9182 results for "cancer rna-seq"

in 29.19 milliseconds.

• BLUEPRINT release August 2016, Bisulfite-Seq for memory B cell, on genome GRCh38

  Bisulfite-Seq data for 3 memory B cell sample(s). 47 run(s), 4 experiment(s), 6 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 EGAD00001002416

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_BG_T=24hrs_RPMI_T=5days, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_BG_T=24hrs_RPMI_T=5days sample(s). 18 run(s), 2 experiment(s), 2 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 EGAD00001002385

• BLUEPRINT release August 2016, Bisulfite-Seq for Acute Myeloid Leukemia, on genome GRCh38

  Bisulfite-Seq data for 19 Acute Myeloid Leukemia sample(s). 338 run(s), 32 experiment(s), 38 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 EGAD00001002419

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_LPS_T=4hrs, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_LPS_T=4hrs sample(s). 18 run(s), 2 experiment(s), 2 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 EGAD00001002432

• BLUEPRINT release August 2016, Bisulfite-Seq for hematopoietic multipotent progenitor cell, on genome GRCh38

  Bisulfite-Seq data for 2 hematopoietic multipotent progenitor cell sample(s). 16 run(s), 3 experiment(s), 4 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 EGAD00001002434

• BLUEPRINT release August 2016, Bisulfite-Seq for conventional dendritic cell, on genome GRCh38

  Bisulfite-Seq data for 2 conventional dendritic cell sample(s). 30 run(s), 2 experiment(s), 4 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 EGAD00001002437

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_BG_T=24hrs, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_BG_T=24hrs sample(s). 21 run(s), 2 experiment(s), 2 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 EGAD00001002441

• BLUEPRINT release August 2016, Bisulfite-Seq for Acute Promyelocytic Leukemia - ATRA, on genome GRCh38

  Bisulfite-Seq data for 1 Acute Promyelocytic Leukemia - ATRA sample(s). 9 run(s), 1 experiment(s), 2 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 EGAD00001002472

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - Attached_T=1hr, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - Attached_T=1hr sample(s). 23 run(s), 2 experiment(s), 2 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 EGAD00001002475

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_T=1hr, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_T=1hr sample(s). 15 run(s), 1 experiment(s), 2 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 EGAD00001002447

• BLUEPRINT release August 2016, Bisulfite-Seq for regulatory T cell, on genome GRCh38

  Bisulfite-Seq data for 2 regulatory T cell sample(s). 41 run(s), 3 experiment(s), 4 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 EGAD00001002492

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_LPS_T=1hr, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_LPS_T=1hr sample(s). 14 run(s), 1 experiment(s), 2 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 EGAD00001002502

• 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, Bisulfite-Seq for germinal center B cell, on genome GRCh38

  Bisulfite-Seq data for 3 germinal center B cell sample(s). 37 run(s), 4 experiment(s), 6 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 EGAD00001002511

• Chromatin 3D interactions mediate genetic effects on gene expression (ChIP-seq)

  ChIP-seq data for Lymphoblastoid Cell Lines (LCL) and Fibroblasts (FIB) from the Gencord Cohort: - 160 LCLs assayed for H3K27ac, H3K4me1 and H3K4me3, - 78 FIB assayed for H3K4me3 and 79 FIB assayed for H3K27ac and H3K4me1 This dataset was generated as part of the following study: Delaneau et al (2019). Chromatin 3D interactions mediate genetic effects on gene expression.

  Dataset EGAD00001004871

• scRNA-seq raw data

  This dataset contains scRNA-seq fastq files (trimmed for quality and adapters using Trim Galore) for 3739 intestinal plasma cells of known or unknown antigen specificities from in total 12 individuals (4 untreated coeliac disease patients, 3 treated coeliac disease patients, 5 controls). The data was generated using the Smart-seq2 protocol and sequenced on the Illumina NextSeq500 platform with 75 bp paired-end reads.

