15136 results for "cancer studies using rna-seq"

in 33.47 milliseconds.

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Study of Autism Genetics Exploration (SAGE)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs001740

• Center for Common Disease Genomics (CCDG)-Neuropsychiatric: A Study of the Genetic Causes of Complex Pediatric Disorders (CAG)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002004

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Autism Genetic Resource Exchange

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs001766

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: The Autism Simplex Collection (TASC)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs001741

• Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness (ESCAPE-BioLINCC)

  Accessing Data Please refer to “Authorized Access” below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP. Objectives To test whether pulmonary artery catheter use was safe and could improve clinical outcomes in participants hospitalized with recurrent heart failure. Background Pulmonary artery catheters have been used to guide adjustment of therapy in multiple settings, but recent studies have raised concern that pulmonary artery catheters may lead to increased mortality in hospitalized participants. Participants A total of 433 participants at 26 sites were enrolled, and randomly assigned to receive therapy guided by clinical assessment and the pulmonary artery catheter or clinical assessment alone. Patients with acute decompensation in which the attending heart failure physician considered pulmonary artery catheterization (PAC) was required or likely to be required within the next 24 hours were entered into a PAC registry. A total of 439 patients were added to the registry. Conclusions Therapy to reduce volume overload during hospitalization for heart failure led to marked improvement in signs and symptoms of elevated filling pressures, with or without the pulmonary artery catheter. Addition of the pulmonary artery catheter to careful clinical assessment did not impact overall mortality and hospitalization. Future trials should test noninvasive assessments with specific treatment strategies that could be used to better tailor therapy for both survival time and survival quality as valued by participants. (Binanay, C. et al., JAMA, 2005)

  Study phs003782

• The circulating cell-free DNA landscape in sepsis is dominated by impaired liver clearance

  Circulating cell-free DNA (cfDNA) is a promising molecular biomarker. However, its utility in severe infection remains poorly understood. Here, we isolated cfDNA from sepsis patients and controls, demonstrating a 41-fold increase in the amount of cfDNA in circulation during disease. We used sequencing to reconstruct cfDNA methylomes, fragmentation profiles, and nucleosome footprints across 58 samples. We observed no difference in cfDNA composition between patients and controls, challenging the idea that cfDNA increases due to higher immune cell death during sepsis. Instead, we suggest that liver dysfunction prevents efficient clearance of cfDNA during disease. This was supported by fragmentation and end-motif patterns, both of which showed evidence of cfDNA being exposed to circulating nucleases for a prolonged period, proportionally to the extent of liver dysfunction. Variation in cfDNA of megakaryocyte-erythroid progenitor origin was also a significant contributor in sepsis, increasing over time. Moreover, we showed that cfDNA retains nucleosome footprints with cell type-specific gene activity information. We developed a novel approach to study nucleosome phasing that successfully recovers tissue-specific signatures. By combining this with single-cell data, we demonstrated that sepsis patients with liver dysfunction have higher amounts of cfDNA derived from Kupffer cells and the liver parenchyma. In conclusion, we present the first high-throughput multi-modal study of cfDNA during sepsis, which will serve as a reference point for future studies on the role of this biomarker in critical illness.

  Study EGAS50000001033

• Targeted sequencing of candidate regions on chromosome 22q predisposing to multiple schwannomas

  Constitutional LZTR1 or SMARCB1 pathogenic variants have been found in ~86% of familial and ~40% of sporadic schwannomatosis cases. Hence, we performed massively parallel sequencing of the entire LZTR1, SMARCB1 and NF2 genomic loci in 35 individuals with schwannomas negative for constitutional first-hit pathogenic variants in the LZTR1/SMARCB1/NF2 coding sequences, however with 22q deletion and/or a different NF2 pathogenic variant in each tumor, including six cases with only one tumor available. Furthermore, we verified whether any other LZTR1/SMARCB1/NF2 (likely) pathogenic variant(s) could be found in 16 cases carrying a SMARCB1 constitutional variant in the 3’ untranslated region (3’-UTR) c.*17C>T, c.*70C>T or c.*82C>T. As no additional variants were found, functional studies were performed to clarify the effect of these 3’-UTR variants on the transcript. The 3’-UTR variants c.*17C>T and c.*82C>T showed pathogenicity by negatively affecting SMARCB1 transcript level. Two novel deep intronic SMARCB1 variants, c.500+883T>G and c.500+887G>A, resulting in out-of-frame missplicing of intron 4, were identified in two unrelated individuals. Further resequencing of the entire repeat - masked genomics sequences of chromosome 22q in individuals negative for pathogenic variants in the SMARCB1/LZTR1/NF2 coding- and non-coding regions revealed five potential schwannomatosis-predisposing candidate genes, i.e. MYO18B, NEFH, SGSM1, SGSM3 and SBF1, pending further verification.

  Study EGAS00001005680

• UK10K_RARE_HYPERCHOL

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches.Familial Hypercholesterolemia is a condition where the affected person has a consistently high level of LDL which can lead to early clogging of the coronary arteries. All patients selected for this study will have been found not to carry the common APOB and PCSK9 mutations, and to have no detectable LDLR mutations by testing for 18 common mutations. For further information with regard to this cohort please contact Steve Humphries(steve.humphries@ucl.ac.uk).

