dc.contributor.author
Pan, Chenxu
dc.contributor.author
Reinert, Knut
dc.date.accessioned
2024-07-01T08:23:08Z
dc.date.available
2024-07-01T08:23:08Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/44013
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-43722
dc.description.abstract
Advances in sequencing technology have facilitated population-scale long-read structural variant (SV) detection. Arguably, one of the main challenges in population-scale analysis is developing effective computational pipelines. Here, we present a new filter-based pipeline for population-scale long-read SV detection. It better captures SV signals at an early stage than conventional assembly-based or alignment-based pipelines. Assessments in this work suggest that the filter-based pipeline helps better resolve intra-read rearrangements. Moreover, it is also more computationally efficient than conventional pipelines and thus may facilitate population-scale long-read applications.
en
dc.format.extent
18 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Filter-based pipelines
en
dc.subject
Intra-read SV detection
en
dc.subject
Population-scale long-read applications
en
dc.subject
Generative model
en
dc.subject
Extended SAM/BAM
en
dc.subject.ddc
500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie
dc.title
Leaf: an ultrafast filter for population-scale long-read SV detection
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
155
dcterms.bibliographicCitation.doi
10.1186/s13059-024-03297-5
dcterms.bibliographicCitation.journaltitle
Genome Biology
dcterms.bibliographicCitation.number
1
dcterms.bibliographicCitation.volume
25
dcterms.bibliographicCitation.url
https://doi.org/10.1186/s13059-024-03297-5
refubium.affiliation
Mathematik und Informatik
refubium.affiliation.other
Institut für Informatik
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refubium.funding
Springer Nature DEAL
refubium.note.author
Die Publikation wurde aus Open Access Publikationsgeldern der Freien Universität Berlin gefördert.
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
1474-760X