14308 results for ""Pennsylvania+German"+ACS+study+'Socius'+open+access"

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• Response to tumor-infiltrating lymphocyte adoptive therapy is associated with preexisting CD8+ T-myeloid cell networks in melanoma

  Adoptive cell therapy (ACT) using ex vivo–expanded tumor-infiltrating lymphocytes (TILs) can eliminate or shrink metastatic melanoma, but its long-term efficacy remains limited to a fraction of patients. Using longitudinal samples from 13 patients with metastatic melanoma treated with TIL-ACT in a phase 1 clinical study, we interrogated cellular states within the tumor microenvironment (TME) and their interactions. We performed bulk and single-cell RNA sequencing, whole-exome sequencing, and spatial proteomic analyses in pre- and post-ACT tumor tissues, finding that ACT responders exhibited higher basal tumor cell–intrinsic immunogenicity and mutational burden. Compared with nonresponders, CD8+ TILs exhibited increased cytotoxicity, exhaustion, and costimulation, whereas myeloid cells had increased type I interferon signaling in responders. Cell-cell interaction prediction analyses corroborated by spatial neighborhood analyses revealed that responders had rich baseline intratumoral and stromal tumor–reactive T cell networks with activated myeloid populations. Successful TIL-ACT therapy further reprogrammed the myeloid compartment and increased TIL-myeloid networks. Our systematic target discovery study identifies potential T-myeloid cell network–based biomarkers that could improve patient selection and guide the design of ACT clinical trials.

  Study EGAS50000001217

• Pipeline Olympics: Continuous benchmarking of computational workflows for DNA methylation sequencing data

  DNA methylation is a widely studied epigenetic mark and a powerful biomarker of cell type, age, environmental exposure, and disease. Genome-wide DNA methylation profiling is usually performed using whole-genome sequencing following selective conversion of unmethylated cytosines into thymines via bisulfite treatment or enzymatic methods. Numerous software tools facilitate processing of raw sequencing data for DNA methylation profiling, but a comprehensive benchmarking has been lacking. In this study, we systematically compared methods and tools for processing DNA methylation sequencing data. We established a dedicated benchmarking dataset comprising genome-wide DNA methylation profiling data for four reference samples using five experimental protocols each. The results of data processing were compared to highly quantitative locus-specific DNA methylation measurements available for 46 genomic loci. Based on this gold-standard reference dataset, we identified workflows that consistently demonstrated high performance. We present our results in an interactive application that provides a “living benchmark” ready for continuous updating as new workflows are proposed and require benchmarking against existing methods. In summary, our study provides guidance for any laboratory studying DNA methylation on a genome-wide scale.

  Study EGAS50000000541

• Development of cell lines and mouse models of bone and soft tissue sarcoma to establish novel treatment

  Malignant giant-cell tumors are extremely rare bone sarcomas that transform from conventional giant-cell tumors during long periods of treatment. We have applied the culture methods and succeeded in establishing patient-derived organoids (PDO) of rare sarcomas. This study aimed to investigate the genomic characteristics of our established novel organoids from human malignant giant-cell tumors. The developed tumors with xenograft were genomically analyzed to compare their characteristics with those of the original tumors. Genetic changes over time throughout treatment were analyzed, and the genomic status of the established organoid was confirmed. Next generation sequencing revealed that the established organoids lacked the H3-3A G34W mutation. The xenografted organoids of the malignant giant-cell tumor had phenotypes genetically similar to those of the original tumor. The established organoids were confirmed to be derived from human malignant giant-cell tumors. In summary, the present study demonstrated a novel organoid model of a malignant giant-cell tumor that was genetically confirmed to be a malignant transformed tumor. Our organoid model could be used to elucidate the molecular pathogenesis of a malignant giant-cell tumor and develop novel treatment modalities.

