MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data

Abstract

Copy number variation (CNV) is a very important phenomenon in tumor genomes and plays a significant role in tumor genesis. Accurate detection of CNVs has become a routine and necessary procedure for a deep investigation of tumor cells and diagnosis of tumor patients. Next-generation sequencing (NGS) technique has provided a wealth of data for the detection of CNVs at base-pair resolution. However, such task is usually influenced by a number of factors, including GC-content bias, sequencing errors, and correlations among adjacent positions within CNVs. Although many existing methods have dealt with some of these artifacts by designing their own strategies, there is still a lack of comprehensive consideration of all the factors. In this paper, we propose a new method, MFCNV, for an accurate detection of CNVs from NGS data. Compared with existing methods, the characteristics of the proposed method include the following: (1) it makes a full consideration of the intrinsic correlations among adjacent positions in the genome to be analyzed, (2) it calculates read depth, GC-content bias, base quality, and correlation value for each genome bin and combines them as multiple features for the evaluation of genome bins, and (3) it addresses the joint effect among the factors via training a neural network algorithm for the prediction of CNVs. We test the performance of the MFCNV method by using simulation and real sequencing data and make comparisons with several peer methods. The results demonstrate that our method is superior to other methods in terms of sensitivity, precision, and F1-score and can detect many CNVs that other methods have not discovered. MFCNV is expected to be a complementary tool in the analysis of mutations in tumor genomes and can be extended to be applied to the analysis of single-cell sequencing data.

Detection of Pathogenic Microbe Composition Using Next-Generation Sequencing Data

“…We test PGMicroD based on both simulation and real datasets, indicating the PGMicroD exhibits superior performance. In future work, we intend to integrate mutations such as single nucleotide variations (Yuan et al, 2020) and copy number variations (Xi et al, 2019;Zhao et al, 2020) to improve the detection of microbial composition. We also plan to establish a more comprehensive reference library for detecting species and improving detection accuracy and create new methods aiming at the filtered reads to identify new species.…”

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

“…The learning rate is an important parameter of a neural network, and is closely related to convergence. The value of the learning rate is set at a default value of 0.1, which is the same as the MFCNV (Zhao et al, 2020).…”

“…Artificial neural networks have powerful nonlinear mapping capabilities, which can address the linked effects of multiple features. MFCNV (Zhao et al, 2020) detects CNVs based on multiple features and a back-propagation neural network classifier. However, neural networks suffer from some limitations, such as slow convergence velocity, relapse into local optima, and premature convergence.…”

DINTD: Detection and Inference of Tandem Duplications From Short Sequencing Reads

“…Here, p(x) is an estimate of the probability that the alignment position is wrong. It can be combined with other features for variation detection (Zhao et al, 2020). If there are TDs in the genome, then a read is mapped to multiple positions.…”