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PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation

Single cell whole-genome sequencing (scWGS) is providing novel insights into the nature of genetic heterogeneity in normal and diseased cells. However, the whole-genome amplification process required for scWGS introduces biases into the resulting sequencing that can confound downstream analysis. Her...

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Autores principales: Sherman, Maxwell A, Barton, Alison R, Lodato, Michael A, Vitzthum, Carl, Coulter, Michael E, Walsh, Christopher A, Park, Peter J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829578/
https://www.ncbi.nlm.nih.gov/pubmed/29186545
http://dx.doi.org/10.1093/nar/gkx1195
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author Sherman, Maxwell A
Barton, Alison R
Lodato, Michael A
Vitzthum, Carl
Coulter, Michael E
Walsh, Christopher A
Park, Peter J
author_facet Sherman, Maxwell A
Barton, Alison R
Lodato, Michael A
Vitzthum, Carl
Coulter, Michael E
Walsh, Christopher A
Park, Peter J
author_sort Sherman, Maxwell A
collection PubMed
description Single cell whole-genome sequencing (scWGS) is providing novel insights into the nature of genetic heterogeneity in normal and diseased cells. However, the whole-genome amplification process required for scWGS introduces biases into the resulting sequencing that can confound downstream analysis. Here, we present a statistical method, with an accompanying package PaSD-qc (Power Spectral Density-qc), that evaluates the properties and quality of single cell libraries. It uses a modified power spectral density to assess amplification uniformity, amplicon size distribution, autocovariance and inter-sample consistency as well as to identify chromosomes with aberrant read-density profiles due either to copy alterations or poor amplification. These metrics provide a standard way to compare the quality of single cell samples as well as yield information necessary to improve variant calling strategies. We demonstrate the usefulness of this tool in comparing the properties of scWGS protocols, identifying potential chromosomal copy number variation, determining chromosomal and subchromosomal regions of poor amplification, and selecting high-quality libraries from low-coverage data for deep sequencing. The software is available free and open-source at https://github.com/parklab/PaSDqc.
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spelling pubmed-58295782018-03-06 PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation Sherman, Maxwell A Barton, Alison R Lodato, Michael A Vitzthum, Carl Coulter, Michael E Walsh, Christopher A Park, Peter J Nucleic Acids Res Methods Online Single cell whole-genome sequencing (scWGS) is providing novel insights into the nature of genetic heterogeneity in normal and diseased cells. However, the whole-genome amplification process required for scWGS introduces biases into the resulting sequencing that can confound downstream analysis. Here, we present a statistical method, with an accompanying package PaSD-qc (Power Spectral Density-qc), that evaluates the properties and quality of single cell libraries. It uses a modified power spectral density to assess amplification uniformity, amplicon size distribution, autocovariance and inter-sample consistency as well as to identify chromosomes with aberrant read-density profiles due either to copy alterations or poor amplification. These metrics provide a standard way to compare the quality of single cell samples as well as yield information necessary to improve variant calling strategies. We demonstrate the usefulness of this tool in comparing the properties of scWGS protocols, identifying potential chromosomal copy number variation, determining chromosomal and subchromosomal regions of poor amplification, and selecting high-quality libraries from low-coverage data for deep sequencing. The software is available free and open-source at https://github.com/parklab/PaSDqc. Oxford University Press 2018-02-28 2017-11-25 /pmc/articles/PMC5829578/ /pubmed/29186545 http://dx.doi.org/10.1093/nar/gkx1195 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Methods Online
Sherman, Maxwell A
Barton, Alison R
Lodato, Michael A
Vitzthum, Carl
Coulter, Michael E
Walsh, Christopher A
Park, Peter J
PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation
title PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation
title_full PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation
title_fullStr PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation
title_full_unstemmed PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation
title_short PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation
title_sort pasd-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829578/
https://www.ncbi.nlm.nih.gov/pubmed/29186545
http://dx.doi.org/10.1093/nar/gkx1195
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