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BamToCov: an efficient toolkit for sequence coverage calculations

MOTIVATION: Many genomics applications require the computation of nucleotide coverage of a reference genome or the ability to determine how many reads map to a reference region. RESULTS: BamToCov is a toolkit for rapid and flexible coverage computation that relies on the most memory efficient algori...

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Detalles Bibliográficos
Autores principales: Birolo, Giovanni, Telatin, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048650/
https://www.ncbi.nlm.nih.gov/pubmed/35199151
http://dx.doi.org/10.1093/bioinformatics/btac125
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author Birolo, Giovanni
Telatin, Andrea
author_facet Birolo, Giovanni
Telatin, Andrea
author_sort Birolo, Giovanni
collection PubMed
description MOTIVATION: Many genomics applications require the computation of nucleotide coverage of a reference genome or the ability to determine how many reads map to a reference region. RESULTS: BamToCov is a toolkit for rapid and flexible coverage computation that relies on the most memory efficient algorithm and is designed for integration in pipelines, given its ability to read alignment files from streams. The tools in the suite can process sorted BAM or CRAM files, allowing the user to extract coverage information via different filtering approaches and to save the output in different formats (BED, Wig or counts). The BamToCov algorithm can also handle strand-specific and/or physical coverage analyses. AVAILABILITY AND IMPLEMENTATION: This program, accessory utilities and their documentation are freely available at https://github.com/telatin/BamToCov. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-90486502022-04-29 BamToCov: an efficient toolkit for sequence coverage calculations Birolo, Giovanni Telatin, Andrea Bioinformatics Applications Notes MOTIVATION: Many genomics applications require the computation of nucleotide coverage of a reference genome or the ability to determine how many reads map to a reference region. RESULTS: BamToCov is a toolkit for rapid and flexible coverage computation that relies on the most memory efficient algorithm and is designed for integration in pipelines, given its ability to read alignment files from streams. The tools in the suite can process sorted BAM or CRAM files, allowing the user to extract coverage information via different filtering approaches and to save the output in different formats (BED, Wig or counts). The BamToCov algorithm can also handle strand-specific and/or physical coverage analyses. AVAILABILITY AND IMPLEMENTATION: This program, accessory utilities and their documentation are freely available at https://github.com/telatin/BamToCov. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-02-23 /pmc/articles/PMC9048650/ /pubmed/35199151 http://dx.doi.org/10.1093/bioinformatics/btac125 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Birolo, Giovanni
Telatin, Andrea
BamToCov: an efficient toolkit for sequence coverage calculations
title BamToCov: an efficient toolkit for sequence coverage calculations
title_full BamToCov: an efficient toolkit for sequence coverage calculations
title_fullStr BamToCov: an efficient toolkit for sequence coverage calculations
title_full_unstemmed BamToCov: an efficient toolkit for sequence coverage calculations
title_short BamToCov: an efficient toolkit for sequence coverage calculations
title_sort bamtocov: an efficient toolkit for sequence coverage calculations
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048650/
https://www.ncbi.nlm.nih.gov/pubmed/35199151
http://dx.doi.org/10.1093/bioinformatics/btac125
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