6365 results for "11+1双色球多少钱一注"

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• Genomic Analyses in Neoadjuvant Immunotherapy-Treated Head and Neck Cancers

  This is a phase 2 multicenter interventional trial involving the preoperative (neoadjuvant) administration of pembrolizumab, an anti-programmed cell death protein-1 (anti-PD-1) agent, to patients with pathologically confirmed, surgically resectable advanced head and neck squamous cell carcinoma (HNSCC). Participating centers were Dana-Farber/Harvard Cancer Center, Alvin J. Siteman Cancer Center, Washington University and Memorial Sloan Kettering Cancer Center. The trial (NCT02296684) consisted of two cohorts: Cohort 1 enrolled and treated patients received one dose of pembrolizumab prior to surgical resection with curative intent, and adjuvant pembrolizumab was planned for patients with high risk pathological features (positive margins and/or extranodal extension) in the surgical specimen. Cohort 2 enrolled and treated patients received 2 doses of pembrolizumab prior to surgical resection with curative intent. Adjuvant treatment was not dictated by the trial protocol for Cohort 2. The primary endpoint was rate of at least 50% pathological tumor response (pTR), defined as the presence of tumor necrosis/keratinous debris in the tumor bed following neoadjuvant pembrolizumab. pTR was graded as follows: pTR0 (<10%), pTR1 (10-49%) and pTR2 (≥50%). Baseline, pre-treatment tumor biopsy and post-neoadjuvant pembrolizumab surgically resected tumor specimens were dissociated, and CD3+CD45+ cells were analyzed for T cell receptor (TCR) expression and 5' gene expression analysis using single cell sequencing technologies for a subset of patients.

  Study phs002864

• BrainCloud: Data from human postmortem brain procurement for the neuropathology section

  This study explores the temporal dynamics and genetic control of transcription and DNA methylation in the human dorsolateral prefrontal cortex in postmortem tissue. This study examines 269 subjects for gene expression (version 1) and 108 subjects for DNA methylation (version 2). The subjects are normal controls without neuropathological and neuropsychiatric diagnosis and range in age from fetal weeks 14-20 through old age (>80). We discover fast changes in gene expression occurring during early brain development. Later in life, the changes are considerably slower. Many genes reverse pattern of expression between fetal and early postnatal development. We identify thousands of strong associations of SNPs with gene expression. We examine DNA methylation in ~14,500 genes at ~27,000 CpG loci focused on 5' promoter regions. The fastest changes in DNA methylation also occur during the prenatal period, slow down markedly after birth and continue to slow further with aging. DNA methylation is strongly associated with genotypic variants and correlates with expression of a subset of genes. DNA for genotyping was obtained from the cerebella and applied to either Illumina 650K or 1 million BeadArrays - only genotypes common to both platforms are analyzed here. Genotypes were called using BeadExpress software. Doi: 10.1038/nature10524 Nature, 478:519-524, 2011; doi:10.1016/j.ajhg.2011.12.020, AJHG 90, 1-13, Feb 10, 2012. The methylation data can be downloaded at: BrainCloud.

  Study phs000417

• Whole exome and transcriptome analysis of UV-exposed epidermis and carcinoma in situ reveals early drivers of carcinogenesis

  Squamous cell carcinoma in situ (SCCIS) is a prevalent precancerous lesion that can progress to cutaneous squamous cell carcinoma (cSCC). Although SCCIS is common, its pathogenesis remains poorly understood. To better understand SCCIS development, we performed laser-captured microdissection of human SCCIS and adjacent epidermis to isolate genomic DNA and RNA for next generation sequencing. Whole exome sequencing (WES) identified UV-signature mutations in multiple genes including Notch 1-3 (GeneID:4851, GeneID:4853, GeneID:4854) in the epidermis and SCCIS and oncogenic TP53 (GeneID:7157) mutations in SCCIS. Novel gene families, including SCHLAFEN genes, contained UV/oxidative-signature disruptive (UVD) epidermal mutations that manifested positive selection in SCCIS. The frequency and distribution of NOTCH and TP53 mutations indicate that NOTCH mutations may precede TP53 mutations. RNA sequencing identified 1166 differentially expressed genes; the top 5 enriched GO biological processes included: 1) immune response, 2) epidermal development, 3) protein phosphorylation, 4) regulation of catalytic activity, and 5) cytoskeletal regulation. The NEURL1 ubiquitin ligase, which targets Notch ligands for degradation, was upregulated in SCCIS. Neuralized 1 protein was found to be elevated in SCCIS suggesting that increased levels could represent a novel mechanism for downregulating Notch during UV-induced carcinogenesis. The data from DNA and RNA sequencing of epidermis and SCCIS provide insights regarding SCCIS formation.

