@@ -70,9 +70,9 @@ In the `tags` key inside a sample or library users can supply tags that belong t
The settings config enables a user to alter the settings for almost all settings available in the tools used for a given pipeline.
This config file should be written in either JSON or YAML format. It can contain setup settings like:
* references,
* cut offs,
* program modes and memory limits (program specific),
* references
* cut offs
* program modes and memory limits (program specific)
* Whether chunking should be used
* set program executables (if for some reason the user does not want to use the systems default tools)
* One could set global variables containing settings for all tools used in the pipeline or set tool specific options one layer
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@@ -128,9 +128,13 @@ It is also possible to set the `"species"` flag. Again, we will default to `unkn
# More advanced use of config files.
### 4 levels of configuring settings
In biopet, a value of a ConfigNamespace (e.g., "reference_fasta") for a tool or a pipeline can be defined in 4 different levels.
* Level-4: As a fixed value hardcoded in biopet source code
* Level-3: As a user specified value in the user config file
* Level-2: As a system specified value in the global config files. On the LUMC's SHARK cluster, these global config files are located at /usr/local/sasc/config.
* Level-1: As a default value provided in biopet source code.
During execution, biopet framework will resolve the value for each ConfigNamespace following the order from level-4 to level-1. Hence, a value defined in the a higher level will overwrite a value define in a lower level for the same ConfigNamespace.
| -o | --outputConfig | Path (**required**) | Name of the config file that gets generated.|
| -s | --outputScript | Path (optional) | Biopet can also output a script that can be directly used for running the pipeline, the call of the pipeline is generated with the config file as input. This parameter sets the name for the script file.|
| -t | --template | Path (optional) | A template file with 2 placeholders *%s* is required for generating the script. The first placeholder will be replaced with the name of the pipeline, the second with the paths to the sample and settings config files. When Biopet has been pre-configured to use the default template file, then setting this parameter is optional. |
| | --expert | | This flag enables the user to configure a more extensive list of parameters for the pipeline. |
\ No newline at end of file
| | --expert | | This flag enables the user to configure a more extensive list of parameters for the pipeline. |
This pipeline is build for variant calling on NGS data (preferably Illumina data).
It is based on the <ahref="https://www.broadinstitute.org/gatk/guide/best-practices"target="_blank">best practices</a>) of GATK in terms of their approach to variant calling.
This pipeline is built for variant calling on NGS data (preferably Illumina data). Part of this pipeline resembles the <ahref="https://www.broadinstitute.org/gatk/guide/best-practices"target="_blank">best practices</a>) of GATK in terms of their approach to variant calling.
The pipeline accepts ```.fastq & .bam``` files as input.
----
## Tools for this pipeline
## Overview of tools and sub-pipelines for this pipeline
Note that one should first create the appropriate [configs](../general/config.md).
Note that one should first create the appropriate sample and pipeline setting [configs](../general/config.md).
### Sample input extensions
Shiva pipeline can start from FASTQ or BAM files. This pipeline will include pre-process steps for the BAM files.
Please refer [to our mapping pipeline](mapping.md) for information about how the input samples should be handled.
Shiva is a special pipeline in the sense that it can also start directly from `bam` files. Note that one should alter the sample config field from `R1` into `bam`.
### Full pipeline
The full pipeline can start from fastq or from bam file. This pipeline will include pre-process steps for the bam files.
When using BAM files as input, Note that one should alter the sample config field from `R1` into `bam`.
A dry run can be performed by simply removing the `-run` flag from the command line call.
[Gears](gears) is run automatically for the data analysed with `Shiva`. There are two levels on which this can be done and this should be specified in the [config](../general/config) file:
*`mapping_to_gears: unmapped` : Unmapped reads after alignment. (default)
*`mapping_to_gears: all` : Trimmed and clipped reads from [Flexiprep](flexiprep).
*`mapping_to_gears: none` : Disable this functionality.
### Only variant calling
It is possible to run Shiva while only performing its variant calling steps.
This has been separated in its own pipeline named `shivavariantcalling`.