  Dataset EGAD00001006436

• DNA-seq FASTQ files from 16 patients affected by acute intermittent porphyria

  Paired-end DNA-seq FASTQ files from 16 patients affected by acute intermittent porphyria. Whole genome sequencing of these samples was performed in an Illumina HiSeq 4000 instrument. Libraries were prepared using the Fisher PE Kit (Kapa Biosystems). Each sample was multiplexed across flowcells and lanes, leading to a total number of 83 pairs of FASTQ files.

  Dataset EGAD00001006950

• Whole genome DNA sequencing for two long-lived humans.

  Two long-lived human samples were sequenced to a mean 20x coverage genome wide. Sequencing was done using the Illumina hi-seq platform, alignment to GRCh37/hg19 reference genome was done using the BWA alignment tool.

  Study EGAS00001000877

• CUT&RUN/ChIP-Seq dataset for Malignant rhabdoid study

  Study describing the dynamics of chromatin organization within malignant rhabdoid tumors. This study describes how this chromatin organization changes upon SMARCB1 rescue within patient-derived organoid models from malignant rhabdoid tumors. Identification of a novel super-enhancer of MYC and identification of patient-specific enhancer utilization to activate MYC expression in these tumors.

  Dataset EGAD00001011821

• METABRIC

  The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (CNVs, SNPs) and acquired somatic copy number aberrations (CNAs) were associated with expression in 40% of genes, although the landscape was dominated by cis and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP, and MAP2K4. Unsupervised analysis of paired DNA/RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, ER-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration 0152hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the 0152CNA-devoid sub-group and a Basal-specific chromosome 5 deletion-driven mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic copy number aberrations on the transcriptome.

  Study EGAS00000000083

• Human and rat skeletal muscle multiomic profiling sequencing data

  Illumina sequencing data (fastq files) representing single-nucleus (sn) ATAC-seq, snRNA-seq, bulk ATAC-seq, and snATACseq+snRNAseq multiomics data from human and rat skeletal muscle samples (19 libraries total). Includes a README file that describes the relationship between libraries, samples, and files.

  Dataset EGAD00001008323

• scRNA-seq of total bone marrow mononuclear cells and CD3+ T cells of multiple myeloma patients and healthy donors

  The study includes 50 scRNA-seq samples of bone marrow aspirates of 11 multiple myeloma patients and 3 healthy donors generated with the 10x Genomics platform (Chromium Single Cell 3’ Solution v2). For the multiple myeloma patients, paired longitudinal samples are available from initial diagnosis and long-term surivor state. For each patient and timepoint, total bone marrow mononuclear cells and CD3+ T cells have been sequenced separately.

  Study EGAS00001006980

• Identification of the dismal subtype of B-ALL with dysregulation of CDX2 and UBTF

  PURPOSE: Advance in the knowledge of genomic basis of B-cell acute lymphoblastic leukemia (B-ALL) has changed the treatment approaches and diagnostic assays and improved their outcome. Despite the recent development of next generation sequencing approaches, there are still some cases that their drivers are unknown. We aimed to define and describe the characteristics of an additional B-ALL subtype by integrating genomic, transcriptomic, and epigenomic approach. METHODS: More than 2,000 B-ALL cases of RNA-seq data from from Eastern Cooperative Oncology Group (ECOG) and the American College of Radiology Imaging Network (ACRIN) (n=764), the Children's Oncology Group (COG), Munich Leukemia Laboratory (MLL) (n=280), and other several collaborators were analyzed to identify the new subtype. Whole genome sequence was performed to detect mutations, stractural variants, and copy number alterations. The tridimentional analysis of chromatin was performed with HiChIP. RESULTS: We identified the new high-risk B-ALL subtype “CDX2/UBTF” that exhibited unique gene expression profiles, which is enriched in female (77.3%), adolescents and young adults (52.6%), and relapse cohort (3.3%). CD10 negativity and IgM positivity are hallmarks of this subtype with NTRK3 expression that can be an effective marker at diagnostic approach and a potential targeted therapy with TRK and mulkinase inbitors, larotrectinib and entrectinib. Genetically, two different alterations define CDX2/UBTF. One is UBTF-ATXN7L3 fusions caused by deletion of 17q21.31, and the other is extopic CDX2 expression through the enhancer hijacking mechanism induced by deletion of 13q12.2. Other genomic features include gain of 1q and PAX5 rearrangement (PAX5-ZCCHC7), might induce upregulation of histone cluster genes and PAX5. CONCLUSION: We described the novel B-ALL subtype “CDX2/UBTF” that has unique clinical and genomic characteristics.