  Study EGAS00001000129

• Persistent STAG2 mutation in recurrent pediatric glioblastoma

  Similar to their adult counterparts, the prognosis for pediatric patients with high-grade gliomas remains poor. At time of recurrence, treatment options are limited and remain without consensus. This report describes the genetic findings, obtained from whole exome sequencing of a pediatric patient with glioblastoma who underwent multiple surgical resections and treatment with standard chemoradiation, as well as a novel recombinant poliovirus vaccine therapy. Strikingly, despite the variety of treatments, there was persistence of a tumor clone, characterized by a deleterious STAG2 mutation, whose deficiency in pre-clinical studies can cause aneuploidy and aberrant mitotic progression, but remains understudied in the clinical setting. There was near elimination of an EGFR mutated and amplified tumor clone after gross total resection, standard chemoradiation, and poliovirus therapy, followed by the emergence of a persistently STAG2 mutated clone, with rare mutations in PTPN11 and BRAF, the latter composed of a novel deleterious mutation previously not reported in pediatric glioblastoma (p.D594G). This was accompanied by a mutation signature shift towards one characterized by increased DNA damage repair defects, consistent with the known underlying STAG2 deficiency. As such, this case represents a novel report following the clinical and genetic progression of a STAG2 mutated glioblastoma, including treatment with a novel and emerging immunotherapy. Although STAG2 deficiency comprises only a small subset of gliomas, this case adds clinical evidence to existing pre-clinical data supporting a role for STAG2 mutations in tumor formation and resistance to standard therapies.

  Study EGAS00001004340

• Genome of the Netherlands

  The Genome of the Netherlands (GoNL) Project characterizes DNA sequence variation, common and rare, for SNVs, short insertions and deletions (indels) and larger deletions in 769 individuals of Dutch ancestry selected from five biobanks under the auspices of the Dutch hub of the Biobanking and Biomolecular Research Infrastructure (BBMRI-NL). The samples come from a representative sample of 250 trio-families from all provinces in the Netherlands. The parent-offspring trios include adult individuals ranging in age from 19 to 87 years (mean=53 years; SD=16 years) from birth cohorts 1910-1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14-15x. Samples where contributed by LifeLines (http://lifelines.nl/lifelines-research/general), The Leiden Longevity Study (http://www.healthy-ageing.nl; http://www.langleven.net), The Netherlands Twin Registry (NTR: http://www.tweelingenregister. org), The Rotterdam studies, (http://www.erasmus-epidemiology.nl/rotterdamstudy) and the Genetic Research in Isolated Populations program (http://www.epib.nl/research/geneticepi/research.html#gip). The sequencing was carried out in collaboration with the Beijing Institute for Genomics (BGI). The analysis was done by a consortium lead by UMCG, LUMC, Erasmus MC, VU university and UMCU, see http://www.nlgenome.nl. Funding for the project was provided by the Netherlands Organization for Scientific Research under award number 184021007, dated July 9, 2009 and made available as a Rainbow Project of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL).

  Study EGAS00001000644

• UK10K COHORT TWINSUK

  The UK10K project proposes a series of complementary genetic approaches to find new low-frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome-wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein-coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will directly analyse quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The TwinsUK samples will be part of the cohort study and will undergo whole genome sequencing. For further information with regard to this cohort please contact Brent Richards (brent.richards@kcl.ac.uk) or Nicole Soranzo (ns6@sanger.ac.uk).

  Study EGAS00001000108

• UK10K NEURO ASD BIONED

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The BioNED (Biomarkers for Childhood onset neuropsychiatric disorders) study has been carrying out detailed phenotypic assessments evaluating children with an autism spectrum disorder. These assessments included ADI-R, ADOS, neuropsychology, EEG etc. There are 56 DNA samples from this study (25 extracted from blood). For further information with regard to this cohort please contact Patrick Bolton (patrick.bolton@kcl.ac.uk).

  Study EGAS00001000111

• UK10K NEURO ASD GALLAGHER

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. This is an Irish sample set of individuals with ASD (approximately 50% with comorbid intellectual disability). Individuals have been diagnosed with ADI/ ADOS, measures of cognition/ adaptive function. They represent a more severe, narrowly defined cohort of ASD subjects. Family histories are available for some with measures of broader phenotype. For further information on this cohort please contact Nadia Bolshakova (bolshakn@tcd.ie).

  Study EGAS00001000112

• Performance comparison of three DNA extraction kits on human whole-exome data from formalin-fixed paraffin-embedded normal and tumor samples

  We generated 42 human whole-exome sequencing data sets from fresh-frozen (FF) and FFPE samples. These samples include normal and tumor tissues from two different organs (liver and colon), that we extracted with three different FFPE extraction kits (QIAamp DNA FFPE Tissue kit and GeneRead DNA FFPE kit from Qiagen, Maxwell\textsuperscript{TM} RSC DNA FFPE Kit from Promega). Variant calling analysis shows a very high rate of concordance between matched FF / FFPE pairs and equivalent performance for the three kits we analyzed. We find a significant variation in the difference of total number of variants called between FF and FFPE samples for the three different FFPE DNA extraction kits. Coverage analysis shows that FFPE samples have less good indicators than FF samples, yet the coverage quality remains above accepted thresholds. We detect limited but significant variations in coverage indicator values between the three FFPE extraction kits. Globally, the GeneRead and QIAamp kits have better variant calling and coverage indicators than the Maxwell kit on the samples used in this study, although this kit performs better on some indicators and has advantages in terms of practical usage. Taken together, our results confirm the potential of FFPE samples analysis for clinical genomic studies, but also indicate that the choice of a FFPE DNA extraction kit should be done with careful testing and analysis beforehand in order to maximize the accuracy of the results.