  Study JGAS000618

• Single-cell multidimensional profiling of tumor cell heterogeneity in supratentorial ependymomas

  Supratentorial ependymomas (ST-EPNs) are aggressive and treatment-resistant pediatric brain tumors that form along the human cortex, with suspected origin from radial glia cells. Although histologically similar, ST-EPNs are molecularly classified into six distinct subgroups, many of them remaining understudied. Here, we integrated molecular cell states and spatial architecture to resolve the cellular origins and aberrant differentiation trajectories of ST-EPNs at single-cell resolution. We show that, while all ST-EPNs display a unified origin from neuroepithelial cells, different subgroups harbor highly diverse cell states and differentiation potential associated with neural or ependymal differentiation. Spatial mapping uncovered a higher-order structural tumor organization driven by presence of the mesenchymal state, and refines classical histological features based on detailed molecular resolution. By benchmarking a morpho-molecular cell-state classification system in different disease models and human tumors, we identified four cellular subtypes defined by the architecture of neurite-like extensions that mediate either self-renewal or fast, saltatory invasive phenotypes reminiscent of neurons during neurodevelopment. Collectively, our study provides a comprehensive framework for the study of ST-EPN cellular states, and reveals biologically relevant tumor compartmentalization with potential clinical implication.

  Study EGAS50000001513

• Whole-genome sequencing of two probands with hereditary spastic paraplegia reveals novel splice-donor region mutation and known pathogenic mutation in SPG11

  Hereditary spastic paraplegias (SPG) are a group of heterogeneous neurodegenerative disorders, which are often presented with overlapping phenotypes such as progressive paraparesis and spasticity. To assist the diagnosis of SPG subtypes, next-generation sequencing is often used to provide supporting evidence. In this study, we report the case of two probands from the same family with SPG symptoms, including bilateral lower limbs weakness, unsteady gait, cognitive decline, dysarthria and slurring of speech since age of 14. Subsequent whole-genome sequencing revealed that the patients are compound heterozygous for mutations in SPG11 gene, including the paternally inherited c.6856C>T (p.Arg2286*) mutation and the novel maternally inherited c.2316+5G>A splice donor region mutation. Mutations in SPG11 are the common cause of autosomal recessive Spastic Paraplegia type 11. According to the ClinVar database, there are already 101 reported pathogenic mutations in SPG11 that are associated with SPG. To our knowledge, this is the first report of SPG11 mutations in our local population. Novel splice mutation identified in this study would enrich the catalog of SPG11 mutations, potentially leading to better genetic diagnosis of SPG.

  Study EGAS00001001849

• An_exome_sequencing_study_of_the_HIV_elite_long_term_non_progressors_and_rapid_progressors__CASCADE_cohorts_

  Background: A rare subgroup of HIV infected individuals naturally controls infection withouttreatment. These ?elite controllers? constitute an important model for the natural control ofHIV infection. Indeed, the study of these individuals may provide insights into strategies forthe development of HIV vaccines. Although several HLA and chemokine alleles are knownto be over-represented in elite controllers, only a small portion of HIV phenotypic variation isexplained by known genetic variants. The elite controller phenotype is rare and distinct,representing the extreme of an infectious disease trait. As such, this phenotype may be partlyexplained by variation in host immune control, which may be characterized by differences inrare functional genetic variants. Genomic regions underlying elite control can be potentiallyidentified by comparing the presence or frequency of variants in this group to thatrepresenting the opposite extreme. In this context, ?rapid progressors? is a group defined byits rapid immunological and clinical disease progression.Aim: To extend an existing study, in order to identify DNA sequence variants involved in thecontrol of HIV infection with greater statistical resolution. Specifically, we aim to sequence upto 200 exomes from multiple cohort studies within the EuroCoord CASCADE collaboration (acollaboration of 25 HIV seroconversion cohort studies across Europe).

  Study EGAS00001000522

• An_evaluation_of_different_strategies_for_large_scale_pooled_sequencing_study_design_

  Second generation sequencing technology has enabled the design of large-scale sequencing experiments in targeted disease-related regions in thousands of cases and controls. Cost implications dictate a pooled DNA sequencing design, evaluation of allele frequency differences based on the resequenced samples and large-scale targeted follow-up in further samples. To evaluate the feasibility of this approach and to assess different study design strategies, we have carried out a pilot study which tests the feasibility of PCR-based and pull-down DNA pooling for (a) SNP discovery and (b) allele frequency comparison purposes. All samples are from the HapMap or 1958 BC. The sample composition of the pool is listed below: Pool 5: 31 HapMap Individuals (NA12249, NA12156, NA12004, NA11831, NA12716, NA11832, NA11993, NA12057, NA11995, NA12006, NA12144, NA12802, NA12146, NA12005, NA12003, NA07000, NA12043, NA12044, NA11992, NA11881, NA11994, NA07345, NA12154, NA06994, NA06985, NA12239, NA07022, NA07034, NA12155, NA07056, NA06993); 19 1958 BC Individuals (WTCCC88214, WTCCC88215, WTCCC88216, WTCCC88217, WTCCC88222, WTCCC88233, WTCCC88240, WTCCC88241, WTCCC88242, WTCCC88247, WTCCC88249, WTCCC88262, WTCCC88264, WTCCC88278, WTCCC88294, WTCCC88298, WTCCC88302, WTCCC88305, WTCCC88321) Some pools were carried out in duplicate to assess reproducibility. We focused on 10 (PD), 7(PCR) chromosomal regions, ~2(PD), ~1.6 (PCR) Mb in total.