  Study EGAS00001005863

• Exploiting immune cell receptor information to quantify index switching in single cell transcriptome sequencing experiment

  By offering high sequencing speed and ultra-high-throughput at a low price, Illumina next-generation sequencing platforms has been widely adopted in recent years. However, an experiment with multiplexed library could be at risk of molecular recombination, known as “index switching”, which causes certain amount of reads to be assigned to an incorrect library. It is reported that a new advance, exclusion amplification (ExAmp) in conjunction with the patterned flow cell technology introduced on HiSeq 3000/HiSeq 4000/HiSeq X sequencing systems, potentially suffers from a higher rate of index switching than conventional bridge amplification. We took advantage of the diverse but highly cell-unique expression of immune cell receptors to quantify index switching on single cell RNA-seq data that were sequenced on HiSeq 3500 and HiSeq 4000. By utilizing the unique T-cell receptor (TCR) expression, we could quantify the spread-of-signal from many different wells (n=51 from total three batches) due to index switching. We used TraCer to reconstruct full-length TCR from all samples, and then used Kallisto to quantify TCR gene expression. We found index switching in all three batches of experiments investigated. The median percentage of incorrectly detected markers was estimated to be 4.2% (interquartile range (IQR): 2.0%-8.7%). We did not detect any consistent pattern of certain indices to be more prone for switching than others, suggesting that index switching is a stochastic process. We confirm that index switching is a problem that affects all samples run in multiplexed libraries on Illumina HiSeq 3500 and HiSeq 4000 platforms.

  Study EGAS00001002911

• Monocyte and macrophage lipid accumulation results in downregulated type-I interferon responses

  Macrophages are critical components of atherosclerotic lesions and their pro- and anti-inflammatory responses influence atherogenesis. Type-I interferons (IFNs) are cytokines that play an essential role in antiviral responses and inflammatory activation and have been shown to promote atherosclerosis. Although the impact of type-I IFNs on macrophage foam cell formation is well-documented, the effect of lipid accumulation in monocytes and macrophages on type-I IFN responses remains unknown. Here we examined IFN stimulated (ISG) and non-ISG inflammatory gene expression in mouse and human macrophages that were loaded with acetylated LDL (acLDL), as a model for foam cell formation. We found that acLDL loading in mouse and human macrophages specifically suppressed expression of ISGs and IFN-β secretion, but not other pro-inflammatory genes. The downregulation of ISGs could be rescued by exogenous IFN-β supplementation. Activation of the cholesterol-sensing nuclear liver X receptor (LXR) recapitulated the cholesterol-initiated type-I IFN suppression. Additional analyses of murine in vitro and in vivo generated foam cells confirmed the suppressed IFN signalling pathways and suggest that this phenotype is mediated via downregulation of interferon regulatory factor binding at gene promoters. Finally, RNA-seq analysis of monocytes of familial hypercholesterolemia (FH) patients also showed type-I IFN suppression which was restored by lipid-lowering therapy and not present in monocytes of healthy donors. Taken together, we define type-I IFN suppression as an athero-protective characteristic of foamy macrophages. These data provide new insights into the mechanisms that control inflammatory responses in hyperlipidaemic settings and can support future therapeutic approaches focusing on reprogramming of macrophages to reduce atherosclerotic plaque progression and improve stability.