  Study EGAS00001002631

• Identification of a Fumarate-Hydratase Deficient-like RCC Subtype with Convergent Pathway Alterations

  The development of molecularly targeted therapies builds upon the identification of tumor clades with shared molecular abnormalities. Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) is a familial syndrome resulting from germline mutation of the two-hit tumor suppressor gene, Fumarate Hydratase (FH). HLRCC renal tumors present often in young adults, exhibit unique histological features and are particularly aggressive. HLRCC is uncommon and poorly understood, and whether there are sporadic renal tumors with similar characteristics, is unclear. Notably, FH can also be inactivated somatically in RCC, and should be included among FH-deficient (FHD) kidney tumors. We identified a tumor clade without FH mutations but with similar morphological and molecular characteristics as well as aggressive clinical behavior, which we name FHDL (FHD-like). We show that FHD/FHDL tumors represent a unique subtype of CIMP (CpG Island Methylator Phenotype) with the highest levels of overall genome methylation and CDKN2A silencing. FHD/FHDL tumors are characterized by convergent activation of the Hippo pathway as determined by frequent and mutually exclusive mutations in NF2 and PTPN14. AKR1B10, an NRF2 target, was upregulated in FHD/FHDL tumors as well as GATA3, which suggests a shared origin in the renal medulla and is consistent with topological studies. There is loss of 5-hydroxymethylcytosine in both FHD and FHDL tumors. These data define a group of particularly aggressive renal cancers with shared molecular features and potential targets for therapeutic intervention.

  Study EGAS00001002646

• Initial whole genome sequencing of plasma cell neoplasms in First Responders exposed to the World Trade Center attack of September 11, 2001

  The World Trade Center (WTC) attack of September 11, 2001 created an unprecedented environmental exposure to known and suspected carcinogens. High incidence of multiple myeloma (MM) and precursor conditions has been reported among first responders to the WTC disaster. To expand on our prior screening studies, and to characterize the genomic impact of the exposure to known and potential carcinogens in the WTC debris, we were motivated to perform whole genome sequencing (WGS) of WTC first responders and recovery workers who developed a plasma cell disorder after the attack. PATIENTS AND METHODS: We performed WGS of 9 CD138-positive bone marrow mononuclear samples from patients who were diagnosed with plasma cell disorders after the WTC disaster. RESULTS: No significant differences were observed in comparing the post-WTC driver and mutational signatures landscape with 110 previously published WGS from 56 patients with MM and the CoMMpass WGS cohort (n=752). Leveraging constant activity of the single base substitution mutational signatures 1 and 5 over time, we estimated that tumor-initiating chromosomal gains were windowed to both pre- and post-WTC exposure. CONCLUSIONS: Although limitations in sample size preclude any definitive conclusions, our findings suggest that the observed increased incidence of plasma cell neoplasms in this population is due to complex and heterogeneous effects of the WTC exposure that may have initiated or contributed to progression of malignancy.

  Study EGAS00001004467

• The Southern African Human Genome Programme

  The Southern African Human Genome Programme (SAHGP) is a national and regional initiative that aims to unlock the unique genetic character of southern African populations. Its vision is to improve quality of life through understanding human genetic diversity. It is timely to advance the SAHGP initiative since global genomic initiatives are increasingly pointing to populations in Africa, more specifically the San and Khoe from southern Africa, as harbouring the most ancient genetic signatures among the living populations of the world. Studies on African populations have already generated a wealth of information upon which the SAHGP can build. This information will be used to promote and support genomic research programmes both nationally and regionally to address critical questions that would benefit the people of our region. The SAHGP aims to make a significant contribution to the understanding of DNA variation among southern Africans and how this impacts on the health of the people of this region. Aims of the SAHGP: To develop capacity for genomic research in southern Africa; To establish sustainable resources for genomic research in the region; and To translate knowledge and information into improvements in human health in the region. The objective is to derive the maximum benefit from the SAHGP, and that the data should be widely shared with the scientific community for the benefit of the people of the sub-continent and beyond.

  Study EGAS00001002639

• AVENIO Expanded ctDNA panel sequence alignments (BAMs)

  The AVENIO ctDNA Expanded Kit is a next-generation sequencing (NGS) liquid biopsy assay with a 77 gene panel (192 kb) containing genes in U.S. National Comprehensive Cancer Network (NCCN) Guidelines and emerging cancer biomarkers. This pan-cancer assay was applied to 100 plasma samples from patients with lung cancer undergoing treatment in the OSCILLATE trial. After 150 bp paired-end sequencing, reads were aligned to the human genome reference with the AVENIO Oncology Analysis Software. These files are the sorted non-deduplicated alignments generated by the analysis software used for subsequent variant, indel and CNV calling.

