Affymetrix SNP array data for human prostate tumor samples
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE62218 | GEO | 2014/10/20
SECONDARY ACCESSION(S): PRJNA263493
REPOSITORIES: GEO
Similar Datasets
Project description:Metabolomics was used to identify metabolic fingerprints of AKT1 activation or MYC overexpression in human prostate tumors. Genomics was used to determine how many of these tumors had a genetic aberration underlying signaling activation. Copy number analysis of Affymetrix 250K SNP arrays was performed for 59 human prostate tumors and 6 normal prostate samples.
2014-10-20 | E-GEOD-62218 | biostudies-arrayexpress
Project description:To shed light on the molecular bases of B-lineage acute lymphoblastic leukemia lacking known rearrangements (B-NEG ALL) and the differences between children and adults, we analyzed 168 B-NEG ALLs - including children, adolescents/young adults (AYA) and adults by genome-wide technologies, namely Next-generation sequencing and copy number aberration (CNA). Affymetrix SNP array analysis was performed according to the manufacturer's directions on DNA extracted from bone marrow sampled at diagnosis and paired germline DNA extracted from peripheral blood/bone marrow at complete remission or saliva.
2016-03-01 | GSE67405 | GEO
Project description:Gene expression microarrays measure the levels of messenger ribonucleic acid (mRNA) in a sample using probe sequences that hybridize with transcribed regions. These probe sequences are designed using a reference genome for the relevant species. However, most model organisms and all humans have genomes that deviate from their reference. These variations, which include single nucleotide polymorphisms, insertions of additional nucleotides, and nucleotide deletions, can affect the microarray's performance. Genetic experiments comparing individuals bearing different population-associated single nucleotide polymorphisms that intersect microarray probes are therefore subject to systemic bias, as the reduction in binding efficiency due to a technical artifact is confounded with genetic differences between parental strains. This problem has been recognized for some time, and earlier methods of compensation have attempted to identify probes affected by genome variants using statistical models. These methods may require replicate microarray measurement of gene expression in the relevant tissue in inbred parental samples, which are not always available in model organisms and are never available in humans.By using sequence information for the genomes of organisms under investigation, potentially problematic probes can now be identified a priori. However, there is no published software tool that makes it easy to eliminate these probes from an annotation. I present equalizer, a software package that uses genome variant data to modify annotation files for the commonly used Affymetrix IVT and Gene/Exon platforms. These files can be used by any microarray normalization method for subsequent analysis. I demonstrate how use of equalizer on experiments mapping germline influence on gene expression in a genetic cross between two divergent mouse species and in human samples significantly reduces probe hybridization-induced bias, reducing false positive and false negative findings.The equalizer package reduces probe hybridization bias from experiments performed on the Affymetrix microarray platform, allowing accurate assessment of germline influence on gene expression.
| S-EPMC4518889 | biostudies-literature
Project description: Not available
| S-DIXA-D-1096 | biostudies-other
Project description:BACKGROUND: Copy number variation (CNV) is essential to understand the pathology of many complex diseases at the DNA level. Affymetrix SNP arrays, which are widely used for CNV studies, significantly depend on accurate copy number (CN) estimation. Nevertheless, CN estimation may be biased by several factors, including cross-hybridization and training sample batch, as well as genomic waves of intensities induced by sequence-dependent hybridization rate and amplification efficiency. Since many available algorithms only address one or two of the three factors, a high false discovery rate (FDR) often results when identifying CNV. Therefore, we have developed a new CNV detection pipeline which is based on hybridization and amplification rate correction (CNVhac). METHODS: CNVhac first estimates the allelic concentrations (ACs) of target sequences by using the sample independent parameters trained through physicochemical hybridization law. Then the raw CN is estimated by taking the ratio of AC to the corresponding average AC from a reference sample set for one specific site. Finally, a hidden Markov model (HMM) segmentation process is implemented to detect CNV regions. RESULTS: Based on public HapMap data, the results show that CNVhac effectively smoothes the genomic waves and facilitates more accurate raw CN estimates compared to other methods. Moreover, CNVhac alleviates, to a certain extent, the sample dependence of inference and makes CNV calling with appreciable low FDRs. CONCLUSION: CNVhac is an effective approach to address the common difficulties in SNP array analysis, and the working principles of CNVhac can be easily extended to other platforms.
| S-EPMC3428662 | biostudies-literature
Project description:Chordomas are rare, low to intermediate grade malignant bone tumors of the axial skeleton, believed to originate from the notochord. Treatment options are limited and mainly restricted to surgical excision, as chordomas have been proven to be largely resistant to conventional ionizing radiation and chemotherapy. Therefore a new cell line was established. The cell line has been characterized by immunohistochemistry,western blot and karyotyping using cobra fish technology. Copy number variations (CNV) and loss-of heterozygosity (LOH) from fibroblasts, tumor tissue, low and high cell line passage was evaluted by using Affymetrix 6.0SNP Arrays. DNA isolated from the patients MNCs was used as control. The cell line MUG-Chor1 is highly similar to genetic profiles of chordomas and provides therefore a powerful tool for further investigations.