  Study EGAS00001005955

• Low mutation burden and frequent loss of CDKN2A/B and SMARCA2, but not PRC2, define pre-malignant neurofibromatosis type 1-associated atypical neurofibromas

  Neurofibromatosis type 1 (NF1) is a tumor-predisposition disorder caused by germline mutations in NF1. NF1 patients have an 8-16% lifetime risk of developing a malignant peripheral nerve sheath tumor (MPNST), a highly aggressive soft-tissue sarcoma, often arising from pre-existing benign plexiform neurofibromas (PN) and atypical neurofibromas (ANF). ANF are distinct from both PN and MPNST, representing an intermediate step in malignant transformation. In the first comprehensive genomic analysis of ANF, we performed tumor/normal exome sequencing (ES) of 16 ANFs. In addition, we conducted ES of three MPNSTs, copy-number meta-analysis of 26 ANF and 28 MPNST, and whole transcriptome RNA-seq analysis of five ANF and five MPNST. We identified low mutation burden (median 1, range 0-5) in the exomes of ANF (only NF1 somatic mutations were recurrent), and frequent deletions of CDKN2A/CDKN2B (69%) and SMARCA2(42%) loci. We determined that polycomb repressor complex 2 (PRC2) genes EED or SUZ12 were frequently mutated, deleted or downregulated in MPNST but not in ANF. Our gene expression study revealed upregulated NRAS, MDM2, CCND1/CCND2/CCND3 and CDK4/CDK6 in both ANF and MPNST, and overexpression of EZH2 in MPNST only. We conclude that the PN-ANF transition is primarily driven by the deletion of CDKN2A/CDKN2B in addition to already present inactivated NF1. Further progression from ANF to MPNST likely involves broad chromosomal rearrangements and frequent inactivation of the PRC2 genes, loss of the DNA repair genes, and copy-number increase of signal transduction, cell cycle and pluripotency self-renewal genes.

  Study phs001993

• Identification of gene mutations and fusion genes in patients with Sézary Syndrome

  Sezary syndrome (SS) is a leukemic form of cutaneous T-cell lymphoma (CTCL) with an aggressive clinical course. The goal of our study is to understand the genetic basis of the disease by looking for driver gene mutations and fusion genes in 15 erythrodermic patients with circulating Sezary cels, fourteen of them fulfilling the diagnostic criteria of SS. Peripheral blood samples were collected for each patient and CD4+ T-lymphocytes and Granulocytes were isolated. DNA and RNA was extracted for matched normal and tumor materials. Whole exome sequencing was performed on matched normal and tumor tissues of 12 patients and RNA sequencing was performed on 10 patients. For 7 of these patients, we have both exome as well as RNA sequencing data. In this study, we present a complex genomic landscape of Sezary syndrome with several point mutations, copy number variations and fusion events which could contribute to the pathogenesis of SS.

  Study EGAS00001001706

• Exome

  Exome Seq from EGAS00001001846

  Dataset EGAD00001002162

• whole exome seq

  whole exome sequencing data

  Dataset EGAD00001004464

• Exome

  Exome Seq for Study EGAS00001001844

  Dataset EGAD00001002159

• Comprehensive molecular profiling of ETMR

  ETMR RIPSeq

  Dataset EGAD00001004802

• snRNA-seq in white matter post-mortem tissue from MS and controls - DAC

  Dac EGAC00001001105

• scRNA-seq reveals alterations of multiple alveolar macrophage states in chronic obstructive pulmonary disease

  Dac EGAC00001001547

• Human Inflammatory Skin Disease scRNA-seq

  Study EGAS00001005271

• GLASS-NL RNA-sequencing Study

  Study EGAS00001007551

• Small molecule screen reveals synergy of cell cycle checkpoint kinase inhibitors with DNA-damaging chemotherapies in medulloblastoma (methylation dataset)

  human medulloblastoma xenograft isolated from mouse brain was frozen and genomic DNA or RNA was extracted. Bisulfite converted DNA was processed and hybridised to illumina EPIC or 450K arrays using standard protocols. RNAseq was performed on total RNA.

  Dataset EGAD00001006583

• Illumina 5'end RNA sequencing

  The dataset contains 200 fastq files of Illumina 5'end RNA sequencing data of 50 PBMC samples. The paired-end data includes 100 fastq files (R1 and R2) of 50 full-length cDNA sequencing libraries and 100 fastq files (R1 and R2) of 50 HLA amplicon sequencing libraries.

