AI
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00010001926 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:High-resolution microarray-based whole genome genotyping (WGG) techniques based on SNP analysis have successfully been applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). Problems in data interpretation arise when WGG is applied on tumor tissue specimens, in which normal cell components and tumor subpopulations frequently exist. Such heterogeneity may lead to reduced detection of cancer cell specific genomic alterations. To circumvent problems with sample heterogeneity, we propose using a segmentation strategy derived from DNA copy number analysis for detection of LOH and allelic imbalance. We generated an experimental dilution series of a tumor cell line mixed with its paired normal cell line and simulated data for such dilutions to test the strategy. We also used data sets generated on both Affymetrix and Illumina WGG platforms, including paired tumor-normal samples and tumors previously characterized by FISH. We tested the segmentation strategy against several reported algorithms. We demonstrate high sensitivity and specificity of the segmentation strategy for detecting both minute and gross allelic imbalances originating from DNA copy number gain, loss, and neutral events in tumor specimens. For example, hemizygous copy number loss can be detected in samples containing only 20-25% tumor cells. Furthermore, the strategy can identify cell subpopulation specific events and accurately estimate the fraction of cells affected by an allelic imbalance. Thus, the segmentation strategy extends the usefulness of WGG platforms for investigation of allelic imbalances in heterogeneous tumor genomes.
2008-09-18 | GSE11976 | GEO
Project description:To obtain a comprehensive survey of the structure and expression level of transcripts across the yeast genome, we used tiling arrays to profile wild-type transcriptomes in ethanol (YPE), glucose (YPD, SDC), and galactose (YPGal), which together encompass the main laboratory growth conditions of yeast. Transcript start and end positions were mapped to the genome by a segmentation algorithm and subsequent manual curation. To identify Cryptic Unstable Transcripts (CUTs), profiles were also measured for a deletion mutant of Rrp6, an important component of the nuclear exosome, which is involved in the degradation of CUTs. Expression profiles are provided in a searchable web-database (http://steinmetzlab.embl.de/NFRsharing).
2008-11-26 | E-TABM-590 | biostudies-arrayexpress
Project description:Near-haploid chromosome numbers have been found in less than 1% of cytogenetically reported tumors, but seem to be more common in certain neoplasms including the malignant cartilage-producing tumor chondrosarcoma. By a literature survey of published karyotypes from chondrosarcomas we could confirm that loss of chromosomes resulting in hyperhaploid-hypodiploid cells is common and that these cells may polyploidize. Sixteen chondrosarcomas were investigated by single nucleotide polymorphism (SNP) array and the majority displayed SNP patterns indicative of a hyperhaploid-hypodiploid origin, with or without subsequent polyploidization. Except for chromosomes 5, 7, 19, 20 and 21, autosomal loss of heterozygosity was commonly found, resulting from chromosome loss and subsequent duplication of monosomic chromosomes giving rise to uniparental disomy. Additional gains, losses and rearrangements of genetic material, and even repeated rounds of polyploidization, may affect chondrosarcoma cells resulting in highly complex karyotypes. Loss of chromosomes and subsequent polyploidization was not restricted to a particular chondrosarcoma subtype and, although commonly found in chondrosarcoma, binucleated cells did not seem to be involved in these events. Tumor DNA was hybridized onto Illumina Human Omni-Quad v1.0 BeadChip or Human CNV370-Quad v3.0 BeadChip (Illumina, San Diego, CA, USA), following standard protocols supplied by the manufacturer. Data were extracted from the GenomeStudio software (Illumina), and subsequently normalized and segmented using thresholded quantile normalization (tQN) and BAFsegmentation, respectively (Staaf et al. Normalization of array-CGH data: influence of copy number imbalances. BMC Genomics 2007, 8:382; Staaf et al. Segmentation-based detection of allelic imbalance and loss-of-heterozygosity in cancer cells using whole genome SNP arrays. Genome Biol 2008, 9:R136). Base pair positions are indicated according to the NCBI build 36 (hg18).