  Dataset EGAD50000000138

• cell-Free DNA Genomic Profiling and its Clinical Implementation in Advanced Prostate Cancer

  Sixty-eight patients with advanced prostate cancer in castration-resistant or castration-sensitive settings undergoing treatment at the University Hospital Basel or the St. Claraspital Basel (Switzerland) were selected for targeted parallel sequencing analysis on liquid biopsy (plasma cfDNA) and matched formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples.

  Study EGAS50000000234

• Long-term Survival Update and Extended RAS Mutational Analysis of the CAIRO2 Trial: Addition of Cetuximab to CAPOX/Bevacizumab in Metastatic Colorectal Cancer

  Here we present updated survival of the CAIRO2 trial and assessed whether the addition of anti-EGFR to anti-VEGF therapy could still be an effective treatment option for patients with extended RAS/BRAF wildtype and left-sided metastatic colorectal cancer (mCRC).

  Study EGAS50000000775

• Somatic_mutation_and_clonal_evolution_in_the_human_bladder_WES_NOVASEQ

  n this study, we aim to characterise the landscape of mutation and clonal selection in the human bladder. The study includes targeted sequencing of laser-dissected microbiopsies from the bladder. The samples utilised in this study will include urothelium from transplant donors with no history of bladder cancer and cystectomy specimens from patients with bladder cancer.

  Study EGAS00001003456

• Genomic characterization of metastatic breast cancers

  While large efforts have been done to characterize early breast cancer, little is known about the genomic landscape of metastatic breast cancer. In the present study, we performed whole exome sequencing of 629 metastatic breast cancers, in order to identify new candidate targets and better stratify patients eligible for innovative therapies.

