usage.md

genomic-medicine-sweden/nallo: Usage

Introduction

genomic-medicine-sweden/nallo is a bioinformatics analysis pipeline to analyse long-read data.

Prerequisites

1. Install Nextflow (>=22.10.1) using the instructions here.
2. Install one of the following technologies for full pipeline reproducibility: Docker, Singularity, Podman, Shifter or Charliecloud.

   Almost all nf-core pipelines give you the option to use conda as well. However, some tools used in the nallo pipeline do not have a conda package so we do not support conda at the moment.

Run genomic-medicine-sweden/nallo with test data

Before running the pipeline with your data, we recommend running it with the test dataset available in the assets/test_data folder provided with the pipeline. You do not need to download any of the data as part of it came directly with the pipeline and the other part will be fetched automatically for you when you use the test profile.

Run the following command, where YOURPROFILE is the package manager you installed on your machine. For example, -profile test,docker or -profile test,singularity:

nextflow run genomic-medicine-sweden/nallo \
    -profile test,<YOURPROFILE> \
    --outdir <OUTDIR>

Check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your institute. If so, you can simply use -profile test,<institute> in your command. This enables the appropriate package manager and sets the appropriate execution settings for your machine. NB: The order of profiles is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

Running the command creates 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, like history of pipeline logs.

Note

The default cpu and memory configurations used in nallo are written keeping the test profile (and dataset, which is tiny) in mind. You should override these values in configs to get it to work on larger datasets. Check the section custom-configuration below to know more about how to configure resources for your platform.

Updating the pipeline

The above command downloads the pipeline from GitHub, caches it, and tests it on the test dataset. When you run the command again, it will fetch the pipeline from cache even if a more recent version of the pipeline is available. To make sure that you're running the latest version of the pipeline, update the cached version of the pipeline by including -latest in the command.

Run genomic-medicine-sweden/nallo with your data

Running the pipeline involves three steps:

1. Prepare a samplesheet
2. Gather all required references
3. Supply samplesheet and references, and run the command

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.

--input '[path to samplesheet file]'

It has to be a comma-separated file with 6 columns, and a header row as shown in the examples below. file can either be a gzipped-fastq file or an aligned or unalinged BAM file (BAM files will be converted to FASTQ and aligned again). phenotype is not used at the moment but still required, set it to 1. If you don't have related samples, family_id could be set to sample name, and paternal_id and maternal_id to a value that is not another sample name.

sample,file,family_id,paternal_id,maternal_id,sex,phenotype
HG002,/path/to/HG002.fastq.gz,FAM,HG003,HG004,1,1
HG005,/path/to/HG005.bam,FAM,HG003,HG004,2,1

Fields	Description
sample	Custom sample name, cannot contain spaces.
file	Absolute path to gzipped FASTQ or BAM file. File has to have the extension ".fastq.gz", .fq.gz" or ".bam".
family_id	"Family ID must be provided and cannot contain spaces. If no family ID is available you can use the same ID as the sample
paternal_id	Paternal ID must be provided and cannot contain spaces. If no paternal ID is available, use any ID not in sample column.
maternal_id	Maternal ID must be provided and cannot contain spaces. If no maternal ID is available, use any ID not in sample column.
sex	Sex (0=unknown; 1=male; 2=female).
phenotype	Affected status of patient (0 = missing; 1=unaffected; 2=affected).

An example samplesheet has been provided with the pipeline.

The typical command for running the pipeline is as follows:

nextflow run genomic-medicine-sweden/nallo -profile docker \
    --input samplesheet.csv \
    --preset <revio/pacbio/ONT_R10> \
    --outdir <OUTDIR> \
    --fasta <reference.fasta> \
    --skip_assembly_wf \
    --skip_repeat_wf \
    --skip_snv_annotation \
    --skip_cnv_calling

Presets

This pipeline comes with three different presets that can be set with the --preset parameter, "revio", "pacbio" and "ONT_R10" (defaults to "revio"). By default, --skip_assembly_wf and --skip_repeat_wf is set to true for ONT_R10 and --skip_methylation_wf is set to true for "pacbio", which means these workflows are not run.

Reference files and parameters

The typical command example above requires no additional files except the reference genome. Nallo has the ability to skip certain parts of the pipeline, for example --skip_repeat_wf. Some workflows require additional files:

• If running without --skip_assembly_wf, download a BED file with PAR regions (hg38) to supply with --dipcall_par.

Note

Make sure chrY PAR is hard masked in reference.

• If running without --skip_repeat_calling, download a BED file with tandem repeats (TRGT) matching your reference genome to supply with --trgt_repeats.

• If running without --skip_repeat_annotation, download a json variant catalog, (e.g. variant_catalog_grch38.json) matching your reference genome to supply with --variant_catalog.

