Empirical study on software and process quality in bioinformatics tools

Ferenc, Katalin; Otto, Konrad; Neto, Francisco Gomes de Oliveira; López, Marcela Dávila; Horkoff, Jennifer; Schliep, Alexander

doi:10.1101/2022.03.10.483804

Cited by 6 publications

(3 citation statements)

References 56 publications

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“…Even with tools that are built to handle growing reference sequence databases, many tools suffer from "dormant rot," the inability to install or run the tool after the absence of updates and maintenance (Deschamps et al, 2023). The bioinformatics landscape is particularly rife with examples of poorly maintained and documented tools, as developers are often incentivized to build and publish new tools instead of ensure long term sustainability (Ferenc et al, 2022).…”

Section: Database Maintenance and Updatingmentioning

confidence: 99%

Ten common issues with reference sequence databases and how to mitigate them

Chorlton

2024

Front. Bioinform.

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Metagenomic sequencing has revolutionized our understanding of microbiology. While metagenomic tools and approaches have been extensively evaluated and benchmarked, far less attention has been given to the reference sequence database used in metagenomic classification. Issues with reference sequence databases are pervasive. Database contamination is the most recognized issue in the literature; however, it remains relatively unmitigated in most analyses. Other common issues with reference sequence databases include taxonomic errors, inappropriate inclusion and exclusion criteria, and sequence content errors. This review covers ten common issues with reference sequence databases and the potential downstream consequences of these issues. Mitigation measures are discussed for each issue, including bioinformatic tools and database curation strategies. Together, these strategies present a path towards more accurate, reproducible and translatable metagenomic sequencing.

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Section: Database Maintenance and Updatingmentioning

confidence: 99%

Ten common issues with reference sequence databases and how to mitigate them

Chorlton

2024

Front. Bioinform.

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“…Inclusion of continuous integration and static code analysis tools allow for rapid feedback loops in code inspection, reducing the time reviewers need to spend reviewing and reducing cost of time and funding of development maintenance [68,69]. Journals are increasingly requiring that the code used for analysis is made available [70].…”

Section: Make Your Processing Pipelines and Code Fairmentioning

confidence: 99%

Ten Simple Rules for Using Public Biological Data for Your Research

Oza¹,

Whitlock²,

Wilk³

et al. 2022

Preprint

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With an increasing amount of "omics" data available publicly, there is a need for a guide on how to successfully download and use this data. The 10 simple rules for using public data are: 1) use public data in your research, 2) evaluate data for your use case, 3) check data reuse requirements and embargoes, 4) be aware of ethics for data reuse, 5) plan for data storage and compute requirements, 6) know what you are downloading, 7) download programmatically and verify integrity, 8) properly cite data, 9) make data FAIR and share, and 10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.

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“…Inclusion of continuous integration and static code analysis tools allow for rapid feedback loops in code inspection, reducing the time reviewers need to spend reviewing, and reducing cost of time and funding of development maintenance [ 62 , 63 ]. Journals are increasingly requiring that the code used for analysis is made available [ 64 ].…”

Section: Introductionmentioning

confidence: 99%

Ten simple rules for using public biological data for your research

et al. 2023

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With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The 10 simple rules for using public biological data are: (1) use public data purposefully in your research; (2) evaluate data for your use case; (3) check data reuse requirements and embargoes; (4) be aware of ethics for data reuse; (5) plan for data storage and compute requirements; (6) know what you are downloading; (7) download programmatically and verify integrity; (8) properly cite data; (9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share; and (10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.

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Empirical study on software and process quality in bioinformatics tools

Cited by 6 publications

References 56 publications

Ten common issues with reference sequence databases and how to mitigate them

Ten common issues with reference sequence databases and how to mitigate them

Ten Simple Rules for Using Public Biological Data for Your Research

Ten simple rules for using public biological data for your research

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