Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

8-chip high-resolution aCGH experiment NA15510 vs. NA18505

ABSTRACT: We introduce high-throughput and massive paired-end mapping (PEM), a large-scale genome sequencing method to identify SVs 3 kb or larger that combines the rescue and capture of paired-ends of 3 kb fragments, massive 454 Sequencing, and a computational approach to map DNA reads onto a reference genome. PEM was used to map SVs in an African and putatively European individual and identified shared and divergent SVs relative to the reference genome. Overall, we fine-mapped more than 1000 SVs and documented that the number of SVs among humans is much larger than initially hypothesized; many of the SVs potentially affect gene function. The breakpoint junction sequences of more than 200 SVs were deduced with a novel pooling strategy and computational analysis. Array-CGH was used for validation. Keywords: array CGH

ORGANISM(S): Homo sapiens

PROVIDER: GSE9002 | GEO | 2007/09/28

SECONDARY ACCESSION(S): PRJNA102481

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

8-chip high-resolution aCGH experiment NA15510 vs. NA18505

Project description:We introduce high-throughput and massive paired-end mapping (PEM), a large-scale genome sequencing method to identify SVs 3 kb or larger that combines the rescue and capture of paired-ends of 3 kb fragments, massive 454 Sequencing, and a computational approach to map DNA reads onto a reference genome. PEM was used to map SVs in an African and putatively European individual and identified shared and divergent SVs relative to the reference genome. Overall, we fine-mapped more than 1000 SVs and documented that the number of SVs among humans is much larger than initially hypothesized; many of the SVs potentially affect gene function. The breakpoint junction sequences of more than 200 SVs were deduced with a novel pooling strategy and computational analysis. Array-CGH was used for validation. Keywords: array CGH 2 samples were analyzed with 8 different Nimblegen chips (385k); thus ~30M probes were used to interrogate copy number variants in NA15510 (using NA18505 as control) at high resolution.

2007-09-28 | E-GEOD-9002 | biostudies-arrayexpress

Long Span DNA Paired-End-Tag (DNA-PET) Sequencing Strategy for the Interrogation of Genomic Structural Mutations

Project description:Structural variations (SVs) contribute significantly to the variability of the human genome and extensive genomic rearrangements are a hallmark of cancer. Genomic DNA paired-end-tag (DNA-PET) sequencing is an attractive approach to identify genomic SVs. The current application of PET sequencing with short insert size DNA is insufficient for the comprehensive mapping of SVs in low complexity and repeat-rich genomic regions. We have developed a robust procedure to generate PET sequencing data using large DNA inserts of 10 - 20 kb for the identification of SVs. We compared the characteristics of the large insert libraries with short insert (1 kb) libraries with the same sequencing depths and costs. Although short insert libraries bear an advantage in identifying small deletions, they do not provide a significantly better breakpoint resolution. Large inserts are superior to short inserts in providing higher physical genome coverage and therefore achieve greater sensitivity for the identification of the different types of SVs, including copy number neutral and complex events. Further, large inserts allow the identification of SVs within repetitive sequences which cannot be spanned by short inserts. Structural variations of three cancer cell lines using short (1 kb) and long (10 kb and 20 kb) insert size DNA fragments

2012-10-04 | E-GEOD-32674 | biostudies-arrayexpress

Comprehensive Long Span Paired-End-Tag Mapping Reveals Characteristic Patterns of Structural Variations in Epithelial Cancer Genomes

Project description:Somatic genome rearrangements are thought to play important roles in cancer development. We optimized a long span paired-end-tag (PET) sequencing approach using 10 Kb genomic DNA inserts to study human genome structural variations (SVs). The use of 10 Kb insert size allows the identification of breakpoints within repetitive or homology containing regions of a few Kb in size and results in a higher physical coverage compared to small insert libraries with the same sequencing effort. We have applied this approach to comprehensively characterize the SVs of 15 cancer and 2 non-cancer genomes and used a filtering approach to strongly enrich for somatic SVs in the cancer genomes. Our analyses revealed that most inversions, deletions, and insertions are germline SVs, whereas tandem duplications, unpaired inversions, inter-chromosomal translocations, and complex rearrangements are overrepresented among somatic rearrangements in cancer genomes. We demonstrate that the quantitative and connective nature of DNA-PET data is precise in delineating the genealogy of complex rearrangement events, we observe signatures which are compatible with breakage-fusion-bridge cycles, and discover that large duplications are among the initial rearrangements that trigger genome instability for extensive amplification in epithelial cancers. Structural variations of 15 human cancer samples and 2 human normal samples were identified by long span paired-end sequencing

2011-04-06 | E-GEOD-26954 | biostudies-arrayexpress

Agilent custom 244K array CGH data for the HuRef individual

Project description:The ideal genome sequence for medical interpretation is complete and diploid, capturing the full spectrum of genetic variation. Toward this end, there has been progress in discovery of single nucleotide polymorphism (SNP) and small (<10bp) insertion/deletions (indels), but annotation of larger structural variation (SV) including copy number variation (CNV) has been less comprehensive, even with available diploid sequence assemblies. We applied a multi-step sequence and microarray-based analysis to identify numerous previously unknown SVs within the first genome sequence reported from an individual. CGH experiments were performed with genomic DNA extracted from the HuRef and six HapMap lymphoblastoid cell lines, hybridized against the reference NA10851. A dye-swap experiment was performed for each experiment. The custom CGH microarray contains probes that target novel sequences that are not on the NCBI reference build 35. Instead, the probes target scaffold sequences that are unique to the Celera R27C assembly.

