A Deep Learning Approach for Detecting Copy Number Variation in Next-Generation Sequencing Data.
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ABSTRACT:
SUBMITTER: Hill T
PROVIDER: S-EPMC6829143 | biostudies-literature | 2019 Nov
REPOSITORIES: biostudies-literature
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A Deep Learning Approach for Detecting Copy Number Variation in Next-Generation Sequencing Data.
Hill Tom T Unckless Robert L RL
G3 (Bethesda, Md.) 20191105 11
Copy number variants (CNV) are associated with phenotypic variation in several species. However, properly detecting changes in copy numbers of sequences remains a difficult problem, especially in lower quality or lower coverage next-generation sequencing data. Here, inspired by recent applications of machine learning in genomics, we describe a method to detect duplications and deletions in short-read sequencing data. In low coverage data, machine learning appears to be more powerful in the detec ...[more]
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Project description:BackgroundDetection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations. Our aim has been to develop a bioinformatic tool for CNV detection from NGS data in medical genetic diagnostic samples.ResultsOur computational pipeline for detection of CNVs in NGS data from targeted gene panels utilizes coverage depth of the captured regions and calculates a copy number ratio score for each region. This is computed by comparing the mean coverage of the sample with the mean coverage of the same region in other samples, defined as a pool. The pipeline selects pools for comparison dynamically from previously sequenced samples, using the pool with an average coverage depth that is nearest to the one of the samples. A sliding window-based approach is used to analyze each region, where length of sliding window and sliding distance can be chosen dynamically to increase or decrease the resolution. This helps in detecting CNVs in small or partial exons. With this pipeline we have correctly identified the CNVs in 36 positive control samples, with sensitivity of 100% and specificity of 91%. We have detected whole gene level deletion/duplication, single/multi exonic level deletion/duplication, partial exonic deletion and mosaic deletion. Since its implementation in mid-2018 it has proven its diagnostic value with more than 45 CNV findings in routine tests.ConclusionsWith this pipeline as part of our diagnostic practices it is now possible to detect partial, single or multi-exonic, and intragenic CNVs in all genes in our target panel. This has helped our diagnostic lab to expand the portfolio of genes where we offer CNV detection, which previously was limited by the availability of MLPA kits.
| S-EPMC8406611 | biostudies-literature
Project description:BackgroundA copy number variation (CNV) is a difference between genotypes in the number of copies of a genomic region. Next generation sequencing (NGS) technologies provide sensitive and accurate tools for detecting genomic variations that include CNVs. However, statistical approaches for CNV identification using NGS are limited. We propose a new methodology for detecting CNVs using NGS data. This method (henceforth denoted by m-HMM) is based on a hidden Markov model with emission probabilities that are governed by mixture distributions. We use the Expectation-Maximization (EM) algorithm to estimate the parameters in the model.ResultsA simulation study demonstrates that our proposed m-HMM approach has greater power for detecting copy number gains and losses relative to existing methods. Furthermore, application of our m-HMM to DNA sequencing data from the two maize inbred lines B73 and Mo17 to identify CNVs that may play a role in creating phenotypic differences between these inbred lines provides results concordant with previous array-based efforts to identify CNVs.ConclusionsThe new m-HMM method is a powerful and practical approach for identifying CNVs from NGS data.
| S-EPMC4021345 | biostudies-literature
Project 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.
| S-EPMC9887524 | biostudies-literature
Project description:Copy number variations (CNVs) affect a wide range of phenotypic traits; however, CNVs in or near segmental duplication regions are often intractable. Using a read depth approach based on next-generation sequencing, we examined genome-wide copy number differences among five taurine (three Angus, one Holstein, and one Hereford) and one indicine (Nelore) cattle. Within mapped chromosomal sequence, we identified 1265 CNV regions comprising ~55.6-Mbp sequence--476 of which (~38%) have not previously been reported. We validated this sequence-based CNV call set with array comparative genomic hybridization (aCGH), quantitative PCR (qPCR), and fluorescent in situ hybridization (FISH), achieving a validation rate of 82% and a false positive rate of 8%. We further estimated absolute copy numbers for genomic segments and annotated genes in each individual. Surveys of the top 25 most variable genes revealed that the Nelore individual had the lowest copy numbers in 13 cases (~52%, ?(2) test; P-value <0.05). In contrast, genes related to pathogen- and parasite-resistance, such as CATHL4 and ULBP17, were highly duplicated in the Nelore individual relative to the taurine cattle, while genes involved in lipid transport and metabolism, including APOL3 and FABP2, were highly duplicated in the beef breeds. These CNV regions also harbor genes like BPIFA2A (BSP30A) and WC1, suggesting that some CNVs may be associated with breed-specific differences in adaptation, health, and production traits. By providing the first individualized cattle CNV and segmental duplication maps and genome-wide gene copy number estimates, we enable future CNV studies into highly duplicated regions in the cattle genome.
