21157 results for "fc 26 coins Buyfc26coins.com is EA Sports official for FC 26 coins The service is affordable and quick..6Xgd"

in 26.22 milliseconds.

• ERG ALTERATIONS DEFINE A NOVEL SUBTYPE OF ACUTE LYMPHOBLASTIC LEUKEMIA

  Structural chromosomal alterations are a hallmark of acute lymphoblastic leukemia (ALL) yet many patients lack an identifiable abnormality on conventional cytogenetic analysis. Here, using integrated analysis of genome-wide DNA copy number and gene expression data of 1764 childhood and adult ALL cases, including whole-genome, exome and mRNA-sequencing of 72 cases, we report a novel subtype of B-progenitor ALL (B-ALL) representing 4-13% of B-ALL cases characterized by a distinct gene expression profile, and focal deletions of ERG (ETS related gene) in 56% of cases. These cases are characterized by expression of a ERG transcript that utilizes a novel exon in intron 6 of ERG spliced to the canonical reading frame of ERG exons 7-10, encoding a 27 kDa C-terminal ERG protein that retains the ETS and transactivating domains but lacks the N-terminal pointed and regulatory domains. This protein is a competitive inhibitor of wild-type ERG and is leukemogenic in vivo. ERG-altered ALL cases have a favourable outcome despite the presence of alterations associated with poor prognosis in non-ERG ALL, such as deletions of IKZF1. These findings identify a new subtype of leukemia characterized by a novel mechanism of ETS transcription factor dysregulation in cancer.

  Study EGAS00001000514

• T cell landscape definition by multi-omics identifies galectin-9 as novel immunotherapy target in chronic lymphocytic leukemia (CLL)

  Failure of immunotherapy after applying checkpoint inhibitors or CAR-T cells is linked to T cell exhaustion. Here, we explored the T cell landscape in chronic lymphocytic leukemia (CLL) by single-cell omics analyses of blood, bone marrow and lymph node samples of patients and spleen samples of a CLL mouse model. By single-cell RNA- sequencing, mass cytometry (CyTOF), and multiplex image analysis of tissue microarrays, we defined the spectrum of phenotypes and transcriptional programs of T cells and and their differentiation state trajectories. We identified disease-specific accumulation of distinct regulatory T cell subsets and T cells harboring an exhausted phenotype exclusively in the CLL lymph node tissue. Integration of TCR data revealed a clonal expansion of CD8+ precursor exhausted T cells, suggesting their reactivity for CLL cells. Interactome analyses identified the TIM3 ligand Galectin-9 as novel immunoregulatory molecule in CLL. Blocking of Galectin-9 in CLL-bearing mice slowed down disease development and reduced the number of TIM3 expressing T cells. Galectin-9 expression correlated with shorter survival of CLL patients. Thus, Galectin-9 contributes to immune escape in CLL and represents a novel target for immunotherapy.

  Study EGAS00001006864

• Transcriptomic Profiling of Peripheral Immune Cells in a Trial of Ruxolitinib with Nivolumab for Anti-PD1 Non-Responsive cHL

  Treatment with checkpoint inhibitors is effective in multiple cancer types. However, even in cancers with high response rates such as Hodgkin lymphoma, some patients fail to respond or relapse following an initial response to checkpoint inhibitors. In this study, we examined patients enrolled in a clinical trial of ruxolitinib with nivolumab in relapsed or refractory Hodgkin lymphoma (NCT03681561). Patients relapsed or refractory to anti-PD1 therapy received ruxolitinib for 8 days followed by nivolumab every 4 weeks and continued ruxolitinib twice daily. For this transcriptomic study, peripheral blood mononuclear cells (PBMCs) were collected at baseline and 8 days of ruxolitinib therapy and analyzed by single-cell and bulk RNA-sequencing.

  Study phs003601

• RNA sequencing data of pediatric BCR::ABL1 acute lymphoblastic leukemia

  The BCR::ABL1 fusion results from the t(9;22)(q34;11.2) translocation, also known as the Philadelphia chromosome. In children, BCR::ABL1-positive leukemia is rare, accounting for approximately 3% of cases. The BCR::ABL1 oncoprotein activates multiple signaling pathways that drive cell proliferation. Historically, this subtype was associated with an extremely poor prognosis; however, the incorporation of tyrosine kinase inhibitors into therapy has markedly improved outcomes, with overall survival now approaching 80% in pediatric patients. Relapse most commonly occurs through the emergence of clones harboring kinase domain mutations, yet 30–40% of relapses arise in the absence of such mutations, a phenomenon that remains poorly understood.

