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Sateesh Peri edited this page Mar 29, 2022 · 11 revisions

nf-core/mycosnp: Usage

⚠️ This documentation is available on the mycosnp website: https://github.com/CDCgov/mycosnp-nf/blob/master/docs/usage.md

Introduction

CDCgov/mycosnp-nf is a bioinformatics best-practice analysis pipeline written to nf-core standards. MycoSNP is a portable workflow for performing whole genome sequencing analysis of fungal organisms, including Candida auris. This method prepares the reference, performs quality control, and calls variants using a reference. MycoSNP generates several output files that are compatible with downstream analytic tools, such as those used for phylogenetic tree-building and gene variant annotations. This document will describe how to prepare input files and run the pipeline.

Requirements

  • Nextflow >= 21.10.3
  • Java 8 or later
  • Bash 3.2 or later
  • Singularity (Optional/Recommended)
  • Docker (Optional/Recommended)
  • Conda (Optional/Recommended)

Installation

  • mycosnp-nf is written in Nextflow, and as such requires Nextflow installation to run. Please see nextflow installation documents for instructions.

  • Alternatively, you can install nextflow and other dependencies via conda like so:

conda create -n nextflow -c bioconda -c conda-forge nf-core=2.2 nextflow=21.10.6 git=2.35.0 openjdk=8.0.312 graphviz
conda activate nextflow
git clone https://github.com/CDCgov/mycosnp-nf
  • Alternatively, mycosnp-nf can be pulled from github and run directly like so:
nextflow run CDCgov/mycosnp-nf -profile singularity,test

Reference input

You will need to provide the reference sequence in fasta format. You can pass the location of the fasta file using the --fasta argument.

--fasta '[path to fasta file]'

Alternatively, you can skip the reference file processing steps by providing the files needed. This can be done in one of two ways.

  • First way is by supplying a directory which has all the reference files from a previous mycosnp run using '--ref_dir'.
  • This expects the following directory format: ** masked fasta in <ref_dir>/masked/*.fa* ** picard dict in /dict/.dict ** samtools fai in /fai/.fai ** bwa mem index directory in /bwa/bwa 
--ref_dir 'results/reference'
  • Second way is by providing each of the files separately.
    • --ref_masked_fasta path/to/ref.fasta
    • --ref_fai path/to/fai/file.fai
    • --ref_bwa path/to/bwa/directory
    • --ref_dict path/to/picard/dict/file.dict
--ref_masked_fasta results-copy/reference/masked/reference.fa --ref_fai results-copy/reference/fai/reference.fa.fai --ref_bwa results-copy/reference/bwa/bwa --ref_dict results-copy/reference/dict/reference.dict

Samplesheet input

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

--input '[path to samplesheet file]'

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,fastq_3,fastq_4
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz,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 4 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_REP3,AEG588A6_S6_L003_R1_001.fastq.gz,AEG588A6_S6_L003_R2_001.fastq.gz,AEG588A6_S6_L004_R1_001.fastq.gz,AEG588A6_S6_L004_R2_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.

Samplesheet creation - automated

A script is available to create a samplesheet from a directory of fastq files. The script will search 1 directory deep and attempt to determine sample id names and pairing/multilane information and will automatically create a samplesheet. Please review the samplesheet for accuracy before using it in the pipeline.

mycosnp-nf/bin/mycosnp_full_samplesheet.sh <directory of fastq files> > new_samplesheet.csv

SRA sequence file additions

You may provide a list of SRA ids as additional inputs sequences into the pipeline. Use the --add_sra_file parameter to specify its location. It has to be a comma-separated file (csv) with one or 2 columns, and NO header row as shown in the examples below.

--add_sra_file '[path to samplesheet file: assets/sra_small.csv]'

Example File:

If two fields are provided, the first field will be used as the sequence name, and the second field will be used to specify the SRA id to download. If one field is provided, it must be the SRA id, and this SRA id will be used as the sequence name.