| S-EPMC3317159 | biostudies-literature
Project description:Although a common cause of disease, copy number variants (CNVs) have not routinely been identified from next-generation sequencing (NGS) data in a clinical context. This study aimed to examine the sensitivity and specificity of a widely used software package, ExomeDepth, to identify CNVs from targeted NGS data sets. We benchmarked the accuracy of CNV detection using ExomeDepth v1.1.6 applied to targeted NGS data sets by comparison to CNV events detected through whole-genome sequencing for 25 individuals and determined the sensitivity and specificity of ExomeDepth applied to these targeted NGS data sets to be 100% and 99.8%, respectively. To define quality assurance metrics for CNV surveillance through ExomeDepth, we undertook simulation of single-exon (n=1000) and multiple-exon heterozygous deletion events (n=1749), determining a sensitivity of 97% (n=2749). We identified that the extent of sequencing coverage, the inter- and intra-sample variability in the depth of sequencing coverage and the composition of analysis regions are all important determinants of successful CNV surveillance through ExomeDepth. We then applied these quality assurance metrics during CNV surveillance for 140 individuals across 12 distinct clinical areas, encompassing over 500 potential rare disease diagnoses. All 140 individuals lacked molecular diagnoses after routine clinical NGS testing, and by application of ExomeDepth, we identified 17 CNVs contributing to the cause of a Mendelian disorder. Our findings support the integration of CNV detection using ExomeDepth v1.1.6 with routine targeted NGS diagnostic services for Mendelian disorders. Implementation of this strategy increases diagnostic yields and enhances clinical care.
| S-EPMC5427176 | biostudies-literature
Project description:Copy number variations (CNV) are known to play a major role in genetic variability and disease pathogenesis in several species including cattle. In this study, we report the identification and characterization of CNV in eight French beef and dairy breeds using whole-genome sequence data from 200 animals. Bioinformatics analyses to search for CNV were carried out using four different but complementary tools and we validated a subset of the CNV by both in silico and experimental approaches.We report the identification and localization of 4178 putative deletion-only, duplication-only and CNV regions, which cover 6% of the bovine autosomal genome; they were validated by two in silico approaches and/or experimentally validated using array-based comparative genomic hybridization and single nucleotide polymorphism genotyping arrays. The size of these variants ranged from 334 bp to 7.7 Mb, with an average size of ~ 54 kb. Of these 4178 variants, 3940 were deletions, 67 were duplications and 171 corresponded to both deletions and duplications, which were defined as potential CNV regions. Gene content analysis revealed that, among these variants, 1100 deletions and duplications encompassed 1803 known genes, which affect a wide spectrum of molecular functions, and 1095 overlapped with known QTL regions.Our study is a large-scale survey of CNV in eight French dairy and beef breeds. These CNV will be useful to study the link between genetic variability and economically important traits, and to improve our knowledge on the genomic architecture of cattle.
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Project description:Copy number variation (CNV) has played an important role in studies of susceptibility or resistance to complex diseases. Traditional methods such as fluorescence in situ hybridization (FISH) and array comparative genomic hybridization (aCGH) suffer from low resolution of genomic regions. Following the emergence of next generation sequencing (NGS) technologies, CNV detection methods based on the short read data have recently been developed. However, due to the relatively young age of the procedures, their performance is not fully understood. To help investigators choose suitable methods to detect CNVs, comparative studies are needed. We compared six publicly available CNV detection methods: CNV-seq, FREEC, readDepth, CNVnator, SegSeq and event-wise testing (EWT). They are evaluated both on simulated and real data with different experiment settings. The receiver operating characteristic (ROC) curve is employed to demonstrate the detection performance in terms of sensitivity and specificity, box plot is employed to compare their performances in terms of breakpoint and copy number estimation, Venn diagram is employed to show the consistency among these methods, and F-score is employed to show the overlapping quality of detected CNVs. The computational demands are also studied. The results of our work provide a comprehensive evaluation on the performances of the selected CNV detection methods, which will help biological investigators choose the best possible method.
