4.6 Article

CNV-MEANN: A Neural Network and Mind Evolutionary Algorithm-Based Detection of Copy Number Variations From Next-Generation Sequencing Data

期刊

FRONTIERS IN GENETICS
卷 12, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2021.700874

关键词

copy number variations; next-generation sequencing; mind evolutionary algorithm; multiple features; neural network

资金

  1. Numbers Shandong Research Special Project of Social Science Planning in Shandong Province [20CSDJ09]
  2. Shandong province colleges and universities youth innovation talent introduction and education program, Research fund project of Liaocheng university [318011922]

向作者/读者索取更多资源

The research proposed an improved CNV detection method CNV-MEANN, which adjusts the neural network structure, utilizes a new feature mapping quality, considers the impact of CNV loss categories on disease prediction, and optimizes the neural network model using a mind evolutionary algorithm, successfully improving the performance of CNV detection methods.
Copy number variation (CNV), is defined as repetitions or deletions of genomic segments of 1 Kb to 5 Mb, and is a major trigger for human disease. The high-throughput and low-cost characteristics of next-generation sequencing technology provide the possibility of the detection of CNVs in the whole genome, and also greatly improve the clinical practicability of next-generation sequencing (NGS) testing. However, current methods for the detection of CNVs are easily affected by sequencing and mapping errors, and uneven distribution of reads. In this paper, we propose an improved approach, CNV-MEANN, for the detection of CNVs, involving changing the structure of the neural network used in the MFCNV method. This method has three differences relative to the MFCNV method: (1) it utilizes a new feature, mapping quality, to replace two features in MFCNV, (2) it considers the influence of the loss categories of CNV on disease prediction, and refines the output structure, and (3) it uses a mind evolutionary algorithm to optimize the backpropagation (neural network) neural network model, and calculates individual scores for each genome bin to predict CNVs. Using both simulated and real datasets, we tested the performance of CNV-MEANN and compared its performance with those of seven widely used CNV detection methods. Experimental results demonstrated that the CNV-MEANN approach outperformed other methods with respect to sensitivity, precision, and F1-score. The proposed method was able to detect many CNVs that other approaches could not, and it reduced the boundary bias. CNV-MEANN is expected to be an effective method for the analysis of changes in CNVs in the genome.

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