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A shortest path-based approach for copy number variation detection from next-generation sequencing data
Copy number variation (CNV) is one of the main structural variations in the human genome and accounts for a considerable proportion of variations. As CNVs can directly or indirectly cause cancer, mental illness, and genetic disease in humans, their effective detection in humans is of great interest...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887524/ https://www.ncbi.nlm.nih.gov/pubmed/36733945 http://dx.doi.org/10.3389/fgene.2022.1084974 |
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author | Liu, Guojun Yang, Hongzhi Yuan, Xiguo |
author_facet | Liu, Guojun Yang, Hongzhi Yuan, Xiguo |
author_sort | Liu, Guojun |
collection | PubMed |
description | Copy number variation (CNV) is one of the main structural variations in the human genome and accounts for a considerable proportion of variations. As CNVs can directly or indirectly cause cancer, mental illness, and genetic disease in humans, their effective detection in humans is of great interest in the fields of oncogene discovery, clinical decision-making, bioinformatics, and drug discovery. The advent of next-generation sequencing data makes CNV detection possible, and a large number of CNV detection tools are based on next-generation sequencing data. Due to the complexity (e.g., bias, noise, alignment errors) of next-generation sequencing data and CNV structures, the accuracy of existing methods in detecting CNVs remains low. In this work, we design a new CNV detection approach, called shortest path-based Copy number variation (SPCNV), to improve the detection accuracy of CNVs. SPCNV calculates the k nearest neighbors of each read depth and defines the shortest path, shortest path relation, and shortest path cost sets based on which further calculates the mean shortest path cost of each read depth and its k nearest neighbors. We utilize the ratio between the mean shortest path cost for each read depth and the mean of the mean shortest path cost of its k nearest neighbors to construct a relative shortest path score formula that is able to determine a score for each read depth. Based on the score profile, a boxplot is then applied to predict CNVs. The performance of the proposed method is verified by simulation data experiments and compared against several popular methods of the same type. Experimental results show that the proposed method achieves the best balance between recall and precision in each set of simulated samples. To further verify the performance of the proposed method in real application scenarios, we then select real sample data from the 1,000 Genomes Project to conduct experiments. The proposed method achieves the best F1-scores in almost all samples. Therefore, the proposed method can be used as a more reliable tool for the routine detection of CNVs. |
format | Online Article Text |
id | pubmed-9887524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98875242023-02-01 A shortest path-based approach for copy number variation detection from next-generation sequencing data Liu, Guojun Yang, Hongzhi Yuan, Xiguo Front Genet Genetics Copy number variation (CNV) is one of the main structural variations in the human genome and accounts for a considerable proportion of variations. As CNVs can directly or indirectly cause cancer, mental illness, and genetic disease in humans, their effective detection in humans is of great interest in the fields of oncogene discovery, clinical decision-making, bioinformatics, and drug discovery. The advent of next-generation sequencing data makes CNV detection possible, and a large number of CNV detection tools are based on next-generation sequencing data. Due to the complexity (e.g., bias, noise, alignment errors) of next-generation sequencing data and CNV structures, the accuracy of existing methods in detecting CNVs remains low. In this work, we design a new CNV detection approach, called shortest path-based Copy number variation (SPCNV), to improve the detection accuracy of CNVs. SPCNV calculates the k nearest neighbors of each read depth and defines the shortest path, shortest path relation, and shortest path cost sets based on which further calculates the mean shortest path cost of each read depth and its k nearest neighbors. We utilize the ratio between the mean shortest path cost for each read depth and the mean of the mean shortest path cost of its k nearest neighbors to construct a relative shortest path score formula that is able to determine a score for each read depth. Based on the score profile, a boxplot is then applied to predict CNVs. The performance of the proposed method is verified by simulation data experiments and compared against several popular methods of the same type. Experimental results show that the proposed method achieves the best balance between recall and precision in each set of simulated samples. To further verify the performance of the proposed method in real application scenarios, we then select real sample data from the 1,000 Genomes Project to conduct experiments. The proposed method achieves the best F1-scores in almost all samples. Therefore, the proposed method can be used as a more reliable tool for the routine detection of CNVs. Frontiers Media S.A. 2023-01-17 /pmc/articles/PMC9887524/ /pubmed/36733945 http://dx.doi.org/10.3389/fgene.2022.1084974 Text en Copyright © 2023 Liu, Yang and Yuan. 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 | Genetics Liu, Guojun Yang, Hongzhi Yuan, Xiguo A shortest path-based approach for copy number variation detection from next-generation sequencing data |
title | A shortest path-based approach for copy number variation detection from next-generation sequencing data |
title_full | A shortest path-based approach for copy number variation detection from next-generation sequencing data |
title_fullStr | A shortest path-based approach for copy number variation detection from next-generation sequencing data |
title_full_unstemmed | A shortest path-based approach for copy number variation detection from next-generation sequencing data |
title_short | A shortest path-based approach for copy number variation detection from next-generation sequencing data |
title_sort | shortest path-based approach for copy number variation detection from next-generation sequencing data |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887524/ https://www.ncbi.nlm.nih.gov/pubmed/36733945 http://dx.doi.org/10.3389/fgene.2022.1084974 |
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