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ARIADNA: machine learning method for ancient DNA variant discovery
Ancient DNA (aDNA) studies often rely on standard methods of mutation calling, optimized for high-quality contemporary DNA but not for excessive contamination, time- or environment-related damage of aDNA. In the absence of validated datasets and despite showing extreme sensitivity to aDNA quality, t...
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/PMC6289774/ https://www.ncbi.nlm.nih.gov/pubmed/30215675 http://dx.doi.org/10.1093/dnares/dsy029 |
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author | Kawash, Joseph K Smith, Sean D Karaiskos, Spyros Grigoriev, Andrey |
author_facet | Kawash, Joseph K Smith, Sean D Karaiskos, Spyros Grigoriev, Andrey |
author_sort | Kawash, Joseph K |
collection | PubMed |
description | Ancient DNA (aDNA) studies often rely on standard methods of mutation calling, optimized for high-quality contemporary DNA but not for excessive contamination, time- or environment-related damage of aDNA. In the absence of validated datasets and despite showing extreme sensitivity to aDNA quality, these methods have been used in many published studies, sometimes with additions of arbitrary filters or modifications, designed to overcome aDNA degradation and contamination problems. The general lack of best practices for aDNA mutation calling may lead to inaccurate results. To address these problems, we present ARIADNA (ARtificial Intelligence for Ancient DNA), a novel approach based on machine learning techniques, using specific aDNA characteristics as features to yield improved mutation calls. In our comparisons of variant callers across several ancient genomes, ARIADNA consistently detected higher-quality genome variants with fast runtimes, while reducing the false positive rate compared with other approaches. |
format | Online Article Text |
id | pubmed-6289774 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-62897742018-12-14 ARIADNA: machine learning method for ancient DNA variant discovery Kawash, Joseph K Smith, Sean D Karaiskos, Spyros Grigoriev, Andrey DNA Res Full Papers Ancient DNA (aDNA) studies often rely on standard methods of mutation calling, optimized for high-quality contemporary DNA but not for excessive contamination, time- or environment-related damage of aDNA. In the absence of validated datasets and despite showing extreme sensitivity to aDNA quality, these methods have been used in many published studies, sometimes with additions of arbitrary filters or modifications, designed to overcome aDNA degradation and contamination problems. The general lack of best practices for aDNA mutation calling may lead to inaccurate results. To address these problems, we present ARIADNA (ARtificial Intelligence for Ancient DNA), a novel approach based on machine learning techniques, using specific aDNA characteristics as features to yield improved mutation calls. In our comparisons of variant callers across several ancient genomes, ARIADNA consistently detected higher-quality genome variants with fast runtimes, while reducing the false positive rate compared with other approaches. Oxford University Press 2018-12 2018-09-11 /pmc/articles/PMC6289774/ /pubmed/30215675 http://dx.doi.org/10.1093/dnares/dsy029 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Kazusa DNA Research Institute. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial 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 | Full Papers Kawash, Joseph K Smith, Sean D Karaiskos, Spyros Grigoriev, Andrey ARIADNA: machine learning method for ancient DNA variant discovery |
title | ARIADNA: machine learning method for ancient DNA variant discovery |
title_full | ARIADNA: machine learning method for ancient DNA variant discovery |
title_fullStr | ARIADNA: machine learning method for ancient DNA variant discovery |
title_full_unstemmed | ARIADNA: machine learning method for ancient DNA variant discovery |
title_short | ARIADNA: machine learning method for ancient DNA variant discovery |
title_sort | ariadna: machine learning method for ancient dna variant discovery |
topic | Full Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6289774/ https://www.ncbi.nlm.nih.gov/pubmed/30215675 http://dx.doi.org/10.1093/dnares/dsy029 |
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