2012-07-12 | GSE25720 | GEO
Project description:To shed light on the molecular bases of B-lineage acute lymphoblastic leukemia lacking known rearrangements (B-NEG ALL) and the differences between children and adults, we analyzed 168 B-NEG ALLs - including children, adolescents/young adults (AYA) and adults by genome-wide technologies, namely Next-generation sequencing and copy number aberration (CNA). Affymetrix SNP array analysis was performed according to the manufacturer's directions on DNA extracted from bone marrow sampled at diagnosis and paired germline DNA extracted from peripheral blood/bone marrow at complete remission or saliva. Copy number analysis of Affymetrix SNP 6.0 arrays was performed for 13 B-NEG ALL samples and their paired normal (non-tumoral) DNA samples, included in the discovery panel, processed in the same experiment and deposited.
2016-03-01 | E-GEOD-67405 | biostudies-arrayexpress
Project description:Gain-of-function mutation of PIK3CA represents one of the most common oncogenic events in human malignancy, making PI3K an attractive target for cancer therapy. Despite the great promise of targeted therapy, drug resistance is likely to develop, causing treatment failure. To elucidate resistance mechanisms to PI3K-targeted therapy, we constructed a mouse model of breast cancer conditionally expressing PIK3CA-H1047R. Surprisingly, the majority of mammary tumors induced by PIK3CA-H1047R expression recurred following PIK3CA-H1047R inactivation. Genomic analyses of recurrent tumors revealed multiple lesions, including spontaneous focal amplification of c-Met or c-Myc. While amplification of c-Met allowed tumor survival dependent on activation of endogenous PI3K, tumors with amplification of c-Myc become independent of the PI3K pathway. Functional analyses further demonstrated that c-Myc contributed to tumors’ independence of oncogene and resistance to PI3K inhibition. Together, our data suggest that MYC elevation in tumors may be a potential mechanism conferring resistance to current PI3K-targeted therapies.
2011-08-02 | GSE27691 | GEO
Project description:BackgroundHigh throughput microarray-based single nucleotide polymorphism (SNP) genotyping has revolutionized the way genome-wide linkage scans and association analyses are performed. One of the key features of the array-based GeneChip Mapping 10K Array from Affymetrix is the automated SNP calling algorithm. The Affymetrix algorithm was trained on a database of ethnically diverse DNA samples to create SNP call zones that are used as static models to make genotype calls for experimental data. We describe here the implementation of clustering algorithms on large training datasets resulting in improved SNP call rates on the 10K GeneChip.ResultsA database of 948 individuals genotyped on the GeneChip Mapping 10K 2.0 Array was used to identify 822 SNPs that were called consistently less than 75% of the time. These SNPs represent on average 8.25% of the total SNPs on each chromosome with chromosome 19, the most gene-rich chromosome, containing the highest proportion of poor performers (18.7%). To remedy this, we created SNiPer, a new application which uses two clustering algorithms to yield increased call rates and equivalent concordance to Affymetrix called genotypes. We include a training set for these algorithms based on individual genotypes for 705 samples. SNiPer has the capability to be retrained for lab-specific training sets. SNiPer is freely available for download at http://www.tgen.org/neurogenomics/data.ConclusionThe correct calling of poor performing SNPs may prove to be key in future linkage studies performed on the 10K GeneChip. It would prove particularly invaluable for those diseases that map to chromosome 19, known to contain a high proportion of poorly performing SNPs. Our results illustrate that SNiPer can be used to increase call rates on the 10K GeneChip without sacrificing accuracy, thereby increasing the amount of valid data generated.
| S-EPMC1280925 | biostudies-literature
Project description:To analyze mRNA expression of Myc/NrasG12V and Myc/Akt1 driven liver tumors (HCC), we generated HCCs with elevated MYC expression and activated Raf-MEK-ERK or Akt1 signaling. To trigger tumor development, we co-delivered transposable elements encoding for Myc and oncogenic NrasG12V or myristoylated Akt1 via hydrodynamic injection into the hepatocytes of C57BL/6 wildtype mice and analyzed the transcriptomes of the developed tumors.
2020-12-01 | GSE162324 | GEO