  Study EGAS50000001798

• Targeted Sequencing of Human Myeloid Malignancies

  This study involves targeted sequencing of samples from myeloid malignancies at different timepoints to assess clonal evolution of malignancy a. 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 EGAD00001002225

• Woodcock et al TenMenDeep Study EGA Dataset A

  Woodcock et al TenMenDeep EGA Dataset A. These are Illumina based deep sequencing data based on bait capture sequencing. See Woodcock et al methods for more detail. Note: the Amplicon sequencing data type is selected because the EGA Website currently has no option to select Bait Capture Sequencing or similar.

  Dataset EGAD00001005381

• Woodcock et al TenMenDeep Study EGA Dataset B

  Woodcock et al TenMenDeep EGA Dataset B. These are Illumina based deep sequencing data based on bait capture sequencing. See Woodcock et al methods for more detail. Note: the Amplicon sequencing data type is selected because the EGA Website currently has no option to select Bait Capture Sequencing or similar.

  Dataset EGAD00001005382

• Genomic catastrophes frequently arise in esophageal adenocarcinoma and drive tumorigenesis.

  Oesophageal adenocarcinoma (EAC) incidence is rapidly increasing in Western countries. A better understanding of EAC underpins efforts to improve early detection and treatment outcomes. While large EAC exome sequencing efforts to date have found recurrent loss-of-function mutations, oncogenic driving events have been underrepresented. Here we use a combination of whole-genome sequencing (WGS) and single-nucleotide polymorphism-array profiling to show that genomic catastrophes are frequent in EAC, with almost a third (32%, n=40/123) undergoing chromothriptic events. WGS of 22 EAC cases show that catastrophes may lead to oncogene amplification through chromothripsis-derived double-minute chromosome formation (MYC and MDM2) or breakage-fusion-bridge (KRAS, MDM2 and RFC3). Telomere shortening is more prominent in EACs bearing localized complex rearrangements. Mutational signature analysis also confirms that extreme genomic instability in EAC can be driven by somatic BRCA2 mutations. These findings suggest that genomic catastrophes have a significant role in the malignant transformation of EAC.

  Study EGAS00001000750

• UROMOL 2020 - RNA-seq data validation

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed total RNA-seq data from 47 patients used for validation. Sequencing of total RNA was performed using KAPA RNA HyperPrep Kit with RiboErase HMR (Roche). RNA input was 100 to 500 ng. The dataset is composed of 94 fastq files.

  Dataset EGAD00001006967

• Ancestry and somatic profile predict acral melanoma origin and prognosis – RNA

  Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, we characterise the genome and transcriptome of 123 acral melanoma tumours from 92 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower median number of structural variants. The tumours with activating BRAF mutations have a transcriptional profile more similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and quadruple wild-type samples (non BRAF/NRAS/NF1/KIT) differed from mutated samples in their structural genomic profile and overall and recurrence-free survival patterns. Transcriptional profiling defined three expression clusters; these characteristics were associated with recurrence-free and overall survival. We highlight potential novel low-frequency drivers, such as PTPRJ, NF2 and RDH5. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

  Dataset EGAD00001015756

• Glioblastoma stem cell lines RNA-seq (Guilhamon et al.)

  RNA was extracted from GSCs using the Qiagen RNeasy Plus kit. RNA sample quality was measured by Qubit (Life Technologies) for concentration and by Agilent Bioanalyzer for RNA integrity. All samples had RIN above 9. Libraries were prepared using the TruSeq Stranded mRNA kit (Illumina). Two hundred nanograms from each sample were purified for polyA tail containing mRNA molecules using poly-T oligo attached magnetic beads, then fragmented post-purification. The cleaved RNA fragments were copied into first strand cDNA using reverse transcriptase and random primers. This is followed by second strand cDNA synthesis using RNase H and DNA Polymerase I. A single “A” base was added and adapter ligated followed by purification and enrichment with PCR to create cDNA libraries. Final cDNA libraries were verified by the Agilent Bioanalyzer for size and concentration quantified by qPCR. All libraries were pooled to a final concentration of 1.8nM, clustered and sequenced on the Illumina NextSeq500 as a pair-end 75 cycle sequencing run using v2 reagents to achieve a minimum of ~40 million reads per sample.

  Dataset EGAD00001006813

• Single cell RNAseq and TCRseq data from tumor and blood samples from 4 patients with muscle invasive bladder cancer

  For this dataset we performed single cell RNAseq paired with single cell TCR-seq on tumor and blood samples from 4 patients. This dataset contains 4 tumor samples as well as 4 blood samples. Each sample is made up of 2 sets of paired fastq files. The first pair contains reads corresponding to RNA transcripts (_Transcripts in file name), while the second pair contain reads corresponding to TCRs (_VDJ in file name). Sequenced on the Illumina NovaSeq6000 platform in a paired-end run using an SP flow cell (v1.5, 300 cycles).

