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MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations

BACKGROUND: Mosaic mutations contribute to numerous human disorders. As such, the identification and precise quantification of mosaic mutations is essential for a wide range of research applications, clinical diagnoses, and early detection of cancers. Currently, the low-throughput nature of single a...

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Autores principales: Doan, Ryan N., Miller, Michael B., Kim, Sonia N., Rodin, Rachel E., Ganz, Javier, Bizzotto, Sara, Morillo, Katherine S., Huang, August Yue, Digumarthy, Reethika, Zemmel, Zachary, Walsh, Christopher A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881461/
https://www.ncbi.nlm.nih.gov/pubmed/33579278
http://dx.doi.org/10.1186/s12920-021-00893-3
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author Doan, Ryan N.
Miller, Michael B.
Kim, Sonia N.
Rodin, Rachel E.
Ganz, Javier
Bizzotto, Sara
Morillo, Katherine S.
Huang, August Yue
Digumarthy, Reethika
Zemmel, Zachary
Walsh, Christopher A.
author_facet Doan, Ryan N.
Miller, Michael B.
Kim, Sonia N.
Rodin, Rachel E.
Ganz, Javier
Bizzotto, Sara
Morillo, Katherine S.
Huang, August Yue
Digumarthy, Reethika
Zemmel, Zachary
Walsh, Christopher A.
author_sort Doan, Ryan N.
collection PubMed
description BACKGROUND: Mosaic mutations contribute to numerous human disorders. As such, the identification and precise quantification of mosaic mutations is essential for a wide range of research applications, clinical diagnoses, and early detection of cancers. Currently, the low-throughput nature of single allele assays (e.g., allele-specific ddPCR) commonly used for genotyping known mutations at very low alternate allelic fractions (AAFs) have limited the integration of low-level mosaic analyses into clinical and research applications. The growing importance of mosaic mutations requires a more rapid, low-cost solution for mutation detection and validation. METHODS: To overcome these limitations, we developed Multiple Independent Primer PCR Sequencing (MIPP-Seq) which combines the power of ultra-deep sequencing and truly independent assays. The accuracy of MIPP-seq to quantifiable detect and measure extremely low allelic fractions was assessed using a combination of SNVs, insertions, and deletions at known allelic fractions in blood and brain derived DNA samples. RESULTS: The Independent amplicon analyses of MIPP-Seq markedly reduce the impact of allelic dropout, amplification bias, PCR-induced, and sequencing artifacts. Using low DNA inputs of either 25 ng or 50 ng of DNA, MIPP-Seq provides sensitive and quantitative assessments of AAFs as low as 0.025% for SNVs, insertion, and deletions. CONCLUSIONS: MIPP-Seq provides an ultra-sensitive, low-cost approach for detecting and validating known and novel mutations in a highly scalable system with broad utility spanning both research and clinical diagnostic testing applications. The scalability of MIPP-Seq allows for multiplexing mutations and samples, which dramatically reduce costs of variant validation when compared to methods like ddPCR. By leveraging the power of individual analyses of multiple unique and independent reactions, MIPP-Seq can validate and precisely quantitate extremely low AAFs across multiple tissues and mutational categories including both indels and SNVs. Furthermore, using Illumina sequencing technology, MIPP-seq provides a robust method for accurate detection of novel mutations at an extremely low AAF.
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spelling pubmed-78814612021-02-17 MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations Doan, Ryan N. Miller, Michael B. Kim, Sonia N. Rodin, Rachel E. Ganz, Javier Bizzotto, Sara Morillo, Katherine S. Huang, August Yue Digumarthy, Reethika Zemmel, Zachary Walsh, Christopher A. BMC Med Genomics Technical Advance BACKGROUND: Mosaic mutations contribute to numerous human disorders. As such, the identification and precise quantification of mosaic mutations is essential for a wide range of research applications, clinical diagnoses, and early detection of cancers. Currently, the low-throughput nature of single allele assays (e.g., allele-specific ddPCR) commonly used for genotyping known mutations at very low alternate allelic fractions (AAFs) have limited the integration of low-level mosaic analyses into clinical and research applications. The growing importance of mosaic mutations requires a more rapid, low-cost solution for mutation detection and validation. METHODS: To overcome these limitations, we developed Multiple Independent Primer PCR Sequencing (MIPP-Seq) which combines the power of ultra-deep sequencing and truly independent assays. The accuracy of MIPP-seq to quantifiable detect and measure extremely low allelic fractions was assessed using a combination of SNVs, insertions, and deletions at known allelic fractions in blood and brain derived DNA samples. RESULTS: The Independent amplicon analyses of MIPP-Seq markedly reduce the impact of allelic dropout, amplification bias, PCR-induced, and sequencing artifacts. Using low DNA inputs of either 25 ng or 50 ng of DNA, MIPP-Seq provides sensitive and quantitative assessments of AAFs as low as 0.025% for SNVs, insertion, and deletions. CONCLUSIONS: MIPP-Seq provides an ultra-sensitive, low-cost approach for detecting and validating known and novel mutations in a highly scalable system with broad utility spanning both research and clinical diagnostic testing applications. The scalability of MIPP-Seq allows for multiplexing mutations and samples, which dramatically reduce costs of variant validation when compared to methods like ddPCR. By leveraging the power of individual analyses of multiple unique and independent reactions, MIPP-Seq can validate and precisely quantitate extremely low AAFs across multiple tissues and mutational categories including both indels and SNVs. Furthermore, using Illumina sequencing technology, MIPP-seq provides a robust method for accurate detection of novel mutations at an extremely low AAF. BioMed Central 2021-02-12 /pmc/articles/PMC7881461/ /pubmed/33579278 http://dx.doi.org/10.1186/s12920-021-00893-3 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Technical Advance
Doan, Ryan N.
Miller, Michael B.
Kim, Sonia N.
Rodin, Rachel E.
Ganz, Javier
Bizzotto, Sara
Morillo, Katherine S.
Huang, August Yue
Digumarthy, Reethika
Zemmel, Zachary
Walsh, Christopher A.
MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations
title MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations
title_full MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations
title_fullStr MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations
title_full_unstemmed MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations
title_short MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations
title_sort mipp-seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881461/
https://www.ncbi.nlm.nih.gov/pubmed/33579278
http://dx.doi.org/10.1186/s12920-021-00893-3
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