  Study phs002019

• Genetic Markers of Caries Risk in Diverse Underserved Children: CIDR

  The study Predicting Caries Risk In Underserved Children, From Toddlers To The School-age Years In Primary Healthcare Settings, is a renewal of an initial project designed to develop a dental caries risk tool for use in primary medical and dental healthcare settings to identify high dental caries risk children, expanding from the toddler (1-4 years old, referred to as Stage 1 grant) to the school-age years (6-9 years old, referred to as Stage 2 grant); and to determine the relationships between dental caries risk profiles and dental caries disease patterns. 1326 children, 12 +/- 3 months of age, and their primary caregivers, were enrolled in the study. This cohort represents a diverse population, including ethnic and racial minorities and low socio-economic status groups. There are a total of six in-person visits (at child age 1, 2.5, 4, 6.5, 8 and 9.5 years), and at each one the primary caregiver (PCG) completes a dental caries risk questionnaire, and the child receives a detailed dental caries examination. Saliva samples were collected during the age 8 visit (2019-2020) from 1,059 racially and ethnically diverse individuals (543 children and 516 PCGs) for the purposes of human genetic testing, to study the genetics of dental caries experience of children and their primary caregivers.

  Study phs003280

• Symptom Clusters in Oncology Patients Receiving Chemotherapy

  The parent longitudinal study evaluated the symptom experience of oncology outpatients receiving chemotherapy. Patients completed questionnaires in their homes a total of six times (assessments 1 through 6). The questionnaires were timed to evaluate symptoms associated with the receipt of two cycles of chemotherapy that were administered approximately a month apart. Assessments 1 and 4 evaluated symptoms prior to the receipt of chemotherapy. Assessments 2 and 5 evaluated symptoms in the week following the administration of chemotherapy. Assessments 3 and 6 evaluated symptoms two weeks following the administration of chemotherapy. For this analysis, morning and evening fatigue data from the enrollment assessment (i.e., prior to the second or third cycle of chemotherapy) and the assessment that was done approximately 1 week after administration of chemotherapy were evaluated. Patients completed the Lee Fatigue Scale to obtain information on morning and evening fatigue severity. For the morning fatigue assessment, patients were asked to describe how they “felt over the past week when they woke up in the morning”. For the evening fatigue assessment, they were asked to describe how they felt over the past week “before they went to bed at night”. Medical records were reviewed for disease and treatment information. Epigenome assays (microarray) were completed for 1170 patients. Whole transcriptome sequencing (bulk RNA-Seq) was completed for 651 patients.

  Study phs003863

• Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma

  Cholangiocarcinoma (CCA) is a hepatobiliary malignancy exhibiting high incidence in countries with endemic liver-fluke infection. We analyzed 489 CCAs from 10 countries, combining whole-genome (71 cases), targeted/exome, copy-number, gene expression, and DNA methylation information. Integrative clustering defined 4 CCA clusters-fluke-positive CCAs (clusters 1/2) are enriched in ERBB2 amplifications and TP53 mutations; conversely, fluke-negative CCAs (clusters 3/4) exhibit high copy-number alterations and PD-1/PD-L2 expression, or epigenetic mutations (IDH1/2, BAP1) and FGFR/PRKA-related gene rearrangements. Whole-genome analysis highlighted FGFR2 3' untranslated region deletion as a mechanism of FGFR2 upregulation. Integration of noncoding promoter mutations with protein-DNA binding profiles demonstrates pervasive modulation of H3K27me3-associated sites in CCA. Clusters 1 and 4 exhibit distinct DNA hypermethylation patterns targeting either CpG islands or shores-mutation signature and subclonality analysis suggests that these reflect different mutational pathways. Our results exemplify how genetics, epigenetics, and environmental carcinogens can interplay across different geographies to generate distinct molecular subtypes of cancer.SIGNIFICANCE: Integrated whole-genome and epigenomic analysis of CCA on an international scale identifies new CCA driver genes, noncoding promoter mutations, and structural variants. CCA molecular landscapes differ radically by etiology, underscoring how distinct cancer subtypes in the same organ may arise through different extrinsic and intrinsic carcinogenic processes.

  Study EGAS00001001653

• Atezolizumab Plus Personalized Neoantigen Vaccination in Patients with Urothelial Cancer: a Phase 1 Trial

