nf-core/magmap
Introduction
Magmap is a workflow designed for mapping metatranscriptomic and metagenomic reads onto a group of genomes.
The collection of genomes can either be specified directly using a table (see the --genomeinfo
parameter) or be the result of filtering with Sourmash.
The latter can use either the genomes specified by --genomeinfo
, a “sketch index” pointing to genomes available for instance at NCBI (see the --indexes
parameter) or a combination, to identify a smaller set to map to.
Genome files provided with --genominfo
must include contigs in fasta format and optionally gff files (Prokka format).
Any genome for which a gff file is missing will be annotated with Prokka.
The pipeline can take output files from CheckM, CheckM2 and GTDB-Tk as input, and will provide processed output from these tools.
Note that the pipeline can map to any collection of genomes, including single genomes and isolates.
Running the workflow
Quickstart
A typical command for running the workflow is:
nextflow run nf-core/magmap -profile docker --outdir results/ --input samples.csv --genomeinfo localgenomes.csv
Samplesheet input
You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It must be a comma-separated file with 3 columns, and a header row as shown in the examples below
sample,fastq_1,fastq_2
T0a,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
T0b,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz
T0c,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz
Multiple runs of the same sample
The sample
identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes:
sample,fastq_1,fastq_2
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz
CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz
Full samplesheet
The pipeline will auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 3 columns to match those defined in the table below.
A final samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 6 samples, where TREATMENT_REP3
has been sequenced twice.
sample,fastq_1,fastq_2
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
CONTROL_REP2,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz
CONTROL_REP3,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz
TREATMENT_REP1,AEG588A4_S4_L003_R1_001.fastq.gz,
TREATMENT_REP2,AEG588A5_S5_L003_R1_001.fastq.gz,
TREATMENT_REP3,AEG588A6_S6_L003_R1_001.fastq.gz,
TREATMENT_REP3,AEG588A6_S6_L004_R1_001.fastq.gz,
Column | Description |
---|---|
sample | Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (_ ). |
fastq_1 | Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”. |
fastq_2 | Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”. |
An example samplesheet has been provided with the pipeline.
Genomes input
Magmap needs two mandatory input to run. One, the samplesheet, was explained above while the second, genome input needs to be specified with the option --genomeinfo
. The file is a .csv
file and it requires three columns: accno, genome_fna, genome_gff. it looks as follow:
accno,genome_fna,genome_gff
GCA_002688505,./genomes/GCA_002688505.fna,./genomes/GCA_002688505.gff
GCA_002688515,./genomes/GCA_002688515.fna,
N.B.: you don’t need to provide gff, Prokka can handle if you only provide fasta files but we recommend when adding gff that the file is generated by using prokka in order to don’t get any conflict with other generated gffs.
Other inputs (optional)
Magmap can handle several types of input that can be used for different purpose.
Indexes input
The indexes input is used by Sourmash to select genomes that can downloaded in a second step and added to the pipeline.
It is provided with the --indexes
parameter and it is a path (local or remote).
https://github.com/nf-core/test-datasets/raw/magmap/testdata/sourmash_test.index.sbt.zip
N.B.: the sbt files can be generated with sourmash (check it here) or some can be found in sourmash documentation website. E.g. “sourmash gtdb sbt” you will find the indexes for gtdb.
metadata input
Magmap accepts several metadata as .csv
files that provides information about the genomes that you will use in the pipeline.
gtdb_metadata
This file contains several information that can be found in gtdb metadata on the official website. You can either use directly the same file found there or you can make a custom one.
gtdb-tk metadata
This file contains several information that can be obtained as gtdb-tk output. You can either use directly the same file found there or you can make a custom one.
checkM metadata
This file contains several information that can be obtained as checkM output. You can either use directly the same file found there or you can make a custom one.
Check duplicates
The pipeline is checking if there are any duplicate names among the genomes that the user provides. If there are duplicates, the pipeline will stop and return a file with the contig names that needs to be changed in their name in order to work. This is done to avoid overlapping in the following steps (e.g. same prokka output for the protein sequences and the gffs).
