=========== CGAT-ruffus

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Overview

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The ruffus module is a lightweight way to add support for running computational pipelines.

Computational pipelines are often conceptually quite simple, especially if we breakdown the process into simple stages, or separate tasks.

Each stage or task in a computational pipeline is represented by a python function Each python function can be called in parallel to run multiple jobs.

Ruffus was originally designed for use in bioinformatics to analyse multiple genome data sets.

More recently, we have extended the functionality of CGAT-ruffus to include cluster integration (Currently support SGE, SLURM and PBS-pro/Torque), paramaterisation, logging, database integration and conda environment switching. CGAT-core <https://github.com/cgat-developers/cgat-core>_ code and documentation <https://cgat-core.readthedocs.io/en/latest/>_.

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Documentation

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Ruffus documentation can be found here <https://cgat-ruffus.readthedocs.io/en/latest/>_ , with installation notes <https://cgat-ruffus.readthedocs.io/en/latest/installation.html>_ , and an in-depth manual <https://cgat-ruffus.readthedocs.io/en/latest/tutorials/new_tutorial/manual_contents.html>_ .

However, to utilise the full power of this workflow management system we recomend using CGAT-core <https://github.com/cgat-developers/cgat-core>_ (documentation <https://cgat-core.readthedocs.io/en/latest/>_).

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Background

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The purpose of a pipeline is to determine automatically which parts of a multi-stage process needs to be run and in what order in order to reach an objective ("targets")

Computational pipelines, especially for analysing large scientific datasets are in widespread use. However, even a conceptually simple series of steps can be difficult to set up and to maintain, perhaps because the right tools are not available.

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Design

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The ruffus module has the following design goals:

• Simplicity. Can be picked up in 10 minutes
• Elegance
• Lightweight
• Unintrusive
• Flexible/Powerful

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Features

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Automatic support for

• Managing dependencies
• Parallel jobs
• Re-starting from arbitrary points, especially after errors
• Display of the pipeline as a flowchart
• Reporting

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A Simple example

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Use the @transform(...) python decorator before the function definitions:

.. code-block:: python

from ruffus import *

# make 10 dummy DNA data files
data_files = [(prefix + ".fastq") for prefix in range("abcdefghij")]
for df in data_files:
    open(df, "w").close()


@transform(data_files, suffix(".fastq"), ".bam")
def run_bwa(input_file, output_file):
    print "Align DNA sequences in %s to a genome -> %s " % (input_file, output_file)
    # make dummy output file
    open(output_file, "w").close()


@transform(run_bwa, suffix(".bam"), ".sorted.bam")
def sort_bam(input_file, output_file):
    print "Sort DNA sequences in %s -> %s " % (input_file, output_file)
    # make dummy output file
    open(output_file, "w").close()

pipeline_run([sort_bam], multithread = 5)

the @transform decorator indicate that the data flows from the run_bwa function to sort_bwa down the pipeline.

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Usage

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Each stage or task in a computational pipeline is represented by a python function Each python function can be called in parallel to run multiple jobs.

1. Import module::

    import ruffus
   

2. Annotate functions with python decorators

3. Print dependency graph if you necessary

   • For a graphical flowchart in jpg, svg, dot, png, ps, gif formats::

     pipeline_printout_graph ("flowchart.svg")

   This requires dot to be installed

   • For a text printout of all jobs ::

     pipeline_printout(sys.stdout)

4. Run the pipeline::

   pipeline_run(list_of_target_tasks, verbose = NNN, [multithread | multiprocess = NNN])