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Estimating Exceptionally Rare Germline and Somatic Mutation Frequencies via Next Generation Sequencing

We used targeted next generation deep-sequencing (Safe Sequencing System) to measure ultra-rare de novo mutation frequencies in the human male germline by attaching a unique identifier code to each target DNA molecule. Segments from three different human genes (FGFR3, MECP2 and PTPN11) were studied....

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Autores principales: Eboreime, Jordan, Choi, Soo-Kung, Yoon, Song-Ro, Arnheim, Norman, Calabrese, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4920415/
https://www.ncbi.nlm.nih.gov/pubmed/27341568
http://dx.doi.org/10.1371/journal.pone.0158340
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author Eboreime, Jordan
Choi, Soo-Kung
Yoon, Song-Ro
Arnheim, Norman
Calabrese, Peter
author_facet Eboreime, Jordan
Choi, Soo-Kung
Yoon, Song-Ro
Arnheim, Norman
Calabrese, Peter
author_sort Eboreime, Jordan
collection PubMed
description We used targeted next generation deep-sequencing (Safe Sequencing System) to measure ultra-rare de novo mutation frequencies in the human male germline by attaching a unique identifier code to each target DNA molecule. Segments from three different human genes (FGFR3, MECP2 and PTPN11) were studied. Regardless of the gene segment, the particular testis donor or the 73 different testis pieces used, the frequencies for any one of the six different mutation types were consistent. Averaging over the C>T/G>A and G>T/C>A mutation types the background mutation frequency was 2.6x10(-5) per base pair, while for the four other mutation types the average background frequency was lower at 1.5x10(-6) per base pair. These rates far exceed the well documented human genome average frequency per base pair (~10(−8)) suggesting a non-biological explanation for our data. By computational modeling and a new experimental procedure to distinguish between pre-mutagenic lesion base mismatches and a fully mutated base pair in the original DNA molecule, we argue that most of the base-dependent variation in background frequency is due to a mixture of deamination and oxidation during the first two PCR cycles. Finally, we looked at a previously studied disease mutation in the PTPN11 gene and could easily distinguish true mutations from the SSS background. We also discuss the limits and possibilities of this and other methods to measure exceptionally rare mutation frequencies, and we present calculations for other scientists seeking to design their own such experiments.
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spelling pubmed-49204152016-07-18 Estimating Exceptionally Rare Germline and Somatic Mutation Frequencies via Next Generation Sequencing Eboreime, Jordan Choi, Soo-Kung Yoon, Song-Ro Arnheim, Norman Calabrese, Peter PLoS One Research Article We used targeted next generation deep-sequencing (Safe Sequencing System) to measure ultra-rare de novo mutation frequencies in the human male germline by attaching a unique identifier code to each target DNA molecule. Segments from three different human genes (FGFR3, MECP2 and PTPN11) were studied. Regardless of the gene segment, the particular testis donor or the 73 different testis pieces used, the frequencies for any one of the six different mutation types were consistent. Averaging over the C>T/G>A and G>T/C>A mutation types the background mutation frequency was 2.6x10(-5) per base pair, while for the four other mutation types the average background frequency was lower at 1.5x10(-6) per base pair. These rates far exceed the well documented human genome average frequency per base pair (~10(−8)) suggesting a non-biological explanation for our data. By computational modeling and a new experimental procedure to distinguish between pre-mutagenic lesion base mismatches and a fully mutated base pair in the original DNA molecule, we argue that most of the base-dependent variation in background frequency is due to a mixture of deamination and oxidation during the first two PCR cycles. Finally, we looked at a previously studied disease mutation in the PTPN11 gene and could easily distinguish true mutations from the SSS background. We also discuss the limits and possibilities of this and other methods to measure exceptionally rare mutation frequencies, and we present calculations for other scientists seeking to design their own such experiments. Public Library of Science 2016-06-24 /pmc/articles/PMC4920415/ /pubmed/27341568 http://dx.doi.org/10.1371/journal.pone.0158340 Text en © 2016 Eboreime et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Eboreime, Jordan
Choi, Soo-Kung
Yoon, Song-Ro
Arnheim, Norman
Calabrese, Peter
Estimating Exceptionally Rare Germline and Somatic Mutation Frequencies via Next Generation Sequencing
title Estimating Exceptionally Rare Germline and Somatic Mutation Frequencies via Next Generation Sequencing
title_full Estimating Exceptionally Rare Germline and Somatic Mutation Frequencies via Next Generation Sequencing
title_fullStr Estimating Exceptionally Rare Germline and Somatic Mutation Frequencies via Next Generation Sequencing
title_full_unstemmed Estimating Exceptionally Rare Germline and Somatic Mutation Frequencies via Next Generation Sequencing
title_short Estimating Exceptionally Rare Germline and Somatic Mutation Frequencies via Next Generation Sequencing
title_sort estimating exceptionally rare germline and somatic mutation frequencies via next generation sequencing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4920415/
https://www.ncbi.nlm.nih.gov/pubmed/27341568
http://dx.doi.org/10.1371/journal.pone.0158340
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