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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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Detalles Bibliográficos
Autores principales: Liu, Guojun, Yang, Hongzhi, Yuan, Xiguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
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.
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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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