# How to create configs

### The sample config

The sample config should be in [__JSON__](http://www.json.org/) or [__YAML__](http://yaml.org/) format. For yaml the file should be named *.yml or *.yaml.

- First field should have the key __"samples"__
- Second field should contain the __"libraries"__
- Third field contains __"R1" or "R2"__ or __"bam"__
- The fastq input files can be provided zipped and unzipped

#### Example sample config

###### yaml:

``` yaml
samples:
  Sample_ID1:
    libraries:
      MySeries_1:
        R1: R1.fastq.gz
        R2: R2.fastq.gz
```

###### json:

``` json
    {  
       "samples":{  
          "Sample_ID1":{  
             "libraries":{  
                "MySeries_1":{  
                   "R1":"Your_R1.fastq.gz",
                   "R2":"Your_R2.fastq.gz"
                }
             }
          }
       }
    }
```

For BAM files as input one should use a config like this:
  
``` yaml
samples:
  Sample_ID_1:
    libraries:  
      Lib_ID_1:
        bam: MyFirst.bam
      Lib_ID_2:
        bam: MySecond.bam
```


Note that there is a tool called [SamplesTsvToJson](../tools/SamplesTsvToJson.md) this enables a user to get the sample config without any chance of creating a wrongly formatted JSON file.


### The settings config
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),
 * 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 
 deeper into the JSON file. E.g. in the example below the settings for Picard tools are altered only for Picard and not global. 


~~~
"picard": { "validationstringency": "LENIENT" } 
~~~

Global setting examples are:
~~~
"java_gc_timelimit": 98,
"numberchunks": 25,
"chunking": true
~~~


----

#### References
Pipelines and tools that use references should now use the reference module.
This gives a more fine-grained control over references and enables a user to curate the references in a structural way.
E.g. pipelines and tools which uses FASTA references should now set value `"reference_fasta"`.
Additionally, we can set `"reference_name"` for the name to be used (e.g. `"hg19"`). If unset, Biopet will default to `unknown`.
It is also possible to set the `"species"` flag. Again, we will default to `unknown` if unset.
#### Example settings config
~~~
{
        "reference_fasta": "/references/hg19_nohap/ucsc.hg19_nohap.fasta",
        "reference_name": "hg19_nohap",
        "species": "homo_sapiens",
        "dbsnp": "/data/LGTC/projects/vandoorn-melanoma/data/references/hg19_nohap/dbsnp_137.hg19_nohap.vcf",
        "joint_variantcalling": false,
        "haplotypecaller": { "scattercount": 100 },
        "multisample": { "haplotypecaller": { "scattercount": 1000 } },
        "picard": { "validationstringency": "LENIENT" },
        "library_variantcalling_temp": true,
        "target_bed_temp": "/data/LGTC/projects/vandoorn-melanoma/analysis/target.bed",
        "min_dp": 5,
        "bedtools": {"exe":"/share/isilon/system/local/BEDtools/bedtools-2.17.0/bin/bedtools"},
        "bam_to_fastq": true,
        "baserecalibrator": { "memory_limit": 8, "vmem":"16G" },
        "samtofastq": {"memory_limit": 8, "vmem": "16G"},
        "java_gc_timelimit": 98,
        "numberchunks": 25,
        "chunking": true,
        "haplotypecaller": { "scattercount": 1000 }
}
~~~

### JSON validation

To check if the created JSON file is correct their are several possibilities: the simplest way is using [this](http://jsonformatter.curiousconcept.com/)
website. It is also possible to use Python, Scala or any other programming languages for validating JSON files but this requires some more knowledge.