Skip to content

usage.md

Latest commit

 

History

History
588 lines (399 loc) · 30 KB

usage.md

File metadata and controls

588 lines (399 loc) · 30 KB

nf-core/sarek: Usage

⚠️ Please read this documentation on the nf-core website: https://nf-co.re/sarek/usage

Introduction

Sarek is a workflow designed to detect germline and somatic variants on whole genome, whole exome, or targeted sequencing data.

Initially designed for human and mouse, it can work on any species if a reference genome is available. Sarek is designed to handle single samples, such as single-normal or single-tumor samples, and tumor-normal pairs including additional relapses.

Running the pipeline

Quickstart

The typical command for running the pipeline is as follows:

nextflow run nf-core/sarek --input samplesheet.csv  --outdir <OUTDIR> --genome GRCh38 -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
results         # Finished results (configurable, see below)
.nextflow_log   # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

Input: Samplesheet configurations

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use the parameter --input to specify its location. It has to be a comma-separated file with at least 3 columns, and a header row as shown in the examples below.

It is recommended to use the absolute path of the files, but a relative path should also work.

If necessary, a tumor sample can be associated to a normal sample as a pair, if specified with the same patient ID, a different sample, and the respective status. An additional tumor sample (such as a relapse for example), can be added if specified with the same patient ID, a different sample, and the status value 1.

Sarek will output results in a different directory for each sample. If multiple samples IDs are specified in the CSV file, Sarek will consider all files to be from different samples.

Output from Variant Calling and/or Annotation will be in a specific directory for each sample and tool configuration (or normal/tumor pair if applicable).

Multiple CSV files can be specified if the path is enclosed in quotes.

--input '[path to samplesheet file(s)]'
Column Description
patient Custom patient ID; designates the patient/subject; must be unique for each patient, but one patient can have multiple samples (e.g. normal and tumor).
gender Sex chromosomes of the patient; i.e. XX, XY..., only used for Copy-Number Variation analysis in a tumor/pair
Optional, Default: NA
status Normal/tumor status of sample; can be 0 (normal) or 1 (tumor).
Optional, Default: 0
sample Custom sample ID for each tumor and normal sample; more than one tumor sample for each subject is possible, i.e. a tumor and a relapse; samples can have multiple lanes for which the same ID must be used to merge them later (see also lane). Sample IDs must be unique for unique biological samples
lane Lane ID, used when the sample is multiplexed on several lanes. Must be unique for each lane in the same sample (but does not need to be the original lane name), and must contain at least one character
Required for --step_mapping
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".
bam Full path to (u)BAM file
bai Full path to BAM index file
cram Full path to CRAM file
crai Full path to CRAM index file
table Full path to recalibration table file
mpileup Full path to pileup file

An example samplesheet has been provided with the pipeline.

Start with mapping (--step mapping [default])

This step can be started either from fastq files or (u)bams. The CSV must contain at least the columns patient, sample, lane, and either fastq_1/fastq_2 or bam.

Examples

Minimal config file:

patient,sample,lane,fastq_1,fastq_2
patient1,test_sample,lane_1,test_1.fastq.gz,test_2.fastq.gz
patient,sample,lane,bam
patient1,test_sample,lane_1,test.bam

In this example, there are 3 read groups:

patient,sample,lane,fastq_1,fastq_2
patient1,test_sample,lane_1,test_L001_1.fastq.gz,test_L001_2.fastq.gz
patient1,test_sample,lane_2,test_L002_1.fastq.gz,test_L002_2.fastq.gz
patient1,test_sample,lane_3,test_L003_1.fastq.gz,test_L003_2.fastq.gz
patient,sample,lane,bam
patient1,test_sample,1,test_L001.bam
patient1,test_sample,2,test_L002.bam
patient1,test_sample,3,test_L003.bam
Full samplesheet

In this example, all possible columns are used. There are 3 read groups for the normal sample, 2 for the tumor sample, 1 for the relapse, including the gender and status information per patient:

patient,gender,status,sample,lane,fastq_1,fastq_2
patient1,XX,0,normal_sample,lane_1,test_L001_1.fastq.gz,test_L001_2.fastq.gz
patient1,XX,0,normal_sample,lane_2,test_L002_1.fastq.gz,test_L002_2.fastq.gz
patient1,XX,0,normal_sample,lane_3,test_L003_1.fastq.gz,test_L003_2.fastq.gz
patient1,XX,1,tumor_sample,lane_1,test2_L001_1.fastq.gz,test2_L001_2.fastq.gz
patient1,XX,1,tumor_sample,lane_2,test2_L002_1.fastq.gz,test2_L002_2.fastq.gz
patient1,XX,1,relapse_sample,lane_1,test3_L001_1.fastq.gz,test3_L001_2.fastq.gz
patient,gender,status,sample,lane,bam
patient1,XX,0,normal_sample,lane_1,test_L001.bam
patient1,XX,0,normal_sample,lane_2,test_L002.bam
patient1,XX,0,normal_sample,lane_3,test_L003.bam
patient1,XX,1,tumor_sample,lane_1,test2_L001.bam
patient1,XX,1,tumor_sample,lane_2,test2_L002.bam
patient1,XX,1,relapse_sample,lane_1,test3_L001.bam

Start with duplicate marking and/or preparing recalibration (--step prepare_recalibration)

Duplicate Marking

For starting from duplicate marking, the CSV file must contain at least the columns patient, sample, bam, bai.

