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HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads

BACKGROUND: Haplotype assembly is the process of assigning the different alleles of the variants covered by mapped sequencing reads to the two haplotypes of the genome of a human individual. Long reads, which are nowadays cheaper to produce and more widely available than ever before, have been used...

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Autores principales: Beretta, Stefano, Patterson, Murray D., Zaccaria, Simone, Della Vedova, Gianluca, Bonizzoni, Paola
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029272/
https://www.ncbi.nlm.nih.gov/pubmed/29970002
http://dx.doi.org/10.1186/s12859-018-2253-8
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author Beretta, Stefano
Patterson, Murray D.
Zaccaria, Simone
Della Vedova, Gianluca
Bonizzoni, Paola
author_facet Beretta, Stefano
Patterson, Murray D.
Zaccaria, Simone
Della Vedova, Gianluca
Bonizzoni, Paola
author_sort Beretta, Stefano
collection PubMed
description BACKGROUND: Haplotype assembly is the process of assigning the different alleles of the variants covered by mapped sequencing reads to the two haplotypes of the genome of a human individual. Long reads, which are nowadays cheaper to produce and more widely available than ever before, have been used to reduce the fragmentation of the assembled haplotypes since their ability to span several variants along the genome. These long reads are also characterized by a high error rate, an issue which may be mitigated, however, with larger sets of reads, when this error rate is uniform across genome positions. Unfortunately, current state-of-the-art dynamic programming approaches designed for long reads deal only with limited coverages. RESULTS: Here, we propose a new method for assembling haplotypes which combines and extends the features of previous approaches to deal with long reads and higher coverages. In particular, our algorithm is able to dynamically adapt the estimated number of errors at each variant site, while minimizing the total number of error corrections necessary for finding a feasible solution. This allows our method to significantly reduce the required computational resources, allowing to consider datasets composed of higher coverages. The algorithm has been implemented in a freely available tool, HapCHAT: Haplotype Assembly Coverage Handling by Adapting Thresholds. An experimental analysis on sequencing reads with up to 60 × coverage reveals improvements in accuracy and recall achieved by considering a higher coverage with lower runtimes. CONCLUSIONS: Our method leverages the long-range information of sequencing reads that allows to obtain assembled haplotypes fragmented in a lower number of unphased haplotype blocks. At the same time, our method is also able to deal with higher coverages to better correct the errors in the original reads and to obtain more accurate haplotypes as a result. AVAILABILITY: HapCHAT is available at http://hapchat.algolab.euunder the GNU Public License (GPL).
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spelling pubmed-60292722018-07-09 HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads Beretta, Stefano Patterson, Murray D. Zaccaria, Simone Della Vedova, Gianluca Bonizzoni, Paola BMC Bioinformatics Methodology Article BACKGROUND: Haplotype assembly is the process of assigning the different alleles of the variants covered by mapped sequencing reads to the two haplotypes of the genome of a human individual. Long reads, which are nowadays cheaper to produce and more widely available than ever before, have been used to reduce the fragmentation of the assembled haplotypes since their ability to span several variants along the genome. These long reads are also characterized by a high error rate, an issue which may be mitigated, however, with larger sets of reads, when this error rate is uniform across genome positions. Unfortunately, current state-of-the-art dynamic programming approaches designed for long reads deal only with limited coverages. RESULTS: Here, we propose a new method for assembling haplotypes which combines and extends the features of previous approaches to deal with long reads and higher coverages. In particular, our algorithm is able to dynamically adapt the estimated number of errors at each variant site, while minimizing the total number of error corrections necessary for finding a feasible solution. This allows our method to significantly reduce the required computational resources, allowing to consider datasets composed of higher coverages. The algorithm has been implemented in a freely available tool, HapCHAT: Haplotype Assembly Coverage Handling by Adapting Thresholds. An experimental analysis on sequencing reads with up to 60 × coverage reveals improvements in accuracy and recall achieved by considering a higher coverage with lower runtimes. CONCLUSIONS: Our method leverages the long-range information of sequencing reads that allows to obtain assembled haplotypes fragmented in a lower number of unphased haplotype blocks. At the same time, our method is also able to deal with higher coverages to better correct the errors in the original reads and to obtain more accurate haplotypes as a result. AVAILABILITY: HapCHAT is available at http://hapchat.algolab.euunder the GNU Public License (GPL). BioMed Central 2018-07-03 /pmc/articles/PMC6029272/ /pubmed/29970002 http://dx.doi.org/10.1186/s12859-018-2253-8 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Beretta, Stefano
Patterson, Murray D.
Zaccaria, Simone
Della Vedova, Gianluca
Bonizzoni, Paola
HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads
title HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads
title_full HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads
title_fullStr HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads
title_full_unstemmed HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads
title_short HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads
title_sort hapchat: adaptive haplotype assembly for efficiently leveraging high coverage in long reads
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029272/
https://www.ncbi.nlm.nih.gov/pubmed/29970002
http://dx.doi.org/10.1186/s12859-018-2253-8
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