B12352,SRR7909282
SRR7909249
B13520,SRR7909394

VCF file additions

You may provide a list VCF files from previous runs of this pipeline as additional inputs sequences into the pipeline. These VCF file must have used the exact same reference file when they were generated. The *.tbi index file must be within the same directory as the vcf file and have the same name. Use the --add_vcf_file parameter to specify its location. It has to be plain text file with the full path to the vcf file on each line, and NO header row as shown in the examples below.

--add_vcf_file '[path to vcf file: assets/vcf_add.txt]'

Example File:

/mydir/results_2021/samples/B12044/variant_calling/haplotypecaller/B12044.g.vcf.gz
/mydir/results_control/samples/B12352/variant_calling/haplotypecaller/B12352.g.vcf.gz
/mydir/results_2020/B12427.g.vcf.gz
/mydir/results/samples/B12430/variant_calling/haplotypecaller/B12430.g.vcf.gz

Pipeline parameters

Sane recommended defaults parameters are used, but full documentation on the default parameters can be viewed in the help documentation or on mycosnp-nf wiki. You can see full pipeline parameters by using '-help' when running the workflow.

nextflow run main.nf --help
# Or
nextflow run CDCgov/mycosnp-nf --help

Some parameters are hidden, but can be seen by using the --show_hidden_params option:

nextflow run main.nf -help --show_hidden_params
# Or
nextflow run CDCgov/mycosnp-nf -help --show_hidden_params

Running the pipeline

The typical command for running the pipeline is as follows:

For a minimal test run:

nextflow run main.nf -profile singularity,test

For a full test run:

nextflow run main.nf -profile singularity,test_full

For a real run:

nextflow run main.nf -profile singularity --input samplesheet.csv --fasta reference_genome.fasta

Additional examples:

nextflow run main.nf \
  -profile singularity \
  --fasta "https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/016/772/135/GCA_016772135.1_ASM1677213v1/GCA_016772135.1_ASM1677213v1_genomic.fna.gz" \
  --custom_config_base ~/nf-core-custom \
  --publish_dir_mode copy \
  --add_sra_file assets/sra_ca_controls.csv \
  --outdir sra_controlsdata

This will launch the pipeline with the singularity 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.

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 CDCgov/mycosnp-nf

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 CDCgov/mycosnp-nf releases page and find the latest version number - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.0.1.

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.

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 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.

  • 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.

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 mycosnp-nf pipeline is failing after multiple re-submissions of the BWA_PRE_PROCESS:FAQCS process due to an exit code of 137 this would indicate that there is an out of memory issue:

[62/149eb0] NOTE: Process `MYCOSNP:BWA_PRE_PROCESS:FAQCS (SAMPLE_1` terminated with an error exit status (137) -- Execution is retried (1)
Error executing process > 'MYCOSNP:BWA_PRE_PROCESS:FAQCS (SAMPLE_1'

Caused by:
    Process `MYCOSNP:BWA_PRE_PROCESS:FAQCS (SAMPLE_1)` terminated with an error exit status (137)

Command executed:
FaQCs \
            -d . \
            -u SAMPLE_1.fastq.gz \
            --prefix SAMPLE_1_clean \
            -t 8 \
        <TRUNCATED>

Command exit status:
    137

Command output:
    (empty)

Command error:
    .command.sh: line 9:  30 Killed    FaQCs -d . -u SAMPLE_1.fastq.gz -t 8 <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 CDCgov/mycosnp-nf 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/modules/faqcs/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_medium. The Nextflow label directive allows us to organize 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_medium label are set in the pipeline's base.config which in this case is defined as 12GB. 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 FAQCS process failure by creating a custom config file that sets at least 12GB of memory, in this case increased to 50GB. The custom config below can then be provided to the pipeline via the -c parameter as highlighted in previous sections.

process {
    withName: FAQCS {
        memory = 50.GB
    }
}

NB: We specify just the process name i.e. FAQCS in the config file and not the full task name string that is printed to screen in the error message or on the terminal whilst the pipeline is running i.e. MYCOSNP:BWA_PRE_PROCESS:FAQCS. You may get a warning suggesting that the process selector isn't recognised but you can ignore that if the process name has been specified correctly. This is something that needs to be fixed upstream in core Nextflow.

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.

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'
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