  Dataset EGAD00001006838

• Single cell RNA-sequencing of treatment naïve PDAC patient samples

  Single cell RNA-sequencing of treatment naïve PDAC patient samples. We have 10 samples, sequenced using the 10X genomics chromium platform with 3 prime chemistry. We are submitting FASTQ files representing the index files (I1), Read1 (R1) and Read2 (R2).

  Dataset EGAD00001008961

• Presence of bacterial infection in brains of patients with Huntington's disease (HD)

  Bacterial isolation in infected brains in patients with Huntington's disease. Here we used next generation sequencing of 16S ribosomal RNA gene PCR amplicons (NGS 16S amplicon analysis)

  Study EGAS00001003678

• Presence of bacterial infection in brains of patients with Parkinson's disease (PD)

  Isolation of bacteria in infected brains in patients with Parkinson's disease. Here we used next generation sequencing of 16S ribosomal RNA gene PCR amplicons (NGS 16S amplicon analysis).

  Study EGAS00001003643

• A method for multiplexed full-length single-molecule sequencing of the human mitochondrial genome

  Here we describe a method to target, multiplex and sequence full-length, native single-molecule the human mitochondrial genome utilizing the RNA-guided DNA endonuclease Cas9.

  Study EGAS00001006203

• Asthma in the Lives of Families Today (ALOFT)

  Social interactions and the overall psychosocial environment have a demonstrated impact on health, particularly for people living in disadvantaged urban areas. Here we investigated the effect of psychosocial experiences on gene expression in peripheral blood immune cells of 251 children with asthma in Metro Detroit. Participants were included from an ongoing longitudinal study Asthma in the Lives of Families Today (ALOFT; recruited from November 2010-July 2018. The ALOFT project was established to identify the behavioral and biological pathways through which family social environments impact youth with asthma. In version 1 we used RNA-sequencing and a new machine learning approach to identify transcriptional signatures of 19 variables including psychosocial factors, blood cell composition and asthma symptoms. Using longitudinal data collected from a subset of the participants we showed that transcriptional signatures track the longitudinal changes in measured phenotypes. Importantly, we found 169 genes associated with asthma that are regulated by psychosocial factors, and 344 significant gene-environment interactions for gene expression levels. These results demonstrate that immune gene expression mediates the link between negative psychosocial experiences and asthma risk. In version 2, we focused on pubertal development. We identified substantial gene expression changes associated with age and pubertal development. We showed that genetic effects on gene expression change dynamically during pubertal development. Gene expression changes during puberty are correlated with gene expression changes associated with asthma and may explain sex differences in prevalence. Our results show that molecular data used to study the genetics of early onset diseases should consider pubertal development as an important factor that modifies the transcriptome. Version 3, uses single cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated Peripheral Blood Mononuclear Cells from 96 African American children. We employed novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we demonstrated that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and demonstrated widespread genetic regulation of the transcriptional dynamics of the gene expression response.

  Study phs002182

• RNA-seq analysis of transcriptome variation with human ESC subclones

  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 EGAD00001004266

• EGFR Mutant SCLC transformed exome seq

  Dataset EGAD00001001436

• ATRT ChIPSeq H3K27ac

  ATRT ChIPSeq H3K27ac

  Dataset EGAD00001004836

• ChIP_PEAKS

  ChIP-seq narrowPeaks. Software: MACS2 v2.1.2

  Dataset EGAD00010001923

• AML scRNA-seq

  Dataset EGAD00001006537

• Epitope-linked Ig-seq of self-reactive plasma cells in celiac disease

  Study EGAS00001003658

• ChIP-seq of plasma cell-free nucleosomes identifies cell-of-origin gene expression programs

  Dac EGAC00001001875

• BLUEPRINT release August 2014, ChIP-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell

  ChIP-Seq data for 1 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 7 run(s), 7 experiment(s), 7 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001000916