Project config file

A project config file is organized into sections. Most are optional. Here are additional details on each of the configuration sections:

Project config section: metadata

The metadata section is the only required section of a PEP. It contains paths to various parts of the project: this must include pointers to at least 1) the sample annotation sheet; and 2) the parent output directory.

Here’s a minimal example with just the two required variables:

  sample_annotation: /path/to/sample_annotation.csv
  output_dir: /path/to/parent/output/folder

The sample_annotation value can be absolute or relative; relative paths are assumed relative to the location of the project_config.yaml file.

The metadata section also allows for another attribute, called sample_subannotation, which points to a second annotation table to accommodate samples with multiple values for one or more attributes. Most projects do not require subannotation tables, but for more details, see the advanced section on sample subannotation.

Project config sections: derived_attributes and data_sources

The sections called derived_attributes and data_sources provide a flexible way to point to data files on disk. These two sections go together (so they aren’t meaningful independently; if you define one you should define the other).

The derived_attributes section provides a way to link short keys to variable-encoded file paths. The variables in the file paths are formatted as {variable}, and are populated by sample attributes (columns in the sample annotation sheet). For example, your files may be stored in /path/to/{sample_name}.fastq, where {sample_name} will be populated individually for each sample in your PEP.


  key1: "/path/to/raw/data/{sample_name}_{sample_type}.bam"
  key2: "/path/from/collaborator/weirdNamingScheme_{external_id}.fastq"
  key3: "${HOME}/{test_id}.fastq"

You can also use shell environment variables (like ${HOME}).

The derived_attributes section simply identifies which column names (or sample attributes) should be populated as data_sources. Corresponding sample attributes will then have as their value derived from the string replacement of sample attributes specified in the config file. This enables you to point to more than one input file for each sample.


derived_attributes: [read1, read2, data_1]

For more details and a complete example, see derived attributes.

Project config section: implied_attributes

implied_attributes lets you infer additional attributes, which can be useful for pipeline arguments. For instance, it may that one sample attribute implies several more. Rather than encoding these each as separate, non-varying columns in the annotation sheet, you may simply indicate in the project_config.yaml that samples of a certain type should automatically inherit additional attributes.


      genome: "hg38"
      macs_genome_size: "hs"

This example says that any sample with organism attribute set to the string “human” should also automatically set additional attributes of genome (with value “hg38”) and macs_genome_size (with value “hs”). These implied attributes may now be used as pipeline arguments or for other analysis.

For more details, see implied attributes.

Project config section: subprojects

Subprojects are useful to define multiple similar projects within a single project config file. Under the subprojects key, you can specify names of subprojects, and then underneath these you can specify any project config variables that you want to overwrite for that particular subproject.

For example:

	  sample_annotation: psa_rrbs_diverse.csv
	  sample_annotation: psa_rrbs_intracancer.csv

This project would specify 2 subprojects that have almost the exact same settings, but change only their metadata.sample_annotation parameter (so, each subproject points to a different sample annotation sheet). Rather than defining two 99% identical project config files, you can use a subproject.

For more details, see subprojects.

Project config section: constants

This section lets you declare additional attributes, for each of which there’s a single value across all samples. This is particularly useful when combined with derived_attributes and/or implied_attributes, especially when there are many samples.


  data_source: src
  read_type: SINGLE
  organism: mouse

Looper sections

The following sections are specific to looper, which is one tool in pepkit. These sections are optional and only useful if you’re planning to use looper to submit pipelines for your project.

Project config section: metadata

The metadata section (described previously) has some additional attributes that are understood by looper. It may optionally include pointers to the submission-script subdirectory (where submit scripts are stored), the results subdirectory, and pipeline interface files.


  sample_annotation: /path/to/sample_annotation.csv
  output_dir: /path/to/parent/output/folder
  results_subdir: results_pipeline
  submission_subdir: submission
  pipeline_interfaces: /path/to/pipeline_interface.yaml

Project config section: pipeline_config

Occasionally, a particular project needs to run a particular flavor of a pipeline. Rather than creating an entirely new pipeline, you can parameterize the differences with a pipeline config file, and then specify that file in the project config file.


