Pipeline interface specification

Table of contents:


In order to run an arbitrary pipeline, we require a formal specification for how the pipeline is to be used. We define this using a pipeline interface file. It maps attributes of a PEP project or sample to the pipeline CLI arguments. Thus, it defines the interface between the project metadata (the PEP) and the pipeline itself.

If you're using existing looper-compatible pipelines, you don't need to create a new interface; just point your project at the one that comes with the pipeline. When creating new looper-compatible pipelines, you'll need to create a new pipeline interface file.

Overview of pipeline interface components

A pipeline interface may contain the following keys:

  • pipeline_name (REQUIRED) - A string identifying the pipeline,
  • pipeline_type (REQUIRED) - A string indicating a pipeline type: "sample" (for run) or "project" (for runp),
  • command_template (REQUIRED) - A Jinja2 template used to construct a pipeline command command to run.
  • input_schema (RECOMMENDED) - A PEP Schema formally defining required inputs for the pipeline
  • output_schema (RECOMMENDED) - A schema describing the outputs of the pipeline
  • compute (RECOMMENDED) - Settings for computing resources
  • var_templates (RECOMMENDED) - A mapping of Jinja2 templates and corresponding names, typically used to encode submission-specific paths that can be submission-specific
  • pre_submit (OPTIONAL) - A mapping that defines the pre-submission tasks to be executed

The pipeline interface should define either a sample pipeline or a project pipeline. Here's a simple example:

pipeline_name: RRBS
pipeline_type: sample
  pipeline: "{looper.piface_dir}/pipelines/pipeline1.py"
  sample_info: "{looper.piface_dir}/{sample.name}/info.txt"
input_schema: path/to/rrbs_schema.yaml
command_template: {pipeline.var_templates.pipeline} --input {sample.data_path} --info {pipeline.sample_info.path}

Pretty simple. The pipeline_name is arbitrary. It's used for messaging and identification. Ideally, it's unique to each pipeline. In this example, we define a single sample-level pipeline.

Details of pipeline interface components


The pipeline name is arbitrary. It should be unique for each pipeline. Looper uses it for a few things:

  1. to construct the job_name variable (accessible via {looper.job_name}). See variable namespaces for more details.

  2. to check for flags. For pipelines that produce flags, looper will be aware of them and not re-submit running jobs.


Looper can run 2 kinds of pipeline: sample pipelines run once per sample; project pipelines run once per project. The type of pipeline must be specified in the pipeline interface as pipeline_type: sample or pipeline_type: project.


The command template is the most critical part of the pipeline interface. It is a Jinja2 template for the command to run for each sample. Within the command_template, you have access to variables from several sources. These variables are divided into namespaces depending on the variable source. You can access the values of these variables in the command template using the single-brace jinja2 template language syntax: {namespace.variable}. For example, looper automatically creates a variable called job_name, which you may want to pass as an argument to your pipeline. You can access this variable with {looper.job_name}. The available namespaces are described in detail in looper variable namespaces.

Because it's based on Jinja2, command templates are extremely flexible. For example, optional arguments can be accommodated using Jinja2 syntax, like this:

command_template: >
  --sample-name {sample.sample_name}
  --genome {sample.genome}
  --input {sample.read1}
  --single-or-paired {sample.read_type}
  {% if sample.read2 is defined %} --input2 {sample.read2} {% endif %}
  {% if sample.peak_caller is defined %} --peak-caller {sample.peak_caller} {% endif %}
  {% if sample.FRIP_ref is defined %} --frip-ref-peaks {sample.FRIP_ref} {% endif %}

Arguments wrapped in Jinja2 conditionals will only be added if the specified attribute exists for the sample.


The input schema formally specifies the input processed by this pipeline. The input schema serves 2 related purposes:

  1. Validation. Looper uses the input schema to ensure that the project fulfills all pipeline requirements before submitting any jobs. Looper uses the PEP validation tool, eido, to validate input data by ensuring that input samples have the attributes and input files required by the pipeline. Looper will only submit a sample pipeline if the sample validates against the pipeline's input schema.

  2. Description. The input schema is also useful to describe the inputs, including both required and optional inputs, thereby providing a standard way to describe a pipeline's inputs. In the schema, the pipeline author can describe exactly what the inputs mean, making it easier for users to learn how to structure a project for the pipeline.

Details for how to write a schema in in writing a schema. The input schema format is an extended PEP JSON-schema validation framework, which adds several capabilities, including

  • required (optional): A list of sample attributes (columns in the sample table) that must be defined
  • required_files (optional): A list of sample attributes that point to input files that must exist.
  • files (optional): A list of sample attributes that point to input files that are not necessarily required, but if they exist, should be counted in the total size calculation for requesting resources.

If no input_schema is included in the pipeline interface, looper will not be able to validate the samples and will simply submit each job without validation.


