5522 results for "*oma"

in 47.78 milliseconds.

• H005 WES CLL

  Whole exome sequencing from matched tumor-control samples of 121 primary lymphoma samples. Sequencing was performed on Illumina HiSeq2000. The dataset contains FASTQ files.

  Dataset EGAD00001002055

• H005 RNA-seq CLL

  Paired-end RNA sequencing using total RNA from 136 primary lymphoma samples. Sequencing was performed on the Illumina HiSeq2000 with 300bp insert size. The dataset contains FASTQ files.

  Dataset EGAD00001002056

• Analysis of tumor periphery and center-specific mutations in renal cell carcinoma

  Renal cell carcinoma (RCC) is a genomically heterogeneous tumor. In the present project, the question whether intratumoral heterogeneity follows a zonal pattern indicating spatial niches was addressed. Whole exome sequencing of 16 paired samples from tumor periphery and center revealed a number of region-specific functional SNVs and Indels. Therefore, RCCs are not composed of evenly admixed tumor cells but show topological differences in their clonal composition.

  Dataset EGAD00001002067

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - 129_cohort

  Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - 129_cohort EAC whole genomic sequencing data - Publication Secrier & Li et al., 2016, Nature Genetics

  Dataset EGAD00001002218

• EATL-II whole-exome sequencing profile

  Enteropathy-associated T-cell lymphoma (EATL), a rare and aggressive intestinal malignancy of intraepithelial T lymphocytes, comprises two disease variants (EATL-I and EATL-II) differing in clinical characteristics and pathological features. Here we report findings derived from whole exome sequencing of 15 EATL-II tumor-normal tissue pairs.

  Dataset EGAD00001002220

• PSCP_mutation analysis in hESCs

  Many studies over the past 10 years, culminating in the recent report of the International Stem Cell Initiative (ISCI, 2011) have shown that hPSC acquire genetic and epigenetic changes during their time in culture. Many of the genetic changes are non-random and recurrent, probably because they provide a selective growth advantage to the undifferentiated cells. Some are shared by embryonal carcinoma cells, the malignant counterparts of ES cells. The origins of these growth advantages are poorly understood, but may come from altered cell cycle dynamics, resistance to apoptosis or altered patterns of differentiation. Less is known about the nature and consequences of epigenetic changes, but it is likely that these similarly affect hPSC behaviour; e.g., enhanced expression of DLK1, an imprinted gene, is associated with altered hPSC growth (Enver et al 2005). Inevitably, these genetic and epigenetic changes will impact on our ability to use hPSC for regenerative medicine, either because malignant transformation of the undifferentiated cells or their differentiated derivatives to be used for transplantation compromises safety, or because they impede the function of those differentiated derivatives, or because they affect the efficiency with which the undifferentiated cells can be expanded and differentiated into desired cell types. Focusing initially upon the existing clinical grade hESC lines, later moving to iPSC, we will Consolidate and extend knowledge of the rate, type and functional impact of the genetic variations that occur during hPSC culture. We will use whole genome and exome sequencing as well as SNP arrays, together with clonal analysis and other cytogenetics techniques. Common changes will be compared with those found in the normal human population, at low frequency in the original cell population or observed during iPSC generation in the HIPSCI project currently based at the WTSI. These studies will provide a better understanding of the range of genetic changes that occur in hPSC beyond the CNVs already identified. In conjunction with cancer genome resources and expertise at WTSI, bioinformatic analyses of these hPSC data will allow us to assess potential impact on hPSC behaviour pertinent to applications in regenerative medicine, notably the likelihood that specific changes arising in undifferentiated PSC cultures may be associated with potential malignant transformation of differentiated progeny. This data is part of a pre-publication release. For information on the proper use of pre-publication data shred by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002231

• PSCP_bisulphite analysis in hESCs

  Many studies over the past 10 years, culminating in the recent report of the International Stem Cell Initiative (ISCI, 2011) have shown that hPSC acquire genetic and epigenetic changes during their time in culture. Many of the genetic changes are non-random and recurrent, probably because they provide a selective growth advantage to the undifferentiated cells. Some are shared by embryonal carcinoma cells, the malignant counterparts of ES cells. The origins of these growth advantages are poorly understood, but may come from altered cell cycle dynamics, resistance to apoptosis or altered patterns of differentiation. Less is known about the nature and consequences of epigenetic changes, but it is likely that these similarly affect hPSC behaviour; e.g., enhanced expression of DLK1, an imprinted gene, is associated with altered hPSC growth (Enver et al 2005). Inevitably, these genetic and epigenetic changes will impact on our ability to use hPSC for regenerative medicine, either because malignant transformation of the undifferentiated cells or their differentiated derivatives to be used for transplantation compromises safety, or because they impede the function of those differentiated derivatives, or because they affect the efficiency with which the undifferentiated cells can be expanded and differentiated into desired cell types. Focusing initially upon the existing clinical grade hESC lines, later moving to iPSC, we will Consolidate and extend knowledge of the rate, type and functional impact of the genetic variations that occur during hPSC culture. We will use whole genome and exome sequencing as well as SNP arrays, together with clonal analysis and other cytogenetics techniques. Common changes will be compared with those found in the normal human population, at low frequency in the original cell population or observed during iPSC generation in the HIPSCI project currently based at the WTSI. These studies will provide a better understanding of the range of genetic changes that occur in hPSC beyond the CNVs already identified. In conjunction with cancer genome resources and expertise at WTSI, bioinformatic analyses of these hPSC data will allow us to assess potential impact on hPSC behaviour pertinent to applications in regenerative medicine, notably the likelihood that specific changes arising in undifferentiated PSC cultures may be associated with potential malignant transformation of differentiated progeny. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002235

