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Article: Cellsnp-lite: an efficient tool for genotyping single cells

TitleCellsnp-lite: an efficient tool for genotyping single cells
Authors
Issue Date2021
PublisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2021, v. 37 n. 23, p. 4569-4571 How to Cite?
AbstractSummary: Single-cell sequencing is an increasingly used technology and has promising applications in basic research and clinical translations. However, genotyping methods developed for bulk sequencing data have not been well adapted for single-cell data, in terms of both computational parallelization and simplified user interface. Here, we introduce a software, cellsnp-lite, implemented in C/C++ and based on well-supported package htslib, for genotyping in single-cell sequencing data for both droplet and well-based platforms. On various experimental datasets, it shows substantial improvement in computational speed and memory efficiency with retaining highly concordant results compared to existing methods. Cellsnp-lite, therefore, lightens the genetic analysis for increasingly large single-cell data. Availability and implementation: The source code is freely available at https://github.com/single-cell-genetics/cellsnp-lite. Supplementary information: Supplementary data are available at Bioinformatics online.
Persistent Identifierhttp://hdl.handle.net/10722/304617
ISSN
2021 Impact Factor: 6.931
2020 SCImago Journal Rankings: 3.599
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHuang, X-
dc.contributor.authorHuang, Y-
dc.date.accessioned2021-10-05T02:32:42Z-
dc.date.available2021-10-05T02:32:42Z-
dc.date.issued2021-
dc.identifier.citationBioinformatics, 2021, v. 37 n. 23, p. 4569-4571-
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/10722/304617-
dc.description.abstractSummary: Single-cell sequencing is an increasingly used technology and has promising applications in basic research and clinical translations. However, genotyping methods developed for bulk sequencing data have not been well adapted for single-cell data, in terms of both computational parallelization and simplified user interface. Here, we introduce a software, cellsnp-lite, implemented in C/C++ and based on well-supported package htslib, for genotyping in single-cell sequencing data for both droplet and well-based platforms. On various experimental datasets, it shows substantial improvement in computational speed and memory efficiency with retaining highly concordant results compared to existing methods. Cellsnp-lite, therefore, lightens the genetic analysis for increasingly large single-cell data. Availability and implementation: The source code is freely available at https://github.com/single-cell-genetics/cellsnp-lite. Supplementary information: Supplementary data are available at Bioinformatics online.-
dc.languageeng-
dc.publisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/-
dc.relation.ispartofBioinformatics-
dc.rightsThis is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version Bioinformatics, 2021, v. 37 n. 23, p. 4569-4571 is available online at: https://doi.org/10.1093/bioinformatics/btab358-
dc.titleCellsnp-lite: an efficient tool for genotyping single cells-
dc.typeArticle-
dc.identifier.emailHuang, X: hxj5@hku.hk-
dc.identifier.emailHuang, Y: yuanhua@hku.hk-
dc.identifier.authorityHuang, Y=rp02649-
dc.description.naturepostprint-
dc.identifier.doi10.1093/bioinformatics/btab358-
dc.identifier.pmid33963851-
dc.identifier.scopuseid_2-s2.0-85118364372-
dc.identifier.hkuros325779-
dc.identifier.volume37-
dc.identifier.issue23-
dc.identifier.spage4569-
dc.identifier.epage4571-
dc.identifier.isiWOS:000733374500039-
dc.publisher.placeUnited Kingdom-

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