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dc.contributor.authorEdrisi, Mohammadamin
Valecha, Monica V
Chowdary, Sunkara B V
Robledo, Sergio
Ogilvie, Huw A
Posada, David
Zafar, Hamim
Nakhleh, Luay
dc.date.accessioned 2022-07-06T18:09:15Z
dc.date.available 2022-07-06T18:09:15Z
dc.date.issued 2022
dc.identifier.citation Edrisi, Mohammadamin, Valecha, Monica V, Chowdary, Sunkara B V, et al.. "Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data." Bioinformatics, 38, no. Supplement_1 (2022) Oxford University Press: i195-i202. https://doi.org/10.1093/bioinformatics/btac254.
dc.identifier.urihttps://hdl.handle.net/1911/112678
dc.description.abstractSingle-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing data, such as SCIΦ and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data.Here, we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCIΦ in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases.Phylovar is implemented in Python and is publicly available at https://github.com/NakhlehLab/Phylovar.
dc.language.iso eng
dc.publisher Oxford University Press
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data
dc.type Journal article
dc.citation.journalTitle Bioinformatics
dc.citation.volumeNumber 38
dc.citation.issueNumber Supplement_1
dc.identifier.digital btac254
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1093/bioinformatics/btac254
dc.type.publication publisher version
dc.citation.firstpage i195
dc.citation.lastpage i202


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