2013-02-12 | E-GEOD-20287 | biostudies-arrayexpress

Long Span DNA Paired-End-Tag (DNA-PET) Sequencing Strategy for the Interrogation of Genomic Structural Mutations

2012-10-04 | GSE32674 | GEO

Comprehensive Long Span Paired-End-Tag Mapping Reveals Characteristic Patterns of Structural Variations in Epithelial Cancer Genomes

2011-04-06 | GSE26954 | GEO

Agilent custom 24M array CGH data for the HuRef individual

Project description:The ideal genome sequence for medical interpretation is complete and diploid, capturing the full spectrum of genetic variation. Toward this end, there has been progress in discovery of single nucleotide polymorphism (SNP) and small (<10bp) insertion/deletions (indels), but annotation of larger structural variation (SV) including copy number variation (CNV) has been less comprehensive, even with available diploid sequence assemblies. We applied a multi-step sequence and microarray-based analysis to identify numerous previously unknown SVs within the first genome sequence reported from an individual. Agilent array CGH experiment was performed according to the manufacturer's directions on DNA extracted from lymphoblastoid cell lines. HuRef genomic DNA was co-hybridized with female sample NA15510 from the Polymorphism Discovery Resource. No replicate nor dye swap was done. The Agilent 24 million features CGH array set was designed with 23.5 million 60-mer oligonucleotide probes tiled along the NCBI Build 36 assembly.

2013-02-12 | E-GEOD-20288 | biostudies-arrayexpress

Genome-wide detection of enhancer-hijacking events from chromatin interaction data in rearranged genomes

Project description:Recent efforts have shown that structural variations (SVs) can disrupt three-dimensional genome organization and induce enhancer hijacking, yet no computational tools exist to identify such events from chromatin interaction data. Here, we develop NeoLoopFinder, a computational framework to identify the chromatin interactions induced by SVs, including interchromosomal translocations, large deletions and inversions. Our framework can automatically resolve complex SVs, reconstruct local Hi-C maps surrounding the breakpoints, normalize copy number variation and allele effects and predict chromatin loops induced by SVs. We applied NeoLoopFinder in Hi-C data from 50 cancer cell lines and primary tumors and identified tens of recurrent genes associated with enhancer hijacking. To experimentally validate NeoLoopFinder, we deleted the hijacked enhancers in prostate adenocarcinoma cells using CRISPR–Cas9, which significantly reduced expression of the target oncogene. In summary, NeoLoopFinder enables identification of critical oncogenic regulatory elements that can potentially reveal therapeutic targets.

2021-03-23 | GSE161493 | GEO

Expression values from 6 time points during the regeneration of secondary vascular System (SVS)

Project description:Secondary vascular system (SVS) development resulted from cambial growth is a currently not well understood process. Therefore, more studies are needed to shed more lights on the molecular mechanisms underpinning the cambial activity. The regeneration of SVS from debarked trunk that can mimic the vascular cambium-driven wood formation has developed and could be used to revealed a larger number of differentially expressed genes during the stages of cambium formation and xylem differentiation in Populus tomentosa. We used microarrays to detail the global programme of gene expression in 6 time points during the regeneration of SVS.

2015-07-21 | GSE71094 | GEO

Genomic DNA sequences of recombinant F2 individuals from an Arabidopsis thaliana Col X Ler cross

Project description:Many environmental, genetic, and epigenetic factors are known to affect the frequency and positioning of meiotic crossovers (COs). Suppression of COs by large, cytologically visible inversions and translocations has long been recognized, but relatively little is known about how smaller structural variants (SVs) affect COs. To examine fine-scale determinants of the CO landscape, including SVs, we used a rapid, cost-effective method for high-throughput sequencing to generate a precise map of over 17,000 COs between the Col-0 and Ler accessions of Arabidopsis thaliana. COs were generally suppressed in regions with SVs, but this effect did not depend on the size of the variant region, and was only marginally affected by the variant type. CO suppression did not extend far beyond the SV borders, and CO rates were slightly elevated in the flanking regions. Disease resistance gene clusters, which often exist as SVs, exhibited high CO rates at some loci, but there was a tendency toward depressed CO rates at loci where large structural differences exist between the two parents. Our high-density map also revealed in fine detail how CO positioning relates to genetic (DNA motifs) and epigenetic (chromatin structure) features of the genome. We conclude that suppression of COs occurs over a narrow region spanning large and small-scale SVs, representing influence on the CO landscape in addition to sequence and epigenetic variation along chromosomes.

2019-08-25 | E-MTAB-8165 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data