• If running without --skip_snv_annotation, download VEP cache to supply with --vep_cache and prepare a samplesheet with annotation databases (echtvar encode) to supply with --snp_db:

snp_dbs.csv

sample,file
gnomad,/path/to/gnomad.v3.1.2.echtvar.popmax.v2.zip
cadd,/path/to/cadd.v1.6.hg38.zip

• If running without --skip_cnv_calling, expected CN regions for your reference genome can be downloaded from HiFiCNV GitHub to supply with --hificnv_xy, --hificnv_xx (expected_cn) and --hificnv_exclude (excluded_regions).

• If you want to include extra samples for mili-sample calling of SVs - prepare a samplesheet with .snf files from Sniffles to supply with --extra_snfs:

extra_snfs.csv

sample,file
HG01123,/path/to/HG01123_sniffles.snf
HG01124,/path/to/HG01124_sniffles.snf

• If running without --skip_call_paralogs, the reference genome needs to be hg38

• If running without --skip_mapping_wf, a VCF of known polymorphic sites (e.g. sites.hg38.vcg.gz) needs to be supplied with --somalier_sites, from which sex will be inferred if possible.

Highlighted parameters:

• You can choose to limit SNV calling to regions in BED file (--bed).

• By default SNV-calling is split into 13 parallel processes, limit this by setting --parallel_snv to a different number.

• By default the pipeline does not perform parallel alignment, but this can be set by setting --split_fastq to split the input and alignment into N files/processes.

All parameters are listed below:

Workflow skip options

Options to skip various steps within the workflow

Parameter	Description	Type	Default	Required	Hidden
skip_raw_read_qc	Skip raw read QC	boolean	False
skip_short_variant_calling	Skip short variant calling	boolean	False
skip_assembly_wf	Skip assembly and downstream processes	boolean	False
skip_mapping_wf	Skip read mapping and downstream processes	boolean	False
skip_methylation_wf	Skip methylation workflow	boolean	False
`skip_repeat_calling	Skip repeat calling workflow	boolean	False
skip_repeat_annotation	Skip repeat annotation workflow	boolean	False
skip_phasing_wf	Skip phasing workflow	boolean	False
skip_snv_annotation	Skip SNV annotation	boolean	False
skip_cnv_calling	Skip CNV workflow	boolean	False
skip_call_paralogs	Skip call paralogs (Paraphase)	boolean	False

Input/output options

Define where the pipeline should find input data and save output data.

Parameter	Description	Type	Default	Required	Hidden
input	Path to comma-separated file containing information about the samples in the experiment. Help You will need to create a design file with information about the samples in your experiment before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row.	string		True
outdir	The output directory where the results will be saved. You have to use absolute paths to storage on Cloud infrastructure.	string		True
email	Email address for completion summary. Help 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.	string
multiqc_title	MultiQC report title. Printed as page header, used for filename if not otherwise specified.	string

Reference genome options

Reference genome related files and options required for the workflow.

Parameter	Description	Type	Default	Required	Hidden
fasta	Reference genome	string
genome	Name of iGenomes reference. Help If using a reference genome configured in the pipeline using iGenomes, use this parameter to give the ID for the reference. This is then used to build the full paths for all required reference genome files e.g. --genome GRCh38. See the nf-core website docs for more details.	string
igenomes_ignore	Do not load the iGenomes reference config. Help Do not load igenomes.config when running the pipeline. You may choose this option if you observe clashes between custom parameters and those supplied in igenomes.config.	boolean	True		True

Institutional config options

Parameters used to describe centralised config profiles. These should not be edited.

Parameter	Description	Type	Default	Required	Hidden
custom_config_version	Git commit id for Institutional configs.	string	master		True
custom_config_base	Base directory for Institutional configs. Help If you're running offline, Nextflow will not be able to fetch the institutional config files from the internet. If you don't need them, then this is not a problem. If you do need them, you should download the files from the repo and tell Nextflow where to find them with this parameter.	string	https://raw.githubusercontent.com/nf-core/configs/master		True
config_profile_name	Institutional config name.	string			True
config_profile_description	Institutional config description.	string			True
config_profile_contact	Institutional config contact information.	string			True
config_profile_url	Institutional config URL link.	string			True

Max job request options

Set the top limit for requested resources for any single job.

Parameter	Description	Type	Default	Required	Hidden
max_cpus	Maximum number of CPUs that can be requested for any single job. Help Use to set an upper-limit for the CPU requirement for each process. Should be an integer e.g. --max_cpus 1	integer	16		True
max_memory	Maximum amount of memory that can be requested for any single job. Help Use to set an upper-limit for the memory requirement for each process. Should be a string in the format integer-unit e.g. --max_memory '8.GB'	string	128.GB		True
max_time	Maximum amount of time that can be requested for any single job. Help Use to set an upper-limit for the time requirement for each process. Should be a string in the format integer-unit e.g. --max_time '2.h'	string	240.h		True

Generic options

Less common options for the pipeline, typically set in a config file.

