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CNV-P: a machine-learning framework for predicting high confident copy number variations.

ABSTRACT: Background:Copy-number variants (CNVs) have been recognized as one of the major causes of genetic disorders. Reliable detection of CNVs from genome sequencing data has been a strong demand for disease research. However, current software for detecting CNVs has high false-positive rates, which needs further improvement. Methods:Here, we proposed a novel and post-processing approach for CNVs prediction (CNV-P), a machine-learning framework that could efficiently remove false-positive fragments from results of CNVs detecting tools. A series of CNVs signals such as read depth (RD), split reads (SR) and read pair (RP) around the putative CNV fragments were defined as features to train a classifier. Results:The prediction results on several real biological datasets showed that our models could accurately classify the CNVs at over 90% precision rate and 85% recall rate, which greatly improves the performance of state-of-the-art algorithms. Furthermore, our results indicate that CNV-P is robust to different sizes of CNVs and the platforms of sequencing. Conclusions:Our framework for classifying high-confident CNVs could improve both basic research and clinical diagnosis of genetic diseases.

SUBMITTER: Wang T

PROVIDER: S-EPMC8645205 | biostudies-literature |

REPOSITORIES: biostudies-literature

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X-CNV: genome-wide prediction of the pathogenicity of copy number variations.

Project description:BackgroundGene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success.ResultsWe have developed a novel computational framework X-CNV ( www.unimd.org/XCNV ), to predict the pathogenicity of CNVs by integrating more than 30 informative features such as allele frequency (AF), CNV length, CNV type, and some deleterious scores. Notably, over 14 million CNVs across various ethnic groups, covering nearly 93% of the human genome, were unified to calculate the AF. X-CNV, which yielded area under curve (AUC) values of 0.96 and 0.94 in training and validation sets, was demonstrated to outperform other available tools in terms of CNV pathogenicity prediction. A meta-voting prediction (MVP) score was developed to quantitively measure the pathogenic effect, which is based on the probabilistic value generated from the XGBoost algorithm. The proposed MVP score demonstrated a high discriminative power in determining pathogenetic CNVs for inherited traits/diseases in different ethnic groups.ConclusionsThe ability of the X-CNV framework to quantitatively prioritize functional, deleterious, and disease-causing CNV on a genome-wide basis outperformed current CNV-annotation tools and will have broad utility in population genetics, disease-association studies, and diagnostic screening.

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CNV-CH: A Convex Hull Based Segmentation Approach to Detect Copy Number Variations (CNV) Using Next-Generation Sequencing Data.

Project description:Copy number variation (CNV) is a form of structural alteration in the mammalian DNA sequence, which are associated with many complex neurological diseases as well as cancer. The development of next generation sequencing (NGS) technology provides us a new dimension towards detection of genomic locations with copy number variations. Here we develop an algorithm for detecting CNVs, which is based on depth of coverage data generated by NGS technology. In this work, we have used a novel way to represent the read count data as a two dimensional geometrical point. A key aspect of detecting the regions with CNVs, is to devise a proper segmentation algorithm that will distinguish the genomic locations having a significant difference in read count data. We have designed a new segmentation approach in this context, using convex hull algorithm on the geometrical representation of read count data. To our knowledge, most algorithms have used a single distribution model of read count data, but here in our approach, we have considered the read count data to follow two different distribution models independently, which adds to the robustness of detection of CNVs. In addition, our algorithm calls CNVs based on the multiple sample analysis approach resulting in a low false discovery rate with high precision.

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HBOS-CNV: A New Approach to Detect Copy Number Variations From Next-Generation Sequencing Data.

Project description:Copy number variation (CNV) is a genomic mutation that plays an important role in tumor evolution and tumor genesis. Accurate detection of CNVs from next-generation sequencing (NGS) data is still a challenging task due to artifacts such as uneven mapped reads and unbalanced amplitudes of gains and losses. This study proposes a new approach called HBOS-CNV to detect CNVs from NGS data. The central point of HBOS-CNV is that it uses a new statistic, the histogram-based outlier score (HBOS), to evaluate the fluctuation of genome bins to determine those of changed copy numbers. In comparison with existing statistics in the evaluation of CNVs, HBOS is a non-linearly transformed value from the observed read depth (RD) value of each genome bin, having the potential ability to relieve the effects resulted from the above artifacts. In the calculation of HBOS values, a dynamic width histogram is utilized to depict the density of bins on the genome being analyzed, which can reduce the effects of noises partially contributed by mapping and sequencing errors. The evaluation of genome bins using such a new statistic can lead to less extremely significant CNVs having a high probability of detection. We evaluated this method using a large number of simulation datasets and compared it with four existing methods (CNVnator, CNV-IFTV, CNV-LOF, and iCopyDav). The results demonstrated that our proposed method outperforms the others in terms of sensitivity, precision, and F1-measure. Furthermore, we applied the proposed method to a set of real sequencing samples from the 1000 Genomes Project and determined a number of CNVs with biological meanings. Thus, the proposed method can be regarded as a routine approach in the field of genome mutation analysis for cancer samples.

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Widespread, focal copy number variations (CNV) in Trypanosoma cruzi strains

Project description:Trypanosoma cruzi is a protozoan parasite and the etiologic agent of Chagas disease, an important public health problem in Latin America. T. cruzi is diploid, almost exclusively asexual, and displays an extraordinarily diverse population structure both genetically and phenotypically. Yet, to date the genotypic diversity of T. cruzi and its relationship, if any, to biological diversity have not been studied at the whole genome level. In this study, we used whole genome oligonucleotide tiling arrays to compare gene content in biologically disparate T. cruzi strains by comparative genomic hybridization (CGH). We observed that T. cruzi strains display widespread and focal copy number variations (CNV) and a substantially greater level of diversity than can be adequately defined by the current genetic typing methods. As expected, CNV were particularly frequent in gene family-rich regions containing mucins and trans-sialidases but were also evident in core genes. Gene groups that showed little variation in copy numbers among the strains tested included those encoding protein kinases and ribosomal proteins, suggesting these loci were less permissive to CNV. Moreover, frequent variation in chromosome copy numbers were observed, and chromosome-specific CNV signatures were shared by genetically divergent T. cruzi strains, suggesting a greater degree of chromosome exchange than previously thought.

