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Filter-based hybridization capture of subgenomes enables resequencing and copy-number detection.

ABSTRACT: To exploit contemporary sequencing technologies for targeted genetic analyses, we developed a hybridization enrichment strategy for DNA capture that uses PCR products as subgenomic traps. We applied this strategy to 115 kilobases of the human genome encompassing 47 genes implicated in cardiovascular disease. Massively parallel sequencing of captured subgenomic libraries interrogated 99.8% of targeted nucleotides >or=20 times ( approximately 40,000-fold enrichment), enabling sensitive and specific detection of sequence variation and copy-number variation.

SUBMITTER: Herman DS

PROVIDER: S-EPMC2773433 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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Filter-based hybridization capture of subgenomes enables resequencing and copy-number detection.

Herman Daniel S DS Hovingh G Kees GK Iartchouk Oleg O Rehm Heidi L HL Kucherlapati Raju R Seidman J G JG Seidman Christine E CE

Nature methods 20090621 7

To exploit contemporary sequencing technologies for targeted genetic analyses, we developed a hybridization enrichment strategy for DNA capture that uses PCR products as subgenomic traps. We applied this strategy to 115 kilobases of the human genome encompassing 47 genes implicated in cardiovascular disease. Massively parallel sequencing of captured subgenomic libraries interrogated 99.8% of targeted nucleotides >or=20 times ( approximately 40,000-fold enrichment), enabling sensitive and specifi ...[more]

PMID: 19543287

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Detection of copy number variations in rice using array-based comparative genomic hybridization.

Project description:BackgroundCopy number variations (CNVs) can create new genes, change gene dosage, reshape gene structures, and modify elements regulating gene expression. As with all types of genetic variation, CNVs may influence phenotypic variation and gene expression. CNVs are thus considered major sources of genetic variation. Little is known, however, about their contribution to genetic variation in rice.ResultsTo detect CNVs, we used a set of NimbleGen whole-genome comparative genomic hybridization arrays containing 718,256 oligonucleotide probes with a median probe spacing of 500 bp. We compiled a high-resolution map of CNVs in the rice genome, showing 641 CNVs between the genomes of the rice cultivars 'Nipponbare' (from O. sativa ssp. japonica) and 'Guang-lu-ai 4' (from O. sativa ssp. indica). The CNVs identified vary in size from 1.1 kb to 180.7 kb, and encompass approximately 7.6 Mb of the rice genome. The largest regions showing copy gain and loss are of 37.4 kb on chromosome 4, and 180.7 kb on chromosome 8. In addition, 85 DNA segments were identified, including some genic sequences. Contracted genes greatly outnumbered duplicated ones. Many of the contracted genes corresponded to either the same genes or genes involved in the same biological processes; this was also the case for genes involved in disease and defense.ConclusionWe detected CNVs in rice by array-based comparative genomic hybridization. These CNVs contain known genes. Further discussion of CNVs is important, as they are linked to variation among rice varieties, and are likely to contribute to subspecific characteristics.

| S-EPMC3156786 | biostudies-literature

Detection of copy number variations in rice using array-based comparative genomic hybridization

2012-07-01 | GSE30542 | GEO

Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization.

Project description:Large-scale high throughput studies using microarray technology have established that copy number variation (CNV) throughout the genome is more frequent than previously thought. Such variation is known to play an important role in the presence and development of phenotypes such as HIV-1 infection and Alzheimer's disease. However, methods for analyzing the complex data produced and identifying regions of CNV are still being refined.We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses.Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.

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CONTRA: copy number analysis for targeted resequencing.

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Detection of copy number variations in rice using array-based comparative genomic hybridization

Project description:Copy number variations (CNVs) can create new genes, change gene dosage, reshape gene structures, and modify elements regulating gene expression. As with all types of genetic variation, CNVs may influence phenotypic variation and gene expression. CNVs are thus considered major sources of genetic variation. Little is known, however, about their contribution to genetic variation in rice. To detect CNVs, we used a set of NimbleGen whole-genome comparative genomic hybridization arrays containing 715,851 oligonucleotide probes with a median probe spacing of 500 bp. We compiled a high-resolution map of CNVs in the rice genome, showing 641 CNVs between the genomes of the rice cultivars M-bM-^@M-^XNipponbareM-bM-^@M-^Y (from O. sativa ssp. japonica) and M-bM-^@M-^XGuang-lu-ai 4M-bM-^@M-^Y (from O. sativa ssp. indica). These CNVs contain some known genes. They are linked to variation among rice varieties, and are likely to contribute to subspecific characteristics. Genomic DNA isolated from Nipponbare and Guang-lu-ai 4. Cy5 labeled DNA from Nipponbare used as reference and Cy3 labeled DNA from Guang-lu-ai 4 was hybridized to the 720K rice tiling array including three replicates. Fluorescence intensity data were normalized with qspline algorithm and ratio data were analyzed with the circular binary segmentation algorithm. Copy number variation calls were made if the averaged Log2 ratio of a segment was shifted by 1.0 from the baseline.