Example:

patient,sample,bam,bai
patient1,test_sample,test_mapped.bam,test_mapped.bam.bai
Prepare Recalibration

For starting directly from preparing recalibration and skipping duplicate marking, the CSV file must contain at least the columns patient, sample, cram, crai with non-recalibrated CRAM files. Additionally, the parameter --skip_tools markduplicates must be set.

Example:

patient,sample,cram,crai
patient1,test_sample,test_mapped.cram,test_mapped.cram.crai

The Sarek-generated CSV file is stored under results/Preprocessing/CSV/duplicates_marked_no_table.csv and will automatically be used as an input when specifying the parameter --step prepare_recalibration.

Full samplesheet

In this example, all possible columns are used including the gender and status information per patient:

patient,gender,status,sample,bam,bai
patient1,XX,0,test_sample,test_mapped.bam,test_mapped.bam.bai
patient1,XX,1,tumor_sample,test2_mapped.bam,test2_mapped.bam.bai
patient1,XX,1,relapse_sample,test3_mapped.bam,test3_mapped.bam.bai
patient,gender,status,sample,cram,crai
patient1,XX,0,normal_sample,test_mapped.cram,test_mapped.cram.crai
patient1,XX,1,tumor_sample,test2_mapped.cram,test2_mapped.cram.crai
patient1,XX,1,relapse_sample,test3_mapped.cram,test3_mapped.cram.crai

Start with base quality recalibration (--step recalibrate)

For starting from base quality recalibration the CSV file must contain at least the columns patient, sample, cram, crai, table containing the paths to non-recalibrated CRAM files and the associated recalibration table.

Example:

patient,sample,cram,crai,table
patient1,test_sample,test_mapped.cram,test_mapped.cram.crai,test.table

The Sarek-generated CSV file is stored under results/Preprocessing/CSV/duplicates_marked.csv and will automatically be used as an input when specifying the parameter --step recalibrate.

Full samplesheet

In this example, all possible columns are used including the gender and status information per patient:

patient,gender,status,sample,cram,crai,table
patient1,XX,0,test_sample,test_mapped.cram,test_mapped.cram.crai,test.table
patient1,XX,1,tumor_sample,test2_mapped.cram,test2_mapped.cram.crai,test2.table
patient1,XX,1,relapse_sample,test3_mapped.cram,test3_mapped.cram.crai,test3.table

Start with variant calling (--step variant_calling)

For starting from the variant calling step, the CSV file must contain at least the columns patient, sample, cram, crai.

Example:

patient,sample,cram,crai
patient1,test_sample,test_mapped.cram,test_mapped.cram.crai

The Sarek-generated CSV file is stored under results/Preprocessing/CSV/recalibrated.csv and will automatically be used as an input when specifying the parameter --step variant_calling.

Full samplesheet

In this example, all possible columns are used including the gender and status information per patient:

patient,gender,status,sample,cram,crai
patient1,XX,0,normal_sample,test_mapped.cram,test_mapped.cram.crai
patient1,XX,1,tumor_sample,test2_mapped.cram,test2_mapped.cram.crai
patient1,XX,1,relapse_sample,test3_mapped.cram,test3_mapped.cram.crai

Start with annotation (--step annotate)

Starting with annotation, is a special case in that it doesn't require an input sample sheet. The input files for Sarek can be specified using the path to a VCF file given to the --input command only with the annotation step (--step annotate). As Sarek will use bgzip and tabix to compress and index the annotated VCF files, it expects the input VCF files to be sorted. Multiple VCF files can be specified, using a glob path, if enclosed in quotes. For example:

--step annotate --input "results/VariantCalling/*/{HaplotypeCaller,Manta,Mutect2,Strelka,TIDDIT}/*.vcf.gz"

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/sarek

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/sarek releases page and find the latest version number - numeric only (eg. 3.0.0). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 3.0.0.

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.

Automatic Resubmission

Core Nextflow arguments

NB: 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, Conda) - see below. When using Biocontainers, most of these software packaging methods pull Docker containers from quay.io e.g FastQC except for Singularity which directly downloads Singularity images via https hosted by the Galaxy project and Conda which downloads and installs software locally from Bioconda.