The output schema formally specifies the output produced by this pipeline. It is used by downstream tools to that need to be aware of the products of the pipeline for further visualization or analysis. Like the input schema, it is based on the extended PEP JSON-schema validation framework, but adds looper-specific capabilities. The base schema has two properties sections, one that pertains to the project, and one that pertains to the samples. The properties sections for both sample and project will recognize these attributes:

  • title, following the base JSON-schema spec.
  • description, following the base JSON-schema spec.
  • path, used to specify a relative path to an output file. The value in the path attribute is a template for a path that will be populated by sample variables. Sample variables can be used in the template using brace notation, like {sample_attribute}.
  • thumbnail_path, templates similar to the path attribute, but used to specify a thumbnail output version.
  • type, the data type of this output. Can be one of: link, image, file.

The attributes added under the Project properties section are assumed to be project-level outputs, whereas attributes under the samples object are sample-level outputs. Here is an example output schema:

description: objects produced by PEPPRO pipeline.
    type: array
      type: object
          path: "aligned_{genome}/{sample_name}_smooth.bw"
          type: string
          description: "A smooth bigwig file"
          path: "aligned_{genome}/{sample_name}_sort.bam"
          type: string
          description: "A sorted, aligned BAM file"
          path: "peak_calling_{genome}/{sample_name}_peaks.bed"
          type: string
          description: "Peaks in BED format"
    title: "TSS enrichment file"
    description: "Plots TSS scores for each sample."
    thumbnail_path: "summary/{name}_TSSEnrichment.png"
    path: "summary/{name}_TSSEnrichment.pdf"
    type: image
    title: "Project peak coverage file"
    description: "Project peak coverages: chr_start_end X sample"
    path: "summary/{name}_peaks_coverage.tsv"
    type: link

Looper uses the output schema in its report function, which produces a browsable HTML report summarizing the pipeline results. The output schema provides the relative locations to sample-level and project-level outputs produced by the pipeline, which looper can then integrate into the output results. If the output schema is not included, the looper report will be unable to locate and integrate the files produced by the pipeline and will therefore be limited to simple statistics.


The compute section of the pipeline interface provides a way to set compute settings at the pipeline level. These variables can then be accessed in the command template. They can also be overridden by values in the PEP config, or on the command line. See the looper variable namespaces for details.

There is one reserved attribute under compute with specialized behavior -- size_dependent_variables which we'll now describe in detail.


The size_dependent_variables section lets you specify variables with values that are modulated based on the total input file size for the run. This is typically used to add variables for memory, CPU, and clock time to request, if they depend on the input file size. Specify variables by providing a relative path to a .tsv file that defines the variables as columns, with input sizes as rows.

The pipeline interface simply points to a tsv file:

pipeline_type: sample
  path: pipelines/pepatac.py
command_template: >
  {pipeline.var_templates.path} ...
  size_dependent_variables: resources-sample.tsv

The resources-sample.tsv file consists of a file with at least 1 column called max_file_size. Add any other columns you wish, each one will represent a new attribute added to the compute namespace and available for use in your command template. Here's an example:

max_file_size cores mem time
0.001 1 8000  00-04:00:00
0.05  2 12000 00-08:00:00
0.5 4 16000 00-12:00:00
1 8 16000 00-24:00:00
10  16  32000 02-00:00:00
NaN 32  32000 04-00:00:00

This example will add 3 variableS: cores, mem, and time, which can be accessed via {compute.cores}, {compute.mem}, and {compute.time}. Each row defines a "packages" of variable values. Think of it like a group of steps of increasing size. For a given job, looper calculates the total size of the input files (which are defined in the input_schema). Using this value, looper then selects the best-fit row by iterating over the rows until the calculated input file size does not exceed the max_file_size value in the row. This selects the largest resource package whose max_file_size attribute does not exceed the size of the input file. Max file sizes are specified in GB, so 5 means 5 GB.

This final line in the resources tsv must include NaN in the max_file_size column, which serves as a catch-all for files larger than the largest specified file size. Add as many resource sets as you want.


This section can consist of multiple variable templates that are rendered and can be reused. The namespaces available to the templates are listed in variable namespaces section. Please note that the variables defined here (even if they are paths) are arbitrary and are not subject to be made relative. Therefore, the pipeline interface author needs take care of making them portable (the {looper.piface_dir} value comes in handy!).


This section can consist of two subsections: python_functions and/or command_templates, which specify the pre-submission tasks to be run before the main pipeline command is submitted. Please refer to the pre-submission hooks system section for a detailed explanation of this feature and syntax.

Validating a pipeline interface

A pipeline interface can be validated using JSON Schema against schema.databio.org/pipelines/pipeline_interface.yaml. Looper automatically validates pipeline interfaces at submission initialization stage.