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Project description:Amplicon-based next generation sequencing (NGS) approaches have been preferentially adopted by the clinical laboratories on the basis of a short turnaround time (TAT) and small DNA input needs. However, little work has been done to assess the amplicon-based NGS methods for copy number variation (CNV) detection in comparison with current standard methods like immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). The correlation between NGS based CNV detection and the later standard methods has remained unexplored. We developed an amplicon-based panel to detect human epidermal receptor growth factor (HER2) amplification in formalin-fixed paraffin-embedded (FFPE) tumor tissue samples from 280 breast cancer and 50 gastric cancer patients. Assessment by IHC and FISH was conducted in parallel, and descriptive statistics were used to assess the concordance. The copy number detected by NGS was correlated with either the average HER2 copy number (signals/cell) (r?=?0.844; p?<?0.001) or the HER2/CEP17 ratio (r?=?0.815; p?<?0.001). We determined a cut-off value for NGS to categorize HER2 amplification status by using 151 HER2 non-amplified FFPE samples. In breast cancer patients, the cut-off value was 2.910, with 95.35%, 98.67% and 97.29% sensitivity, specificity and concordance, respectively. However, this cut-off value displayed low sensitivity in gastric cancer patients (64.71%), and the following macrodissection procedure was not effective for increasing sensitivity (57.14%). Evaluation of HER2 copy number with NGS in our study was comparable with IHC and FISH in breast cancer patients, but concordance in gastric cancer was only moderate. The greater discordance in gastric cancer may reflect the underlying biological mechanisms, and further study is warranted. NGS-based HER2 assessment may decrease the equivocal HER2 determinations in breast cancer patients assessed by FISH/IHC.
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Project description:Copy number variations (CNVs) confer significant effects on genetic innovation and phenotypic variation. Previous CNV studies in swine seldom focused on in-depth characterization of global CNVs.Using whole-genome assembly comparison (WGAC) and whole-genome shotgun sequence detection (WSSD) approaches by next generation sequencing (NGS), we probed formation signatures of both segmental duplications (SDs) and individualized CNVs in an integrated fashion, building the finest resolution CNV and SD maps of pigs so far. We obtained copy number estimates of all protein-coding genes with copy number variation carried by individuals, and further confirmed two genes with high copy numbers in Meishan pigs through an enlarged population. We determined genome-wide CNV hotspots, which were significantly enriched in SD regions, suggesting evolution of CNV hotspots may be affected by ancestral SDs. Through systematically enrichment analyses based on simulations and bioinformatics analyses, we revealed CNV-related genes undergo a different selective constraint from those CNV-unrelated regions, and CNVs may be associated with or affect pig health and production performance under recent selection.Our studies lay out one way for characterization of CNVs in the pig genome, provide insight into the pig genome variation and prompt CNV mechanisms studies when using pigs as biomedical models for human diseases.
| S-EPMC4111851 | biostudies-literature
Project description:The human Y chromosome provides a fertile ground for structural rearrangements owing to its haploidy and high content of repeated sequences. The methodologies used for copy number variation (CNV) studies have developed over the years. Low-throughput techniques based on direct observation of rearrangements were developed early on, and are still used, often to complement array-based or sequencing approaches which have limited power in regions with high repeat content and specifically in the presence of long, identical repeats, such as those found in human sex chromosomes. Some specific rearrangements have been investigated for decades; because of their effects on fertility, or their outstanding evolutionary features, the interest in these has not diminished. However, following the flourishing of large-scale genomics, several studies have investigated CNVs across the whole chromosome. These studies sometimes employ data generated within large genomic projects such as the DDD study or the 1000 Genomes Project, and often survey large samples of healthy individuals without any prior selection. Novel technologies based on sequencing long molecules and combinations of technologies, promise to stimulate the study of Y-CNVs in the immediate future.
| S-EPMC5418319 | biostudies-literature