  Study EGAS00001003290

• Programmatic submission based on XML

  Programmatic submissions (XML based) For further information please check our Submission FAQs, submission quickguide as well as submission terms! Introduction Besides the Submitter Portal tool, EGA supports programmatic sequence and clinical data metadata submissions. If you are not sure what this means, you may want to explore our brief metadata introduction. Programmatic submissions are recommended for array-based submission. Moreove, it may be of help if your submission is recurrent or it is difficult to manage manually due to its sheer size. Otherwise, we highly recommend using the Submitter Portal to perform submissions. In this page we will guide you through the required steps to programmatically submit data to the EGA. Programmatic submissions require your metadata to be structured for an easy and straightforward validation and archival. It basically consists in formatting your metadata as Extensible markup language (XML) files and submitting them to the EGA using the WEBIN Before submitting metadata to the EGA, it is important to ensure that the information in your XML files is compliant with our standards. You can see further details on how these standards are maintained at EGA at our EGA Schemas documentation page. Using WEBIN, you can validate your XML files against EGA's schemas to ensure that your metadata is compliant before submission. WEBIN services WEBIN production service WEBIN test service We advise you to submit your metadata to the test service when submitting to the production service for the first time. The test service is identical to the production service except that all submissions will be discarded in the following 24 hours. This allows you to learn about the submission process without having to worry about data being submitted. Authentication Authentication is required each time a submission is made. The submission service uses HTTPS protocol for metadata encryption and identification to provide a secure submission environment. Data file upload Both Runs and Analyses reference files (e.g. FASTQ need to be uploaded to the EGA before these metadata objects are submitted. In other words, if you submit a Run that references a file that we cannot find associated with your account, the metadata submission will fail. See further details on how to upload your files in our File Upload documentation. Metadata model of the EGA Our metadata model is formed by multiple metadata objects. Check further details in our documentation at our EGA Schema documentation page. Working with EGA XMLs files Now that the basic concepts of the EGA metadata have been described, you can start preparing your programmatic submission through XML. Here you will find the guidance on how to prepare the XML files. Programmatic Submission Tutorial Video Take a look at the Programmatic Submission Tutorial Video, which explains the workflow of a programmatic submission and goes over an example metadata submission. Programmatic Submission Tutorial Video. When building your XML files, we recommend using text editors (e.g.Sublime Text or VisualStudio) that allow you to visualise the structure of the XML with ease. Furthermore, these editors constantly check the consistency of the XML structure. Alternatively, and if the submission consists of a big number of objects (specially analyses), you may find the tool star2xml handy. This tool allows for a direct conversion between metadata in a tabular format (e.g. a spreadsheet) into XMLs. Identifying objects: Aliases and center names Every EGA object must be uniquely identified within the submission account using their alias attribute. The aliases can be used in submissions to make references between EGA objects. Let us dig into EGA's use of aliases and center names: alias: every object should have a name that is unique within your submission account. Once submitted successfully, every alias will be assigned a unique and permanent accession (EGA ID). refname: when an object references another by its alias, the alias of the referenced object goes into the "refname" attribute of the referencing object. For example, if a sample has the alias "sample1", and an experiment uses this sample, then the experiment's "EXPERIMENT/SAMPLE/refname" attribute should be "sample1". center_name: The "center_name" attribute is required within the submission XML and, if not provided when the object is submitted, it will be automatically filled using your default EGA account center_name. This element is the "controlled vocabulary acronym or abbreviation that is provided to the account holder when the account is first generated". If the submitter is brokering a submission for another institute, the submitter should use their special broker account name in broker_name while the data centre acronym remains in center_name. Log-in details should have been provided when you requested a submission account. Please contact our Helpdesk team if you have any questions. run_center: Many submitting centers contract out the actual sample sequencing to another center. In these cases, the sequencing center should be acknowledged in the run_center attribute. Again, this is controlled vocabulary and the acronym should be sought from EGA helpdesk before submitting. Please contact our Helpdesk team if you have any questions. Prepare your XMLs The goal of this section is to provide sufficient information to be able to create the metadata XML documents required for programmatic submissions. Please note, the EGA utilises the XML schemas maintained at the European Nucleotide Archive (ENA). It is important due to the fact that by using a similar system, some pieces of documentation from the ENA's programmatic submission can also help you with your programmatic submission to the EGA. For example, you can submit programmatically without using a Submission XML by following the steps at Submission actions without submission XML. A submission does not have to contain all different types of XMLs. For example, it is possible to submit only a few samples; or a study that is later to be referenced. You can submit each object one by one, or submit all in a batch: you choose what method of submission works best for you. We do recommend, nevertheless, that you submit the objects to be referenced (e.g. samples or studies) first, and the objects that reference these (e.g. experiments or datasets) afterwards. You can see a graphical view of these objects and their relationships at our EGA Schemas page. Independently of the submission scenario, you will always require a Dataset XML. The entity of a dataset is what is used to control access to the given data, in the form of runs or analyses. In other words, when a requester is granted access, it is through the dataset and the objects (e.g. runs or analyses) that the dataset contains, granting access to them in one go. Given the nature of the EGA, a dataset XML will always be required for the data access. First, we will differentiate between submissions of "raw" and "processed" data: Runs and Analyses, respectively. Run data submissions Raw data derives from instruments "as is". For example, a plain sequence file (e.g. FASTQ or unaligned BAM files) would be considered raw data. A typical raw (unaligned) sequence read submission consists of 8 XMLs: Submission Study Sample Experiment Run DAC Policy Dataset When technical reads (e.g. barcodes, adaptors or linkers) are included in the submitted raw sequences, a spot descriptor must be submitted to describe the position of the technical reads so that they can be removed. The following data files can be submitted without providing spot descriptor information in the experiment/run XML: BAM files (single reads) SFF files (single reads without barcodes) FastQ files (single reads without any technical reads) Complete Genomics files Analysis data submissions Processed data is, in some way, refined raw data. This includes raw data that has been processed by some form of analysis method (e.g. alignment, noise reduction, etc.). For example, an aligned sequence (e.g. BAM file), that was created using raw FASTQ files, would be a processed file. This category includes most types of data: sequence alignment files (e.g. BAM or CRAM), clinical data (e.g. phenopackets), sequence variation files (e.g. VCF), sequence annotation, etc. A typical EGA analysis data submission consists of 7 EGA XML: Submission Study Sample Analysis DAC Policy Dataset We accept three different types of analysis data submissions: BAM files (for multiple read alignments) VCF files (for sequence variations) Phenotype files (in any format) In anycase, keep in mind that samples must be created in order to be referenced in the analyses. In other words, the provenance of the information within the BAM, VCF and phenotype files Example XMLs Below you can find a non-extensive list of example XMLs with descriptive fields (i.e. explaining what to provide in each field). Furthermore, you can also find real examples (i.e. the true value of the provided fields) in our GitHub repository. Submission XML The submission XML is used to validate, submit or update any number of other objects. The submission XML refers to other XMLs. New submissions use the ADD action to submit new objects. Object updates are done using the MODIFY action and objects can be validated using the VERIFY action. Descriptive submission XML example True values submission XML example Study XML The study XML is used to describe the study containing a title, a study type and abstract as it would appear in a publication. Descriptive study XML example True values study XML example Please use the following notation within the property "STUDY_LINKS" when including PubMed citations in the Study XML: <STUDY_LINKS> <STUDY_LINK> <XREF_LINK> <DB>PUBMED</DB> <ID>18987735</ID> </XREF_LINK> </STUDY_LINK> </STUDY_LINKS> Sample XML The sample XML is used to describe the samples used to obtain the data, whether they were sequenced, measured in any other way, or have an associated phenotype. The mandatory fields include information about the taxonomy of the sample, sex, subject ID and phenotype. For example, the mandatory attribute fields for each sample would look like these, within the array of "SAMPLE_ATTRIBUTES": <SAMPLE_ATTRIBUTES> <SAMPLE_ATTRIBUTE> <TAG>subject_id</TAG> <VALUE>free text!