Evidence map›Paper›PMID 31544213›Full record

ArticleGigaScience2019

Bionitio: demonstrating and facilitating best practices for bioinformatics command-line software.

Peter Georgeson, Anna Syme, Clare Sloggett, Jessica Chung, Harriet Dashnow, Michael Milton, Andrew Lonsdale, David Powell, Torsten Seemann, Bernard Pope

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Peter GeorgesonMelbourne Bioinformatics, The University of Melbourne, 187 Grattan Street, Carlton, Victoria, Australia 3053.
Anna SymeMelbourne Bioinformatics, The University of Melbourne, 187 Grattan Street, Carlton, Victoria, Australia 3053.
Clare SloggettMelbourne Bioinformatics, The University of Melbourne, 187 Grattan Street, Carlton, Victoria, Australia 3053.
Jessica ChungMelbourne Bioinformatics, The University of Melbourne, 187 Grattan Street, Carlton, Victoria, Australia 3053.
Harriet DashnowBioinformatics, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, Victoria, Australia 3052.
Michael MiltonMelbourne Bioinformatics, The University of Melbourne, 187 Grattan Street, Carlton, Victoria, Australia 3053.
Andrew LonsdaleBioinformatics, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, Victoria, Australia 3052.
David PowellMonash Bioinformatics Platform, Biomedicine Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, 15 Innovation Walk, Monash University, Clayton, Victoria, Australia 3800.
Torsten SeemannMelbourne Bioinformatics, The University of Melbourne, 187 Grattan Street, Carlton, Victoria, Australia 3053.
Bernard PopeMelbourne Bioinformatics, The University of Melbourne, 187 Grattan Street, Carlton, Victoria, Australia 3053.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBioinformatics software tools are often created ad hoc, frequently by people without extensive training in software development. In particular, for beginners, the barrier to entry in bioinformatics software development is high, especially if they want to adopt good programming practices. Even experienced developers do not always follow best practices. This results in the proliferation of poorer-quality bioinformatics software, leading to limited scalability and inefficient use of resources; lack of reproducibility, usability, adaptability, and interoperability; and erroneous or inaccurate results.

findingsWe have developed Bionitio, a tool that automates the process of starting new bioinformatics software projects following recommended best practices. With a single command, the user can create a new well-structured project in 1 of 12 programming languages. The resulting software is functional, carrying out a prototypical bioinformatics task, and thus serves as both a working example and a template for building new tools. Key features include command-line argument parsing, error handling, progress logging, defined exit status values, a test suite, a version number, standardized building and packaging, user documentation, code documentation, a standard open source software license, software revision control, and containerization.

conclusionsBionitio serves as a learning aid for beginner-to-intermediate bioinformatics programmers and provides an excellent starting point for new projects. This helps developers adopt good programming practices from the beginning of a project and encourages high-quality tools to be developed more rapidly. This also benefits users because tools are more easily installed and consistent in their usage. Bionitio is released as open source software under the MIT License and is available at https://github.com/bionitio-team/bionitio.

Indexed as

Computational BiologySoftwarebest practicesbioinformaticssoftware developmenttraining

Identifiers

PMID31544213
PMCPMC6755254

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.