Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA121209 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The Ivy Glioblastoma Atlas Project (Ivy GAP) is a detailed anatomically based transcriptomic atlas of human glioblastoma tumors. As collaborators, the Ivy Foundation funded the Allen Institute and the Swedish Neuroscience Institute to design and create the atlas. The Paul G. Allen Family Foundation also supported the project. This resource consists of a viewer interface that resolves the manually- and machine-annotated histologic images (H&E and RNA in situ hybridization) at 0.5 µm/pixel, a transcriptome browser to view and mine the anatomically-based RNA-Seq samples, an application programming interface, help documentation that describes the methods and how to use the resource, as well as SNP array data and the supporting longitudinal clinical information and MRI time course data. The resource is made available to the public without charge as part of the Ivy GAP (http://glioblastoma.alleninstitute.org/) via the Allen Institute data portal (http://www.brain-map.org), the Ivy GAP Clinical and Genomic Database (http://ivygap.org/) via the Swedish Neuroscience Institute (http://www.swedish.org/services/neuroscience-institute), and The Cancer Imaging Archive (https://wiki.cancerimagingarchive.net/display/Public/Ivy+GAP). The Ivy GAP processed data at GEO includes normalized RNA-Seq FPKM files used for analysis in "An anatomic transcriptional atlas of glioblastoma,” which is under review. Other processed data files as well as sample and donor meta-data and QC metrics are available at http://glioblastoma.alleninstitute.org/static/download.html. The raw RNA-Seq and SNP array data will be submitted to dbGaP.
2017-12-01 | GSE107559 | GEO
Project description:The Ivy Glioblastoma Atlas Project (Ivy GAP) is a detailed anatomically based transcriptomic atlas of human glioblastoma tumors. As collaborators, the Ivy Foundation funded the Allen Institute and the Swedish Neuroscience Institute to design and create the atlas. The Paul G. Allen Family Foundation also supported the project. This resource consists of a viewer interface that resolves the manually- and machine-annotated histologic images (H&E and RNA in situ hybridization) at 0.5 µm/pixel, a transcriptome browser to view and mine the anatomically-based RNA-Seq samples, an application programming interface, help documentation that describes the methods and how to use the resource, as well as SNP array data and the supporting longitudinal clinical information and MRI time course data. The resource is made available to the public without charge as part of the Ivy GAP (http://glioblastoma.alleninstitute.org/) via the Allen Institute data portal (http://www.brain-map.org), the Ivy GAP Clinical and Genomic Database (http://ivygap.org/) via the Swedish Neuroscience Institute (http://www.swedish.org/services/neuroscience-institute), and The Cancer Imaging Archive (https://wiki.cancerimagingarchive.net/display/Public/Ivy+GAP). The Ivy GAP processed data at GEO includes normalized RNA-Seq FPKM files used for analysis in "An anatomic transcriptional atlas of glioblastoma,” which is under review. Other processed data files as well as sample and donor meta-data and QC metrics are available at http://glioblastoma.alleninstitute.org/static/download.html. The raw RNA-Seq and SNP array data will be submitted to dbGaP.
2017-12-01 | GSE107558 | GEO
Project description:In order to benchmark the reproducibility of Affymetrix Genome-Wide Human SNP Array 6.0 for detecting copy-number alterations, we performed replicate hybridizations of 3 tumor cell lines and 2 paired normal cell lines obtained from the American Type Culture Collection (ATCC). We calculated copy numbers at each SNP probeset by a custom copy-number pipeline (PMID: 18772890). For each cell line, copy number data from replicate arrays are supplied in the accompanying matrix files. For each SNP probeset, we calculated the median copy number across replicate arrays. We compared the copy-number alterations detected by Circular Binary Segmentation segmentation of these arrays with statistical analyses of short sequence reads obtained from the Illumina/Solexa 1G GenomeAnalyzer. Shotgun sequencing results can be found in the NCBI Short Read Archive, accession number SRP000246 Keywords: disease state analysis
2008-11-14 | GSE13372 | GEO
Project description:Genome-wide copy number analysis using SNP-arrays (OncoScans) in multinodular goitres from individuals with the c.1552G>A;p.E518K mutation in DGCR8 show allelic imbalance at Chr22 in all samples. Likewise this event is confirmed in papillary thyroid tumors harboring the same alteration somatically. The only alteration common in all MNG and FvPTC samples was the allelic imbalance at the Chr22 in line with all samples showing an homozygous genotype at the DGCR8 locus
2019-11-01 | GSE135374 | GEO
Project description:Renal tumors with complex morphology require extensive workup for accurate classification. Chromosomal aberrations that define subtypes of renal epithelial neoplasms have been reported. We explored if whole-genome chromosome copy number and loss-of-heterozygosity analysis with single nucleotide polymorphism (SNP) arrays can be used to identify these aberrations. Keywords: Chromosome copy number and LOH analysis with SNP Genotyping Arrays
2008-01-24 | GSE9469 | GEO
Project description:Renal tumors with complex morphology require extensive workup for accurate classification. Chromosomal aberrations that define subtypes of renal epithelial neoplasms have been reported. We explored if whole-genome chromosome copy number and loss-of-heterozygosity analysis with single nucleotide polymorphism (SNP) arrays can be used to identify these aberrations in cases where morphology was unable to definitively classify these tumors. Keywords: Chromosome copy number and LOH analysis (virtual karyotyping) with SNP Genotyping Arrays Keywords: Genome variation profiling by SNP array
2008-11-01 | GSE11447 | GEO
Project description:We describe a method for automatic detection of absolute segmental copy numbers and genotype status in complex cancer genome profiles measured by SNP arrays. The method is based on pattern recognition of segmented and smoothed copy number and allelic imbalance profiles. Overall copy number assignments were verified by DNA indexes of breast carcinomas and karyotypes of cell lines. The method performs well even for poor quality data, low tumor content, and highly rearranged tumor genomes.
2009-10-30 | GSE18799 | GEO
Project description:Single primer enrichment technology as a tool for massive genotyping: bridging the gap between arrays and genotyping-by-sequencing
| PRJNA484339 | ENA
Project description:Development of a clinically relevant animal models of RCC for preclinical investigations. For DNA copy number analysis, the Sty I (250K) SNP array of the 500K Human Mapping Array (Affymetrix) was used. Arrays were scanned by GeneChip Scanner 3000 7G. Probe-level signal intensities were normalized to a baseline array with median intensity using invariant set normalization and SNP-level signal intensities were obtained using a model-based (PM/MM) method. Keywords: SNP array data, renal cell carcinoma
2011-05-05 | GSE29062 | GEO