As this calling pipeline starts from BAM files, it will naturally not perform any pre-processing steps.
| haplotypecaller_allele | <ahref="https://www.broadinstitute.org/gatk/gatkdocs/org_broadinstitute_gatk_tools_walkers_haplotypecaller_HaplotypeCaller.php">haplotypecaller</a> | Only genotype a given list of alleles with HaplotypeCaller |
| unifiedgenotyper_allele | <ahref="https://www.broadinstitute.org/gatk/gatkdocs/org_broadinstitute_gatk_tools_walkers_genotyper_UnifiedGenotyper.php">unifiedgenotyper</a> | Only genotype a given list of alleles with UnifiedGenotyper |
| shiva | species | String | unknown_species | Name of species, like H.sapiens | all |
| shiva | reference_name | String | unknown_reference_name | Name of reference, like hg19 | all |
| shiva | reference_fasta | String | | reference to align to | all |
| shiva | dbsnp_vcf | String | | vcf file of dbsnp records | haplotypecaller, haplotypecaller_gvcf, haplotypecaller_allele, unifiedgenotyper, unifiedgenotyper_allele |
| shiva | variantcallers | List[String] | | variantcaller to use, see list | all |
| shiva | input_alleles | String | | vcf file contains sites of interest for genotyping (including HOM REF calls). Only used when haplotypecaller_allele or unifiedgenotyper_allele is used. | haplotypecaller_allele, unifiedgenotyper_allele |
Since Shiva uses the [Mapping](mapping.md) pipeline internally, mapping config values can be specified as well.
For all the options, please see the corresponding documentation for the mapping pipeline.
### Exome variant calling
----
If one calls variants with Shiva on exome samples and a ```amplicon_bed``` file is available, the user is able to add this file to the config file.
When the file is given, the coverage over the positions in the bed file will be calculated plus the number of variants on each position. If there is an interest
in a specific region of the genome/exome one is capable to give multiple ```regionOfInterest.bed``` files with the option ```regions_of_interest``` (in list/array format).
A short recap: the option ```amplicon_bed``` can only be given one time and should be composed of the amplicon kit used to obtain the exome data.
The option ```regions_of_interest``` can contain multiple bed files in ```list``` format and can contain any region a user wants. If multiple regions are given,
the pipeline will make an coverage plot over each bed file separately.
## Advanced usage
### Modes
### Reporting modes
Shiva furthermore supports three modes. The default and recommended option is `multisample_variantcalling`.
During this mode, all bam files will be simultaneously called in one big VCF file. It will work with any number of samples.
On top of that, Shiva provides two separate modes that only work with a single sample.
Additionally, Shiva provides two separate modes that only work with a single sample.
Those are not recommended, but may be useful to those who need to validate replicates.
Mode `single_sample_variantcalling` calls a single sample as a merged bam file.
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@@ -175,41 +151,88 @@ The config for these therefore is:
If one calls variants with Shiva on exome samples and an ```amplicon_bed``` file is available, the user is able to add this file to the config file.
When the file is given, the coverage over the positions in the bed file will be calculated plus the number of variants on each position. If there is an interest
in a specific region of the genome/exome one is capable to give multiple ```regionOfInterest.bed``` files with the option ```regions_of_interest``` (in list/array format).
A short recap: the option ```amplicon_bed``` can only be given one time and should be composed of the amplicon kit used to obtain the exome data.
The option ```regions_of_interest``` can contain multiple bed files in ```list``` format and can contain any region a user wants. If multiple regions are given,
the pipeline will make an coverage plot over each bed file separately.
### VEP annotation
Shiva can be linked to our VEP based annotation pipeline to annotate the VCF files.
**example config**
```yaml
toucan:
vep_version:86
enable_scatter:false
```
**Additional XHMM CNV calling example**
### SV calling
In addition to standard variant calling, Shiva also supports SV calling.
One can enable this option by setting the `sv_calling` config option to `true`.
**example config**
```yaml
shiva:
sv_calling:true
sv_callers:
-breakdancer
-delly
-clever
pysvtools:
flanking:100
```
### CNV calling
In addition to standard variant calling, Shiva also supports CNV calling.
One can enable this option by setting the `cnv_calling` config option to `true`.
For CNV calling Shiva uses the [Kopisu](kopisu.md) as a sub-pipeline.