  # pipeline configuration files used in project.
  # Key string must match the _name of the pipeline script_ (including extension)
  # Relative paths are relative to this project config file.
  # Default (null) means use the generic config for the pipeline. null
  # Or you can point to a specific config to be used in this project: wgbs_flavor1.yaml

This will instruct looper to pass -C wgbs_flavor1.yaml to any invocations of (for this project only). Your pipelines will need to understand the config file (which will happen automatically if you use pypiper).

Project config section: pipeline_args

Sometimes a project requires tweaking a pipeline, but does not justify a completely separate pipeline config file. For simpler cases, you can use the pipeline_args section, which lets you specify command-line parameters via the project config. This lets you fine-tune your pipeline, so it can run slightly differently for different projects.


pipeline_args:  # pipeline identifier: must match the name of the pipeline script
	# here, include all project-specific args for this pipeline
	"--flavor": simple
	"--flag": null

For flag-like options (options without parameters), you should set the value to the yaml keyword null (which means no value). Looper will pass the key to the pipeline without a value. The above specification will now pass --flavor=simple and --flag (no parameter) whenever is invoked – for this project only. This is a way to control (and record!) project-level pipeline arg tuning. The only keyword here is pipeline_args; all other variables in this section are specific to particular pipelines, command-line arguments, and argument values.

Project config section: compute

You can specify project-specific compute settings in a compute section. However, you’re better off specifying this globally using a pepenv environment configuration. Instructions are at the pepenv repository <>_. If you do need project-specific control over compute settings (like submitting a certain project to a certain resource account), you can do this by specifying variables in a project config compute section, which will override global pepenv values for that project only.

	  partition: project_queue_name

Project config example

Here’s an example. Additional fields can be added as well and will be ignored.

  # Relative paths are considered relative to this project config file.
  # Typically, this project config file is stored with the project metadata
  # sample_annotation: one-row-per-sample metadata
  sample_annotation: table_experiments.csv
  # merge_table: input for samples with more than one input file
  merge_table: table_merge.csv
  # compare_table: comparison pairs or groups, like normalization samples
  compare_table: table_compare.csv
  # output_dir: the parent, shared space for this project where results go
  output_dir: /fhgfs/groups/lab_bock/shared/projects/example
  # results and submission subdirs are subdirectories under parent output_dir
  # results: where output sample folders will go
  # submission: where cluster submit scripts and log files will go
  results_subdir: results_pipeline
  submission_subdir: submission
  # pipeline_interfaces: the pipeline_interface.yaml file or files for Looper pipelines
  # scripts (and accompanying pipeline config files) for submission.
  pipeline_interfaces: /path/to/shared/projects/example/pipeline_interface.yaml

  # specify the ABSOLUTE PATH of input files using variable path expressions
  # entries correspond to values in the data_source column in sample_annotation table
  # {variable} can be used to replace environment variables or other sample_annotation columns
  # If you use {variable} codes, you should quote the field so python can parse it.
  bsf_samples: "$RAWDATA/{flowcell}/{flowcell}_{lane}_samples/{flowcell}_{lane}#{BSF_name}.bam"
  encode_rrbs: "/path/to/shared/data/encode_rrbs_data_hg19/fastq/{sample_name}.fastq.gz"

# supported genomes/transcriptomes and organism -> reference mapping
        genome: hg38
        transcriptome: hg38_cdna
        genome: mm10
        transcriptome: mm10_cdna

  # pipeline configuration files used in project.
  # Default (null) means use the generic config for the pipeline.
  rrbs: null
  # Or you can point to a specific config to be used in this project:
  # rrbs: rrbs_config.yaml
  # wgbs: wgbs_config.yaml
  # cgps: cpgs_config.yaml

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