  This is a phase 1 single center clinical trial evaluating a multi-peptide personalized neoantigen vaccine, PGV001, in combination with atezolizumab for patients with urothelial cancer. Patients were enrolled in either the adjuvant or metastatic setting. Those in the adjuvant setting included patients considered high risk of recurrence (pathological stage ypT2-T4 or ypN+ for those treated with prior neoadjuvant chemotherapy or pT3-T4 or pN+ for those not treated with prior neoadjuvant chemotherapy). Those in the metastatic setting included patients progressing after prior chemotherapy or those with at least stable disease after chemotherapy treated as “switch maintenance” therapy. The primary endpoint was feasibility (including the number of neoantigens identified per patient, vaccine production time, the proportion of patients consenting to the vaccine development phase for whom a vaccine product was prepared, and the proportion of patients eligible for the treatment phase who completed the priming cycle 1 of PGV001 plus atezolizumab) and safety. Secondary endpoints included the objective response rate, progression-free survival or disease-free survival, overall survival, and immunomodulatory effects of treatment. Urothelial tumor tissue and normal tissue or peripheral blood specimens were utilized for DNA sequencing. HLA typing was performed utilizing genomic DNA isolated from whole blood, and RNA sequencing was performed utilizing tumor tissue specimens. Neoantigen prediction and final selection for incorporation into the vaccine was determined by the OpenVax computational pipeline. 12 patients were initially enrolled into the vaccine development phase and 10 patients initiated the treatment phase. PGV001 was administered with poly-ICLC and tetanus peptide on subsequent days for up to 10 doses, including an initial priming cycle 1. Atezolizumab was administered every 3 weeks for up to 1 year in the adjuvant setting or 2 years in the metastatic setting. The vaccine was feasible and safe, the median vaccine production time was 20.3 weeks, and 100% of patients that initiated the treatment phase completed the priming cycle. The most common treatment-related adverse events were local grade 1 injection site reactions, fever, and fatigue. Three of the 4 patients in the adjuvant setting remained disease-free at median follow-up of 39.5 months. There was a 40% objective response rate among patients with measurable disease in the metastatic setting. The single patient treated as “switch maintenance” therapy remained with no evidence of disease at 39.4 month follow-up. All patients demonstrated on-treatment neoantigen-specific T cell reactivity, as measured by ELISPOT and flow cytometry assays. Overall, PGV001 plus atezolizumab was feasible, safe, and warrants further investigation. Whole exome sequencing from tumor tissue and normal tissue or whole blood specimens and RNA sequencing from tumor tissue specimens will be available through dbGaP.

  Study phs003922

• Whole Genome Association Study of Bipolar Disorder

  The goal of the project is to identify genes that make individuals more susceptible to bipolar disorder (manic depressive illness) and to better understand the brain pathways involved in the disease. Dataset versioning Version 1: European-American (EA) ancestry only Version 2: Version 1 plus African-American (AA) ancestry Version 3: EA and AA samples plus updated diagnostic criteria Consent groups and participant set General research use (GRU): 1767 controls (1081 EA controls, 686 AA controls) This consent group includes all controls for the Bipolar study, which are a subset of controls for the Schizophrenia study (subset of Schizophrenia: GRU). Bipolar and related disorders (BARD): 841 cases (691 EA cases, 150 AA cases) This consent group includes a subset of the Bipolar cases. Bipolar disorder only (BDO): 653 cases (388 EA cases, 265 AA cases) This consent group includes a subset of the Bipolar cases.

  Study phs000017

• National Cancer Institute Genome-Wide Association Study of Renal Cell Carcinoma

  The National Cancer Institute (NCI) genome-wide association study (GWAS) of renal cell carcinoma (RCC) was conducted to investigate common genetic variants associated with RCC risk. The GWAS includes 1,453 RCC cases and 3,531 controls of European background from 4 studies (3 cohort, 1 case-control), scanned using the Illumina InfiniumHumanHap 550, 610 and 660W chips. This project was supported by the Intramural Research Program of the National Institutes of Health and NCI. Data from this GWAS were pooled with those from another GWAS of RCC (2,639 cases and 5,392 controls) conducted in Europe by the International Agency for Research on Cancer and the Centre National de Gènotypage. Findings from this collaboration are described in an upcoming report (Purdue et al. Nature Genetics 2011;43(1):60-65; PMID: 21131975). Only data from the NCI scan are included in this dbGaP submission.

  Study phs000351

• Genomic Correlates of Response and Resistance to Immune Checkpoint Blockade in Solid Tumors

  Immune checkpoint inhibitors yield clinical benefit in many cancer types, but molecular predictors of response have not yet been robustly characterized. In this study, we pursued whole exome sequencing (WES) of pre-treatment tumors from patients treated with immune checkpoint therapies - including monoclonal antibodies targeting programmed cell death-1 (PD-1) and cytotoxic T-lymphocyte-associated protein-4 (CTLA-4). Using these data, we aim to apply computational pipelines for mutation-calling, neoantigen prediction, and other analyses to validate pre-existing hypotheses regarding response to immune checkpoint therapies and discover new relationships with greater power. Further, by pursuing genomic characterization of tumors from patients with a variety of cancer types, we hope to describe molecular features of intrinsically sensitive or resistant tumors that are both context-specific and shared across cancer types.

  Study phs001565

• Natural History of and Genetic Modifiers in Spinocerebellar Ataxias

  Spinocerebellar ataxias (SCA) are genetic neurological diseases that cause imbalance, poor coordination, and speech difficulties. There are different kinds of SCA and this study will focus on types 1, 2, 3, and 6 (SCA 1, SCA 2, SCA 3, also known as Machado-Joseph disease and SCA 6). The diseases are rare, slowly progressive, cause increasingly severe neurological difficulties and are variable across and within genotypes. The purpose of this research study is to bring together a group of experts in the field of SCA for the purpose of learning more about the disease. The research questions are: How does the disease progress over time? What are the best ways to measure disease progression? Do some genes, other than the gene that is abnormal in the SCA disease, have any effect on the way the disease behaves?

