Skip to content

genomic-medicine-sweden/nallo: Usage

Prerequisites

  1. Install Nextflow (>=24.04.2) using the instructions here.
  2. Install one of the following technologies for full pipeline reproducibility: Docker, Singularity, Podman, Shifter or Charliecloud.

Warning

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

Getting started

Before running the pipeline with your data, we recommend running it with the test profile. You do not need to download any of the data as it 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>

Note

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.

Running genomic-medicine-sweden/nallo with your data

Running the pipeline on real data involves three steps:

  1. Prepare a samplesheet with your data
  2. Gather the required files and references
  3. Supply the samplesheet, reference files and run the pipeline

Samplesheet

First, 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 seven columns and a header row, as shown in the example below:

project,sample,file,family_id,paternal_id,maternal_id,sex,phenotype
testrun,HG002,/path/to/HG002.fastq.gz,FAM,HG003,0,1,2
testrun,HG003,/path/to/HG003.bam,FAM,0,0,2,1
Fields Description
project Project name must be provided and cannot contain spaces, needs to be the same for all samples.
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 use the same ID as sample.
paternal_id Paternal ID must be provided and cannot contain spaces. If no paternal ID is available, use 0.
maternal_id Maternal ID must be provided and cannot contain spaces. If no maternal ID is available, use 0.
sex Sex must be provided as 0, 1 or 2 (0=unknown; 1=male; 2=female). If sex is unknown it will be assigned automatically if possible.
phenotype Affected status of patient (0 = missing; 1=unaffected; 2=affected).

Presets

This pipeline comes with three different presets that should be set with the --preset parameter: revio (default), pacbio or ONT_R10. The preset parameter controls certain technology specific tools and parameters.

Preset effects on subworkflows

  • --skip_genome_assembly and --skip_repeat_wf will be set to true for ONT_R10
  • --skip_methylation_pileups will be set to true for pacbio

Subworkflows

As indicated above, this pipeline is divided into multiple subworkflows, each with their own input requirements and outputs. By default all subworklows are active, and thus all mandatory input files are required.

Required parameters

The only mandatory parameters for all subworkflows are the --input and --outdir parameters, all other parameters are determined by the active subworkflows.

For example, if you would run nextflow run genomic-medicine-sweden/nallo -profile docker --outdir results --input samplesheet.csv, the pipeline will would to guide you through which files are required:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  --skip_genome_assembly is NOT active, the following files are required: --par_regions
  --skip_snv_annotation is NOT active, the following files are required: --echtvar_snv_databases
  --skip_alignment is NOT active, the following files are required: --somalier_sites
  --skip_snv_annotation is NOT active, the following files are required: --vep_cache
  ...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

A thorough description of the required files are provided below.

Skipping subworkflows

If you want to skip a subworkflow, you will need to explicitly state to skip all subworkflows that rely on it.

For example, nextflow run genomic-medicine-sweden/nallo -profile docker --outdir results --input samplesheet.csv --skip_alignment will tell you

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  --skip_alignment is active, the pipeline has to be run with: --skip_qc --skip_genome_assembly --skip_call_paralogs --skip_snv_calling --skip_snv_annotation --skip_cnv_calling --skip_phasing --skip_rank_variants --skip_repeat_calling --skip_repeat_annotation --skip_methylation_pileups
  ...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Because almost all other subworkflows relies on the mapping subworkflow.

Reference files and parameters

All parameters are listed in the parameters section, but the most useful parameters needed to run the pipeline described in more detail below.

Alignment

The majority of subworkflows depend on the alignment subworkflow which requires --fasta and --somalier_sites.

Parameter Description
fasta Reference genome, either gzipped or uncompressed (e.g. GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz)
somalier_sites A VCF with known polymorphic sites from which sex will be inferred, if possible (e.g. sites.hg38.vcg.gz)

Turned off with --skip_alignment.

QC

This subworkflow depends on the alignment subworkflow, but requires no additional files.

Turned off with --skip_qc.

Assembly

This subworkflow contains both genome assembly and assembly variant calling. The assembly variant calling needs the sex of samples. For samples with unknown sex this is inferred with the help of the aligned reads. Therefore it depends on the alignment subworkflow. It requires a BED file with PARs.

Parameter Description
par_regions A BED file with PAR regions (e.g. GRCh38_PAR.bed)

Warning

Make sure chrY PAR is hard masked in the reference genome.

Turned off with --skip_genome_assembly.

Call paralogs

This subworkflow depends on the mapping subworkflow, but requires no additional files.

Warning

Only GRCh38 is supported.

Turned off with --skip_call_paralogs.

SNV calling

This subworkflow depends on the alignment subworkflow, and requires PARs.

Parameter Description
par_regions A BED file with PAR regions (e.g. GRCh38_PAR.bed)

Turned off with --skip_snv_calling.

CNV calling

This subworkflow depends on the alignment and SNV calling subworkflows, and requires the following additional files:

Parameter Description
hificnv_expected_xy_cn Expected XY copy number regions for your reference genome (e.g. expected_cn.hg38.XY.bed)
hificnv_expected_xx_cn Expected XX copy number regions for your reference genome (e.g. expected_cn.hg38.XX.bed)
hificnv_excluded_regions BED file specifying regions to exclude (e.g. cnv.excluded_regions.hg38.bed.gz)

Turned off with --skip_cnv_calling.

Phasing

This subworkflow phases variants and haplotags aligned BAM files, and such relies on the alignment and SNV calling subworkflows, but requires no additional files.

Turned off with --skip_phasing.

