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. It has to be a comma-separated file with 3 columns, and a header row as shown in the examples below.
--input '[path to samplesheet file]'
sample,cram,crai
SAMPLE1,AEG588A1_S1_L002_R1_001.cram,AEG588A1_S1_L002_R2_001.cram.crai
SAMPLE2,AEG588A1_S1_L003_R1_002.cram,AEG588A1_S1_L003_R2_002.cram.crai
SAMPLE3,AEG588A1_S1_L004_R1_003.cram,AEG588A1_S1_L004_R2_003.cram.craiIf a ground truth is available, provide it as a VCF file with the ground_truth_vcf parameter. Note that sample names in the VCF must overlap with the sample names in the cram files for them to be matched correctly.
In the samplesheet, add a column high_cov to specify which cram files are high coverage and should be downsampled before imputation. Set the desired sequencing depth after downsampling with the downsample_coverage parameter.
samplesheet.csv:
sample,cram,crai,high_cov
/path/to/sample1.cram,/path/to/sample1.cram.crai,true
/path/to/sample2.cram,/path/to/sample2.cram.crai,false
And then run the pipeline with:
nextflow run birneylab/stitchimpute \
-profile <docker/singularity/.../institute> \
--input samplesheet.csv \
--outdir <OUTDIR> \
--ground_truth_vcf /path/to/ground_truth.vcf.gz \
--downsample_coverage 0.5Downsampling is done in order to have an accurate representation of the performance of the imputation for sample with a given coverage level. For this reason, it is suggested to set downsample_coverage to a value similar to the coverage of most samples in the analysis cohort.
This pipeline actually consist of three related pipelines, which can be selected with the mode parameter.
The imputation workflow is selected by setting the mode parameter to "imputation". This is the default setting if mode is not specified.
The samples are imputed using STITCH with a specific set of parameters (K and nGen at least must be set, see the documentation of STITCH for more details).
The parameter optimization workflow is selected by setting the mode parameter to "grid_search".
In this workflow K and nGen do not need to be specified.
A set of combinations for the 2 parameters must be provided via the grid_search_params parameter.
This should point to a csv file containing a column with header K and one with header nGen.
Each row contains a combination of K and nGen which the pipeline will use for imputation.
The imputation will be repeated with different parameters for as many times as there are non-header lines in the file specified.
An example of grid_search_params file is the following:
K,nGen
8,1
8,2
16,2
32,2
The SNP set refinement workflow is selected by setting the mode parameter to "snp_set_refinement".
In this mode K and nGen need to be supplied as in the "imputation" mode.
In this mode a first imputation is done, and then the imputation performance is evaluated for each SNP internally by STITCH (info_score), or externally against a ground truth.
A second imputation is then performed only using SNPs that satisfy a certain threshold for the imputation quality metric.
Then a third imputation is performed and so on.
This is the method suggested in the original STITCH paper for obtaining a reliable SNP set (https://www.nature.com/articles/ng.3594).
The parameter snp_filtering_criteria must be set and point to a csv file with no header, and a single column.
Each row should contain a threshold value that is used to filter out SNPs with a performance metric lower than the threshold.
The metric considered is the info_score if ground_truth_vcf is not set, and the Pearson correlation otherwise.
Which performance metric should be used can also be specified manually with the filter_var parameter.
An example of snp_filtering_criteria file is the following:
0.8
0.8
0.9
0.9
The number of filtering iterations performed is equal to the number of rows in the snp_filtering_criteria file.
For each iteration, the filter value of the corresponding line is applied.
For ease of use, the ideal settings for stitch for medaka samples have been specified in a profile called medaka.
This can be activated with the flag -profile medaka.
Always use this profile when working with medaka samples.
For some caveats I cannot set a reference genome directly (medaka in Ensembl is compressed with gzip instead than bgzip) so you still need to provided a path to a valid reference using the fasta parameter.
The typical command for running the pipeline is as follows:
nextflow run birneylab/stitchimpute --input ./samplesheet.csv --outdir ./results --genome GRCh37 -profile dockerThis 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
<OUTDIR> # 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-cmust 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 birneylab/stitchimpute -profile docker -params-file params.yamlwith params.yaml containing:
input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh37'
<...>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 birneylab/stitchimputeIt 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 birneylab/stitchimpute 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.
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.
NB: These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).
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.
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.
Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.
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.
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.
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.
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.
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.
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).
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'