  Study phs001332

• NHLBI TOPMed - NHGRI CCDG: Malmo Preventive Project (MPP)

  The Malmö Preventive Project (MPP) was a community-based disease prevention program including 33,346 inhabitants from the city of Malmö in Southern Sweden. Complete birth cohorts between 1921-1949 were invited, and the participation rate was 71%. Participants underwent screening between 1974 to 1992 for cardiovascular risk factors, alcohol abuse, and breast cancer. Between 2002-2006, surviving participants were invited to a reexamination which included blood sampling from which DNA has been extracted. Subjects with prevalent or incident AF were identified from national registers as previously described, and cases with DNA were then matched in a 1:1 fashion to controls with DNA from the same cohort by sex, age (±1 year), and date of baseline exam (±1 year). Also, controls required a follow-up exceeding that of the corresponding AF case.

  Study phs001544

• Transcriptomic profiles of tumor samples from patients with stage I-III TNBC treated with anthracycline-taxane chemotherapy plus fasting-mimicking diet plus/minus metformin in the context of the BREAKFAST trial (NCT04248998)

  BREAKFAST is a randomized, non-comparative, phase II trial enrolling stage I-III (cT>1cm) TNBC pts treated with neoadjuvant doxorubicin-cyclophosphamide q3w for 4 cycles, followed by weekly paclitaxel for 12 cycles. Pts were randomized 1:1 to receive: CT + triweekly 5-day FMD cycles (arm A), or CT + FMD + daily metformin (1700 mg) (arm B). The primary study objective was to investigate if one or both experimental treatments were able to increase pCR rates when compared to anthracycline-taxane CT alone according to historical data. Secondary/exploratory endpoints included biomarker analyses based on tumor transcriptomics analyses. Specifically, we collected fresh-frozen tumor samples at baseline and 14-21 days after the first treament cycle and we analyzed the RNA-seq profiles to identify early predictors of treatment activity.

  Study EGAS50000000690

• The function of circular RMST in neuroendocrine tumours

  Circular RNA (circRNA) is a class of noncoding RNA with regulatory potentials. Its role in the transdifferentiation of prostate and lung adenocarcinoma into neuroendocrine prostate cancer (NEPC) and small cell lung cancer (SCLC) remains unexplored. Here we identified circRMST as an exceptionally abundant circRNA predominantly expressed in NEPC using total RNA-seq profiling of patient samples. In addition, circRMST is also abundantly expressed in SCLC. Functional studies using shRNA, siRNA, CRISPR-Cas13 and Cas9 consistently demonstrate that circRMST is essential for tumour growth and the expression of ASCL1, a master regulator of neuroendocrine fate. Mechanistically, circRMST physically interacts with lineage transcription factors NKX2-1 and SOX2. Loss of circRMST induces NKX2-1 protein degradation through autophagy-lysosomal pathway and alters the genomic binding of SOX2, collectively leading to the loss of ASCL1 transcription.

  Study EGAS50000000897

• Pilot study for Illumina TST170 NGS panel on cutaneous T cell lymphoma samples

  A number of genomic studies using next generation sequencing (NGS) techniques have attempted to understand the underlying genetic basis of cutaneous T-cell lymphoma and found alterations in genes that are involved in T-cell activation, NF-kB and JAK-signal transducer and activator of transcription (STAT) pathways. 8 diagnostic skin biopsy samples (2xplaque, 6xtumour) on FFPE blocks were selected from 8 MF patients (stage IB:n=1, IIB:n=6, IVA2:n=1). An H&E was performed in order to assess percentage of tumor cells, viable cells, necrosis and immune infiltration, to determine the number of slides required for NGS. Freshly cut unstained slides were manually macrodissected prior to DNA and RNA extraction. Targeted deep sequencing was performed using panel of 170 genes associated with common solid tumours (Illumina TruSight Tumour 170 NGS panel). VCF files generated via the Illumina’s Basespace platform.

  Study EGAS00001002567

• Detection of low-frequency DNA variants by targeted sequencing of the Watson and Crick strands

  Identification and quantification of low-frequency mutations remain challenging despite improvements in the baseline error rate of next-generation sequencing technologies. Here, we describe a method, termed SaferSeqS, that addresses this challenge by (1) efficiently introducing identical molecular barcodes in the Watson and Crick strands of template molecules and (2) enriching target sequences with strand-specific PCR. The method achieves high sensitivity and specificity and detects variants at frequencies below 1 in 100,000 DNA template molecules with a background mutation rate of <5 × 10-7 mutants per base pair (bp). We demonstrate that it can evaluate mutations in a single amplicon or simultaneously in multiple amplicons, assess limited quantities of cell-free DNA with high recovery of both strands and reduce the error rate of existing PCR-based molecular barcoding approaches by >100-fold.

  Study EGAS00001005048

• Single cell RNAseq of PBMC from bladder cancer patients

  Frozen PBMC samples containing at least 1 million cells were thawed for 1 minute at 37C and washed twice with RPMI complete media (10% FBS with glutamate and Pen/Strep). All of the samples had >80% viable cells. Sample processing for single-cell RNA-seq was done using Chromium Single Cell 3’ Library and Gel bead kit v2 (PN-120237) following manufacturer’s user guide (CG00052, 10x Genomics, Pleasanton, CA). The total cell density was used to impute the volume of single cell suspension needed in the reverse transcription (RT) master mix, aiming to achieve ~ 6,000 cells per sample. cDNAs and libraries were prepared following manufacturer’s user guide (10x Genomics). cDNA amplification and indexed libraries were prepared using 12 and 14 cycles of PCR, respectively. Libraries were profiled, quantified, and sequenced as 5’ single-cell gene expression libraries.