Methylation pileups

This subworkflow relies on alignment and short variant calling subworkflows, but requires no additional files.

Turned off with --skip_methylation_pileups.

Repeat calling

This subworkflow requires haplotagged BAM files, and such relies on aligment, SNV calling and phasing subworkflows. It requires the following additional files:

Parameter Description
trgt_repeats a BED file with tandem repeats matching your reference genome (e.g. pathogenic_repeats.hg38.bed)

Turned off with --skip_repeat_calling.

Repeat annotation

This subworkflow relies on the alignment, SNV calling, phasing and repeat calling subworkflows. It requires the following additional files:

Parameter Description
stranger_repeat_catalog a variant catalog matching your reference (e.g. stranger_repeat_catalog_grch38.json)

Turned off with --skip_repeat_annotation.

SNV annotation

This subworkflow relies on the alignment and SNV calling, and requires the following additional files:

Parameter Description
vep_cache VEP cache matching your reference genome, either as a .tar.gz archive or path to a directory (e.g. homo_sapiens_vep_110_GRCh38.tar.gz)
vep_plugin_files 1 A csv file with VEP plugin files, pLI and LoFtool are required. Example provided below.
echtvar_snv_databases 2 A csv file with annotation databases from echtvar encode (e.g. gnomad.v3.1.2.echtvar.popmax.v2.zip)
variant_consequences_snvs A list of SO terms listed in the order of severity from most severe to lease severe for annotating genomic and mitochondrial SNVs. Sample file here. You can learn more about these terms here

1 Example file for input with --vep_plugin_files

vep_files
https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/nallo/reference/vep_plugins/spliceai_21_scores_raw_indel_-v1.3-.vcf.gz.tbi
https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/nallo/reference/vep_plugins/spliceai_21_scores_raw_indel_-v1.3-.vcf.gz
https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/nallo/reference/vep_plugins/spliceai_21_scores_raw_snv_-v1.3-.vcf.gz.tbi
https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/nallo/reference/vep_plugins/pLI_values.txt
https://raw.githubusercontent.com/genomic-medicine-sweden/test-datasets/nallo/reference/vep_plugins/LoFtool_scores.txt

2 Example file for input with --echtvar_snv_databases:

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

Tip

Optionally, to calcuate CADD scores for small indels, supply a path to a folder containing cadd annotations with --cadd_resources and prescored indels with --cadd_prescored_indels. Equivalent of the data/annotations/ and data/prescored/ folders described here. CADD scores for SNVs can be annotated through echvtvar and --echtvar_snv_databases.

Turned off with --skip_snv_annotation.

Rank SNVs and INDELs

This subworkflow ranks SNVs, and relies on the alignment, SNV calling and SNV annotation subworkflows. It requires the following additional files:

Parameter Description
genmod_score_config_snvs  Used by GENMOD when ranking variants. Sample file here
genmod_reduced_penetrance A list of loci that show reduced penetrance in people. Sample file here

Turned off with --skip_rank_variants.

SV annotation

This subworkflow relies on the alignment subworkflow, and requires the following additional files:

Parameter Description
svdb_sv_databases 1 Csv file with databases (VCFs) used for structural variant annotation

1 Example file for input with --svdb_sv_databases:

filename,in_freq_info_key,in_allele_count_info_key,out_freq_info_key,out_allele_count_info_key
https://github.com/genomic-medicine-sweden/test-datasets/raw/b9ff54b59cdd39df5b6e278a30b08d94075a644c/reference/colorsdb.test_data.vcf.gz,AF,AC,colorsdb_af,colorsdb_ac

These databases could for example come from CoLoRSdb.

Turned off with --skip_sv_annotation.

Rank SVs

This subworkflow ranks SVs, and relies on the mapping, SV calling and SV annotation subworkflows, and requires the following additional files:

Parameter Description
genmod_score_config_svs  Used by GENMOD when ranking variants. Sample file here
genmod_reduced_penetrance A list of loci that show reduced penetrance in people. Sample file here

--skip_rank_variants.

Filter variants

This subworkflow filters SNVs and SVs. It required at least the alignment and SNV calling workflows, but most of the time also the SNV annotation and ranking workflows.

Parameter Description
filter_variants_hgnc_ids 1  Used by filter_vep to filter variants on HGNC IDs. Requires a tsv/csv file with a hgnc_ids column, that has one numerical HGNC ID per row, e.g. 4281 or HGNC:4281.
filter_snvs_expression An expression that is passed to bcftools view to filter SNVs, e.g. --filter_snvs_expression "-e 'INFO/AQ>60'"
filter_svs_expression An expression that is passed to bcftools view to filter SVs, e.g.--filter_snvs_expression "-e 'INFO/AQ>60'"

1 Example file for input with --filter_variants_hgnc_ids:

hgnc_id
4865
14150

Filtering of variants only happens if any of these three parameters is active.

Other highlighted parameters

  • Limit SNV calling to regions in BED file (--target_bed).
  • By default SNV-calling is split into 13 parallel processes, this speeds up the variant calling significantly. Change this by setting --snv_calling_processes to a different number.
  • By default the pipeline splits the input files into 8 pieces, performs parallel alignment and then merges the files. This can be changed to a different number with --alignment_processes, or turned off by supplying a value of 1. Parallel alignment comes with some additional overhead, but can speed up the pipeline significantly.

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. 0.2.0). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 0.2.0. 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.

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

Download pipeline and containers

The pipeline and container images can be downloaded using nf-core download, e.g.:

nf-core download genomic-medicine-sweden/nallo -r 0.3.2

The offline section from the nf-core docs should be followed for more information about offline usage.