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 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.

  • 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
  • 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 or Charliecloud.
  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters

-resume

Specify this when restarting a pipeline. Nextflow will used cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously.

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.

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 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).

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.

For example, if the nf-core/rnaseq pipeline is failing after multiple re-submissions of the STAR_ALIGN process due to an exit code of 137 this would indicate that there is an out of memory issue:

[62/149eb0] NOTE: Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) -- Execution is retried (1)
Error executing process > 'NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)'

Caused by:
    Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137)

Command executed:
    STAR \
        --genomeDir star \
        --readFilesIn WT_REP1_trimmed.fq.gz  \
        --runThreadN 2 \
        --outFileNamePrefix WT_REP1. \
        <TRUNCATED>

Command exit status:
    137

Command output:
    (empty)

Command error:
    .command.sh: line 9:  30 Killed    STAR --genomeDir star --readFilesIn WT_REP1_trimmed.fq.gz --runThreadN 2 --outFileNamePrefix WT_REP1. <TRUNCATED>
Work dir:
    /home/pipelinetest/work/9d/172ca5881234073e8d76f2a19c88fb

Tip: you can replicate the issue by changing to the process work dir and entering the command `bash .command.run`

To bypass this error you would need to find exactly which resources are set by the STAR_ALIGN process. The quickest way is to search for process STAR_ALIGN in the nf-core/rnaseq Github repo. We have standardised the structure of Nextflow DSL2 pipelines such that all module files will be present in the modules/ directory and so, based on the search results, the file we want is modules/nf-core/software/star/align/main.nf. If you click on the link to that file you will notice that there is a label directive at the top of the module that is set to label process_high. The Nextflow label directive allows us to organise workflow processes in separate groups which can be referenced in a configuration file to select and configure subset of processes having similar computing requirements. The default values for the process_high label are set in the pipeline's base.config which in this case is defined as 72GB. Providing you haven't set any other standard nf-core parameters to cap the maximum resources used by the pipeline then we can try and bypass the STAR_ALIGN process failure by creating a custom config file that sets at least 72GB of memory, in this case increased to 100GB. The custom config below can then be provided to the pipeline via the -c parameter as highlighted in previous sections.

process {
    withName: 'NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN' {
        memory = 100.GB
    }
}

NB: We specify the full process name i.e. NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN in the config file because this takes priority over the short name (STAR_ALIGN) and allows existing configuration using the full process name to be correctly overridden. If you get a warning suggesting that the process selector isn't recognised check that the process name has been specified correctly.

Updating containers

The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. If for some reason you need to use a different version of a particular tool with the pipeline then you just need to identify the process name and override the Nextflow container definition for that process using the withName declaration. For example, in the nf-core/viralrecon pipeline a tool called Pangolin has been used during the COVID-19 pandemic to assign lineages to SARS-CoV-2 genome sequenced samples. Given that the lineage assignments change quite frequently it doesn't make sense to re-release the nf-core/viralrecon everytime a new version of Pangolin has been released. However, you can override the default container used by the pipeline by creating a custom config file and passing it as a command-line argument via -c custom.config.

  1. Check the default version used by the pipeline in the module file for Pangolin

  2. Find the latest version of the Biocontainer available on Quay.io

  3. Create the custom config accordingly:

    • For Docker:

      process {
    withName: PANGOLIN {
        container = 'quay.io/biocontainers/pangolin:3.0.5--pyhdfd78af_0'
    }
}

    • For Singularity:

      process {
    withName: PANGOLIN {
        container = 'https://depot.galaxyproject.org/singularity/pangolin:3.0.5--pyhdfd78af_0'
    }
}

    • For Conda:

      process {
    withName: PANGOLIN {
        conda = 'bioconda::pangolin=3.0.5'
    }
}

NB: If you wish to periodically update individual tool-specific results (e.g. Pangolin) generated by the pipeline then you must ensure to keep the work/ directory otherwise the -resume ability of the pipeline will be compromised and it will restart from scratch.

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.

Tutorials

Which tool for which data type

How to run sarek when not all reference files are in igenomes

How to deal with a (custom) annotation cache

Examples for both snpeff and VEP

Containers

With Nextflow DSL2, each process use its own Conda environment or container from biocontainers.