  Study EGAS00001004008

• Single cell RNAseq of PBMC from RCC patients

  Frozen PBMC samples containing at least 1 million cells were thawed for 1 minute at 37C and washed twice with RPMI complete media (10% FBS with glutamate and Pen/Strep). All of the samples had >80% viable cells. Sample processing for single-cell RNA-seq was done using Chromium Single Cell 3’ Library and Gel bead kit v2 (PN-120237) following manufacturer’s user guide (CG00052, 10x Genomics, Pleasanton, CA). The total cell density was used to impute the volume of single cell suspension needed in the reverse transcription (RT) master mix, aiming to achieve ~ 6,000 cells per sample. cDNAs and libraries were prepared following manufacturer’s user guide (10x Genomics). cDNA amplification and indexed libraries were prepared using 12 and 14 cycles of PCR, respectively. Libraries were profiled, quantified, and sequenced as 5’ single-cell gene expression libraries.

  Study EGAS00001004451

• NanoString nCounter® PanCancer IO 360™on anti-PD1/anti-PD1+CTLA4 in patients with metastatic melanoma

  Patients with advanced melanoma were treated with standard-of-care single agent anti-PD-1 (nivolumab or pembrolizumab) or a combined anti-PD-1+anti-CTLA-4 (ipilimumab) therapy. Patients were retrospectively identified, based on formalin-fixed paraffin-embedded (FFPE) tissue availability. Total RNA was isolated from macro-dissected FFPE tissue sections using the AllPrep DNA/RNA FFPE Kit (Qiagen) or High Pure FFPET RNA Isolation Kit (Roche) according to the manufacturer’s instructions. RNA quantity was assessed on Qubit, and RNA integrity was assessed using the Tapestation system (Agilent). Total RNA samples (60ng/ul, total 200ng) were used as input for the NanoString PanCancer IO360 Panel, run on the nCounter MAX/FLEX prep station and scanner. Samples were hybridised for 20hrs, and each cartridge contained a panel standard.

  Study EGAS00001006977

• Normalized read counts for 84 PDAC samples

  Bulk RNAseq analysis of 84 PDAC samples : Normalized read counts

  Dataset EGAD50000000537

• DAC-2020-03-26-Lemola (DAC-039))

  DAC-2020-03-26-Lemola (DAC-039)), raw data in EGA, metadata in Harvard Dataverse

  Dataset EGAD50000000897

• EuCanImage_DEMO_UC1

  This dataset contains the standardized clinical information related to the Use case 1 - Liver Cancer (HCC) from EuCanImage. The Data is Synthetic.

  Dataset EGAD50000002083

• HipSci - Macular Dystrophy - RNA Sequencing - July 2017

  HipSci - Macular Dystrophy - RNA Sequencing - July 2017

  Dataset EGAD00001003541

• RBSC11

  One retinoblastoma sample was studied by single-cell RNA-sequencing (10X genomics Chromium).

  Dataset EGAD00001007497

• SF4324 Primary Wildtype GBM Female, 57.

  Single Cell RNA-Seq of Primary GBM. Gender Female, Age, 57.

  Dataset EGAD00001006017

• GCAT| Sex and Age

  Sex, age at recruitment (2014-2018), and birthdate of GCAT Cohort individuals.

  Dataset EGAD00001007729

• Raw count table

  Raw count matrix of the 124 bulk tumor RNAseq samples from patients with hormone sensitive or castration resistant prostate cancer.

  Dataset EGAD00001008762

• HipSci - Monogenic Diabetes - Exome Sequencing - January 2016

  HipSci - Monogenic Diabetes - Exome Sequencing - January 2016

  Dataset EGAD00001001951

• A non-canonical lymphoblast in refractory childhood T-cell leukaemia

  This dataset contains 75 WGS samples of T-ALL (including matched remission samples where available).

  Dataset EGAD00001015727

• RNA sequence of HUB_5 cell culture

  RNA sequence data for conditionally reprogrammed cells from patient HUB_5

  Dataset EGAD00001001923

• HipSci - Monogenic Diabetes - Exome Sequencing - April 2015

  HipSci - Monogenic Diabetes - Exome Sequencing - April 2015

  Dataset EGAD00001001949

• ScRNA-seq of human kidney immune cells of patients with ANCA-associated glomerulonephritis, Lupus Nephritis against a healthy nephrectomy control

  ANCA-associated glomerulonephritis (AGN) associates with a high risk of end-stage kidneydisease. The role of kidney immune cells in local inflammation remains unclear. This study investigates kidney immune cell diversity and function. Kidney tissue from AGN patients (n=5) and a lupus nephritis (LN) patient (n=1) were aquired during a biopsy procedure for a clinical indication. Needle-core biopsies were obtained for histopathological examination, and an additional pass was performed to retrieve kidney tissue for scRNA-seq. Healthy kidney tissue (n=1) was obtained from a kidney that was surgically removed do tue due to a (non-invasive) papillary urothelial carcinoma. Immediately after collection, kidney tissue was processed into a single-cell suspension and sorted using a 4-color flow cytometry panel to isolate living, CD45+immune cells. To aid in the multi-omic characterization, surface markers and T and B cell repertoires were sequenced in 2 samples (1 AGN patient and the nephrectomy control). These samples were incubated with an oligo-antibody TotalSeq-C cocktail containing 130 unique cell surface antigens.

