Introduction

This is a sister pipeline to TreeVal which generates a plurality of data for the curation of reference-quality genomes. curationpretext is a subset of TreeVal that produces soley the Pretext maps and accessory files

Currently, the pipeline expects input data to be in a specific format.

The --input should be .fasta or .fa (the same format but differing suffix).

The --cram should point to the folder containing .cram files along with a .crai per .cram.

The --longread should point to the folder containing .fasta.gz files.

The --longread_type should be the data type of your data, e.g, ont, illumina, hifi.

The --aligner should be the prefered aligner for analysis, e.g, bwamem2 or minimap2.

The --teloseq should be the expected telomeric sequence in your sample

If you do not have these file formats we have also included instructions on converting from common formats to our preferred format. If there is a popular public preference for a particular format, we can modify the pipeline to utilise those formats. Just submit an issue.

Prior to running CurationPretext

Details

Download the pipeline! git clone https://github.com/sanger-tol/curationpretext.git Or use: git clone https://github.com/sanger-tol/curationpretext.git --branch 1.0.0 --single-branch

This will pull the released version and not an in development version.

Now move into the folder with cd curationpretext

We provide a complete set of data that can be used to test the pipeline locally.

By default the test.config file is set up to run on GitHub, however, should you want to test this locally you can follow the below instructions.

First, choose a download location ${PRETEXT_TEST_DATA} and run this command (this assumes you are inside the curationpretext directory):

PRETEXT_TEST_DATA=$(pwd)
curl https://tolit.cog.sanger.ac.uk/test-data/resources/treeval/TreeValTinyData.tar.gz | tar xzf -

Then replace some of the variables in the config file:

sed -i'' -e "s|/home/runner/work/curationpretext/curationpretext|${PRETEXT_TEST_DATA}|" conf/test.config

You should then check this with cat conf/test.config you should now see paths that make sense rather than what would have been /home/runner paths.

If using singularity like we do you should also set your $NXF_SINGULARITY_CACHEDIR={PATH OF YOUR CHOOSING}. This will be where nextflow stores your singularity containers, for this and any subsequent runs. So clean it out when you update the pipeline otherwise it will fill with oldd containers.

Then, you should be able to run the pipeline (taking into account changes needed to run jobs on your local compute environment) with the test profile as follows:

nextflow run . -profile test,singularity

HiC data Preparation

Details

Illumina HiC read files should be presented in an unmapped CRAM format, each must be accompanied by an index file (.crai) generated by samtools index. If your unmapped HiC reads are in FASTQ format, you should first convert them to CRAM format by using samtools import methods. Examples are below:

Conversion of FASTQ to CRAM 

samtools import -@8 -r ID:{prefix} -r CN:{hic-kit} -r PU:{prefix} -r SM:{sample_name} {prefix}_R1.fastq.gz {prefix}_R2.fastq.gz -o {prefix}.cram

Indexing of CRAM 

samtools index {prefix}.cram

Longread Data Preparation

Details

Before running the pipeline data has to be in the fasta.gz format. Because of the software we use this data with it must also be long-read data as well as single stranded. This means you could use ONT too (except duplex reads).

The below commands should help you convert from mapped bam to fasta.gz, or from fastq to fasta.

If your data isn't already in these formats, then let us know and we'll see how we can help.

BAM -> FASTQ

This command iterates through your bam files and converts them to fastq via samtools.

cd { TO FOLDER OF BAM FILES }
mkdir fastq
for i in *bam
do
  echo $i
  j=${i%.bam}
  echo $j
  samtools bam2fq ${i} > fastq/${j}.fq
done

FASTQ -> FASTA

This command creates a fasta folder (to store our fasta files), moves into the fastq folder and then converts fastq to fasta using seqtk seq.

mkdir fasta
cd fastq
for i in *fq; do
  echo $i
  j=${i%.fq}
  echo $j
  seqtk seq -a $i > ../fasta/${j}.fasta
done

FASTA -> FASTA.GZ

This simply gzips the fasta files.

for i in .fasta; do
  echo $i
  gzip $i
done

Or if you're a command line ninja 

samtools bam2fq {prefix}.bam| seqtk seq -a - | gzip - > {prefix}.fasta.gz

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run sanger-tol/curationpretext \
  --input { input.fasta } \
  --cram { path/to/cram/ } \
  --longread { path/to/pacbio/fasta/ } \
  --longread_type { default is "hifi" }
  --sample { default is "pretext_rerun" } \
  --teloseq { deafault is "TTAGGG" } \
  --outdir { OUTDIR } \
  -profile <docker/singularity/{institute}> \
  -entry MAPS_ONLY # This line is opnly needed for the truncated pipeline, FULL runs do not need this line at all.

Above arguments surrounded with {} are user-defined values, those in <> are choices made between the shown values.

Note that the pipeline will create the following files in your working directory:

work                    # Directory containing the nextflow working files
<OUTDIR>/pipeline_info  # Finished results in specified location (defined with --outdir)
<OUTDIR>/hic_files      # Finished results in specified location (defined with --outdir)
.nextflow_log           # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

⚠️ Do not use -c <file> to specify parameters as this will result in errors. Custom config files specified with -c must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args). The above pipeline run specified with a params file in yaml format:

nextflow run sanger-tol/curationpretext -profile docker -params-file params.yaml -entry <ALL_FILES/MAPS_ONLY>

with params.yaml containing:

input: "./samplesheet.csv"
outdir: "./results/"
teloseq: "GRCh37"
sample: "data"
longread: "longread_path"
cram: "cram_path"

You can also generate such YAML/JSON files via nf-core/launch.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull sanger-tol/curationpretext

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 sanger-tol/curationpretext releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.

To further assist in 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.

💡 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

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, Apptainer, Conda) - see below.

We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended, since it can lead to different results on different machines dependent on the computer 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
  • conda
    • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.

-resume

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see this blog post.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.

-c

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases you may wish to change which container or conda environment a step of the pipeline uses for a particular tool. By default nf-core pipelines use containers and software from the biocontainers or bioconda projects. However in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your 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'