  Dataset EGAD50000001381

• Whole Exome Sequencing of controls performed at the Broad Institute on a cohort from Bristol, UK

  These data were generated as part of a collaboration between University of Bristol, UK and the Stanley Center at the Broad Institute. This project sequenced and analysed the whole exomes of 2969 samples from the Avon Longitudinal Study of Parents and Children (ALSPAC). ALSPAC is a population-based pregnancy cohort with participants acting as ‘controls’ for this project. Genomic DNA from each sample was sequenced to a mean depth of 20x. The exome used Twist capture and samples were sequenced on Illumina NovaSeq 6000 machines producing CRAM files. ALSPAC recruited pregnant women in the Avon County of south-west England between 1991 and 1992. Of the 15,447 pregnancies, 14,901 children were alive at 1 year of age.  DNA was extracted from child blood samples taken between ages 7 and 24.

  Dataset EGAD50000000716

• Detection and Targeting of Splicing Deregulation in Pediatric Acute Myeloid Leukemia Stem Cells

  This study evaluates the gene expression and splicing changes between pediatric patients with and without Acute Myeloid Leukemia (AML) and pediatric and adult patients with AML. We obtained bone marrow or peripheral blood from patients and isolated CD34+ stem and/or progenitor cells from pediatric patients with AML (10) or without AML (6) and adult patients with AML (5). In parallel, a targeted analysis for known, deleterious, genetic mutations in the KRAS, NRAS, and KMT2C genes identified 2, 5, and 6 AML patients with alterations, respectively. Additionally, Tapestri analysis further identified a mutation in the intronic region of the SF3B1 gene in multiple pediatric AML patients. RNA-Seq was performed on the purified cell populations at The Scripps Research Institute Next Generation Core on an Illumina NextSeq 500 sequencer with 150bp paired-end reads.These datasets revealed that, similar to adult AML, pediatric AML samples were enriched for Leukemia Stem Cells (LSC) markers. A deeper analysis showed widespread dysregulation of splicing in pediatric AML samples compared to age-matched non-leukemic samples. In addition, the splicing regulator RBFOX2 was downregulated with an accompanying increase in specific CD47 splice isoforms in the pediatric AML samples. Thus, the increased presence of LSCs in pediatric AML is possibly driven by global splicing deregulation, and the treatment with splicing modulators like Rebecsinib has a potential therapeutic value for eradicating LSCs in these patients.

  Study phs003196

• Successful BRAF/MEK-inhibition in a young patient with BRAF V600E-mutated extrapancreatic acinar cell carcinoma (HIPO-021)

  Pancreatic acinar cell carcinoma (PAC) is a rare disease with a poor prognosis. Treatment options for metastatic PAC are limited and often follow chemotherapeutic regimens for pancreatic ductal adenocarcinoma. Although recurrent genomic alterations, such as BRAF fusions and defects in genes involved in homologous recombination DNA repair, have been described in PAC, data on the clinical efficacy of molecularly guided, targeted treatment are scarce. Here we describe the case of a 27-yr-old patient with BRAF V600E-mutated PAC who was successfully treated with a combination of BRAF and MEK inhibitors. The patient presented to our clinic with abdominal pain and weight loss. Imaging showed extensive retroperitoneal disease as well as mediastinal lymphadenopathy. Because of elevated alpha-fetoprotein (AFP) levels and inconclusive histologic findings, a germ cell tumor was suspected; however, PEI chemotherapy was unsuccessful. A repeat biopsy yielded the diagnosis of PAC and treatment with FOLFIRINOX was initiated. Comprehensive molecular profiling within the MASTER (Molecularly Aided Stratification for Tumor Eradication Research) precision oncology program revealed a somatic BRAF V600E mutation and a germline PALB2 stop-gain mutation. Therapy was therefore switched to BRAF/MEK inhibition, resulting in almost complete remission and disease control for 12 mo and a remarkable improvement in the patient's general condition. These results indicate that BRAF alterations are a valid therapeutic target in PAC that should be routinely assessed in this patient population.