• Melanoma C32 ENU resistance to Combination Therapy

  This study involves mutagenizing C32, a melanoma cell line, with ENU to identify those mutations which engender resistance to a targeted treatment.

  Dataset EGAD00001002234

• RNA expression profiling of melanoma patient-derived xenograft

  Patient-derived xenografts (n=96) were derived from metastatic melanoma patients. RNA expression profiling will be preformed to study 1. HLA-typing and 2. the effect of the tumour microenvironment on tumour growth This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002230

• BAP1 sequence of uveal melanoma and mesothelioma samples

  Detection of BAP1 mutations in DNA from uveal melanoma and mesothelioma samples.

  Dataset EGAD00001002229

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - chemo_cohort

  Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - chemo_cohort

  Dataset EGAD00001002241

• Transcriptome/Exome for glioblastoma intra-tumoral heterogeneity

  Total of 49 tumor specimens from 20 patients were subjected for whole-exome and/or whole-transcriptome sequencing including matched normal/blood. Tumor samples are acquired based on 4 categories; 1) locally adjacent tumors, 2) multifocal/multicentric tumors, 3) 5-ALA (+/-) tumors and 4) Longitudinal tumors.

  Dataset EGAD00001002248

• Single-cell Transcriptome for glioblastoma intra-tumoral heterogeneity

  Single-Cell RNA Sequencing of 355 cells isolated from 7 tissue fragments of 3 patients corresponding to locally adjacent tumor, multifocal with recurrence and sections segregated by a marker of tumor cellularity (5-ALA).

  Dataset EGAD00001002249

• BLUEPRINT release August 2016, ChIP-Seq for Burkitt Lymphoma, on genome GRCh38

  ChIP-Seq data for 3 Burkitt Lymphoma sample(s). 13 run(s), 13 experiment(s), 13 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002329

• BLUEPRINT release August 2016, RNA-Seq for Mantle Cell Lymphoma, on genome GRCh38

  RNA-Seq data for 5 Mantle Cell Lymphoma sample(s). 5 run(s), 5 experiment(s), 5 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002336

• BLUEPRINT release August 2016, Bisulfite-Seq for Mantle Cell Lymphoma, on genome GRCh38

  Bisulfite-Seq data for 5 Mantle Cell Lymphoma sample(s). 65 run(s), 5 experiment(s), 10 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002505

• BLUEPRINT release August 2016, ChIP-Seq for Germinal Center B-Cell-Like Diffuse Large B-Cell Lymphoma, on genome GRCh38

  ChIP-Seq data for 3 Germinal Center B-Cell-Like Diffuse Large B-Cell Lymphoma sample(s). 10 run(s), 10 experiment(s), 10 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002506

• BLUEPRINT release August 2016, ChIP-Seq for Multiple Myeloma, on genome GRCh38

  ChIP-Seq data for 4 Multiple Myeloma sample(s). 34 run(s), 28 experiment(s), 28 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002379

• BLUEPRINT release August 2016, ChIP-Seq for Mantle Cell Lymphoma, on genome GRCh38

  ChIP-Seq data for 5 Mantle Cell Lymphoma sample(s). 35 run(s), 35 experiment(s), 35 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002397

• BLUEPRINT release August 2016, RNA-Seq for Multiple Myeloma, on genome GRCh38

  RNA-Seq data for 14 Multiple Myeloma sample(s). 14 run(s), 14 experiment(s), 14 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002401

• BLUEPRINT release August 2016, Bisulfite-Seq for Multiple Myeloma, on genome GRCh38

  Bisulfite-Seq data for 5 Multiple Myeloma sample(s). 63 run(s), 7 experiment(s), 10 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002521

• Genome and transcriptome sequence data from a uterine sarcoma patient

  Genome and transcriptome sequence data from a uterine sarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002538

• Genome and transcriptome sequence data from a cholangiocarcinoma patient

  Genome and transcriptome sequence data from a cholangiocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002537

• Genome and transcriptome sequence data from a melanoma patient

  Genome and transcriptome sequence data from a melanoma patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002546

• Genome and transcriptome sequence data from a Ewing sarcoma patient

  Genome and transcriptome sequence data from a Ewing sarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002552