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Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions
Recent advances in massively parallel shotgun sequencing opened up new options for affordable non-invasive prenatal testing (NIPT) for fetus aneuploidy from DNA material extracted from maternal plasma. Tests typically compare chromosomal distributions of a tested sample with a control set of healthy...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6678500/ https://www.ncbi.nlm.nih.gov/pubmed/31336782 http://dx.doi.org/10.3390/ijms20143414 |
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author | Gazdarica, Juraj Budis, Jaroslav Duris, Frantisek Turna, Jan Szemes, Tomas |
author_facet | Gazdarica, Juraj Budis, Jaroslav Duris, Frantisek Turna, Jan Szemes, Tomas |
author_sort | Gazdarica, Juraj |
collection | PubMed |
description | Recent advances in massively parallel shotgun sequencing opened up new options for affordable non-invasive prenatal testing (NIPT) for fetus aneuploidy from DNA material extracted from maternal plasma. Tests typically compare chromosomal distributions of a tested sample with a control set of healthy samples with unaffected fetuses. Deviations above certain threshold levels are concluded as positive findings. The main problem with this approach is that the variance of the control set is dependent on the number of sequenced fragments. The higher the amount, the more precise the estimation of actual chromosomal proportions is. Testing a sample with a highly different number of sequenced reads as used in training may thus lead to over- or under-estimation of their variance, and so lead to false predictions. We propose the calculation of a variance for each tested sample adaptively, based on the actual number of its sequenced fragments. We demonstrate how it leads to more stable predictions, mainly in real-world diagnostics with the highly divergent inter-sample coverage. |
format | Online Article Text |
id | pubmed-6678500 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66785002019-08-19 Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions Gazdarica, Juraj Budis, Jaroslav Duris, Frantisek Turna, Jan Szemes, Tomas Int J Mol Sci Article Recent advances in massively parallel shotgun sequencing opened up new options for affordable non-invasive prenatal testing (NIPT) for fetus aneuploidy from DNA material extracted from maternal plasma. Tests typically compare chromosomal distributions of a tested sample with a control set of healthy samples with unaffected fetuses. Deviations above certain threshold levels are concluded as positive findings. The main problem with this approach is that the variance of the control set is dependent on the number of sequenced fragments. The higher the amount, the more precise the estimation of actual chromosomal proportions is. Testing a sample with a highly different number of sequenced reads as used in training may thus lead to over- or under-estimation of their variance, and so lead to false predictions. We propose the calculation of a variance for each tested sample adaptively, based on the actual number of its sequenced fragments. We demonstrate how it leads to more stable predictions, mainly in real-world diagnostics with the highly divergent inter-sample coverage. MDPI 2019-07-11 /pmc/articles/PMC6678500/ /pubmed/31336782 http://dx.doi.org/10.3390/ijms20143414 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gazdarica, Juraj Budis, Jaroslav Duris, Frantisek Turna, Jan Szemes, Tomas Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions |
title | Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions |
title_full | Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions |
title_fullStr | Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions |
title_full_unstemmed | Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions |
title_short | Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions |
title_sort | adaptable model parameters in non-invasive prenatal testing lead to more stable predictions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6678500/ https://www.ncbi.nlm.nih.gov/pubmed/31336782 http://dx.doi.org/10.3390/ijms20143414 |
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