</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>sex</TAG> <VALUE>female/male/unknown</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>phenotype</TAG> <VALUE>Free text, EFO terms (e.g. EFO:0000574) are recommended</VALUE> </SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTES> Sample is one of the most important objects to be described biologically, it is highly recommended that “TAG-VALUE” pairs are generated as SAMPLE_ATTRIBUTES to describe the sample in as much detail as possible. For example, were we to give the population ancestry of the sample, we could add a new attribute to the array, in which, for example, we would indicate that the sample derives from an individual of "Mende in Sierra Leone" (MSL), with an african ancestry: <SAMPLE_ATTRIBUTE> <TAG>Population</TAG> <VALUE>MSL</VALUE> </SAMPLE_ATTRIBUTE> Given that VALUE and TAG are free text, the combinations are limitless in order to give you full flexibility on the information you want to provide. We recommend you use the Experimental Factor Ontology (EFO) to describe the phenotypes of your samples. You can provide more than one phenotype by adding more items to the array of SAMPLE_ATTRIBUTES. Phenotypes considered essential for understanding the data submission should be provided. Each phenotype described should be listed as a separate sample attribute <SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTE>. There is no limit to the number of phenotypes that can be submitted. If a suitable EFO accession cannot be found for your phenotype attribute, please consider using another controlled ontology database (e.g. HPO, MONDO, etc.) before using free text. Descriptive sample XML example True values sample XML example Experiment XML The experiment XML is used to describe the experimental setup, including instrument platform and model details, library preparation details, and any additional information required to correctly interpret the submitted data. Where any of these values differ between runs, a new experiment object must exist, since runs are grouped by experiments. Each experiment references a study and a sample by alias, or if previously-submitted, by accession. Pooled data must be demultiplexed by barcode for submission. Descriptive experiment ( Illumina paired read ) XML example True values experiment ( Illumina paired read ) XML example Run XML The run XML is used to associate data files with experiments and typically comprises a single data file (e.g. a FASTQ file). Please note that pooled samples should be de-multiplexed prior submission and submitted as different runs. Descriptive run XML example True values run XML example Analysis XML Given that an analysis can be used to submit any type of processed data to the EGA, we will list below an example of each of the three most common types of analysis XMLs submitted to the EGA: sequence alignments (e.g. BAM files); sequence variation (e.g. VCF files); and clinical metadata or phenotypes (e.g. phenopackets). Regardless of the type of processed data submitted in the analysis, the analysis must be associated with a Study and can reference multiple types of other objects, from samples to experiments, if they are available at the EGA. Just like with Runs, whenever a file is submitted to the EGA through an analysis object, the file MD5 checksums must be present, in order for the EGA to validate file integrity upon transfer. This also includes index files when applicable (e.g. .bai.md5 files). Ideally, any analysis that uses a reference sequence for some kind of alignment (e.g. BAM, CRAM or VCF files), would contain metadata about the alignment, such as INSDC reference assemblies and sequences, by either using accessions (e.g. CM000663.1) or common labels (e.g. GRCh37). Read alignment (BAM) Analysis XML The Analysis can be used to submit BAM alignments to EGA. Only one BAM file can be submitted in each analysis and the samples used within the BAM read groups must be associated with Samples. Descriptive bam alignments XML example True values bam alignments XML example Sequence variation (VCF) Analysis XML The Analysis can be used to submit VCF files to EGA. Only one VCF file can be submitted in each analysis and the samples used within the VCF files must be associated with Samples. Download analysis XML (VCF) Phenotype files The Analysis XML can be used to submit phenotype files to the EGA. Only one phenotype file can be submitted in each analysis and the samples used within the phenotype files must be associated with EGA Samples. Download analysis XML (Phenotype) DAC XML The DAC XML describes the Data Access Committee (DAC) affiliated to the data submission. The DAC may consist of a group or a single individual and is responsible for the data access decisions based on the application procedure described in the POLICY.XML. As with any other object, if it was already submitted to the EGA, there is no need to submit it again: you can reference an existing object within the EGA. Hence, A DAC XML does not need to be provided if your submission is affiliated to an existing EGA DAC.. Further information on DACs can be found here, and you can always contact our Helpdesk team if you have further inquiries. Descriptive dac XML example True values dac XML example Policy XML The Policy XML describes the Data Access Agreement (DAA) to be affiliated to the named Data Access Committee. Descriptive policy XML example True values study XML example Dataset XML The dataset XML describes the data files, defined by the Run.XML and Analysis.XML, that make up the dataset and links the collection of data files to a specified Policy. The dataset xml is commonly the last metadata object to be submitted, since it references multiple other entities. Please consider the number of datasets that your submission consists of. For example, a case-control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Descriptive dataset XML example True values dataset XML example Validating and submitting your EGA Validating EGA's XMLs through Webin After you have ensured that the XMLs are properly formatted and contain all the required information. You can proceed to validate and submit your data. Use the curl command to validate your XML file: Once you have prepared your XML file and asserted you have access to Webin, you can validate your XML file programmatically against EGA's schemas using the curl command. There are multiple ways in which you can validate your XMLs. This variety has to do with the fact that: (1) there are 2 instances of Webin (test and production); and (2) that validation is a default step during submission. In other words, any time that you submit your data through Webin, it will be validated automatically before being accepted. This allows for 4 possible routes of validation, all having the same validation result: validating or submitting to either the production service or the test service of Webin. For example, directly validating a "study" object XML in the testing service (wwwdev…) would look like the following: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" In this command, you would need to replace <USERNAME> and <PASSWORD> with your EGA account username and password, respectively. You would also replace <INPUT_FILE> with the path to your XML file. A mock example would look like the following: curl -u ega-test-data@ebi.ac.uk:egarocks -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" The validation attempt can have different results depending on the given arguments: If your XML file is valid according to EGA's schemas, you will see a message indicating that your XML file is compliant. For example, see below for our mock example, where the "success" was "true" (i.e. no validation errors found). Nevertheless, notice how the "<STUDY accession=" is empty: it is because we were simply validating, so the study did not get an accession or ID. <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:19:28.850+01:00" submissionFile="submission-EBI-TEST_1681222768850.xml" success="true"> <STUDY accession="" alias="Mock example" status="PRIVATE"/> <SUBMISSION accession="" alias="SUBMISSION-11-04-2023-15:19:28:840"/> <MESSAGES> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If there are any errors or warnings, the tool will display them, allowing you to correct them before submitting your data to EGA. For example, in the following response, it is said that the object we were trying to submit was already existing, and therefore the "success" was "false". <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:12:35.609+01:00" submissionFile="submission-EBI-TEST_1681222355609.xml" success="false"> <STUDY alias="Example!_Human Microbiome Project SP56J" status="PRIVATE" holdUntilDate="2023-03-11Z"/> <SUBMISSION alias="SUBMISSION-11-04-2023-15:12:35:576"/> <MESSAGES> <ERROR>In study, alias: "Example!_Human Microbiome Project SP56J". The object being added already exists in the submission account with accession: "ERP127584".</ERROR> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If the curl command retrieves no response at all, please double check if your username and password are correctly provided. Also notice the "ACTION=..." argument passed to the Curl command. This specifies the action to take during the call to Webin, so we do not need a "Submission" XML just for a validation attempt. See more at submission actions without submission XML. Furthermore, validation of multiple files or objects (e.g. sample, experiment, study…) can be done in a single command by adding more arguments (i.e. '-F'). For example: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" -F "SAMPLE=@sample.xml" -F "DATASET=@dataset.xml" As mentioned above, beside "validate" action in the test environment, you can also validate your metadata by three other methods: "Validate" in the production server. From our example above, you simply need to take the "dev" away from the URL. curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the development server. From our example above, you would simply need to replace the action: from "validate" to "add". Whatever is submitted to this service will be discarded in 24h, so whether something gets submitted or not would not matter in the long run. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the productionserver. A combination of the previous two methods, which would render this attempt into a submission. This path is just to be taken when you are sure your metadata is compliant and what you want to submit. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" What happens after the submission of a dataset XML? Once you have completed the registration of your dataset/s please contact our Helpdesk Team to provide a release date for your study. Please note that all datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitter or DAC contact contact the EGA Helpdesk for the study to be released. We strongly advise you not to delete your data until EGA Helpdesk confirms that your data has been successfully archived.