  Study EGAS00001004282

• Study on the Genetics of Alcoholism (COGA): African American Family GWAS

  COGA is a family study of alcoholism, in which the subjects have been drawn from the Collaborative Study on the Genetics of Alcoholism (COGA), a large, ongoing family-based study that includes subjects from seven sites around the US. COGA has gathered detailed, standardized data on study participants, including diagnostic and neurophysiological assessments. This project has already proved successful in identifying several genes that influence the risk for alcoholism and neurophysiological endophenotypes, which have been independently replicated. COGA data were included as part of two Genetic Analysis Workshops, and the phenotypes are familiar to the genetics community. Alcoholic probands were recruited from treatment facilities, assessed by personal interview, and after securing permission, other family members were also assessed. A set of comparison families was drawn from the same communities as the families recruited through an alcoholic proband. Assessment involved a detailed personal interview developed for this project, the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA), which gathers detailed information on alcoholism related symptoms along with other drugs and psychiatric symptoms. Many participants also came to the laboratories for electroencephalographic studies. Neurophysiological features that have been shown to be useful endophenotypes for which we have linkage and in some cases association results are included on a subset of the case-control sample: the beta power of the resting electroencephalogram (EEG), the P3(00) amplitude of the visual event-related potential (ERP), and the theta and delta event-related oscillations (EROs) underlying the P3. As part of COGA, a set of informative African American families were selected to have Genome-Wide Association data obtained within families. Genotyping was performed using the Illumina Omni2.5_080814_1 chip to genotype 3,438 subjects selected from densely affected families. Genotyping was performed at CIDR. This sample complements a set of densely affected European American families previously made available under dbGaP study accession phs000763. In addition, exome sequencing data on a subset of individuals with GWAS were added in version 2.For version 3, a subset had 30X Whole Genome Sequencing (WGS) as part of the NIDA Sequencing Initiative. The subset contained two distinct sets: Sibling pairs where one sibling had at least two dependence diagnoses in the set (alcohol, cannabis, cocaine, and opioid), and the other had none, and non-related Case-Control pairs matched for age and ethnicity where the cases had alcohol and at least 2 other dependence diagnoses and controls had none. After sequencing, some sibling pairs are re-classified as half siblings, Three VCF files (small variants, structural variants, and copy number variations) are provided. Additional substance abuse variables are made available in version 3. We note that the full sample data are deposited in four dbGaP submissions and the sequenced samples are split across all four: CIDR: Collaborative Study on the Genetics of Alcoholism Case Control Study (phs000125). GWAS data on cases (primarily probands) and controls drawn from the families. Families with highest density of alcohol dependence and/or extreme event-related oscillation data (phs000763). GWAS data on 119 extended families of European descent are available here, along with extensive documentation. Study on the Genetics of Alcoholism (COGA): African American Family GWAS (phs000976). GWAS data on all available COGA families of African descent are available. COGA: Smokescreen GWAS (phs001208). GWAS data on all remaining COGA DNA samples, primarily of other racial background, were genotyped on the Smoke Screen array. A listing of all sequenced pairs is provided in the documentation to facilitate the merging of these samples.

  Study phs000976

• Genetic and epigenetic variation at regulatory regions contribute to cancer evolution under endocrine treatment

  Comprehensive profiling of hormone-dependent breast cancer (HDBC) has identified hundreds of protein-coding alterations contributing to cancer initiation, but only a handful have been linked to endocrine therapy resistance, potentially contributing to 40% of relapses. If other mechanisms underlie the evolution of HDBC under adjuvant therapy is currently unknown. In this work, we employ integrative functional genomics to dissect the contribution of cis-regulatory elements (CREs) to cancer evolution by focusing on 12 megabases of non-coding DNA, including clonal enhancers, gene promoters, and boundaries of topologically associating domains. Massive parallel perturbation in vitro reveals context-dependent roles for many of these CREs, with a specific impact on dormancy entrance and endocrine therapy resistance9. Profiling of CRE somatic alterations in a unique, longitudinal cohort of patients treated with endocrine therapies identifies non-coding changes involved in therapy resistance. Overall, our data uncover actionable transient transcriptional programs critical for dormant persister cells and unveil new regulatory nodes driving evolutionary trajectories towards disease progression.

  Study EGAS00001006340

• Alzheimer's Disease Genetics Consortium (ADGC) Genome Wide Association Study -NIA Alzheimer's Disease Centers Cohort

  The National Institute on Aging (NIA) Alzheimer's Disease Centers (ADCs) cohort includes subjects ascertained and evaluated by the clinical and neuropathology cores of the 29 NIA-funded ADCs. Data collection is coordinated by the National Alzheimer's Coordinating Center (NACC). NACC coordinates collection of phenotype data from the 29 ADCs, cleans all data, coordinates implementation of definitions of AD cases and controls, and coordinates collection of samples. The ADC cohort consists of autopsy-confirmed and clinically-confirmed AD cases, and cognitively normal elders (CNEs) with complete neuropathology data who were older than 60 years at age of death, and living CNEs evaluated using the Uniform dataset (UDS) protocol who were documented to not have mild cognitive impairment (MCI) and were between 60 and 100 years of age at assessment. ADCs sent frozen tissue from autopsied subjects and DNA samples from some autopsied subjects and from living subjects to the National Cell Repository for Alzheimer's Disease (NCRAD). DNA was prepared by NCRAD for genotyping and sent to the genotyping site at Children's Hospital of Philadelphia. ADC samples were genotyped and analyzed in separate batches. [Reprinted from AC Naj et al. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease. Nature Genetics 43, 436-441 (2011). doi:10.1038/ng.801. PMID: 21460841.]