Parameter	Description	Type	Default	Required	Hidden
help	Display help text.	boolean			True
version	Display version and exit.	boolean			True
publish_dir_mode	Method used to save pipeline results to output directory. Help The Nextflow publishDir option specifies which intermediate files should be saved to the output directory. This option tells the pipeline what method should be used to move these files. See Nextflow docs for details.	string	copy		True
email_on_fail	Email address for completion summary, only when pipeline fails. Help An email address to send a summary email to when the pipeline is completed - ONLY sent if the pipeline does not exit successfully.	string			True
plaintext_email	Send plain-text email instead of HTML.	boolean			True
max_multiqc_email_size	File size limit when attaching MultiQC reports to summary emails.	string	25.MB		True
monochrome_logs	Do not use coloured log outputs.	boolean			True
hook_url	Incoming hook URL for messaging service Help Incoming hook URL for messaging service. Currently, MS Teams and Slack are supported.	string			True
multiqc_config	Custom config file to supply to MultiQC.	string			True
multiqc_logo	Custom logo file to supply to MultiQC. File name must also be set in the MultiQC config file	string			True
multiqc_methods_description	Custom MultiQC yaml file containing HTML including a methods description.	string
validate_params	Boolean whether to validate parameters against the schema at runtime	boolean	True		True
validationShowHiddenParams	Show all params when using --help Help By default, parameters set as hidden in the schema are not shown on the command line when a user runs with --help. Specifying this option will tell the pipeline to show all parameters.	boolean			True
validationSkipDuplicateCheck	nf-validation related parameter	boolean			True
validationS3PathCheck	Boolean whether to validate validate AWS S3 paths	boolean			True
monochromeLogs	Boolean whether to color nf-validation logs	boolean			True

Workflow options

Parameter	Description	Type	Default	Required	Hidden
preset	Choose a preset depending on data type	string	revio	True
variant_caller	Choose variant caller	string	deepvariant
phaser	Choose phasing software	string	whatshap
hifiasm_mode	Run hifiasm in hifi-only or hifi-trio mode	string	hifi-only
split_fastq	Split alignment into n jobs	integer	0
parallel_snv	Split SNV calling into n chunks	integer	13

Extra file inputs

Different processes may need extra input files

Parameter	Description	Type	Default	Required	Hidden
dipcall_par	Provide a bed file of chrX PAR regions for dipcall	string
extra_snfs	Extra input files for Sniffles	string
tandem_repeats	Tandem repeat BED-file for sniffles	string
trgt_repeats	BED-file for repeats to be genotyped	string
snp_db	Extra echtvar-databases to annotate SNVs with	string
vep_cache	Path to directory of vep_cache	string
bed	BED file with regions of interest	string
hificnv_xy		string
hificnv_xx		string
hificnv_exclude	HiFiCNV BED file specifying regions to exclude	string
somalier_sites	A VCF of known polymorphic sites	string
variant_catalog	Variant catalog json-file for Stranger	string
validationFailUnrecognisedParams	Validation of parameters fails when an unrecognised parameter is found. Help By default, when an unrecognised parameter is found, it returns a warning.	boolean			True
validationLenientMode	Validation of parameters in lenient more. Help Allows string values that are parseable as numbers or booleans. For further information see JSONSchema docs.	boolean			True

Updating the pipeline

nextflow pull genomic-medicine-sweden/nallo

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:

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 genomic-medicine-sweden/nallo releases page and find the latest pipeline version - numeric only (eg. 0.1.0). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 0.1.0. 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 reproducbility, 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.

Tip

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

Note

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.

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 enviroment.

• 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).
• 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 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 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 organisation 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'

Running the pipeline without internet access

The pipeline and container images can be downloaded using nf-core tools. For running offline, you of course have to make all the reference data available locally, and specify --fasta, etc., see above.

Contrary to the paragraph about Nextflow on the page linked above, it is not possible to use the "-all" packaged version of Nextflow for this pipeline. The online version of Nextflow is necessary to support the necessary nextflow plugins. Download instead the file called just nextflow. Nextflow will download its dependencies when it is run. Additionally, you need to download the nf-validation plugin explicitly:

./nextflow plugin install nf-validation

Now you can transfer the nextflow binary as well as its directory $HOME/.nextflow to the system without Internet access, and use it there. It is necessary to use an explicit version of nf-validation offline, or Nextflow will check for the most recent version online. Find the version of nf-validation you downloaded in $HOME/.nextflow/plugins, then specify this version for nf-validation in your configuration file:

plugins {
        // Set the plugin version explicitly, otherwise nextflow will look for the newest version online.
        id 'nf-validation@1.1.3'
}

This should go in your Nextflow confgiguration file, specified with -c <YOURCONFIG> when running the pipeline.