2012-06-30 | E-GEOD-30542 | biostudies-arrayexpress

Copy Number Variation detection from 1000 Genomes Project exon capture sequencing data.

Project description:BackgroundDNA capture technologies combined with high-throughput sequencing now enable cost-effective, deep-coverage, targeted sequencing of complete exomes. This is well suited for SNP discovery and genotyping. However there has been little attention devoted to Copy Number Variation (CNV) detection from exome capture datasets despite the potentially high impact of CNVs in exonic regions on protein function.ResultsAs members of the 1000 Genomes Project analysis effort, we investigated 697 samples in which 931 genes were targeted and sampled with 454 or Illumina paired-end sequencing. We developed a rigorous Bayesian method to detect CNVs in the genes, based on read depth within target regions. Despite substantial variability in read coverage across samples and targeted exons, we were able to identify 107 heterozygous deletions in the dataset. The experimentally determined false discovery rate (FDR) of the cleanest dataset from the Wellcome Trust Sanger Institute is 12.5%. We were able to substantially improve the FDR in a subset of gene deletion candidates that were adjacent to another gene deletion call (17 calls). The estimated sensitivity of our call-set was 45%.ConclusionsThis study demonstrates that exonic sequencing datasets, collected both in population based and medical sequencing projects, will be a useful substrate for detecting genic CNV events, particularly deletions. Based on the number of events we found and the sensitivity of the methods in the present dataset, we estimate on average 16 genic heterozygous deletions per individual genome. Our power analysis informs ongoing and future projects about sequencing depth and uniformity of read coverage required for efficient detection.

| S-EPMC3563612 | biostudies-literature

Reliability of algorithmic somatic copy number alteration detection from targeted capture data.

Project description:MotivationWhole exome and gene panel sequencing are increasingly used for oncological diagnostics. To investigate the accuracy of SCNA detection algorithms on simulated and clinical tumor samples, the precision and sensitivity of four SCNA callers were measured using 50 simulated whole exome and 50 simulated targeted gene panel datasets, and using 119 TCGA tumor samples for which SNP array data were available.ResultsOn synthetic exome and panel data, VarScan2 mostly called false positives, whereas Control-FREEC was precise (>90% correct calls) at the cost of low sensitivity (<40% detected). ONCOCNV was slightly less precise on gene panel data, with similarly low sensitivity. This could be explained by low sensitivity for amplifications and high precision for deletions. Surprisingly, these results were not strongly affected by moderate tumor impurities; only contaminations with more than 60% non-cancerous cells resulted in strongly declining precision and sensitivity. On the 119 clinical samples, both Control-FREEC and CNVkit called 71.8% and 94%, respectively, of the SCNAs found by the SNP arrays, but with a considerable amount of false positives (precision 29% and 4.9%).DiscussionWhole exome and targeted gene panel methods by design limit the precision of SCNA callers, making them prone to false positives. SCNA calls cannot easily be integrated in clinical pipelines that use data from targeted capture-based sequencing. If used at all, they need to be cross-validated using orthogonal methods.Availability and implementationScripts are provided as supplementary information.Contactgunther.jansen@molecularhealth.com.Supplementary informationSupplementary data are available at Bioinformatics online.

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Fuzzy methods for the detection of copy number variations in comparative genomic hybridization arrays.

Project description:Genomic copy number variations (CNVs) are considered as a significant source of genetic diversity and widely involved in gene expression and regulatory mechanism, genetic disorders and disease risk, susceptibility to certain diseases and conditions, and resistance to medical drugs. Many studies have targeted the identification, profiling, analysis, and associations of genetic CNVs. We propose herein two new fuzzy methods, taht is, one based on the fuzzy inference from the pre-processed input, and another based on fuzzy C-means clustering. Our solutions present a higher true positive rate and a lower false negative with no false positive, efficient performance and consumption of least resources.

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