  Study phs000372

• NHLBI TOPMed: Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE)

  ECLIPSE was a longitudinal observational study of 2164 COPD subjects and a smaller number of smoking controls (337) and nonsmoking controls (245) followed regularly for three years, with three chest CT scans (at baseline, one year, and three years) (Vestbo et al., 2008, PMID: 18216052). Subjects were enrolled at clinical centers in the US, Canada, Europe, and New Zealand. Inclusion criteria included subjects ages 40-75, at least 10 pack-years of smoking, and spirometry in GOLD grades 2-4 (COPD cases) or normal spirometry with post-bronchodilator FEV1 >85% (predicted) and FEV1/FVC>0.7 (controls). Study visits were performed at enrollment, three months, and every six months thereafter with spirometry, questionnaires, and other clinical evaluations. The ECLIPSE CT scans have been analyzed with the VIDA software for emphysema and airway phenotypes. ECLIPSE has provided key insights into the clinical epidemiology of COPD, including COPD exacerbations (Hurst, et. al., 2010, PMID: 20843247) and lung function decline in COPD (Vestbo, et. al., 2011, PMID: 21991892). ECLIPSE has been used in a number of genetic studies of COPD susceptibility and protein biomarkers (Faner, et. al., 2014, PMID: 24310110). Genome-wide gene expression microarray data are available in 147 induced sputum samples from COPD subjects and 248 peripheral blood samples from COPD and control subjects. Phenotype data for ECLIPSE subjects is available through dbGaP phs001252.

  Study phs001472

• Common early-life exposure of the colorectal epithelium to the microbiome-derived mutagen colibactin

  Colibactin is a mutagen produced by bacterial strains in the colorectal microbiomes of some healthy people. It generates somatic mutations characterised by the mutational signatures SBS88 and ID18 in normal human colorectal epithelial cells in vivo. Increasing prevalence of colibactin mutagenesis during the 20th century is hypothesised to have caused recent international increases in early-onset colorectal cancer incidence. Here, we establish the early-life prevalence, timing, mutation burdens and distribution through the colon of colibactin mutagenesis by sequencing normal colorectal epithelial cell genomes to detect SBS88 and ID18 in children and adolescents from the UK and Denmark biopsied between 2016 and 2024. Colibactin mutagenesis was found in ~40% individuals. It starts soon after birth and often ceases by age five years, although some children show longer exposures. Mutation burdens are higher in the left than right colon and show substantial variation between exposed individuals, in some causing >100-fold increases in total mutation loads compared to unexposed children of the same age. Therefore, colorectal mutagenesis by colibactin is currently common in some populations, occurs during early childhood and can produce markedly elevated somatic mutation burdens. The future public health implications of this widespread mutagen exposure will depend on clarification of its role in colorectal cancer causation.

  Dataset EGAD00001015830

• Memory-like HCV-specific CD8+ T cells retain a chronic scar after cure of chronic HCV infection

  Chronic hepatitis C virus (HCV) infection is associated with CD8+ T-cell exhaustion characterized by limited effector functions and thus compromised anti-viral activity. Exhausted HCV-specific CD8+ T cells are comprised of memory-like and terminally exhausted CD8+ T-cell subsets. So far, little is not known about the molecular profile and fate of these cells after elimination of chronic antigen stimulation by direct acting antiviral therapy (DAA). Here, we report an antigen-driven molecular core signature underlying exhausted CD8+ T-cell subset heterogeneity in chronic viral infection with a progenitor/progeny relationship of memory-like and terminally exhausted HCV-specific CD8+ T cells via an intermediate stage. Furthermore, transcriptional profiling reveals that the memory-like cells remain after DAA-mediated cure while terminally exhausted HCV-specific CD8+ T-cell subsets are lost. Thus, the memory polarization of the overall HCV-specific CD8+ T-cell response after cure does not result from re-differentiation of exhausted T cells. Consequently, antigen elimination has little impact on the exhausted core signature of memory-like CD8+ T cells that remains clearly different from bona fide T-cell memory. These results identify a molecular signature of T-cell exhaustion that is imprinted like a chronic scar in HCV-specific CD8+ T cells even after HCV cure, highlighting the requirement of re-programming to elicit full effector potential of exhausted T cells.