Filter/remove sequences from the samples (e.g. rRNA sequences with SILVA database)
The pipeline can remove potential contaminants using the BBduk program. Specify a fasta file, gzipped or not, with the —sequence_filter sequences.fasta parameter. For further documentation, see the BBduk official website.
nextflow run nf-core/magmap -profile docker --outdir results/ --input samples.csv --genomeinfo localgenomes.csv --sequence_filter path/to/file
Sourmash (optional)
with Sourmash you can filter the genomes to be used by magmap in the mapping step. This function is optional but can speed up the process and let you get a better genomes /reads mapping ratio since you are removing all the genomes that are not passing the threshold (that you can select).
nextflow run nf-core/magmap -profile docker --outdir results/ --input samples.csv --genomeinfo localgenomes.csv --sourmash true
ORF caller option
The pipeline uses Prokka to call genes/ORFs from the genomes. This is suitable for prokaryotes and it provides a gff as output for downstream analysis. It also performs functional annotation of ORFs.
Running the pipeline
The typical command for running the pipeline is as follows:
nextflow run nf-core/magmap --input ./samplesheet.csv --outdir ./results --genomeinfo ./genomes.csv -profile docker
This will launch the pipeline with the docker
configuration profile. See below for more information about profiles.
Note that the pipeline will create the following files in your working directory:
work # Directory containing the nextflow working files
<OUTDIR> # Finished results in specified location (defined with --outdir)
.nextflow_log # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.
If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.
Pipeline settings can be provided in a yaml
or json
file via -params-file <file>
.
Do not use -c <file>
to specify parameters as this will result in errors. Custom config files specified with -c
must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args).
The above pipeline run specified with a params file in yaml format:
nextflow run nf-core/magmap -profile docker -params-file params.yaml
with params.yaml
containing:
input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh37'
<...>
You can also generate such YAML
/JSON
files via nf-core/launch.
Updating the pipeline
When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you’re running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:
nextflow pull nf-core/magmap
Reproducibility
It is a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you’ll be running the same version of the pipeline, even if there have been changes to the code since.
First, go to the nf-core/magmap releases page and find the latest pipeline version - numeric only (eg. 1.3.1
). Then specify this when running the pipeline with -r
(one hyphen) - eg. -r 1.3.1
. Of course, you can switch to another version by changing the number after the -r
flag.
This version number will be logged in reports when you run the pipeline, so that you’ll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.
To further assist in reproducibility, you can use share and re-use parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.
If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.
Core Nextflow arguments
These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).
-profile
Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.
Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.
We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.
The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the nf-core/configs documentation.
Note that multiple profiles can be loaded, for example: -profile test,docker
- the order of arguments is important!
They are loaded in sequence, so later profiles can overwrite earlier profiles.
If -profile
is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH
. This is not recommended, since it can lead to different results on different machines dependent on the computer environment.
test
- A profile with a complete configuration for automated testing
- Includes links to test data so needs no other parameters
docker
- A generic configuration profile to be used with Docker
singularity
- A generic configuration profile to be used with Singularity
podman
- A generic configuration profile to be used with Podman
shifter
- A generic configuration profile to be used with Shifter
charliecloud
- A generic configuration profile to be used with Charliecloud
apptainer
- A generic configuration profile to be used with Apptainer
wave
- A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow
24.03.0-edge
or later).
- A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow
conda
- A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.
-resume
Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.
You can also supply a run name to resume a specific run: -resume [run-name]
. Use the nextflow log
command to show previous run names.
-c
Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.
Custom configuration
Resource requests
Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.
To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.
Custom Containers
In some cases you may wish to change which container or conda environment a step of the pipeline uses for a particular tool. By default nf-core pipelines use containers and software from the biocontainers or bioconda projects. However in some cases the pipeline specified version maybe out of date.
To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.
Custom Tool Arguments
A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.
To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.
nf-core/configs
In most cases, you will only need to create a custom config as a one-off but if you and others within your organization are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs
git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c
parameter. You can then create a pull request to the nf-core/configs
repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs
), and amending nfcore_custom.config
to include your custom profile.
See the main Nextflow documentation for more information about creating your own configuration files.
If you have any questions or issues please send us a message on Slack on the #configs
channel.
Azure Resource Requests
To be used with the azurebatch
profile by specifying the -profile azurebatch
.
We recommend providing a compute params.vm_type
of Standard_D16_v3
VMs by default but these options can be changed if required.
Note that the choice of VM size depends on your quota and the overall workload during the analysis. For a thorough list, please refer the Azure Sizes for virtual machines in Azure.
Running in the background
Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.
The Nextflow -bg
flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.
Alternatively, you can use screen
/ tmux
or similar tool to create a detached session which you can log back into at a later time.
Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).
Nextflow memory requirements
In some cases, the Nextflow Java virtual machines can start to request a large amount of memory.
We recommend adding the following line to your environment to limit this (typically in ~/.bashrc
or ~./bash_profile
):
NXF_OPTS='-Xms1g -Xmx4g'