  Documentation submission/metadata/submission/programmatic-submission-xml

• Mapping the Human Connectome - Structure, Function, and Heritability: Healthy Young Adults (Age 22-35 Years) Including Twins and their Non-Twin Siblings

  The Human Connectome Project (HCP) has acquired vast amounts of data about the pattern of long-distance connections (wiring) in the brains of large numbers of healthy participants aged 22-35 using cutting-edge MRI and MEG neuroimaging, and extensive behavioral testing. Types of MR data collected included diffusion MRI (dMRI), resting-state functional MRI (r-fMRI), and structural MRI at both 3T and 7T, and task-evoked functional MRI (t-fMRI) at 3T. Following an extensive period of development and optimization of data acquisition and analysis methods, at Washington University in St. Louis we studied 1,206 participants comprising twins and their non-twin siblings. A subset of 184 twins was scanned again at the University of Minnesota using a 7T scanner. A different subset of 95 twins was studied at St. Louis University using combined resting state and/or task-activated MEG. We were able to collect genetic data on 1142 of our 1206 participants, including 149 pairs of genetically-confirmed monozygotic twins (298 participants) and 94 pairs of genetically-confirmed dizygotic twins (188 participants). Overall, there are 457 different families in the study, as determined by genetic analysis. Our rich set of imaging, behavioral and genetic data - available through the Human Connectome database - will enable many types of analysis of brain circuitry, its relationship to behavior, and the contributions of genetic and environmental factors to brain circuits.

  Study phs001364

• Single cell transcriptomics in expanded Tregs of APS-1 patients

  Aim of study: Exploring single cell transcriptomics in expanded Tregs of with autoimmune polyendocrine syndrome type 1 (APS-1) patients (global gene expression, immune panel, TCR). N= 9 APS-1 patients and 9 healthy controls Protocol 1. Regulatory T cells (Tregs) were isolated from EDTA-blood, activated with anti-CD3/CD28 and IL2, expanded in cell culture for 14 days and stored at -150℃ in AB serum or FBS + 5% DMSO until use. 2. Cells were thawed, run through a Live/Dead column and carefully counted. 10000 Tregs were used as input for the 10x protocol. 3. cDNA was generated and amplified using Single Cell VDJ 5’ Gel Beads, Chromium Next GEM Chip K Single cell Kit, Next GEM Single Cell 5’ GEM Kit v2 and 5’ Feature Barcode Kit (all from 10x genomics). 4. Gene expression (GEX) libraries were generated by using the Library Construction Kit from 10x genomics. 5. T-cell receptor (TCR) libraries were generated using the Single Cell Human TCR Amplification Kit and the Library Construction Kit from 10x genomics. 6. 10x Genomics Target Hybridization Kit and Human Immunology Panel was used with GEX libraries. Only 8 patient samples and 8 control samples were used for the immune panel. 7. All libraries have a unique sample index (Dual Index TT Set A). 8. All samples were sequenced on Illumina NovaSeq 6000 on a NovaSeq S2 flow cell.

