Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA170386 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE36950: SNP array for CNV calling AUTS2 project [Affymetrix] GSE37141: Oligo array for CNV calling AUTS2 project [Agilent] GSE37142: SNP array for CNV calling AUTS2 project [Illumina] GSE37654: Oligo array for calling CNV's for AUTS2 project [NimbleGen] GSE37656: Oligo array for CNV calling AUTS2 project [Bluegnome] Refer to individual Series
2012-07-11 | E-GEOD-37657 | biostudies-arrayexpress
Project description:SNP array for CNV calling AUTS2 project [Affymetrix]
| PRJNA170381 | ENA
Project description:Oligo array for CNV calling AUTS2 project [Bluegnome]
| PRJNA170385 | ENA
Project description:Oligo array for CNV calling AUTS2 project [Agilent]
| PRJNA170382 | ENA
Project description:Oligo array for calling CNV's for AUTS2 project [NimbleGen]
| PRJNA170383 | ENA
Project description:41 lung adenocarcinoma from never-smokers hybridized on Illumina SNP arrays on 13 HumanCNV370-Quadv3 chips. High-resolution array comparative genomic hybridization analysis of lung adenocarcinoma in 41 never smokers for identification of new minimal common regions (MCR) of gain or loss. The SNP array analysis validated copy-number aberrations and revealed that RB1 and WRN were altered by recurrent copy-neutral loss of heterozygosity.The present study has uncovered new aberrations containing cancer genes. The oncogene FUS is a candidate gene in the 16p region that is frequently gained in never smokers. Multiple genetic pathways defined by gains of MYC, deletions of RB1 and WRN or gains on 7p and 7q are involved in lung adenocarcinoma in never smokers. A 'Cartes d'Identite des Tumeurs' (CIT) project from the French National League Against Cancer (http://cit.ligue-cancer.net) 41 samples hybridized on Illumina SNP arrays. Submitter : Fabien PETEL petelf@ligue-cancer.net . Project leader : Pr Pierre FOURET pierre.fouret@psl.aphp.fr
2011-07-28 | E-TABM-1169 | biostudies-arrayexpress
Project description:Generating sufficient DNA for high-throughput genetic analysis has always been a challenge for clinical settings where the amount of source DNA is limited. Multiple displacement amplification (MDA) has been proposed as a promising candidate for such situations. Previous work with lower-resolution arrays confirmed the utility of single-cell MDA products for large-size (~30 Mb) genome variation screening. We tested the performance of single-cell MDA products on the SNP 6.0 arrays to examine the performance of single-cell MDA in SNP genotyping, copy number polymorphism, de novo copy number variation (CNV) and loss of heterozygosity (LOH) analysis. Our data show that for SNP genotyping, single-cell MDA did not obtain complete genome coverage or high sequence fidelity. For CNV calling, single-cell MDA introduced stochastic amplification artifacts in log2 ratio profiles, reducing the robustness of CNV calling; however, by adjusting smooth window size, it is still possible to analyze large chromosomal aberrations, and homozygous deletions as small as 500 kb can still be identified from the noisy log2 ratio profiles. Our results also suggest that even with a modified protocol (reduction of reaction volume, addition of a molecular crowding reagent, minimization of reaction time), single-cell MDA presented little improvement over the unmodified protocol, but by increasing the number of cells as template to 5–10 cells, SNP 6.0 array results comparable to those of 10 ng genomic DNA MDA could be obtained. Algorithms like PICNIC improved the CNV calling, suggesting that better algorithms can better utilize single-cell MDA array results.
2011-03-25 | GSE28132 | GEO
Project description:Illumina SNP and CNV data for the HuRef individual
| PRJNA189290 | ENA
Project description:Background: High-resolution microarray technology is routinely used in basic research and clinical practice to efficiently detect copy number variants (CNVs) across the entire human genome. A new generation of arrays combining high probe densities with optimized designs will comprise essential tools for genome analysis in the coming years. We systematically compared the genome-wide CNV detection power of all 17 available array designs from the Affymetrix, Agilent, and Illumina platforms by hybridizing the well-characterized genome of 1000 Genomes Project subject NA12878 to all arrays, and performing data analysis using both manufacturer-recommended and platform-independent software. We benchmarked the resulting CNV call sets from each array using a gold standard set of CNVs for this genome derived from 1000 Genomes Project whole genome sequencing data. Results: The arrays tested comprise both SNP and aCGH platforms with varying designs and contain between ~0.5 to ~4.6 million probes. Across the arrays CNV detection varied widely in number of CNV calls (4 - 489), CNV size range (~40 bp to ~8 Mbp), and percentage of non-validated CNVs (0 - 86 %). We discovered strikingly strong effects of specific array design principles on performance. For example, some SNP array designs with the largest numbers of probes and extensive exonic coverage produced a considerable number of CNV calls that could not be validated, compared to designs with probe numbers that are sometimes an order of magnitude smaller. This effect was only partially ameliorated using different analysis software and optimizing data analysis parameters. Conclusions: High-resolution microarrays will continue to be used as reliable, cost- and time-efficient tools for CNV analysis. However, different applications tolerate different limitations in CNV detection. Our study quantified how these arrays differ in total number and size range of detected CNVs as well as sensitivity, and determined how each array balances these attributes. This analysis will inform appropriate array selection for future CNV studies, and allow better assessment of the CNV-analytical power of both published and ongoing array-based genomics studies. Furthermore, our findings emphasize the importance of concurrent use of multiple analysis algorithms and independent experimental validation in array-based CNV detection studies. NA12878 hybridization to Affymetrix CytoScan HD array for CNV detection
2017-03-23 | GSE96897 | GEO
Project description:Background: High-resolution microarray technology is routinely used in basic research and clinical practice to efficiently detect copy number variants (CNVs) across the entire human genome. A new generation of arrays combining high probe densities with optimized designs will comprise essential tools for genome analysis in the coming years. We systematically compared the genome-wide CNV detection power of all 17 available array designs from the Affymetrix, Agilent, and Illumina platforms by hybridizing the well-characterized genome of 1000 Genomes Project subject NA12878 to all arrays, and performing data analysis using both manufacturer-recommended and platform-independent software. We benchmarked the resulting CNV call sets from each array using a gold standard set of CNVs for this genome derived from 1000 Genomes Project whole genome sequencing data. Results: The arrays tested comprise both SNP and aCGH platforms with varying designs and contain between ~0.5 to ~4.6 million probes. Across the arrays CNV detection varied widely in number of CNV calls (4 - 489), CNV size range (~40 bp to ~8 Mbp), and percentage of non-validated CNVs (0 - 86 %). We discovered strikingly strong effects of specific array design principles on performance. For example, some SNP array designs with the largest numbers of probes and extensive exonic coverage produced a considerable number of CNV calls that could not be validated, compared to designs with probe numbers that are sometimes an order of magnitude smaller. This effect was only partially ameliorated using different analysis software and optimizing data analysis parameters. Conclusions: High-resolution microarrays will continue to be used as reliable, cost- and time-efficient tools for CNV analysis. However, different applications tolerate different limitations in CNV detection. Our study quantified how these arrays differ in total number and size range of detected CNVs as well as sensitivity, and determined how each array balances these attributes. This analysis will inform appropriate array selection for future CNV studies, and allow better assessment of the CNV-analytical power of both published and ongoing array-based genomics studies. Furthermore, our findings emphasize the importance of concurrent use of multiple analysis algorithms and independent experimental validation in array-based CNV detection studies. array Comparative Genome Hybridization of NA12878 versus NA10851 on Agilent platform
2017-03-23 | GSE96905 | GEO