DeepCNV: a deep learning approach for authenticating copy number variations.
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ABSTRACT:
SUBMITTER: Glessner JT
PROVIDER: S-EPMC8681111 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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 detection of CNVs than the gold-standard methods of coverage estimation alone, and of equal power in high coverage data. We also demonstrate how replicating training sets allows a more precise detection of CNVs, even identifying novel CNVs in two genomes previously surveyed thoroughly for CNVs using long read data.
| S-EPMC6829143 | biostudies-literature
Project description:Stroke is the third leading cause of death worldwide after heart disease and all forms of cancers. Monogenic disorders, genetic, and environmental risk factors contribute to damaging cerebral blood vessels and, consequently, cause stroke. Developments in genomic research led to the discovery of numerous copy number variants (CNVs) that have been recently identified as a new tool for understanding the genetic basis of many diseases. This review discusses the current understanding of the types of stroke, the existing knowledge on the involvement of specific CNVs in stroke as well as the limitations of the methods used for detecting CNVs like SNP-microarray. To confirm an unequivocally association between CNVs and stroke and extend the current findings, it would be desirable to use another methodology to detect smaller CNVs or CNVs in genomic regions poorly covered by this technique, for instance, CGH-array.
| S-EPMC5110597 | biostudies-literature
Project description:Copy number variation is a defining characteristic of human subtelomeres. Human subtelomeric segmental duplication regions ('Subtelomeric Repeats') comprise about 25% of the most distal 500 kb and 80% of the most distal 100 kb in human DNA. Huge allelic disparities seen in subtelomeric DNA sequence content and organization are postulated to have an impact on the dosage of transcripts embedded within the duplicated sequences, on the transcription of genes in adjacent single copy DNA regions, and on the chromatin structures mediating telomere functions including chromosome stability. In addition to the complex duplicon substructure and huge allelic variations in extended subtelomere regions, both copy number variation and alternative sequence organizations for DNA characterize the sequences immediately adjacent to terminal (TTAGGG)n tracts ('subterminal DNA'). The structural variation in subterminal DNA is likely to have important consequences for expression of subterminal transcripts such as a newly-discovered gene family encoding actin-interacting proteins and a non-coding telomeric repeat containing RNA (TERRA) transcript family critical for telomere integrity. Major immediate challenges include discovering the full extent and nature of subtelomeric structural and copy number variation in humans, and developing methods for tracking individual allelic variants in the context of total genomic DNA.
| S-EPMC2731494 | biostudies-literature
Project description:BackgroundCopy number variations (CNVs), which are important source for genetic and phenotypic variation, have been shown to be associated with disease as well as important QTLs, especially in domesticated animals. However, little is known about the CNVs in silkworm.ResultsIn this study, we have constructed the first CNVs map based on genome-wide analysis of CNVs in domesticated silkworm. Using next-generation sequencing as well as quantitative PCR (qPCR), we identified ~319 CNVs in total and almost half of them (~ 49%) were distributed on uncharacterized chromosome. The CNVs covered 10.8 Mb, which is about 2.3% of the entire silkworm genome. Furthermore, approximately 61% of CNVs directly overlapped with SDs in silkworm. The genes in CNVs are mainly related to reproduction, immunity, detoxification and signal recognition, which is consistent with the observations in mammals.ConclusionsAn initial CNVs map for silkworm has been described in this study. And this map provides new information for genetic variations in silkworm. Furthermore, the silkworm CNVs may play important roles in reproduction, immunity, detoxification and signal recognition. This study provided insight into the evolution of the silkworm genome and an invaluable resource for insect genomics research.
| S-EPMC3997817 | biostudies-literature
Project description:Copy number variations (CNVs) are being used as genetic markers or functional candidates in gene-mapping studies. However, unlike single nucleotide polymorphism or microsatellite genotyping techniques, most CNV detection methods are limited to detecting total copy numbers, rather than copy number in each of the two homologous chromosomes. To address this issue, we developed a statistical framework for intensity-based CNV detection platforms using family data. Our algorithm identifies CNVs for a family simultaneously, thus avoiding the generation of calls with Mendelian inconsistency while maintaining the ability to detect de novo CNVs. Applications to simulated data and real data indicate that our method significantly improves both call rates and accuracy of boundary inference, compared to existing approaches. We further illustrate the use of Mendelian inheritance to infer SNP allele compositions in each of the two homologous chromosomes in CNV regions using real data. Finally, we applied our method to a set of families genotyped using both the Illumina HumanHap550 and Affymetrix genome-wide 5.0 arrays to demonstrate its performance on both inherited and de novo CNVs. In conclusion, our method produces accurate CNV calls, gives probabilistic estimates of CNV transmission and builds a solid foundation for the development of linkage and association tests utilizing CNVs.
| S-EPMC2588508 | biostudies-other
Project description:BackgroundCopy-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.MethodsHere, 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.ResultsThe 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.ConclusionsOur framework for classifying high-confident CNVs could improve both basic research and clinical diagnosis of genetic diseases.