  Study EGAS00001000134

• Transgenerational transmission of reproductive and metabolic dysfunction in the male progeny of polycystic ovary syndrome

  The transgenerational maternal effects of PCOS in female progeny have been revealed. As there are evidence that a male equivalent of PCOS may exist, we asked whether sons born to mother with PCOS (PCOS-sons) transmit reproductive and metabolic phenotypes to their male progeny. Here, in a Swedish nationwide register-based cohort and a clinical case-control study from Chile we found that PCOS-sons are more often obese and dyslipidemic. Their serum miRNAs are found to potentially regulate PCOS-risk genes. Our prenatal androgenized PCOS-like mouse model with or without diet-induced obesity confirmed that reproductive and metabolic dysfunctions in F1 male offspring are passed down to F3. Small non-coding RNAs (sncRNAs) sequencing of F1-F3 sperm revealed distinct differentially expressed (DE) sncRNAs across generations in the androgenized, obese, and obese and androgenized lineages, respectively. Notably, common targets between transgenerational DEsncRNAs in mouse sperm and in PCOS-sons serum indicate similar effects of maternal hyperandrogenism. These findings strengthen the translational relevance highlighting a previously underappreciated risk of reproductive and metabolic dysfunction via the male germline transmission and potential molecular markers to study in future generations.

  Study EGAS00001007079

• HCA_Thymus_Disease

  Single cell RNA sequencing on thymic cells from children with syndromic diseases such as Trisomy 21 and inflammatory/autoimmune diseases such as myasthenia gravis. Our group has previously performed atlasing of the healthy human thymus using single cell transcriptomics and obtained an unprecedented understanding of human thymus throughout development. In this study, we intend to survey diseased human thymus and compare this to healthy to obtain a better understanding of thymic dysfunction in human diseases. The diseases we will survey include congenital conditions such as Down's syndrome, CHARGE syndrome, DiGeorge syndrome as well as acquired diseases such as myasthenia gravis and thymic hyperplasia. We intend to perform genetic and genomic study on the samples, including expression analysis. This will not only help us to understand the cells and molecular pathways affected in dysfunctional thymus; but may also lead to generalisable understandings in human genetic diseases and inflammatory/autoimmune diseases. 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/

  Study EGAS00001004310

• Clonal evolution in myeloma: the impact of maintenance lenalidomide and depth of response on the genetics and sub-clonal structure of relapsed disease in uniformly treated newly diagnosed patients

  The emergence of treatment resistant sub-clones is a key feature of relapse in multiple myeloma. Therapeutic attempts to extend remission and prevent relapse include the maximisation of response and use of maintenance therapy. We used whole exome sequencing to study the genetics of paired presentation and relapse samples from 56 newly diagnosed patients, following induction therapy, randomised to receive either lenalidomide maintenance or observation as part of the Myeloma XI trial. Patients included were considered high risk, relapsing within 30 months of maintenance randomisation. Patients achieving a complete response had predominantly branching evolutionary patterns leading to relapse, characterised by a greater mutational burden, an altered mutational profile, bi-allelic inactivation of tumour suppressor genes, and acquired structural aberrations. Conversely, in patients achieving a partial response the evolutionary features were predominantly stable with a similar mutational and structural profile. There were no significant differences between patients relapsing after maintenance lenalidomide vs observation. This study shows that the depth of response is a key determinant of the evolutionary patterns seen at relapse.