  Dataset EGAD50000000229

• The molecular landscape of glioma in patients with Neurofibromatosis 1.

  Neurofibromatosis type 1 (NF1) is a common tumor predisposition syndrome in which glioma is one of the most prominent tumor type. Gliomagenesis in NF1 results in heterogeneous spectrum of tumors, from low grade to high grade glioma, occurring during the entire patient lifespan. In this study, we present the molecular landscape of low- and high-grade glioma in children and adults affected by NF1 (NF1-glioma). 59 tumor samples from 56 NF1-glioma patients with 43 matched normal were profiled with Whole Exome Sequencing, DNA Methylation array (31 tumors) and RNA sequencing (29 tumors).

  Study EGAS00001003186

• Single cell T cell Landscape and T Cell Receptor Repertoire Profiling of AML in Context of PD-1 Blockade Therapy

  Eight pretreatment and 14 posttreatment bone marrow (BM) aspirates from 8 patients with R/R AML treated with azacitidine and nivolumab along with 2 control BM from healthy donors were profiled with scRNA sequencing with matching scTCR -, scBCR - and scATAC sequencing. Additionally, the targeted DNA sequencing in at least 1 time point per patient (total evaluated 17/22 samples, two samples were performed using CLIA-certified molecular diagnostic assay and data is not available) was performed. The Fastq files and/or bam files are available in this dataset.

  Study EGAS00001004894

• Pathogenic Neurofibromatosis type 1 (NF1) RNA splicing resolved by targeted RNAseq

  Neurofibromatosis type 1 (NF1) is caused by loss-of-function variants in the NF1 gene. Approximately 10% of these variants affect RNA splicing and are either missed by conventional DNA diagnostics or are misinterpreted by in silico splicing predictions. Therefore, a targeted RNAseq-based approach was designed to detect pathogenic RNA splicing and associated pathogenic DNA variants. an in-house developed tool (QURNAS) was used to calculate the enrichment score (ERS) for each splicing event. RNA enrichment of NF1 and SPRED1 was done using SPET (NUGEN - NF1 only) and using SureSelect (Agilent - NF1 and SPRED1).

  Study EGAS00001005530

• EGAD00000000045

  Genomic sequencing and transcriptome shotgun sequencing of a metastatic tumour and its recurrence after drug therapy in a single patient

  Dataset EGAD00000000045

• Noninvasive detection of cancer-associated genome-wide hypomethylation and copy number aberrations by plasma DNA bisulfite sequencing

  Dataset EGAD00001000856

• cqmuGWAS1

  DNA for 1546 individuals from Chongqing was extract from peripheral blood and genotyped by Illumina Omni Zhonghua-8 version 1 gene chips.

  Dataset EGAD00010001527

• oacoch-M-1

  Methylation changes in OA patients with chronic exposure to cobalt and chromium

  Dataset EGAD00010001040

• CIRdb-CEU1

  Single Nucleotide Polymorphisms in autosomes of Canary Islanders

  Dataset EGAD00010002294

• Transcriptome of (peripheral blood), from donor Sample A, replicate 2 FACS processing technical replicates

  FACS processing technical replicates

  Dataset EGAD00001010087

• Single-cell multiomics reveals the interplay of clonal evolution and cellular plasticity in hepatoblastoma

  Dataset EGAD00001010049

• Targeted capture ctDNA Library CRCQV42Run021-6

  Targeted capture ctDNA Library CRCQV42Run021-6 from patient PBC0001304, saliva sample

  Dataset EGAD00001010447

• RNAseq data for EGAS00001004572

  Bulk RNA-seq data of tumours in EGAS00001004572.

  Dataset EGAD00001006876

• Targeted capture ctDNA Library CRCQV42Run051-9

  Targeted capture ctDNA Library CRCQV42Run051-9 from patient PBC0003587, plasma sample

  Dataset EGAD00001010822

• SF10432 Wildtype Primary GBM Female, 50

  Single Cell-RNA Seq of Wildtype Primary GBM for Female, Age 50.

  Dataset EGAD00001006019

• Sardinia Population SNPs frequencies

  List of SNPs, and their frequencies, extracted from a low pass whole genome sequencing of 3,514 individuals.

  Dataset EGAD00001003141

• Fecal WMS HV metadata

  Metadata for fecal WMS data from NCT02749630 healthy volunteers.

