Janis Prepare

  • # * = - .

prepare is functionality of Janis to improve the process of running pipelines. Specifically, Janis prepare will perform a few key actions:

  • Downloads reference datasets
  • Performs simple transforms data into the correct format (eg: VCF -> gVCF)
  • Checks quality of some inputs:
    • for example, contigs in a bed file match what’s in a declared reference


Prepare only works with Janis pipelines


The janis prepare command line is almost exactly the same as the janis run. You should supply it with inputs (either through an inputs yaml or on the command line) and any other configuration options. You must supply an output directory (or declare an output_dir in your janis config), the job file and a run script is written to this directory. The job file is also always written to stdout.

For example:

# Write inputs file
cat <<EOT >> inputs.yaml
sampleName: NA12878
- - /<fastqdata>/WGS_30X_R1.fastq.gz
  - /<fastqdata>//WGS_30X_R2.fastq.gz

# run janis prepare
janis prepare \
    -o "$HOME/janis/WGSGermlineGATK-run/" \
    --inputs inputs.yaml \
    --source-hint hg38 \

This will: - Write an inputs file to disk, - download all the hg38 reference files - transform any data types that might need to be transformed, eg:

  • gridss blacklist requires a bed, but the source hint gives a gzipped bed (ENCFF001TDO.bed.gz)
  • snps_dbsnp wants a compressed and tabix indexed VCF, but the source hint gives a regular VCF.
  • reference build the appropriate indexes for the hg38 assembly (as these aren’t downloaded by default)
  • Perform some sanity checks on the data you’ve provided, eg:
    • the contigs in the gridss_blacklist will be checked against those found in the assembly’s reference.
  • Write a job file and run script into the output directory.

Downloading reference datasets

A source is declared on an input through the input documentation, there are two methods for doing this:

  1. Single file - this file is always used regardless of the inputs
  2. A dictionary of source hints. This might be useful for specifying a reference type, eg hg38 or hg19. However this does involve the pipeline author finding public datasets for each hint.


import janis_core as j

myworkflow = j.WorkflowBuilder("wf")

# Input that can be used for ALL workflows

# Input that is only localised if the hint is specified
            "hg19": "https://www.encodeproject.org/files/ENCFF001TDO/@@download/ENCFF001TDO.bed.gz",
            "hg38": "https://www.encodeproject.org/files/ENCFF356LFX/@@download/ENCFF356LFX.bed.gz",

Secondary files and localising sources

By default, secondary files are downloaded with the primary file. The pipeline author can optionally request that secondary files be not downloaded with skip_sourcing_secondary_files=True. In particular, this is important for the exemplar WGS pipelines, because the BWA indexes (".amb", ".ann", ".bwt", ".pac", ".sa") were generated with a different version of BWA.

Although the Janis Transformation step of the janis prepare will perform the re-index, this could take a few hours. If you can speed up this step, please raise an issue <https://github.com/PMCC-BioinformaticsCore/janis-bioinformatics/issues/new> or open a Pull Request!!

Types of reference paths

Janis can localise remote paths through its FileScheme mechanic. Currently, Janis supports the following fileschemes:

  • Local (useful for keeping local references to a pipeline definition when sharing a pipeline internally)
  • HTTP http:// OR http:// prefix (using a GET request)
  • GCS gs:// prefix (public buckets only)

Requires more work: - S3: Implementation required

Input Documentation Source

An InputDocumentation class can be used to document the different options about an input. See the :class:janis.InputDocumentation initialiser below. You can supply it directly to the doc field of an input, but you can also provide a dictionary which will get converted into an InputDocumentation, for example:

w = j.WorkflowBuilder("wf")

# using the InputDocumentation class
w.input("inp1", str, doc=j.InputDocumentation(doc="This is inp1", quality=j.InputQualityType.user))

# Use a dictionary
w.input("inp2", str, doc={"doc": "This is inp2", "quality": "user"})
class janis.InputDocumentation(doc: Optional[str], quality: Union[janis_core.tool.documentation.InputQualityType, str] = <InputQualityType.user: 'user'>, example: Union[str, List[str], None] = None, source: Union[str, List[str], Dict[str, Union[str, List[str]]], None] = None, skip_sourcing_secondary_files=False)[source]
__init__(doc: Optional[str], quality: Union[janis_core.tool.documentation.InputQualityType, str] = <InputQualityType.user: 'user'>, example: Union[str, List[str], None] = None, source: Union[str, List[str], Dict[str, Union[str, List[str]]], None] = None, skip_sourcing_secondary_files=False)[source]

Extended documentation for inputs

  • doc (str) – Documentation string
  • quality (InputQualityType | "user" | "static" | "configuration") – quality of input, whether the inputs are best classified by user (data), static (references), configuration (like constants, but tweakable)
  • example (str | List[str]) – An example of the filename, displayed in the generated example input.yaml
  • source (str | List[str] | Dict[str, str | List[str]]) – A URI of this input, that Janis could localise if it’s not provided. For example, you might want to specify a gs://<path>
  • skip_sourcing_secondary_files (bool) – Skip localising the secondary files from the source. You might want to do this if the secondary files depend on the version of the tool (eg: BWA)


A strong benefit of the rich data types that Janis provides, means that we can do perform basic transformations to prepare your data for the pipeline. Simply, we’ve taught Janis how to perform some basic transformations (using tools in Janis!), and then Janis can determine how to convert your data, for example:

If you provide a VCF, and the pipeline requires a gzipped and tabix’d VCF, we construct a prepare stage that performs this for you:

VCF -> VcfGZ -> VcfGzTabix

More information


These Janis transformations are performed AFTER the reference localisation, so janis can transform your downloaded file to the correct format if possible.

Quality Checks

These are a number of fairly custom checks to catch some frequent errors that we’ve seen:

  • Input checker: makes sure your inputs can be found (only works for local paths)
  • Contig checker: If you define a single input of FASTA type and any number of inputs with BED type, Janis will check that the contigs you declare in the BEDs are found in the .fasta.fai index. This check only returns warnings, and will NOT stop you from running a workflow.

Janis performs some of these checks on every run, some are only performed on the janis prepare.

Developer notes

The Janis prepare steps are all implemented using PipelineModifiers:

  • FileFinderLocatorModifier
  • InputFileQualifierModifier
  • InputTransformerModifier
  • InputChecker
  • ContigChecker

If entering through the prepare cli method, the run_prepare_processing flag is set which initialises a custom set of pipeline modifiers to execute while preparing the job file.

Janis Transformations

Janis transformations are fairly simple, they’re defined in the relevant tool registry (eg: janis-bioinformatics), and exposed through the janis.datatype_transformations entrypoint. These JanisTransformations are added to a graph, and then we just perform a breadth first search on this graph looking for the shortest number of steps to connect two data types. Transformations are directional, and no logic is performed to evaluate the weight or effort of a step.

The Conversion Problem (Janis guide) describes JanisTransformations in a blog sort of style.

Job File

Although not strictly related to Janis Prepare, it was an important change that was made for janis-prepare to work correctly. Functionally, a PreparedJob describes everything needed to run a workflow (except the workflow). It’s an amalgamation of different sections of the janis configuration, adjusted inputs and runtime configurations. It’s serializable, which means it can automatically be written to disk and parsed back in.

A run.sh script can be generated, which just calls the job file with the workflow reference, something like:

# This script was automatically generated by Janis on 2021-01-13 11:14:40.121673.

janis run \
    -j /Users/franklinmichael/janis/hello/20210113_111440/janis/job.yaml \