  Study EGAS00001003539

• Monitoring of leukemia clones in B-cell acute lymphoblastic leukemia at diagnosis and during treatment by single-cell DNA amplicon sequencing

  Acute lymphoblastic leukemia (ALL) is characterized by the presence of chromosomal changes, including numerical changes, translocations and deletions, which are often associated with additional single nucleotide mutations. In this study we used single-cell targeted DNA sequencing to determine the clonal heterogeneity of B-cell ALL at diagnosis and during chemotherapy treatment. With a customly designed DNA amplicon library targeting the most common mutations (in 110 genes) present in ALL, we analyzed bone marrow samples and/or blood samples from 12 B-ALL cases at diagnosis. For 4 cases where we detected multiple mutational clones at diagnosis, we also studied blood samples during the first weeks of chemotherapy treatment. This study shows that B-ALL cases with either a ETV6-RUNX1 or a BCR-ABL1 fusion have a low mutational burden, while cases with PAX5 alterations or hyperdyploidy have multiple clones at diagnosis, often with mutations in the JAK-STAT signaling pathway and the RAS signaling pathway. As expected, it can be observed that these clones disappear during treatment of the leukemia with almost no mutated cells left after two weeks of treatment.

  Study EGAS00001005029

• noninvasive lung cancer subtyping

  Accurate diagnosis of lung cancer is important for treatment decision-making. Currently, the gold standard for diagnosing histological subtypes of lung cancer relies on tumor biopsies. Recently, liquid biopsy, particularly cell-free DNA (cfDNA), has shown promising results in cancer detection and classification. In this study, we investigated the potential of cfDNA methylome for the noninvasive classification of lung cancer histological subtypes. Specifically, we focused on the two most prevalent lung cancer subtypes, lung adenocarcinoma and lung squamous cell carcinoma. Using a fragment-based marker discovery approach, we identified robust subtype-specific methylation markers from tumor samples. These markers were successfully validated in independent cohorts and associated with subtype-specific transcription activity. Leveraging these markers, we constructed a subtype classification model using cfDNA methylation profiles, achieving an AUC of 0.808 in the cross-validation and an AUC of 0.747 in the independent validation. Additionally, tumor copy number variations inferred from cfDNA methylome analysis revealed potential implications for treatment selection. In summary, our study demonstrates the potential of cfDNA methylome analysis for noninvasively lung cancer subtyping, offering insights for cancer monitoring and early detection.

  Study EGAS00001007717

• H3Africa - Deciphering Developmental Disorders in Africa

  Developmental disorders are typically rare conditions, causing an impairment in physical, learning, language, or behavioural areas. Genetic heterogeneity and highly variable clinical manifestations make diagnosing developmental disorders particularly challenging. Developmental disorders are increasing in prevalence in low- and middle-income countries, partly due to effective prevention and treatment of infectious diseases leading to a decrease in childhood mortality. New sequencing technologies are used effectively in the diagnosis of rare diseases and results can impact clinical practice significantly. Despite this international trend, very little implementation of these new approaches has taken place in low-resource settings, especially in Africa. The Deciphering Developmental Disorders in Africa (DDD-Africa) study aims to take advantage of an unbiased genotype-driven approach to improve healthcare services for patients. The study aims to recruit and perform detailed clinical phenotyping and exome sequencing (ES) on 500 African patients with a developmental disorder in whom a genetic diagnosis has not been confirmed. Where available, parents are also included in sequencing efforts. The project has two active research sites in Johannesburg (South Africa) and Kinshasa (Democratic Republic of Congo) representing diversity in geography, clinical, socio-economic, genetic and environmental factors.

  Study EGAS00001008319

• H3Africa - Identification and characterization of novel hereditary neurological disease genes in Mali.

  Despite the vast diversity of its populations, genetic studies in Africa have been limited. African populations, Malians in particular, have a high rate of intra-ethnic and consanguineous marriage, resulting in increased prevalence of autosomal recessive diseases. Family-based genetic studies can be limited in developed countries due to small sibships. The average fertility rate in Mali is over 6 births per woman, offering a unique opportunity to find new disease genes or mutations that can then be studied in other populations. Hereditary neurological diseases are very debilitating diseases, and developing countries, particularly in Africa, pay a high price in terms of disability-adjusted life years. Although most are currently untreatable, increasing awareness about hereditary neurological disorders can reduce this burden. This study will help to identify and characterize novel hereditary neurological disease genes in Mali. These genes are likely to be important in the normal function of the nervous system and to have important pathophysiological implications for African and other populations. This study will also train physicians and students in the characterization of neurodegenerative diseases as well as in genetic technology and molecular biology, and build a suitable research environment that will retain them.

  Study EGAS00001003016

• Myeloproliferative Neoplasms (MPN) Exome Validation Study

  Experiments using targeted pulldown methods will be sequenced to validate findings in the exomes of patients with Myeloproliferative Neoplasms (MPN).