  Dataset EGAD00001006259

• WGS and WES of 78 pairs Chinese gastric cancer

  Gastric Cancer (GC) is a highly heterogeneous disease. To identify potential clinically actionable therapeutic targets that may inform individualized treatment strategies, we performed whole-exome sequencing on 78 GCs of differing histologies and anatomic locations, as well as whole-genome sequencing on two GC cases, each with 3 primary tumours and 2 matching lymph node metastases. The data showed two distinct GC subtypes with either high-clonality (HiC) or low-clonality (LoC).

  Study EGAS00001001056

• WGS of cell-free DNA derived from plasma of CRC patients and healthy controls, and WGS of matched tumor tissue and saliva

  This dataset contains: 1.) Whole-genome sequencing (WGS) data (~6x) of 259 cfDNA samples obtained from 50 colorectal cancer (CRC) patients and 61 healthy controls. Paired-end sequencing was performed with 2x101 bp reads on the NovaSeq 6000 system. Data is provided as mapped .bam files (aligned to GRCh38/hg38). 2.) WGS data (~1x) of 50 tumor biopsy and 45 saliva samples from CRC patients. Paired-end sequencing was performed with 2x101 bp reads on the NovaSeq 6000 system. Data is provided as mapped .bam files (aligned to GRCh38/hg38).

  Dataset EGAD00001009309

• Intercepting Progression from Pre-Invasive to Invasive Lung Adenocarcinoma

  An improved understanding of the biology of lung cancer is needed to intercept the disease at an early point in its progression. In this project we have used multidimensional methods to profile the tumor microenvironment (TME) and to determine the crosstalk between cancer cells and the TME in pre-invasive to invasive human lung non-solid adenocarcinomas. Comparative analysis of the cellular and molecular events associated with the distinct histological stages will lead to identification of the critical events triggering progression and thereby identify targets to intercept disease progression. The data being submitted represents the genomic profiles of patient samples with pre-invasive and invasive human lung adenocarcinomas.

