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FragGeneScanRs: faster gene prediction for short reads
BACKGROUND: FragGeneScan is currently the most accurate and popular tool for gene prediction in short and error-prone reads, but its execution speed is insufficient for use on larger data sets. The parallelization which should have addressed this is inefficient. Its alternative implementation FragGe...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148508/ https://www.ncbi.nlm.nih.gov/pubmed/35643462 http://dx.doi.org/10.1186/s12859-022-04736-5 |
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author | Van der Jeugt, Felix Dawyndt, Peter Mesuere, Bart |
author_facet | Van der Jeugt, Felix Dawyndt, Peter Mesuere, Bart |
author_sort | Van der Jeugt, Felix |
collection | PubMed |
description | BACKGROUND: FragGeneScan is currently the most accurate and popular tool for gene prediction in short and error-prone reads, but its execution speed is insufficient for use on larger data sets. The parallelization which should have addressed this is inefficient. Its alternative implementation FragGeneScan+ is faster, but introduced a number of bugs related to memory management, race conditions and even output accuracy. RESULTS: This paper introduces FragGeneScanRs, a faster Rust implementation of the FragGeneScan gene prediction model. Its command line interface is backward compatible and adds extra features for more flexible usage. Its output is equivalent to the original FragGeneScan implementation. CONCLUSIONS: Compared to the current C implementation, shotgun metagenomic reads are processed up to 22 times faster using a single thread, with better scaling for multithreaded execution. The Rust code of FragGeneScanRs is freely available from GitHub under the GPL-3.0 license with instructions for installation, usage and other documentation (https://github.com/unipept/FragGeneScanRs). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04736-5. |
format | Online Article Text |
id | pubmed-9148508 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91485082022-05-30 FragGeneScanRs: faster gene prediction for short reads Van der Jeugt, Felix Dawyndt, Peter Mesuere, Bart BMC Bioinformatics Software BACKGROUND: FragGeneScan is currently the most accurate and popular tool for gene prediction in short and error-prone reads, but its execution speed is insufficient for use on larger data sets. The parallelization which should have addressed this is inefficient. Its alternative implementation FragGeneScan+ is faster, but introduced a number of bugs related to memory management, race conditions and even output accuracy. RESULTS: This paper introduces FragGeneScanRs, a faster Rust implementation of the FragGeneScan gene prediction model. Its command line interface is backward compatible and adds extra features for more flexible usage. Its output is equivalent to the original FragGeneScan implementation. CONCLUSIONS: Compared to the current C implementation, shotgun metagenomic reads are processed up to 22 times faster using a single thread, with better scaling for multithreaded execution. The Rust code of FragGeneScanRs is freely available from GitHub under the GPL-3.0 license with instructions for installation, usage and other documentation (https://github.com/unipept/FragGeneScanRs). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04736-5. BioMed Central 2022-05-28 /pmc/articles/PMC9148508/ /pubmed/35643462 http://dx.doi.org/10.1186/s12859-022-04736-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Van der Jeugt, Felix Dawyndt, Peter Mesuere, Bart FragGeneScanRs: faster gene prediction for short reads |
title | FragGeneScanRs: faster gene prediction for short reads |
title_full | FragGeneScanRs: faster gene prediction for short reads |
title_fullStr | FragGeneScanRs: faster gene prediction for short reads |
title_full_unstemmed | FragGeneScanRs: faster gene prediction for short reads |
title_short | FragGeneScanRs: faster gene prediction for short reads |
title_sort | fraggenescanrs: faster gene prediction for short reads |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148508/ https://www.ncbi.nlm.nih.gov/pubmed/35643462 http://dx.doi.org/10.1186/s12859-022-04736-5 |
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