  Dataset EGAD00001000619

• BMI EWAS summary stats

  Summary statistics for a multi-cohort epigenome-wide association study. This includes summary statistics (effect-size, standard error, p-value) for 470,000 methylation markers.

  Dataset EGAD00010001029

• Gene Expression Profiling for study EGAS00001004165

  Gene Expression Profiling from 44 Mantle Cell Lymphoma cases

  Dataset EGAD00010001842

• Illumina BeadArray SNP arrays

  SNP measurement. Illumina BeadArray SNP arrays for the study "Molecular characteristics in Burkitt lymphoma over age groups"

  Dataset EGAD00010002137

• Thoracic Patient-Derived Xenografts

  Targeted capture sequencing of 341 cancer associated genes in thoracic patient-derived xenografts.

  Study phs001192

• USH genes sequencing

  Sequencing of a panel including the whole genomic sequence of Usher genes.

  Study EGAS50000000476

• Aggressive PDACs show hypomethylation of repetitive elements and the execution of an intrinsic IFN program linked to a ductal cell-of-origin

  Study EGAS00001004660

• TOPARP-B patient cell-free DNA WGS

  Sequencing files for TOPARP-B patients as described in manuscript

  Study EGAS50000000280

• FFPE_normals_v2_gbm_wtsi_panel

  Targeted pulldown of approx 60 ffpe normal samples to use as normal controls

  Study EGAS00001002124

• Whole exome sequencing of 103 pairs BLCA-CN

  We sequenced 50M exomes of 103 pairs bladder cancer of chinese

  Study EGAS00001000677

• BLUEPRINT EpiVar Whole Genome Sequencing

  BLUEPRINT EpiVar Whole Genome Sequencing

  Study EGAS00001000689

• BLUEPRINT EpiVar ChIP-seq for naive CD4+ T-cells

  BLUEPRINT EpiVar ChIP-seq for naive CD4+ T-cells

  Study EGAS00001000753

• Exome_sequencing_Parkinson_s_disease_patients

  In this project we will sequence the exomes of 250 patients with Parkinson's disease

  Study EGAS00001000151

• Reference epigenome SMC08 mRNA-Seq data generated from KEP study

  SMC08 mRNA-Seq paired end data

  Dataset EGAD00001007175

• Reference epigenome SMC09 mRNA-Seq data generated from KEP study

  SMC09 mRNA-Seq paired end data

  Dataset EGAD00001007176

• Reference epigenome IPS01_N_Fibroblast_smRNA-Seq data generated from KEP study

  A IPS01_N_Fibroblast_smRNA-Seq single end data for iPSC(Oct4)

  Dataset EGAD00001003501

• Reference epigenome Pancreas-Islet07 WGBS data generated from KEP study

  Pancreas-Islet07 WGBS paired end data

  Dataset EGAD00001007146

• Reference epigenome Fat-adipocyte03 WGBS data generated from KEP study

  Fat-adipocyte03 WGBS paired end data

  Dataset EGAD00001007147

• Reference epigenome Fat-adipocyte04 WGBS data generated from KEP study

  Fat-adipocyte04 WGBS paired end data

  Dataset EGAD00001007148

• Reference epigenome SMC07 mRNA-Seq data generated from KEP study

  SMC07 mRNA-Seq paired end data

  Dataset EGAD00001007174

• Reference epigenome SMC05 mRNA-Seq data generated from KEP study

  SMC05 mRNA-Seq paired end data

  Dataset EGAD00001007173

• Reference epigenome CKD25_C_Podo_WGBS data generated from KEP study

  A CKD25_C_Podo_WGBS paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003470

• Reference epigenome CKD24_C_Podo_WGBS data generated from KEP study

  A CKD24_C_Podo_WGBS paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003469

• Reference epigenome SMC04 mRNA-Seq data generated from KEP study

  SMC04 mRNA-Seq paired end data

  Dataset EGAD00001007172

• Reference epigenome Fat-adipocyte05 WGBS data generated from KEP study

  Fat-adipocyte05 WGBS paired end data

  Dataset EGAD00001007149

• Reference epigenome ADMSC05 mRNA-Seq data generated from KEP study

  ADMSC05 mRNA-Seq paired end data

  Dataset EGAD00001007153

• Reference epigenome ADMSC06 mRNA-Seq data generated from KEP study

  ADMSC06 mRNA-Seq paired end data

  Dataset EGAD00001007154

• Reference epigenome ADMSC07 mRNA-Seq data generated from KEP study

  ADMSC07 mRNA-Seq paired end data

  Dataset EGAD00001007155

• Reference epigenome ADMSC08 mRNA-Seq data generated from KEP study

  ADMSC08 mRNA-Seq paired end data

  Dataset EGAD00001007156

• Reference epigenome IPS04_X_Fibroblast_mRNA-Seq data generated from KEP study

  A IPS04_X_Fibroblast_mRNA-Seq paired end data for iPSC(Oct4)