  Study phs002818

• DAC Portal documentation

  DAC Portal Welcome to the DAC Portal documentation! If you are involved in governance or legal aspects, technical or operational aspects, or serving as a data steward, this page will be helpful for you. By exploring these materials, you can define your own Data Access Committee (DAC) and policies, understand the minimal requirements for a DAC and a policy object, and comprehend how the EGA data access requests are managed. DAC Portal Index Setting up your account DACs and Policies Pending Request table Manage Data Requests History page Audit your DACs, policies, and datasets Deprecation User Preferences DAC API - A programmatic approach Setting up your account Register yourself as an EGA user. The Helpdesk team will validate your account (this could take up to 48 hours). After validation, you will receive an email with a link to verify your email - Make sure you click on the link to verify your account! If you don’t receive the email, please check your spam folder. Once your account is active you can login to the DAC Portal. We recommend you to check out the Take The Tour! DACs and Policies Create a DAC Click on Create a DAC Add the title and description to your DAC. Once ready, click on the ‘Create’ button. You will have to wait until the Helpdesk Team validates the creation of your DAC. Once approved by Helpdesk, your DAC will be assigned with a persistent identifier (EGAC). Edit a DAC Once your DAC is registered and approved, you can edit it and add contacts. Remember, you can add as many contacts as needed. And, you can also remove/add contacts anytime. To add a contact, you must write the username or email of a registered EGA user in the Members field. Once you have typed the whole username or email, a drop-down menu will appear where you can select the contact. Select a contact from the drop-down menu. Make sure you write the full username or email of the contact so it appears as an option. Make sure that you select one main contact. Set up the role you want to grant to the new contact of the DAC. We recommend you to check out the FAQ to learn more about the different roles! After adding all the necessary contacts and assigning roles, click on Update. This will send an email notification to the new DAC member, letting them know about the invitation to managing the DAC. Once they log into the DAC Portal, they will be able to either accept or turn down the invitation. Create a policy First, please note that all information registered in a policy metadata object will be publicly available on the EGA website. For each released dataset, all users will be able to read the policy under the “Data Access” tab. Select Create a Policy from the Policies tab. Select the DAC that you want your policy to depend on. Add the title and terms and conditions for accessing and using your data. If you already have an external website for data access requests, you can add the URL directly here (see example here). You can find a template of a Data Access Agreement (DAA) document in our Policy documentation page. The DAA template is provided for guidance only and should be adapted to suit your own purpose depending on the security policies, terms for publication or embargoes, and restrictions on data use or sharing. You can also add Data Use Ontologies (DUO) to your policy. These DUO codes are used to semantically tag the terms and conditions of using the data linked to the policy (example). For more information, you can refer to the Data Uso Ontology documentation. Once completed, make sure to click the Create button in order to register your policy. A persistent identifier (EGAP) will be assigned automatically. Edit a policy Please, note that you will not be able to edit the policy if it shows in orange. Meaning that you are a member of the DAC linked to that policy. Members don’t have edit rights. Only admins can edit objects. You can check your registered policies on My policies, available at the menu in the top-right corner. Select your policy. It will then display the information of that policy, allowing you to edit the information. Once you are done with the modifications, click Update. Pending Requests Table Table Settings Upon selecting a specific DAC, you will be presented with a customisable table showcasing all pending data access requests. This table allows you to tailor the displayed information according to your preferences by selecting desired columns. Available Columns (Displayed with the Eye Icon): Date: The date when the data access request was submitted. Full Name: Name and surname of the user submitting the request. Email: Email address associated with the user's request. Username: The username linked to the EGA account of the user submitting the request. Organisation: Affiliated organisation of the user submitting the request. Dataset: EGA accession ID for the requested EGA dataset. Dataset Title: Title of the requested dataset. DAC Comment: Space provided for internal comments related to the request. Expiration: Option to specify an expiration date for granted permissions. Check more information on the pending request table here! Table Filters By default, all data access requests are displayed. However, you have the option to apply filters to refine your view. Follow these steps to apply filters: Click on the More button. This action will reveal the different columns available for applying filters. As you select columns, the checked fields will be displayed at the top of the request table. Choose the specific values within the selected columns that you want to filter for. Click the Search button to apply the selected filters. Do you find yourself frequently applying the same filters? You can save multiple combinations of filters for easy access at any time! Here's how: Click the Save button. Provide a descriptive name for the filter combination. Click Save to confirm. To load saved filters, simply select the saved filter from the dropdown menu to apply it automatically. Check more information on filters here! Advanced Search For users requiring more granular control over their data access requests, the DAC Portal offers an Advanced Search feature using JIRA Query Language (JQL). With JQL, you can craft precise and complex queries to filter and retrieve specific data access requests based on various criteria such as date, user details, dataset attributes, and more. Using JQL's flexibility, you can create custom queries to meet your specific needs, allowing for advanced filtering options beyond the standard filters provided. You can find the Advanced Search feature following these steps: Click the … button Select Advanced To view the allowed fields for filtering and explore all available options, simply click on the info icon. Once you have written the filtering values, click the Search button to apply. Check more information on filters here! Manage Data Requests Upon logging into the DAC Portal, you'll notice a sand clock icon next to the DAC ID, indicating the number of pending requests. Click on the DAC to review these requests. Accept requests Click on the right side of the toggle button to grant access to the user. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with granting permissions. Deny requests Click on the left side of the toggle button to deny access to the user. Provide a reason for the denial. Note that the user will receive an email with this reason. Click on the Done button. