CGAT-core: a python framework for building scalable, reproducible computational biology workflows

Question

1. What contributions have the authors mentioned in the paper "Cgat-core: a python framework for building scalable, reproducible computational biology workflows [version 1; peer review: 1 approved, 1 approved with reservations]" ?

2. What is the main reason why CGAT-core is open source?

3. What are the main features of CGAT-core?

4. What are the utility functions for CGAT-core?

Accepted Answer

To illustrate their workflow framework, the authors present a pipeline for the analysis of RNAseq data using pseudo-alignment.

Accepted Answer

The ease of pipeline development enables CGAT-core to bridge the gap between exploratory data analysis and building production workflows.

Accepted Answer

CGAT-core workflows are Python scripts, and as such are stand-alone command line utilities that do not require the installation of a dedicated service.

Accepted Answer

In order to reproducibly execute their workflows, the authors provide utility functions for argument parsing, logging and record keeping within scripts (cgatcore. experiment).

Accepted Answer

CGAT-Ruffus implements a wide range of decorators that allow complex operations on input files including: conversion of a single input file to a single output file; splitting of a file into multiple files (and vice versa) and conditional merging of multiple input files into a smaller number of outputs.

Accepted Answer

In a recent survey11, the tool rated highest for ease of pipeline development was CGATRuffus12, a Python package that wraps pipeline steps in discrete Python functions, called ‘tasks’.

Accepted Answer

The need for tools to manage workflows is well established, with a wide range of options available from graphical user interfaces such as Galaxy1 and Taverna2, aimed at non-programmers, to Snakemake, Nextflow, Toil, CGAT-Ruffus and others3–10 developed with computational biologists in mind.

Accepted Answer

After going through the documentation (and a fair bit of trial and error due to not being particularly used to using Ruffus syntax) The authorwas able to create and run a very simple workflow from scratch that included paired-end read trimming (cutadapt) and alignment (STAR) and used the parameterisation added by CGAT-core.

Accepted Answer

In my mind, the addition of parameterisation and conda environment switching were the two biggest stumbling blocks to using Ruffus previously and CGAT-core seems to nicely address both of these.

Accepted Answer

It is possible to run standard job submission commands like qsub etc. as a command-line statement and run the pipelines in –no-cluster mode.

Accepted Answer

In my mind, the addition of parameterisation and conda environment switching were the two biggest stumbling blocks to using Ruffus previously and CGAT-core seems to nicely address both of these.

CGAT-core: a python framework for building scalable, reproducible computational biology workflows

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Most frequently asked questions

1. What contributions have the authors mentioned in the paper "Cgat-core: a python framework for building scalable, reproducible computational biology workflows [version 1; peer review: 1 approved, 1 approved with reservations]" ?

2. What is the main reason why CGAT-core is open source?

3. What are the main features of CGAT-core?

4. What are the utility functions for CGAT-core?

5. What is the main purpose of CGAT-Ruffus?

6. What is the popular tool in the survey?

7. What is the need for tools to manage workflows?

8. What was the main reason for the workflow to be able to run from scratch?

9. What are the main issues that were the biggest stumbling blocks to using Ruffus?

10. how do you run qsub and srun?

11. What are the main problems with Ruffus?

References

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Near-optimal probabilistic RNA-seq quantification

Nextflow enables reproducible computational workflows

Unipro UGENE

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