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      • Abstract: found
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      Is Open Access

      Ten simple rules for making research software more robust

      editorial
      Author(s): 1 , * , 2
      Publication date (Electronic):
      Journal: PLoS Computational Biology
      Publisher: Public Library of Science

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          Abstract

          Software produced for research, published and otherwise, suffers from a number of common problems that make it difficult or impossible to run outside the original institution or even off the primary developer’s computer. We present ten simple rules to make such software robust enough to be run by anyone, anywhere, and thereby delight your users and collaborators.

          Author summary

          Many researchers have found out the hard way that there’s a world of difference between “works for me on my machine” and “works for other people on theirs.” Many common challenges can be avoided by following a few simple rules; doing so not only improves reproducibility but can accelerate research.

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          Most cited references17

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          Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

          Mihaela Pertea, Daehwan Kim, Geo M Pertea … (2017)
          High-throughput sequencing of messenger RNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate, and flexible software to reduce the raw read data to comprehensible results. HISAT, StringTie, and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample, and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol’s execution time depends on the computing resources, but typically takes under 45 minutes of computer time. Pertea et al. describe a protocol to analyze RNA-seq data using HISAT, StringTie, and Ballgown (the “new Tuxedo” package). The protocol can be used for assembly of transcripts, quantification of gene expression levels and differential expression analysis.
          Article 0 comments Cited 1797 times – based on 0 reviews     Review now
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            The Bioperl toolkit: Perl modules for the life sciences.

            Jason E Stajich, David Block, Kris Boulez … (2002)
            The Bioperl project is an international open-source collaboration of biologists, bioinformaticians, and computer scientists that has evolved over the past 7 yr into the most comprehensive library of Perl modules available for managing and manipulating life-science information. Bioperl provides an easy-to-use, stable, and consistent programming interface for bioinformatics application programmers. The Bioperl modules have been successfully and repeatedly used to reduce otherwise complex tasks to only a few lines of code. The Bioperl object model has been proven to be flexible enough to support enterprise-level applications such as EnsEMBL, while maintaining an easy learning curve for novice Perl programmers. Bioperl is capable of executing analyses and processing results from programs such as BLAST, ClustalW, or the EMBOSS suite. Interoperation with modules written in Python and Java is supported through the evolving BioCORBA bridge. Bioperl provides access to data stores such as GenBank and SwissProt via a flexible series of sequence input/output modules, and to the emerging common sequence data storage format of the Open Bioinformatics Database Access project. This study describes the overall architecture of the toolkit, the problem domains that it addresses, and gives specific examples of how the toolkit can be used to solve common life-sciences problems. We conclude with a discussion of how the open-source nature of the project has contributed to the development effort.
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              Best Practices for Scientific Computing

              Greg Wilson, D. A. Aruliah, C. Titus Brown … (2014)
              Scientists spend an increasing amount of time building and using software. However, most scientists are never taught how to do this efficiently. As a result, many are unaware of tools and practices that would allow them to write more reliable and maintainable code with less effort. We describe a set of best practices for scientific software development that have solid foundations in research and experience, and that improve scientists' productivity and the reliability of their software.
              Article 0 comments Cited 76 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): PLoS Comput Biol
                Journal ID (iso-abbrev): PLoS Comput. Biol
                Journal ID (publisher-id): plos
                Journal ID (pmc): ploscomp
                Title: PLoS Computational Biology
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Print): 1553-734X
                ISSN (Electronic): 1553-7358
                Publication date Collection: April 2017
                Publication date (Electronic): 13 April 2017
                Volume: 13
                Issue: 4
                Electronic Location Identifier: e1005412
                Affiliations
                [1 ]Genome Sequence Informatics, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
                [2 ]Software Carpentry Foundation, Austin, Texas, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-0677-6902
                http://orcid.org/0000-0001-8659-8979
                Article
                Publisher ID: PCOMPBIOL-D-16-01683
                DOI: 10.1371/journal.pcbi.1005412
                PMC ID: 5390961
                PubMed ID: 28407023
                SO-VID: a5db6498-b8f5-4e28-8f5b-591a361273a9
                Copyright © © 2017 Taschuk, Wilson
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Page count
                Figures: 0, Tables: 0, Pages: 10
                Funding
                This work was partially funded by the Ontario Institute for Cancer Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Editorial
                Subject: Computer and Information Sciences
                Subject: Computer Software
                Subject: Computer and Information Sciences
                Subject: Software Engineering
                Subject: Software Development
                Subject: Engineering and Technology
                Subject: Software Engineering
                Subject: Software Development
                Subject: Computer and Information Sciences
                Subject: Software Engineering
                Subject: Software Tools
                Subject: Engineering and Technology
                Subject: Software Engineering
                Subject: Software Tools
                Subject: Computer and Information Sciences
                Subject: Software Engineering
                Subject: Engineering and Technology
                Subject: Software Engineering
                Subject: Research and Analysis Methods
                Subject: Research Assessment
                Subject: Reproducibility
                Subject: Research and Analysis Methods
                Subject: Database and Informatics Methods
                Subject: Bioinformatics
                Subject: Research and Analysis Methods
                Subject: Database and Informatics Methods
                Subject: Bioinformatics
                Subject: Sequence Analysis
                Subject: Sequence Alignment
                Subject: Computer and Information Sciences
                Subject: Operating Systems

                ScienceOpen disciplines: Quantitative & Systems biology
                Data availability:
                ScienceOpen disciplines: Quantitative & Systems biology

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