2011-08-06 | GSE23576 | GEO

The Role of Number of Copies, Structure, Behavior and Copy Number Variations (CNV) of the Y Chromosome in Male Infertility.

Project description:The World Health Organization (WHO) defines infertility as the inability of a sexually active, non-contracepting couple to achieve spontaneous pregnancy within one year. Statistics show that the two sexes are equally at risk. Several causes may be responsible for male infertility; however, in 30-40% of cases a diagnosis of idiopathic male infertility is made in men with normal urogenital anatomy, no history of familial fertility-related diseases and a normal panel of values as for endocrine, genetic and biochemical markers. Idiopathic male infertility may be the result of gene/environment interactions, genetic and epigenetic abnormalities. Numerical and structural anomalies of the Y chromosome represent a minor yet significant proportion and are the topic discussed in this review. We searched the PubMed database and major search engines for reports about Y-linked male infertility. We present cases of Y-linked male infertility in terms of (i) anomalies of the Y chromosome structure/number; (ii) Y chromosome misbehavior in a normal genetic background; (iii) Y chromosome copy number variations (CNVs). We discuss possible explanations of male infertility caused by mutations, lower or higher number of copies of otherwise wild type, Y-linked sequences. Despite Y chromosome structural anomalies are not a major cause of male infertility, in case of negative results and of normal DNA sequencing of the ascertained genes causing infertility and mapping on this chromosome, we recommend an analysis of the karyotype integrity in all cases of idiopathic fertility impairment, with an emphasis on the structure and number of this chromosome.

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DeepCNV: a deep learning approach for authenticating copy number variations.

Project description:Copy number variations (CNVs) are an important class of variations contributing to the pathogenesis of many disease phenotypes. Detecting CNVs from genomic data remains difficult, and the most currently applied methods suffer from an unacceptably high false positive rate. A common practice is to have human experts manually review original CNV calls for filtering false positives before further downstream analysis or experimental validation. Here, we propose DeepCNV, a deep learning-based tool, intended to replace human experts when validating CNV calls, focusing on the calls made by one of the most accurate CNV callers, PennCNV. The sophistication of the deep neural network algorithm is enriched with over 10 000 expert-scored samples that are split into training and testing sets. Variant confidence, especially for CNVs, is a main roadblock impeding the progress of linking CNVs with the disease. We show that DeepCNV adds to the confidence of the CNV calls with an optimal area under the receiver operating characteristic curve of 0.909, exceeding other machine learning methods. The superiority of DeepCNV was also benchmarked and confirmed using an experimental wet-lab validation dataset. We conclude that the improvement obtained by DeepCNV results in significantly fewer false positive results and failures to replicate the CNV association results.

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AMYCNE: Confident copy number assessment using whole genome sequencing data.

Project description:Copy number variations (CNVs) within the human genome have been linked to a diversity of inherited diseases and phenotypic traits. The currently used methodology to measure copy numbers has limited resolution and/or precision, especially for regions with more than 4 copies. Whole genome sequencing (WGS) offers an alternative data source to allow for the detection and characterization of the copy number across different genomic regions in a single experiment. A plethora of tools have been developed to utilize WGS data for CNV detection. None of these tools are designed specifically to accurately estimate copy numbers of complex regions in a small cohort or clinical setting. Herein, we present AMYCNE (automatic modeling functionality for copy number estimation), a CNV analysis tool using WGS data. AMYCNE is multifunctional and performs copy number estimation of complex regions, annotation of VCF files, and CNV detection on individual samples. The performance of AMYCNE was evaluated using AMY1A ddPCR measurements from 86 unrelated individuals. In addition, we validated the accuracy of AMYCNE copy number predictions on two additional genes (FCGR3A and FCGR3B) using datasets available through the 1000 genomes consortium. Finally, we simulated levels of mosaic loss and gain of chromosome X and used this dataset for benchmarking AMYCNE. The results show a high concordance between AMYCNE and ddPCR, validating the use of AMYCNE to measure tandem AMY1 repeats with high accuracy. This opens up new possibilities for the use of WGS for accurate copy number determination of other complex regions in the genome in small cohorts or single individuals.

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Widespread, focal copy number variations (CNV) in Trypanosoma cruzi strains

2011-08-05 | E-GEOD-23576 | biostudies-arrayexpress

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Project description:This SuperSeries is composed of the SubSeries listed below.

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CNV-seq, a new method to detect copy number variation using high-throughput sequencing.

Project description:BackgroundDNA copy number variation (CNV) has been recognized as an important source of genetic variation. Array comparative genomic hybridization (aCGH) is commonly used for CNV detection, but the microarray platform has a number of inherent limitations.ResultsHere, we describe a method to detect copy number variation using shotgun sequencing, CNV-seq. The method is based on a robust statistical model that describes the complete analysis procedure and allows the computation of essential confidence values for detection of CNV. Our results show that the number of reads, not the length of the reads is the key factor determining the resolution of detection. This favors the next-generation sequencing methods that rapidly produce large amount of short reads.ConclusionSimulation of various sequencing methods with coverage between 0.1x to 8x show overall specificity between 91.7 - 99.9%, and sensitivity between 72.2 - 96.5%. We also show the results for assessment of CNV between two individual human genomes.

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