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Accurate single-cell genotyping utilizing information from the local genome territory
Single-nucleotide variant (SNV) detection in the genome of single cells is affected by DNA amplification artefacts, including imbalanced alleles and early PCR errors. Existing single-cell genotyper accuracy often depends on the quality and coordination of both the target single-cell and external dat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191788/ https://www.ncbi.nlm.nih.gov/pubmed/33619552 http://dx.doi.org/10.1093/nar/gkab106 |
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author | Tu, Kailing Lu, Keying Zhang, Qilin Huang, Wei Xie, Dan |
author_facet | Tu, Kailing Lu, Keying Zhang, Qilin Huang, Wei Xie, Dan |
author_sort | Tu, Kailing |
collection | PubMed |
description | Single-nucleotide variant (SNV) detection in the genome of single cells is affected by DNA amplification artefacts, including imbalanced alleles and early PCR errors. Existing single-cell genotyper accuracy often depends on the quality and coordination of both the target single-cell and external data, such as heterozygous profiles determined by bulk data. In most single-cell studies, information from different sources is not perfectly matched. High-accuracy SNV detection with a limited single data source remains a challenge. We developed a new variant detection method, SCOUT (Single Cell Genotyper Utilizing Information from Local Genome Territory), the greatest advantage of which is not requiring external data while base calling. By leveraging base count information from the adjacent genomic region, SCOUT classifies all candidate SNVs into homozygous, heterozygous, intermediate and low major allele SNVs according to the highest likelihood score. Compared with other genotypers, SCOUT improves the variant detection performance by 2.0–77.5% in real and simulated single-cell datasets. Furthermore, the running time of SCOUT increases linearly with sequence length; as a result, it shows 400% average acceleration in operating efficiency compared with other methods. |
format | Online Article Text |
id | pubmed-8191788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-81917882021-06-11 Accurate single-cell genotyping utilizing information from the local genome territory Tu, Kailing Lu, Keying Zhang, Qilin Huang, Wei Xie, Dan Nucleic Acids Res Methods Online Single-nucleotide variant (SNV) detection in the genome of single cells is affected by DNA amplification artefacts, including imbalanced alleles and early PCR errors. Existing single-cell genotyper accuracy often depends on the quality and coordination of both the target single-cell and external data, such as heterozygous profiles determined by bulk data. In most single-cell studies, information from different sources is not perfectly matched. High-accuracy SNV detection with a limited single data source remains a challenge. We developed a new variant detection method, SCOUT (Single Cell Genotyper Utilizing Information from Local Genome Territory), the greatest advantage of which is not requiring external data while base calling. By leveraging base count information from the adjacent genomic region, SCOUT classifies all candidate SNVs into homozygous, heterozygous, intermediate and low major allele SNVs according to the highest likelihood score. Compared with other genotypers, SCOUT improves the variant detection performance by 2.0–77.5% in real and simulated single-cell datasets. Furthermore, the running time of SCOUT increases linearly with sequence length; as a result, it shows 400% average acceleration in operating efficiency compared with other methods. Oxford University Press 2021-02-22 /pmc/articles/PMC8191788/ /pubmed/33619552 http://dx.doi.org/10.1093/nar/gkab106 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://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 Tu, Kailing Lu, Keying Zhang, Qilin Huang, Wei Xie, Dan Accurate single-cell genotyping utilizing information from the local genome territory |
title | Accurate single-cell genotyping utilizing information from the local genome territory |
title_full | Accurate single-cell genotyping utilizing information from the local genome territory |
title_fullStr | Accurate single-cell genotyping utilizing information from the local genome territory |
title_full_unstemmed | Accurate single-cell genotyping utilizing information from the local genome territory |
title_short | Accurate single-cell genotyping utilizing information from the local genome territory |
title_sort | accurate single-cell genotyping utilizing information from the local genome territory |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191788/ https://www.ncbi.nlm.nih.gov/pubmed/33619552 http://dx.doi.org/10.1093/nar/gkab106 |
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