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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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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. |
format | Online Article Text |
id | pubmed-5829578 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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