  Dataset EGAD00001008819

• Reference epigenome ADMSC06 WGBS data generated from KEP study

  ADMSC06 WGBS paired end data

  Dataset EGAD00001007137

• Reference epigenome ADMSC08 WGBS data generated from KEP study

  ADMSC08 WGBS paired end data

  Dataset EGAD00001007139

• Targeted capture ctDNA Library CRCQV42Run022-18

  Targeted capture ctDNA Library CRCQV42Run022-18 from patient PBC0002826, saliva sample

  Dataset EGAD00001010453

• Reference epigenome IPS-NPC02 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007288

• Reference epigenome IPS01_N_Fibroblast_WGBS data generated from KEP study

  A IPS01_N_Fibroblast_WGBS paired end data for iPSC(Oct4)

  Dataset EGAD00001003473

• Reference epigenome ADMSC07 WGBS data generated from KEP study

  ADMSC07 WGBS paired end data

  Dataset EGAD00001007138

• Targeted capture ctDNA Library CRCQV42Run42-17

  Targeted capture ctDNA Library CRCQV42Run42-17 from patient PBC0001375, saliva sample

  Dataset EGAD00001010828

• Targeted capture ctDNA Library CRCQV42Run051-8

  Targeted capture ctDNA Library CRCQV42Run051-8 from patient PBC0002294, plasma sample

  Dataset EGAD00001010821

• Targeted capture ctDNA Library CRCQV42Run021-23

  Targeted capture ctDNA Library CRCQV42Run021-23 from patient PBC0001516, saliva sample

  Dataset EGAD00001010444

• Targeted capture ctDNA Library CRCQV42Run020-14

  Targeted capture ctDNA Library CRCQV42Run020-14 from patient PBC0001312, saliva sample

  Dataset EGAD00001010426

• Targeted capture ctDNA Library CRCQV42Run021-24

  Targeted capture ctDNA Library CRCQV42Run021-24 from patient PBC0001653, saliva sample

  Dataset EGAD00001010445

• Targeted capture ctDNA Library CRCQV42Run025-17

  Targeted capture ctDNA Library CRCQV42Run025-17 from patient PBC0003595, saliva sample

  Dataset EGAD00001010481

• Reference epigenome ADMSC05 WGBS data generated from KEP study

  ADMSC05 WGBS paired end data

  Dataset EGAD00001007136

• Targeted capture ctDNA Library CRCQV42Run016-17

  Targeted capture ctDNA Library CRCQV42Run016-17 from patient PBC0001467, saliva sample

  Dataset EGAD00001010401

• HipSci - Monogenic Diabetes - RNA Sequencing - January 2016

  HipSci - Monogenic Diabetes - RNA Sequencing - January 2016

  Dataset EGAD00001001955

• HipSci - Monogenic Diabetes - RNA Sequencing - April 2015

  HipSci - Monogenic Diabetes - RNA Sequencing - April 2015

  Dataset EGAD00001001953

• RCRF release 1

  Transcriptome sequencing from the Rare Cancer Research Foundation: leiomyosarcoma, large cell neuroendocrine carcinoma, clear cell carcinoma

  Dataset EGAD00001010198

• Targeted capture ctDNA Library CRCQV34Run011-22

  Targeted capture ctDNA Library CRCQV34Run011-22 from patient PBC0002294, saliva sample

  Dataset EGAD00001010373

• Targeted capture ctDNA Library CRCQV34Run011-23

  Targeted capture ctDNA Library CRCQV34Run011-23 from patient PBC0001328, saliva sample

  Dataset EGAD00001010374

• Targeted capture ctDNA Library CRCQV40Run027-13

  Targeted capture ctDNA Library CRCQV40Run027-13 from patient PBC0005116, saliva sample

  Dataset EGAD00001010389

• Targeted capture ctDNA Library CRCQV42Run016-16

  Targeted capture ctDNA Library CRCQV42Run016-16 from patient PBC0001396, saliva sample

  Dataset EGAD00001010400

• Targeted capture ctDNA Library CRCQV42Run041-13

  Targeted capture ctDNA Library CRCQV42Run041-13 from patient PBC0001413, saliva sample

  Dataset EGAD00001010627

• Targeted capture ctDNA Library CRCQV42Run041-5

  Targeted capture ctDNA Library CRCQV42Run041-5 from patient PBC0001375, saliva sample

  Dataset EGAD00001010635

• Targeted capture ctDNA Library CRCQV42Run051-10

  Targeted capture ctDNA Library CRCQV42Run051-10 from patient PBC0001665, plasma sample

  Dataset EGAD00001010804

• Targeted capture ctDNA Library CRCQV42Run028-23

  Targeted capture ctDNA Library CRCQV42Run028-23 from patient PBC0002533, saliva sample

  Dataset EGAD00001010508

• Small variants in HAE of patients from Canary Islands (ITER)

  Small variants in HAE of several Canary Islanders sequenced with Illumina WES.

  Dataset EGAD00001009318

• Paired-end RNA-seq analysis of GBM, additional patient.

  Dataset contains one paired-end RNA-seq sample.

