2020
DOI: 10.3390/v12070758
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Validation of Variant Assembly Using HAPHPIPE with Next-Generation Sequence Data from Viruses

Abstract: Next-generation sequencing (NGS) offers a powerful opportunity to identify low-abundance, intra-host viral sequence variants, yet the focus of many bioinformatic tools on consensus sequence construction has precluded a thorough analysis of intra-host diversity. To take full advantage of the resolution of NGS data, we developed HAplotype PHylodynamics PIPEline (HAPHPIPE), an open-source tool for the de novo and reference-based assembly of viral NGS data, with both consensus sequence assembly and a focus on the … Show more

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Cited by 5 publications
(8 citation statements)
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“…To place our software in the context of existing bioinformatics tools, we discuss and compare several alternative viral assembly pipelines all with respect to HAPHPIPE in our validation study ( Gibson et al. 2020b ).…”
Section: Discussionmentioning
confidence: 99%
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“…To place our software in the context of existing bioinformatics tools, we discuss and compare several alternative viral assembly pipelines all with respect to HAPHPIPE in our validation study ( Gibson et al. 2020b ).…”
Section: Discussionmentioning
confidence: 99%
“…As most existing viral NGS platforms focus on HIV-1 exclusively, this represents a significant advantage of HAPHPIPE for users analyzing many different viral species. For a thorough comparison of HAPHPIPE’s methods with those of other tools, please see Gibson et al. (2020b) .…”
Section: Discussionmentioning
confidence: 99%
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“…A visual assessment in Geneious Prime 2022.0.1 ( www.geneious.com ) was carried out to check for good coverage across the genome, or any dips that might indicate an inter-subtype recombinant sequence. If this was the case, the multi-reference BAM files were examined, or an alternative de-novo assembly with HAPHPIPE and SPAdes was attempted [ 5 , 29 ]. Either the single reference assembly (or de-novo assembly if improvement could be found) was then fed into the HAPHPIPE framework for fine tuning with three rounds of iterative improvement.…”
Section: Methodsmentioning
confidence: 99%