  Dataset EGAD00001003491

• Reference epigenome SMC01 mRNA-Seq data generated from KEP study

  SMC01 mRNA-Seq paired end data

  Dataset EGAD00001007169

• Reference epigenome SMC03 mRNA-Seq data generated from KEP study

  SMC03 mRNA-Seq paired end data

  Dataset EGAD00001007171

• Reference epigenome SMC02 mRNA-Seq data generated from KEP study

  SMC02 mRNA-Seq paired end data

  Dataset EGAD00001007170

• Carcinoid study - RNASeq dataset

  Fernandez-Cuesta et al, 2014, Nature Communication, RNA Sequencing data set

  Dataset EGAD00001000795

• HCC.GNE exome dataset

  Exome read sequences for 30 tumor-normal pairs for the study "Diverse modes of genomic alterations in Hepatocellular Carcinoma".

  Dataset EGAD00001000885

• Reference epigenome ADMSC01 mRNA-Seq data generated from KEP study

  ADMSC01 mRNA-Seq paired end data

  Dataset EGAD00001007150

• Reference epigenome ADMSC03 mRNA-Seq data generated from KEP study

  ADMSC03 mRNA-Seq paired end data

  Dataset EGAD00001007152

• CD74-NRG1 study - RNASeq dataset

  RNAseq data, Publication Fernandez-Cuesta et al., 2014, CD74-NRG1 fusions in lung adenocarcinoma

  Dataset EGAD00001000716

• Reference epigenome OB57_D_PreA_WGBS data generated from KEP study

  A OB57_D_PreA_WGBS paired end data for Preadipocyte(fat)

  Dataset EGAD00001003480

• Reference epigenome IPS01_N_Fibroblast_mRNA-Seq data generated from KEP study

  A IPS01_N_Fibroblast_mRNA-Seq paired end data for iPSC(Oct4)

  Dataset EGAD00001003488

• Reference epigenome IPS04_X_Fibroblast_smRNA-Seq data generated from KEP study

  A IPS04_X_Fibroblast_smRNA-Seq single end data for iPSC(Oct4)

  Dataset EGAD00001003504

• BASIS Genome Validation Study

  Bespoke validation experiments will be performed on ER+ Breast Cancer cases to confirm the presence of mutations found in whole genome sequencing.

  Dataset EGAD00001000661

• Effects of nucleases on cell-free extrachromosomal circular DNA

  Effects of nucleases on cell-free extrachromosomal circular DNA

  Study EGAS00001005873

• Enrichment of homologous recombination repair alteration in prostate cancer brain metastases

  Whole-exome sequencing profiling of prostate cancer brain metastases

  Study EGAS00001005091

• RNA-sequencing of HCC patients undergoing sorafenib treatment

  RNA-sequencing of hepatocellular carcinomas at various stages of sorafenib treatment

  Study EGAS00001005662

• Brain_Disease_Wellcome_Leap_Delta_Tissue_RNA

  Utilising Single-Nucleus sequencing to investigate how the tumour microenvironment regulates glioblastoma cell states.

  Study EGAS00001005800

• COVID_19_Challenge_2_WOOE_RNA_Managed_Access

  Single-cell profiling of healthy adult volunteers that were inoculated with SARS-CoV-2.

  Study EGAS00001007636

• Reference epigenome Islet-derived-MSC08 WGBS data generated from KEP study

  Islet-derived_MSC08 WGBS paired end data

  Dataset EGAD00001007876

• Reference epigenome Islet-derived-iPSC04 WGBS data generated from KEP study

  Islet-derived_iPSC04 WGBS paired end data

  Dataset EGAD00001007877

• Reference epigenome Islet-derived-MSC06 WGBS data generated from KEP study

  Islet-derived_MSC06 WGBS paired end data

  Dataset EGAD00001007875

• pilot

  Pilot study for genome-wide sequencing of cell-free DNA from nipple aspirate fluid and plasma of breast cancer patient

  Dataset EGAD00001010836

• BGI study for primary and metastatic Chinese lung adenocarcinomas

  We sequenced 205 patients who were suffering NSCLC with Exome sequencing method.

