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A Novel Algorithm for Detecting Microsatellite Instability Based on Next-Generation Sequencing Data
OBJECTIVES: Microsatellite instability (MSI) is the condition of genetic hypermutability caused by spontaneous acquisition or loss of nucleotides during the DNA replication. MSI has been discovered to be a useful immunotherapy biomarker clinically. The main DNA-based method for MSI detection is poly...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9280483/ https://www.ncbi.nlm.nih.gov/pubmed/35847873 http://dx.doi.org/10.3389/fonc.2022.916379 |
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author | Li, Shijun Wang, Bo Chang, Miaomiao Hou, Rui Tian, Geng Tong, Ling |
author_facet | Li, Shijun Wang, Bo Chang, Miaomiao Hou, Rui Tian, Geng Tong, Ling |
author_sort | Li, Shijun |
collection | PubMed |
description | OBJECTIVES: Microsatellite instability (MSI) is the condition of genetic hypermutability caused by spontaneous acquisition or loss of nucleotides during the DNA replication. MSI has been discovered to be a useful immunotherapy biomarker clinically. The main DNA-based method for MSI detection is polymerase chain reaction (PCR) amplification and fragment length analysis, which are costly and laborious. Thus, we developed a novel method to detect MSI based on next-generation sequencing (NGS) data. METHODS: We chose six markers of MSI. After alignment and reads counting, a histogram was plotted showing the counts of different lengths for each marker. We then designed an algorithm to discover peaks in the generated histograms so that the peak numbers discovered in NGS data resembled that in PCR-based method. RESULTS: We selected nine samples as the training dataset, 101 samples for validation, and 68 samples as the test dataset from Chifeng Municipal Hospital, Inner Mongolia, China. The NGS-based method achieved 100% accuracy for the validation dataset and 98.53% accuracy for the test dataset, in which only one false positive was detected. CONCLUSIONS: Accurate MSI judgments were achieved using NGS data, which could provide comparable MSI detection with the gold standard, PCR-based methods. |
format | Online Article Text |
id | pubmed-9280483 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92804832022-07-15 A Novel Algorithm for Detecting Microsatellite Instability Based on Next-Generation Sequencing Data Li, Shijun Wang, Bo Chang, Miaomiao Hou, Rui Tian, Geng Tong, Ling Front Oncol Oncology OBJECTIVES: Microsatellite instability (MSI) is the condition of genetic hypermutability caused by spontaneous acquisition or loss of nucleotides during the DNA replication. MSI has been discovered to be a useful immunotherapy biomarker clinically. The main DNA-based method for MSI detection is polymerase chain reaction (PCR) amplification and fragment length analysis, which are costly and laborious. Thus, we developed a novel method to detect MSI based on next-generation sequencing (NGS) data. METHODS: We chose six markers of MSI. After alignment and reads counting, a histogram was plotted showing the counts of different lengths for each marker. We then designed an algorithm to discover peaks in the generated histograms so that the peak numbers discovered in NGS data resembled that in PCR-based method. RESULTS: We selected nine samples as the training dataset, 101 samples for validation, and 68 samples as the test dataset from Chifeng Municipal Hospital, Inner Mongolia, China. The NGS-based method achieved 100% accuracy for the validation dataset and 98.53% accuracy for the test dataset, in which only one false positive was detected. CONCLUSIONS: Accurate MSI judgments were achieved using NGS data, which could provide comparable MSI detection with the gold standard, PCR-based methods. Frontiers Media S.A. 2022-06-30 /pmc/articles/PMC9280483/ /pubmed/35847873 http://dx.doi.org/10.3389/fonc.2022.916379 Text en Copyright © 2022 Li, Wang, Chang, Hou, Tian and Tong https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Li, Shijun Wang, Bo Chang, Miaomiao Hou, Rui Tian, Geng Tong, Ling A Novel Algorithm for Detecting Microsatellite Instability Based on Next-Generation Sequencing Data |
title | A Novel Algorithm for Detecting Microsatellite Instability Based on Next-Generation Sequencing Data |
title_full | A Novel Algorithm for Detecting Microsatellite Instability Based on Next-Generation Sequencing Data |
title_fullStr | A Novel Algorithm for Detecting Microsatellite Instability Based on Next-Generation Sequencing Data |
title_full_unstemmed | A Novel Algorithm for Detecting Microsatellite Instability Based on Next-Generation Sequencing Data |
title_short | A Novel Algorithm for Detecting Microsatellite Instability Based on Next-Generation Sequencing Data |
title_sort | novel algorithm for detecting microsatellite instability based on next-generation sequencing data |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9280483/ https://www.ncbi.nlm.nih.gov/pubmed/35847873 http://dx.doi.org/10.3389/fonc.2022.916379 |
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