  Dataset EGAD00001011990

• Targeted capture ctDNA Library CRCQV42Run026-19

  Targeted capture ctDNA Library CRCQV42Run026-19 from patient PBC0002872, saliva sample

  Dataset EGAD00001010492

• Reference epigenome IPS04_X_Fibroblast_WGBS data generated from KEP study

  A IPS04_X_Fibroblast_WGBS paired end data for iPSC(Oct4)

  Dataset EGAD00001003476

• Targeted capture ctDNA Library CRCQV34Run002-18

  Targeted capture ctDNA Library CRCQV34Run002-18 from patient PBC0002062, saliva sample

  Dataset EGAD00001010333

• Targeted capture ctDNA Library CRCQV34Run002-20

  Targeted capture ctDNA Library CRCQV34Run002-20 from patient PBC0002108, saliva sample

  Dataset EGAD00001010334

• Targeted capture ctDNA Library CRCQV34Run002-21

  Targeted capture ctDNA Library CRCQV34Run002-21 from patient PBC0002429, saliva sample

  Dataset EGAD00001010335

• Targeted capture ctDNA Library CRCQV34Run002-22

  Targeted capture ctDNA Library CRCQV34Run002-22 from patient PBC0002459, saliva sample

  Dataset EGAD00001010336

• Targeted capture ctDNA Library CRCQV34Run002-23

  Targeted capture ctDNA Library CRCQV34Run002-23 from patient PBC0002595, saliva sample

  Dataset EGAD00001010337

• Targeted capture ctDNA Library CRCQV34Run007-15

  Targeted capture ctDNA Library CRCQV34Run007-15 from patient PBC0001255, saliva sample

  Dataset EGAD00001010356

• Targeted capture ctDNA Library CRCQV34Run007-18

  Targeted capture ctDNA Library CRCQV34Run007-18 from patient PBC0001845, saliva sample

  Dataset EGAD00001010357

• Targeted capture ctDNA Library CRCQV34Run007-20

  Targeted capture ctDNA Library CRCQV34Run007-20 from patient PBC0002680, saliva sample

  Dataset EGAD00001010358

• Targeted capture ctDNA Library CRCQV34Run011-21

  Targeted capture ctDNA Library CRCQV34Run011-21 from patient PBC0002255, saliva sample

  Dataset EGAD00001010372

• Targeted capture ctDNA Library CRCQV34Run011-14

  Targeted capture ctDNA Library CRCQV34Run011-14 from patient PBC0001224, saliva sample

  Dataset EGAD00001010368

• Targeted capture ctDNA Library CRCQV34Run011-18

  Targeted capture ctDNA Library CRCQV34Run011-18 from patient PBC0001432, saliva sample

  Dataset EGAD00001010370

• Targeted capture ctDNA Library CRCQV34Run011-20

  Targeted capture ctDNA Library CRCQV34Run011-20 from patient PBC0001673, saliva sample

  Dataset EGAD00001010371

• Targeted capture ctDNA Library CRCQV40Run027-10

  Targeted capture ctDNA Library CRCQV40Run027-10 from patient PBC0004414, saliva sample

  Dataset EGAD00001010387

• Targeted capture ctDNA Library CRCQV40Run027-7

  Targeted capture ctDNA Library CRCQV40Run027-7 from patient PBC0003364, saliva sample

  Dataset EGAD00001010396

• Targeted capture ctDNA Library CRCQV40Run027-8

  Targeted capture ctDNA Library CRCQV40Run027-8 from patient PBC0004076, saliva sample

  Dataset EGAD00001010397

• Targeted capture ctDNA Library CRCQV40Run027-9

  Targeted capture ctDNA Library CRCQV40Run027-9 from patient PBC0004350, saliva sample

  Dataset EGAD00001010398

• Targeted capture ctDNA Library CRCQV42Run019-18

  Targeted capture ctDNA Library CRCQV42Run019-18 from patient PBC0001295, saliva sample

  Dataset EGAD00001010421

• Targeted capture ctDNA Library CRCQV42Run020-15

  Targeted capture ctDNA Library CRCQV42Run020-15 from patient PBC0001315, saliva sample

  Dataset EGAD00001010427

• Targeted capture ctDNA Library CRCQV42Run020-23

  Targeted capture ctDNA Library CRCQV42Run020-23 from patient PBC0001310, saliva sample

  Dataset EGAD00001010428

• Targeted capture ctDNA Library CRCQV42Run021-19

  Targeted capture ctDNA Library CRCQV42Run021-19 from patient PBC0001299, saliva sample

  Dataset EGAD00001010441

• Targeted capture ctDNA Library CRCQV42Run021-21

  Targeted capture ctDNA Library CRCQV42Run021-21 from patient PBC0001323, saliva sample

  Dataset EGAD00001010442

• Targeted capture ctDNA Library CRCQV42Run021-22

  Targeted capture ctDNA Library CRCQV42Run021-22 from patient PBC0001470, saliva sample

  Dataset EGAD00001010443

• Targeted capture ctDNA Library CRCQV42Run022-16

  Targeted capture ctDNA Library CRCQV42Run022-16 from patient PBC0002406, saliva sample

  Dataset EGAD00001010451