  Dataset EGAD00001001398

• Premalignant SOX2 in ovarian cancer patients

  Whole Genome Sequencing data set for the study "Premalignant SOX2 in ovarian cancer patients"

  Dataset EGAD00001002734

• HGP-clean

  Himalayan population genetic study QC filtered data

  Dataset EGAD00010001471

• stilts-G-1

  Human Core Exome Genotyping for 1471 samples from the STudy Into Lean and Thin Subjects (STILTS) cohort

  Dataset EGAD00010001622

• Exome sequencing of FFPE and patient-derived cultures

  Exome sequencing of FFPE and patient-derived cultures from patients enrolled in clinical study NCT03860376

  Dataset EGAD50000000245

• Papua New Guinean Lowlanders Dataset (PNGLD)

  DAC to oversee high-coverage whole genome sequences collected to study patterns of genomic variation across the southern lowland of Papua New Guinea.

  Dac EGAC50000000032

• Duplex sequencing

  An additional 320 buccal swab samples were subjected to error-corrected sequencing (duplex sequencing).

  Study EGAS50000000443

• Validation_for_human_early_embryonic_substitutions_

  PCR and MiSeq validation for early embryonic substitution candidates from 400 Breast cancer patients

  Study EGAS00001001218

• Reference epigenome SMC01_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC01_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003845

• Low_input_LC__WGS_

  R&D project to develop low input library construction methods.

  Study EGAS00001001855

• Reference epigenome SMC02_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC02_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003846

• Reference epigenome SMC09_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC09_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003853

• Reference epigenome KNIH010 miRNA-seq data generated from KEP study

  A KNIH010 miRNA-seq single end data for podocytes

  Dataset EGAD00001002769

• Reference epigenome SMC02_WGBS data generated from KEP study

  A SMC02_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003872

• SCLC study Peifer et al. - WES dataset

  SCLC - Whole genome sequencing data Publication Peifer et al., 2012, Nature Genetics

  Dataset EGAD00001000703

• Reference epigenome KNIH011 miRNA-seq data generated from KEP study

  A KNIH011 miRNA-seq single end data for podocytes

  Dataset EGAD00001002770

• Reference epigenome SMC03_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC03_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003847

• Reference epigenome SMC07_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC07_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003851

• Reference epigenome SMC05_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC05_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003849

• Reference epigenome SMC06_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC06_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003850

• Reference epigenome SMC03_WGBS data generated from KEP study

  A SMC03_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003888

• Reference epigenome SMC05_WGBS data generated from KEP study

  A SMC05_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003873

• Reference epigenome CKD23_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003497

• Reference epigenome SMC04_WGBS data generated from KEP study

  A SMC04_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003889

• Reference epigenome Fat-adipocyte03 miRNA-Seq data generated from KEP study

  Fat-adipocyte03 miRNA-Seq single end data

  Dataset EGAD00001007190

• Reference epigenome Pancreas-Islet06 miRNA-Seq data generated from KEP study

  Pancreas-Islet06 miRNA-Seq single end data

  Dataset EGAD00001007187

• Reference epigenome Fat-adipocyte04 miRNA-Seq data generated from KEP study

  Fat-adipocyte04 miRNA-Seq single end data

  Dataset EGAD00001007191

• Reference epigenome Fat-adipocyte05 miRNA-Seq data generated from KEP study

  Fat-adipocyte05 miRNA-Seq single end data

  Dataset EGAD00001007192

• Reference epigenome SMC04_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC04_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003848

• Reference epigenome SMC08_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC08_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003852

• Reference epigenome CKD27_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003500

• Reference epigenome Pancreas-Islet07 miRNA-Seq data generated from KEP study

  Pancreas-Islet07 miRNA-Seq single end data

  Dataset EGAD00001007188

• Reference epigenome SMC06_WGBS data generated from KEP study

  A SMC06_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003874

• Reference epigenome SMC07_WGBS data generated from KEP study

  A SMC07_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003875

• Reference epigenome Pancreas-Islet08 miRNA-Seq data generated from KEP study

  Pancreas-Islet08 miRNA-Seq single end data

  Dataset EGAD00001007189