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with denying permissions. Tips! Apply a filter to view all requests to be managed at once. Use the toggle in the row with column names to grant or deny permissions for multiple requests simultaneously. You can grant and deny permissions in the same action, simplifying the process. The confirmation box will provide a summary of all actions, including grants, denials, DAC comments, and expiration dates. History Page The History page serves as a dedicated space to view information regarding all requests managed by all DAC members. Here, you can review active permissions and revoke them as needed. Go to the History page by clicking on the "HISTORY" button from a DAC page in the DAC Portal. Here are the different row types you may find: Current permissions: row in green with a toggle button to revoke permissions on the right. Approved requests: row in green, with no toggle button. Request denied: row in red with “request denied” on the right. Permission revoked: row in red with “permission revoked” on the right. Distinctions to Note! Between Request Denied and Permission Revoked: Request Denied: Refers to requests that were rejected from the outset, indicating that access to the dataset was never granted. Permission Revoked: Indicates that permissions were previously granted but have since been revoked. Users with permissions revoked have previously accessed the dataset. Between Approved Request and Current Permissions: Approved Request: Represents an entry when a data access request has been approved in the past. Current Permissions: Denotes ongoing permissions where a user has present access to the dataset. An approved request may now appear as Permission Revoked in the present. By observing these different rows, users can gain insight into the complete history of a user's interactions and permissions regarding a dataset over time. To revoke access to a specific user for a dataset, follow these steps: Go to History page Look for the row with the specific permissions. You can use the filters! Click on the toggle button to revoke access. Add a denial reason. Bear in mind that the requester will receive the denial reason! Click on the Apply button. Check more information on the Hisotry page here! Audit your metadata objects In the DAC Portal, you can efficiently manage and audit various metadata objects pertinent to your role as a Data Controller. Upon accessing the DAC Portal, you will encounter three primary tabs on the homepage: DAC: Contains information about Data Access Committees (DACs). Policies: Provides insights into linked policies. Datasets: Displays datasets and relevant details. Within each tab, you'll find a comprehensive list of the objects you manage, these being grouped by type (DAC, Policies, Datasets), as well as by your role (member/admin). Whilst the lists give you a quick look, to check how things are connected, we've added a table at the bottom DACs and policies. DACs Let’s check which policies are linked to a specific DAC: Go to "My DACs." Select a DAC. Click on "EDIT" to see more details. You are now on this page: Scroll down to see the linked policies. Click on “List of linked policies of this DAC” and you will see a list of all policies linked to your selected DAC. Policies Do you want to view a list of datasets connected to a particular policy? Follow the same steps mentioned earlier, but head to the policy tab this time. In the policy tab, you'll find a list of all your policies. Here, you may notice two different icons next to the policy ID (EGAP): DAC Icon (): This represents the DAC. Hover your mouse over the icon to see the DAC ID. Dataset icon (): This indicates datasets falling under that policy. The number next to the icon tells you how many datasets are linked to the policy. For example, if you see "2" next to the icon, it means there are two datasets linked to that policy. If you want to check the linked datasets and their relevant information, simply click on a specific policy. You'll then find the "List of linked datasets of this policy" at the bottom of the page. Datasets Finally, in the dataset tab, by default you will see a list of all the datasets you can manage with all your DACs and policies. Yet, we've included two handy ways to organise them: DAC vs. policy: You can group by datasets, either by DAC or policy. Released vs. unreleased: You can sort out datasets based on their release status. Feel free to experiment with both options! For instance, if you want to see which DACs have unreleased datasets, simply select DAC and unreleased, and you'll get the details you need! Deprecation Do you have a bunch of metadata objects like DACs and policies that you don’t need anymore? This section shows you how to get rid of them! But what does "deprecation" mean for EGA? It's basically changing the status of a metadata object to "deprecated," which means we won't be using it in the future. In simple terms, it's like saying these objects are no longer useful. However, because we believe in making metadata FAIR, once an object has a persistent identifier, we can't just delete it. So, instead of deleting, we deprecate it. Here's a helpful tip! If you want to make a metadata object disappear from the DAC Portal, deprecate it. You won’t see it in the portal anymore! Let’s say you want to deprecate a DAC. Let’s do it! Go to "My DACs." Select a DAC. Click "EDIT" to see more details. You are now in this page: Click on the Deprecate button It will then appear a message. There are two options here: Your DAC is not linked to any policy, hence it’s ready to be deprecated. Click on “Yes. Confirm” to deprecate your DAC object! Your DAC is linked to at least one policy, and you need to either: Deprecate the linked policy first, or Link the policy to a different DAC Now, let’s say you don’t want to get rid of the policy altogether, but you want to change the DAC it’s linked to. Here's how: Go to the policy tab. Find the policy you want to change (for example, EGAP50000000019). Choose a new DAC to link it to. Click on Update. See the Edit Policy section for more details. After ensuring that the DAC we want to deprecate isn’t linked to any policy, return to the "My DACs" section and follow the steps outlined previously. This will lead you to a confirmation message. You can only deprecate DACs and Policies. To deprecate a dataset, please contact our Helpdesk team. Check more information on how deprecation works here! User Preferences We have implemented email notifications in the DAC Portal. Here’s the complete list: DACs: Pending requests Approved by Helpdesk Rejected by Helpdesk DAC Invitation Requesters: Data access request approved Data access request denied Permissions revoked Upcoming expiration date As a DAC member, you will be able to decide whether you want to receive the DAC notifications or not. For that, go to the top-right corner menu, select User Preference. You will be able to decide whether you want to receive notifications for: Approved by Helpdesk Rejected by Helpdesk DAC Invitation For pending requests, you will be able to select how often do you want to receive the notification: Daily Weekly Fortnightly As a DAC member, you are responsible for managing data access requests. Consequently, you will receive notifications for pending requests. If you prefer not to manage these requests, please arrange to be removed as a contact for your DAC. Here's a tip! Do you have a pending request that you don’t want to receive a notification? Add a DAC comment! The EGA understands that a request resolution can take some time, for this reason, if you add a comment (make sure you save it by clicking the APPLY button!) we will filter those requests at the time of sending the notification! DAC API - A programmatic approach In addition to the new DAC Portal, we are excited to announce the release of the DAC API. This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can click the button below. Check out the DAC API specification!

  Documentation access/data-access-committee/dac-portal