For annotation, cache has to be downloaded, or specifically designed containers are available with cache.

sareksnpeff, our snpeff container is designed using Conda.

sareksnpeff-docker status

Based on nfcore/base:1.12.1, it contains:

  • snpEff 4.3.1t
  • Cache for GRCh37, GRCh38, GRCm38, CanFam3.1 or WBcel235

sarekvep, our vep container is designed using Conda.

sarekvep-docker status

Based on nfcore/base:1.12.1, it contains:

  • GeneSplicer 1.0
  • VEP 99.2
  • Cache for GRCh37, GRCh38, GRCm38, CanFam3.1 or WBcel235

Using downloaded cache

Both snpEff and VEP enable usage of cache. If cache is available on the machine where Sarek is run, it is possible to run annotation using cache. You need to specify the cache directory using --snpeff_cache and --vep_cache in the command lines or within configuration files. The cache will only be used when --annotation_cache and cache directories are specified (either in command lines or in a configuration file).

Example:

nextflow run nf-core/sarek --tools snpEff --step annotate --sample <file.vcf.gz> --snpeff_cache </path/to/snpEff/cache> --annotation_cache
nextflow run nf-core/sarek --tools VEP --step annotate --sample <file.vcf.gz> --vep_cache </path/to/VEP/cache> --annotation_cache

Download cache

A Nextflow helper script has been designed to help downloading snpEff and VEP caches. Such files are meant to be shared between multiple users, so this script is mainly meant for people administrating servers, clusters and advanced users.

nextflow run download_cache.nf --snpeff_cache </path/to/snpEff/cache> --snpeff_db <snpEff DB version> --genome <GENOME>
nextflow run download_cache.nf --vep_cache </path/to/VEP/cache> --species <species> --vep_cache_version <VEP cache version> --genome <GENOME>

Using VEP CADD plugin

To enable the use of the VEP CADD plugin:

  • Download the CADD files
  • Specify them (either on the command line, like in the example or in a configuration file)
  • use the --cadd_cache flag

Example:

nextflow run nf-core/sarek --step annotate --tools VEP --sample <file.vcf.gz> --cadd_cache \
    --cadd_indels </path/to/CADD/cache/InDels.tsv.gz> \
    --cadd_indels_tbi </path/to/CADD/cache/InDels.tsv.gz.tbi> \
    --cadd_wg_snvs </path/to/CADD/cache/whole_genome_SNVs.tsv.gz> \
    --cadd_wg_snvs_tbi </path/to/CADD/cache/whole_genome_SNVs.tsv.gz.tbi>

Downloading CADD files

An helper script has been designed to help downloading CADD files. Such files are meant to be share between multiple users, so this script is mainly meant for people administrating servers, clusters and advanced users.

nextflow run download_cache.nf --cadd_cache </path/to/CADD/cache> --cadd_version <CADD version> --genome <GENOME>

How to set sarek up to use sentieon

Sentieon

Sentieon is a commercial solution to process genomics data with high computing efficiency, fast turnaround time, exceptional accuracy, and 100% consistency.

Please refer to the nf-core/configs repository on how to make a pipeline-specific configuration file based on the munin-sarek specific configuration file.

Or ask us on the nf-core Slack on the following channels: #sarek or #configs.

Alignment

Sentieon BWA matches BWA-MEM with > 2X speedup.

This tool is enabled by default within Sarek if both --sentieon and --step mapping are specified.

Germline SNV/INDEL Variant Calling - DNAseq

Precision FDA award-winning software. Matches GATK 3.3-4.1, and without down-sampling. Results up to 10x faster and 100% consistent every time.

This tool is enabled within Sarek if both --sentieon and --tools DNAseq are specified.

Germline SNV/INDEL Variant Calling - DNAscope

Improved accuracy and genome characterization. Machine learning enhanced filtering producing top variant calling accuracy.

This tool is enabled within Sarek if both --sentieon and --tools DNAscope are specified.

Somatic SNV/INDEL Variant Calling - TNscope

Winner of ICGC-TCGA DREAM challenge. Improved accuracy, machine learning enhanced filtering. Supports molecular barcodes and unique molecular identifiers.

This tool is enabled within Sarek if both --sentieon and --tools TNscope are specified.

Structural Variant Calling

Germline and somatic SV calling, including translocations, inversions, duplications and large INDELs

This tool is enabled within Sarek if both --sentieon and --tools DNAscope are specified.

Troubleshooting & FAQ

Spark related issues

If you have problems running processes that make use of Spark such as MarkDuplicates. You are probably experiencing issues with the limit of open files in your system. You can check your current limit by typing the following:

ulimit -n

The default limit size is usually 1024 which is quite low to run Spark jobs. In order to increase the size limit permanently you can:

Edit the file /etc/security/limits.conf and add the lines:

*     soft   nofile  65535
*     hard   nofile  65535

Edit the file /etc/sysctl.conf and add the line:

fs.file-max = 65535

Edit the file /etc/sysconfig/docker and add the new limits to OPTIONS like this:

OPTIONS=”—default-ulimit nofile=65535:65535"

Re-start your session.

Note that the way to increase the open file limit in your system may be slightly different or require additional steps.