2011-09-25 | E-GEOD-25985 | biostudies-arrayexpress
Project description:Pituitary tumours are generally benign. However, many are invasive and some of these are aggressive with high proliferation and recurrence rates. Only metastatic tumours are considered malignant and are rare (0.2%). To identify molecular events associated with the aggressive and malignant phenotypes, we combined a comparative genomic hybridization and transcriptomic analysis of 13 prolactin (PRL) tumours classified as non-invasive (NI; n=5), invasive (I; n=2) or aggressive-invasive (AI; n=6). Each tumour showed copy number alterations which appeared to be varied and discrete in the NI and I tumour groups, and more numerous and extensive in the AI tumour group. Allelic loss within the p arm region of chromosome 11 was detected in five of the AI tumours. This region contains the cytobands 11p15.2, 11p15.1, 11p14.3, 11p14.2, 11p14.1, 11p13, 11p12 and 11p11.2. Furthermore, an allelic loss in the 11q arm was also observed in three of these five tumours, which were considered malignant based on the presence of metastases. The comparison of genomic and transcriptomic data showed that allelic loss impacted upon the expression of genes located in the imbalanced region. An original data filtering strategy allowed us to highlight five genes (DGKZ, CD44, TSG101, GTF2H1 and HTATIP2), within the missing 11p region, potentially responsible for triggering the aggressive and malignant phenotypes of PRL tumours. Our novel combined genomic and transcriptomic analysis underlines the importance of chromosome 11 allelic loss in aggressive and malignant PRL tumours and led us to propose a new strategy to find markers of progression in rare tumours. Transcriptomic analysis of Codelink Human Whole Genome Bioarray was performed for 13 prolactin tumors: 6 aggressive-invasive, 2 invasive, and 5 non-invasive.
2011-03-16 | E-GEOD-22812 | biostudies-arrayexpress
Project description:Pituitary tumours are generally benign. However, many are invasive and some of these are aggressive with high proliferation and recurrence rates. Only metastatic tumours are considered malignant and are rare (0.2%). To identify molecular events associated with the aggressive and malignant phenotypes, we combined a comparative genomic hybridization and transcriptomic analysis of 13 prolactin (PRL) tumours classified as non-invasive (NI; n=5), invasive (I; n=2) or aggressive-invasive (AI; n=6). Each tumour showed copy number alterations which appeared to be varied and discrete in the NI and I tumour groups, and more numerous and extensive in the AI tumour group. Allelic loss within the p arm region of chromosome 11 was detected in five of the AI tumours. This region contains the cytobands 11p15.2, 11p15.1, 11p14.3, 11p14.2, 11p14.1, 11p13, 11p12 and 11p11.2. Furthermore, an allelic loss in the 11q arm was also observed in three of these five tumours, which were considered malignant based on the presence of metastases. The comparison of genomic and transcriptomic data showed that allelic loss impacted upon the expression of genes located in the imbalanced region. An original data filtering strategy allowed us to highlight five genes (DGKZ, CD44, TSG101, GTF2H1 and HTATIP2), within the missing 11p region, potentially responsible for triggering the aggressive and malignant phenotypes of PRL tumours. Our novel combined genomic and transcriptomic analysis underlines the importance of chromosome 11 allelic loss in aggressive and malignant PRL tumours and led us to propose a new strategy to find markers of progression in rare tumours.
2011-03-16 | GSE22812 | GEO
Project description:RNA sequencing and other experimental methods producing large amounts of data are increasingly dominant in molecular biology. However, the noise properties of these techniques are not fully appreciated. We assessed how reproducible are the measurements of allele-specific expression between replicate RNA-seq experiments from the same RNA sample. Surprisingly, estimates of allelic imbalance (AI) varied between technical replicates up to 8-fold higher than expected from commonly applied noise models. We show that AI overdispersion substantially varies between replicates and experimental series, appears to arise during the construction of sequencing libraries, and can be measured by comparing technical replicates. We demonstrate that compensation for AI overdispersion greatly reduces technical variation and enables reliable differential analysis of allele-specific expression across samples and across experiments. Conversely, not taking AI overdispersion into account can lead to a substantial number of false positives in analysis of allele-specific gene expression.
2021-03-26 | GSE143310 | GEO
Project description:Segmentation-based detection of allelic imbalance and LOH in cancer cells using whole genome SNP arrays
| PRJNA105853 | ENA
Project description:Novel Transcriptional Activity and Extensive Allelic Imbalance in the Human MHC Region
| PRJNA428235 | ENA
Project description:Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or cancer genome, to identify only those variants that impact enhancer function, remains a major challenge. Prioritization of non-coding genome variation benefits from explainable AI to predict and interpret the impact of a mutation on gene regulation. Here we apply a specialized deep learning model to 10 phased melanoma genomes and identify functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.
2021-03-03 | GSE159965 | GEO
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