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dc.contributor.authorLähnemann, David
dc.contributor.authorKöster, Johannes
dc.contributor.authorFischer, Ute
dc.contributor.authorBorkhardt, Arndt
dc.contributor.authorMcHardy, Alice C
dc.contributor.authorSchönhuth, Alexander
dc.date.accessioned2022-01-13T12:46:09Z
dc.date.available2022-01-13T12:46:09Z
dc.date.issued2021-11-18
dc.identifier.citationNat Commun. 2021 Nov 18;12(1):6744. doi: 10.1038/s41467-021-26938-w.en_US
dc.identifier.pmid34795237
dc.identifier.doi10.1038/s41467-021-26938-w
dc.identifier.urihttp://hdl.handle.net/10033/623142
dc.description.abstractAccurate single cell mutational profiles can reveal genomic cell-to-cell heterogeneity. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. The resulting data violates assumptions of variant callers developed for bulk sequencing. Thus, only dedicated models accounting for amplification bias and errors can provide accurate calls. We present ProSolo for calling single nucleotide variants from multiple displacement amplified (MDA) single cell DNA sequencing data. ProSolo probabilistically models a single cell jointly with a bulk sequencing sample and integrates all relevant MDA biases in a site-specific and scalable-because computationally efficient-manner. This achieves a higher accuracy in calling and genotyping single nucleotide variants in single cells in comparison to state-of-the-art tools and supports imputation of insufficiently covered genotypes, when downstream tools cannot handle missing data. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly. ProSolo is implemented in an extendable framework, with code and usage at: https://github.com/prosolo/prosolo.en_US
dc.language.isoenen_US
dc.publisherNPGen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleAccurate and scalable variant calling from single cell DNA sequencing data with ProSolo.en_US
dc.typeArticleen_US
dc.identifier.eissn2041-1723
dc.contributor.departmentBRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany.en_US
dc.identifier.journalNature communicationsen_US
dc.source.volume12
dc.source.issue1
dc.source.beginpage6744
dc.source.endpage
refterms.dateFOA2022-01-13T12:46:09Z
dc.source.journaltitleNature communications
dc.source.countryEngland


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International