Literature DB >> 20847218

Ruffus: a lightweight Python library for computational pipelines.

Leo Goodstadt1.   

Abstract

SUMMARY: Computational pipelines are common place in scientific research. However, most of the resources for constructing pipelines are heavyweight systems with graphical user interfaces. Ruffus is a library for the creation of computational pipelines. Its lightweight and unobtrusive design recommends it for use even for the most trivial of analyses. At the same time, it is powerful enough to have been used for complex workflows involving more than 50 interdependent stages.
AVAILABILITY AND IMPLEMENTATION: Ruffus is written in python. Source code, a short tutorial, examples and a comprehensive user manual are freely available at http://www.ruffus.org.uk. The example program is available at http://www.ruffus.org.uk/examples/bioinformatics

Mesh:

Year:  2010        PMID: 20847218     DOI: 10.1093/bioinformatics/btq524

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  46 in total

1.  Omics Pipe: a community-based framework for reproducible multi-omics data analysis.

Authors:  Kathleen M Fisch; Tobias Meißner; Louis Gioia; Jean-Christophe Ducom; Tristan M Carland; Salvatore Loguercio; Andrew I Su
Journal:  Bioinformatics       Date:  2015-01-30       Impact factor: 6.937

2.  Planning bioinformatics workflows using an expert system.

Authors:  Xiaoling Chen; Jeffrey T Chang
Journal:  Bioinformatics       Date:  2017-04-15       Impact factor: 6.937

3.  Nestly--a framework for running software with nested parameter choices and aggregating results.

Authors:  Connor O McCoy; Aaron Gallagher; Noah G Hoffman; Frederick A Matsen
Journal:  Bioinformatics       Date:  2012-12-06       Impact factor: 6.937

4.  Accurate transcriptome-wide prediction of microRNA targets and small interfering RNA off-targets with MIRZA-G.

Authors:  Rafal Gumienny; Mihaela Zavolan
Journal:  Nucleic Acids Res       Date:  2015-01-27       Impact factor: 16.971

5.  Constructing lightweight and flexible pipelines using Plugin-Based Microbiome Analysis (PluMA).

Authors:  Trevor Cickovski; Giri Narasimhan
Journal:  Bioinformatics       Date:  2018-09-01       Impact factor: 6.937

6.  Agile parallel bioinformatics workflow management using Pwrake.

Authors:  Hiroyuki Mishima; Kensaku Sasaki; Masahiro Tanaka; Osamu Tatebe; Koh-Ichiro Yoshiura
Journal:  BMC Res Notes       Date:  2011-09-08

7.  MetAMOS: a modular and open source metagenomic assembly and analysis pipeline.

Authors:  Todd J Treangen; Sergey Koren; Daniel D Sommer; Bo Liu; Irina Astrovskaya; Brian Ondov; Aaron E Darling; Adam M Phillippy; Mihai Pop
Journal:  Genome Biol       Date:  2013-01-15       Impact factor: 13.583

8.  Unifying package managers, workflow engines, and containers: Computational reproducibility with BioNix.

Authors:  Justin Bedő; Leon Di Stefano; Anthony T Papenfuss
Journal:  Gigascience       Date:  2020-11-18       Impact factor: 6.524

9.  uap: reproducible and robust HTS data analysis.

Authors:  Christoph Kämpf; Michael Specht; Alexander Scholz; Sven-Holger Puppel; Gero Doose; Kristin Reiche; Jana Schor; Jörg Hackermüller
Journal:  BMC Bioinformatics       Date:  2019-12-12       Impact factor: 3.169

10.  Bioinformatic pipelines in Python with Leaf.

Authors:  Francesco Napolitano; Renato Mariani-Costantini; Roberto Tagliaferri
Journal:  BMC Bioinformatics       Date:  2013-06-21       Impact factor: 3.169

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