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Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data
MOTIVATION: Single-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 SCI [Formula: see text] and scVILP, leverage the evolutionary history of the cells to overcome the technical er...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235480/ https://www.ncbi.nlm.nih.gov/pubmed/35758771 http://dx.doi.org/10.1093/bioinformatics/btac254 |
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author | Edrisi, Mohammadamin Valecha, Monica V Chowdary, Sunkara B V Robledo, Sergio Ogilvie, Huw A Posada, David Zafar, Hamim Nakhleh, Luay |
author_facet | Edrisi, Mohammadamin Valecha, Monica V Chowdary, Sunkara B V Robledo, Sergio Ogilvie, Huw A Posada, David Zafar, Hamim Nakhleh, Luay |
author_sort | Edrisi, Mohammadamin |
collection | PubMed |
description | MOTIVATION: Single-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 SCI [Formula: see text] 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. RESULTS: 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 SCI [Formula: see text] 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. AVAILABILITY AND IMPLEMENTATION: Phylovar is implemented in Python and is publicly available at https://github.com/NakhlehLab/Phylovar. |
format | Online Article Text |
id | pubmed-9235480 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92354802022-06-29 Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data Edrisi, Mohammadamin Valecha, Monica V Chowdary, Sunkara B V Robledo, Sergio Ogilvie, Huw A Posada, David Zafar, Hamim Nakhleh, Luay Bioinformatics ISCB/Ismb 2022 MOTIVATION: Single-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 SCI [Formula: see text] 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. RESULTS: 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 SCI [Formula: see text] 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. AVAILABILITY AND IMPLEMENTATION: Phylovar is implemented in Python and is publicly available at https://github.com/NakhlehLab/Phylovar. Oxford University Press 2022-06-27 /pmc/articles/PMC9235480/ /pubmed/35758771 http://dx.doi.org/10.1093/bioinformatics/btac254 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This 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. |
spellingShingle | ISCB/Ismb 2022 Edrisi, Mohammadamin Valecha, Monica V Chowdary, Sunkara B V Robledo, Sergio Ogilvie, Huw A Posada, David Zafar, Hamim Nakhleh, Luay Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data |
title | Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data |
title_full | Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data |
title_fullStr | Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data |
title_full_unstemmed | Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data |
title_short | Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data |
title_sort | phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell dna sequencing data |
topic | ISCB/Ismb 2022 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235480/ https://www.ncbi.nlm.nih.gov/pubmed/35758771 http://dx.doi.org/10.1093/bioinformatics/btac254 |
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