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HGGA: hierarchical guided genome assembler

BACKGROUND: De novo genome assembly typically produces a set of contigs instead of the complete genome. Thus additional data such as genetic linkage maps, optical maps, or Hi-C data is needed to resolve the complete structure of the genome. Most of the previous work uses the additional data to order...

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Autores principales: Walve, Riku, Salmela, Leena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9077837/
https://www.ncbi.nlm.nih.gov/pubmed/35525918
http://dx.doi.org/10.1186/s12859-022-04701-2
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author Walve, Riku
Salmela, Leena
author_facet Walve, Riku
Salmela, Leena
author_sort Walve, Riku
collection PubMed
description BACKGROUND: De novo genome assembly typically produces a set of contigs instead of the complete genome. Thus additional data such as genetic linkage maps, optical maps, or Hi-C data is needed to resolve the complete structure of the genome. Most of the previous work uses the additional data to order and orient contigs. RESULTS: Here we introduce a framework to guide genome assembly with additional data. Our approach is based on clustering the reads, such that each read in each cluster originates from nearby positions in the genome according to the additional data. These sets are then assembled independently and the resulting contigs are further assembled in a hierarchical manner. We implemented our approach for genetic linkage maps in a tool called HGGA. CONCLUSIONS: Our experiments on simulated and real Pacific Biosciences long reads and genetic linkage maps show that HGGA produces a more contiguous assembly with less contigs and from 1.2 to 9.8 times higher NGA50 or N50 than a plain assembly of the reads and 1.03 to 6.5 times higher NGA50 or N50 than a previous approach integrating genetic linkage maps with contig assembly. Furthermore, also the correctness of the assembly remains similar or improves as compared to an assembly using only the read data.
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spelling pubmed-90778372022-05-08 HGGA: hierarchical guided genome assembler Walve, Riku Salmela, Leena BMC Bioinformatics Research BACKGROUND: De novo genome assembly typically produces a set of contigs instead of the complete genome. Thus additional data such as genetic linkage maps, optical maps, or Hi-C data is needed to resolve the complete structure of the genome. Most of the previous work uses the additional data to order and orient contigs. RESULTS: Here we introduce a framework to guide genome assembly with additional data. Our approach is based on clustering the reads, such that each read in each cluster originates from nearby positions in the genome according to the additional data. These sets are then assembled independently and the resulting contigs are further assembled in a hierarchical manner. We implemented our approach for genetic linkage maps in a tool called HGGA. CONCLUSIONS: Our experiments on simulated and real Pacific Biosciences long reads and genetic linkage maps show that HGGA produces a more contiguous assembly with less contigs and from 1.2 to 9.8 times higher NGA50 or N50 than a plain assembly of the reads and 1.03 to 6.5 times higher NGA50 or N50 than a previous approach integrating genetic linkage maps with contig assembly. Furthermore, also the correctness of the assembly remains similar or improves as compared to an assembly using only the read data. BioMed Central 2022-05-07 /pmc/articles/PMC9077837/ /pubmed/35525918 http://dx.doi.org/10.1186/s12859-022-04701-2 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 Research
Walve, Riku
Salmela, Leena
HGGA: hierarchical guided genome assembler
title HGGA: hierarchical guided genome assembler
title_full HGGA: hierarchical guided genome assembler
title_fullStr HGGA: hierarchical guided genome assembler
title_full_unstemmed HGGA: hierarchical guided genome assembler
title_short HGGA: hierarchical guided genome assembler
title_sort hgga: hierarchical guided genome assembler
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9077837/
https://www.ncbi.nlm.nih.gov/pubmed/35525918
http://dx.doi.org/10.1186/s12859-022-04701-2
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