different groups or to include long reads for hybrid assembly with metaSPAdes, you can specify a CSV samplesheet input file with 5 columns and the following header: sample,group,short_reads_1,short_reads_2,long_reads. See usage docs.

By default, the pipeline expects paired-end data. If you have single-end data, you need to specify --single_end on the command line when you launch the pipeline. A normal glob pattern, enclosed in quotation marks, can then be used for --input. For example:

--single_end --input '*.fastq'  

It is not possible to run a mixture of single-end and paired-end files in one run.

Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (~/.nextflow/config) then you don't need to specify this on the command line for every run.

MEGAHIT only generates reproducible results when run single-threaded.

When using this parameter do not change the number of CPUs for the megahit process with a custom config file. This would result in an error.

Default: The number of CPUs is specified in the base.config file, and increased with each retry.

SPAdes is designed to be deterministic for a given number of threads. To generate reproducible results fix the number of CPUs using this parameter.

When using this parameter do not change the number of CPUs for the spades process with a custom config file. This would result in an error.

Default: -1 (the number of CPUs is specified in the base.config or in a custom config file, and increased with each retry).

SPAdes is designed to be deterministic for a given number of threads. To generate reproducible results fix the number of CPUs using this parameter.

When using this parameter do not change the number of CPUs for the spadeshybrid process with a custom config file. This would result in an error.

Default: -1 (the number of CPUs is specified in the base.config or in a custom config file, and increased with each retry).

MetaBAT2 is run by default with a fixed seed within this pipeline, thus producing reproducible results. You can set it also to any other positive integer to ensure reproducibility. Set the parameter to 0 to use a random seed.

Default base-quality trimming is set to trim by 'windows', as in FastP. Specifying this flag will use trim via contiguous stretch of low quality bases (Ns) instead.

Replaces --trimwindows 4 with --trimqualities in AdapterRemoval

This parameter is mutually exclusive with --host_genome. Host read removal is done with Bowtie2.
Both the iGenomes FASTA file as well as corresponding, already pre-built Bowtie 2 index files will be used.

This parameter is mutually exclusive with --host_fasta. The reference can be masked. Host read removal is done with Bowtie2.

The default value focuses on length instead of quality to improve assembly size.
In order to assign equal weights to read lengths and read qualities set this parameter to 1.
This might be useful, for example, to benefit indirectly from the removal of short host reads (causing lower qualities for reads not overlapping filtered short reads).

E.g. ftp://ftp.ccb.jhu.edu/pub/infphilo/centrifuge/data/p_compressed+h+v.tar.gz.

The database file must be a compressed tar archive that contains at least the three files hash.k2d, opts.k2d and taxo.k2d. E.g. ftp://ftp.ccb.jhu.edu/pub/data/kraken2_dbs/minikraken_8GB_202003.tgz.

E.g. https://tbb.bio.uu.nl/bastiaan/CAT_prepare/CAT_prepare_20210107.tar.gz. This parameter is mutually exclusive with --cat_db_generate. The file needs to contain a folder named *taxonomy* and *database* that hold the respective files.

Download the taxonomy files from NCBI taxonomy, the nr database and generate CAT database. This parameter is mutually exclusive with --cat_db. Useful to build a CAT database with the same DIAMOND version as used for running CAT classification, avoiding compatibility problems.

Useful to allow reproducibility, as old versions of prebuild CAT databases do not always remain accessible and underlying NCBI taxonomy and nr databases change.

For information which GTDB reference databases are compatible with the used GTDB-tk version see https://ecogenomics.github.io/GTDBTk/installing/index.html#gtdb-tk-reference-data.

Completeness assessed with BUSCO analysis (100% - %Missing). Must be greater than 0 (min. 0.01) to avoid GTDB-tk errors. If too low, GTDB-tk classification results can be impaired due to not enough marker genes!

Contamination approximated based on BUSCO analysis (%Complete and duplicated). If too high, GTDB-tk classification results can be impaired due to contamination!

A low number of CPUs helps to reduce the memory required/reported by GTDB-Tk. See also the GTDB-Tk documentation.

Will be slower. Set to false to turn this off.

An example is adjusting k-mers ("-k 21,33,55,77") or adding advanced options. But not -t, -m, -o or --out-prefix, because these are already in use.

An example is adjusting presets (e.g. "--presets meta-large"), k-mers (e.g. "-k 21,33,55,77") or adding other advanced options. For example, increase the minimum k-mer in the event of an error message such as "Too many vertices in the unitig graph, you may increase the kmer size to remove tons of erroneous kmers." in the MEGAHIT log file. But not --threads, --memory, -o or input read files, because these are already in use.

Available: all, group or own. Note that own cannot be specified in combination with --coassemble_group.

Note that specifying all without additionally specifying --coassemble_group results in n^2 mapping processes for each assembly method, where n is the number of samples.

For forwarding into downstream analysis, i.e. QUAST and BUSCO, and reporting.

Contigs that do not fulfill the thresholds of --min_length_unbinned_contigs and --max_unbinned_contigs are pooled for downstream analysis and reporting, except contigs that also do not fullfill --min_contig_size are not considered further.

Contigs that do not fulfill the thresholds of --min_length_unbinned_contigs and --max_unbinned_contigs are pooled for downstream analysis and reporting, except contigs that also do not fullfill --min_contig_size are not considered further.

Bowtie2 alignment mode options, for example: --very-fast , --very-sensitive-local -N 1 , ...

E.g. https://busco-data.ezlab.org/v5/data/lineages/bacteria_odb10.2020-03-06.tar.gz. Available databases are listed here: https://busco-data.ezlab.org/v5/data/lineages/.

If provided, BUSCO analysis will be run in offline mode. Data can be downloaded from https://busco-data.ezlab.org/v5/data/ (files still need to be unpacked manually). Run in combination with automated lineage selection.

Useful to allow reproducibility, as BUSCO datasets are frequently updated and old versions do not always remain accessible.

By default, BUSCO creates a large number of intermediate files every run. This may cause problems on some clusters which have file number limits in plate, particularly with large numbers of bins. Enabling this option cleans these files, reducing the total file count of the work directory.

The pipeline can also download this for you if not specified, and you can save the resulting directory into your output directory by specifying --save_checkm_data. You should move this directory to somewhere else on your machine (and supply back to the pipeline in future runs again with --checkm_db.

If specified, the directories and files decompressed from the tar.gz file downloaded from the CheckM FTP server will be stored in your output directory alongside your CheckM results.

Score threshold for single-copy gene selection algorithm to keep selecting bins, with a value ranging from 0-1.

For description of scoring algorithm, see: Sieber, Christian M. K., et al. 2018. Nature Microbiology 3 (7): 836–43. https://doi.org/10.1038/s41564-018-0171-1.

Modifies DAS Tool parameter --score_threshold

raw_bins_only: only bins (and unbinned contigs) from the binners.
refined_bins_only: only bins (and unbinned contigs) from the bin refinement step .
both: bins and unbinned contigs from both the binning and bin refinement steps.

If specified, the corresponding DIAMOND file downloaded from the GUNC server will be stored in your output directory alongside your GUNC results.