| S-EPMC8645205 | biostudies-literature
Project description:Genomic rearrangements can cause both Mendelian and complex disorders. Currently, several major mechanisms causing genomic rearrangements, such as non-allelic homologous recombination (NAHR), non-homologous end joining (NHEJ), fork stalling and template switching (FoSTeS), and microhomology-mediated break-induced replication (MMBIR), have been proposed. However, to what extent these mechanisms contribute to gene-specific pathogenic copy-number variations (CNVs) remains understudied. Furthermore, few studies have resolved these pathogenic alterations at the nucleotide-level. Accordingly, our aim was to explore which mechanisms contribute to a large, unique set of locus-specific non-recurrent genomic rearrangements causing the genetic neurocutaneous disorder neurofibromatosis type 1 (NF1). Through breakpoint-spanning PCR as well as array comparative genomic hybridization, we have identified the breakpoints in 85 unrelated individuals carrying an NF1 intragenic CNV. Furthermore, we characterized the likely rearrangement mechanisms of these 85 CNVs, along with those of two additional previously published NF1 intragenic CNVs. Unlike the most typical recurrent rearrangements mediated by flanking low-copy repeats (LCRs), NF1 intragenic rearrangements vary in size, location, and rearrangement mechanisms. We propose the DNA-replication-based mechanisms comprising both FoSTeS and/or MMBIR and serial replication stalling to be the predominant mechanisms leading to NF1 intragenic CNVs. In addition to the loop within a 197-bp palindrome located in intron 40, four Alu elements located in introns 1, 2, 3, and 50 were also identified as intragenic-rearrangement hotspots within NF1.
| S-EPMC4573439 | biostudies-literature
Project description:Genome-wide screenings for copy number variations (CNVs) in patients with schizophrenia have demonstrated the presence of several CNVs that increase the risk of developing the disease and a growing number of large rare CNVs; the contribution of these rare CNVs to schizophrenia remains unknown. Using Affymetrix 6.0 arrays, we undertook a systematic search for CNVs in 172 patients with schizophrenia and 160 healthy controls, all of Italian origin, with the aim of confirming previously identified loci and identifying novel schizophrenia susceptibility genes. We found five patients with a CNV occurring in one of the regions most convincingly implicated as risk factors for schizophrenia: NRXN1 and the 16p13.1 regions were found to be deleted in single patients and 15q11.2 in 2 patients, whereas the 15q13.3 region was duplicated in one patient. Furthermore, we found three distinct patients with CNVs in 2q12.2, 3q29 and 17p12 loci, respectively. These loci were previously reported to be deleted or duplicated in patients with schizophrenia but were never formally associated with the disease. We found 5 large CNVs (>900 kb) in 4q32, 5q14.3, 8q23.3, 11q25 and 17q12 in five different patients that could include some new candidate schizophrenia susceptibility genes. In conclusion, the identification of previously reported CNVs and of new, rare, large CNVs further supports a model of schizophrenia that includes the effect of multiple, rare, highly penetrant variants.
| S-EPMC2954184 | biostudies-literature
Project description:Accurate and efficient detection of copy number variants (CNVs) is of critical importance owing to their significant association with complex genetic diseases. Although algorithms that use whole-genome sequencing (WGS) data provide stable results with mostly valid statistical assumptions, copy number detection on whole-exome sequencing (WES) data shows comparatively lower accuracy. This is unfortunate as WES data are cost-efficient, compact, and relatively ubiquitous. The bottleneck is primarily due to the noncontiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sample batching during sequencing. Here, we present a novel deep learning model, DECoNT, which uses the matched WES and WGS data, and learns to correct the copy number variations reported by any off-the-shelf WES-based germline CNV caller. We train DECoNT on the 1000 Genomes Project data, and we show that we can efficiently triple the duplication call precision and double the deletion call precision of the state-of-the-art algorithms. We also show that our model consistently improves the performance independent of (1) sequencing technology, (2) exome capture kit, and (3) CNV caller. Using DECoNT as a universal exome CNV call polisher has the potential to improve the reliability of germline CNV detection on WES data sets.
| S-EPMC9248885 | biostudies-literature
Project description:Human genome contains many variations, often called mutations, which are difficult to detect and have remained a challenge for years. A substantial part of the genome encompasses repeats and when such repeats are in the coding region they may lead to change in the gene expression profile followed by pathological conditions. Structural variants are alterations which change one or more sequence feature in the chromosome such as change in the copy number, rearrangements, and translocations of a sequence and can be balanced or unbalanced. Copy number variants (CNVs) may increase or decrease the copies of a given region and have a pivotal role in the onset of many diseases including cardiovascular disorders. Cardiovascular disorders have a magnitude of well-established risk factors and etiology, but their correlation with CNVs is still being studied. In this article, we have discussed history of CNVs and a summary on the diseases associated with CNVs. To detect such variations, we shed light on the number of techniques introduced so far and their limitations. The lack of studies on cardiovascular diseases to determine the frequency of such variants needs clinical studies with larger cohorts. This review is a compilation of articles suggesting the importance of CNVs in multitude of cardiovascular anomalies. Finally, future perspectives for better understanding of CNVs and cardiovascular disorders have also been discussed.
| S-EPMC6291864 | biostudies-other