  Study EGAS50000000181

• Immunopeptidomics of colorectal cancer organoids reveals a sparse HLA class I neoantigen landscape and no increase in neoantigens with interferon or MEK-inhibitor treatment

  Study EGAS00001003886

• Matched Pair Cancer Cell line Whole Genomes

  Matched Pair Cancer Cell line Whole Genomes

  Dataset EGAD00001000145

• ICGC prostate cancer miRNA sequencing

  ICGC prostate cancer miRNA sequencing

  Dataset EGAD00001000304

• Esophageal cancer

  Genome-wide NanoRCS of cfDNA from plasma of Esophageal cancer patients

  Dataset EGAD50000000221

• Pilot data of PDiamond Lung Cancer Data

  RNAseq pilot data of PDiamond Korean lung cancer data

  Dataset EGAD50000000843

• Genomic analysis of Japanese gastric cancer

  fastq of 345 Japanese gastric cancer

  Dataset EGAD00001004051

• Copy number profile of ctDNA in ovarian cancer patients

  This study analyzes the copy number profiles from circulating tumor DNA (ctDNA) in plasma samples withdrawn from ovarian cancer patients, in a subset of samples from the MITO16A clinical trial.

  Study EGAS00001008226

• Whole exome and transcriptome sequencing of biliary tract cancer

  To uncover the full repertoire of genetic and transcriptomic landscape of biliary tract cancer (BTC), we molecularly characterized BTC based on genetic and transcriptomic alteration profiles.

  Study EGAS00001000950

• Sequencing of Plasma DNA from breast cancer patients

  Dataset contains plasma DNA whole genome sequencing on 4 breast cancer patients. It also includes matched germline and tumour whole genome sequencing data. Two benign cancer patients were also sequenced and their plasma DNA and matched germline whole genome sequencing data are included in the dataset. Samples were sequenced on Illumina HiSeqX Ten.

  Dataset EGAD00001006869

• Heart Failure Network - Xanthine Oxidase Inhibition for Hyperuricemic Heart Failure Patients (HFN EXACT-BioLINCC)

  ObjectivesTo determine the effect of allopurinol after 24 weeks on a composite clinical endpoint that classifies subject's clinical status (improved, worsened, unchanged) in patients with heart failure and high uric acid levels.BackgroundMorbidity and mortality rates for patients with heart failure are high, despite guideline-recommended therapy. Heart failure is characterized by an imbalance between left ventricular (LV) performance and myocardial energy consumption. There is a growing body of evidence that suggests oxidative stress contributes to ventricular and vascular remodeling, and disease progression in heart failure. Targeting potential source(s) of oxidative stress, e.g. Xanthine oxidase (XO), was the focus of recent clinical trials and epidemiological studies. Increased XO activity has been shown to lead to production of superoxide and uric acid (UA). Serum uric acid levels are included in heart failure risk scores, and hyperuricemia is present in about 25% of patients with heart failure. Hyperuricemia is associated with exercise intolerance, reduced survival, and worsening symptoms. The EXACT-HF trial tested allopurinol, an inhibitor of XO, as a potential target therapy for hyperuricemic heart failure patients.Subjects253 subjects were enrolled in the EXACT-HF study. 128 participants were randomized to the allopurinol arm, and of those participants, 119 completed the trial and 9 did not. 125 participants were randomized to the placebo arm, and of those participants, 116 completed the trial and 9 did not.DesignEXACT-HF was a multi-center, double-blind, placebo controlled, 24-week trial of allopurinol. Eligible participants had to be receiving a stable regimen for at least two weeks (3 months for beta-blockers) prior to randomization. Participants were randomized by an automated system to either the allopurinol or placebo arm, and started treatment within 12 hours of completing the baseline visit. During the first week, participants in both treatment arms received 300mg daily of the respective medications. For the following 23 weeks, participants in both treatment arms received 600mg daily of the respective medications. Patients unable to tolerate 600 mg were maintained on 300 mg.The primary endpoint was a composite clinical endpoint (CCE) that classified a subject's clinical status as improved, worsened, or unchanged at 24 weeks. The CCE was determined based on the following: death; hospitalization, emergency room visit or emergent clinic visit for worsening HF; medication change for worsening HF; and Patient Global Assessment using a 7-point scale. Secondary endpoints at 12 and 24 weeks included changes in quality of life as assessed by the Kansas City Cardiomyopathy Questionnaire, and submaximal exercise capacity as assessed by a 6-minute walk test.ConclusionsParticipants who received allopurinol had significantly less serum uric acid laboratory levels after 24 weeks; however, no significant difference was observed in the primary and secondary endpoints between the allopurinol and placebo-treated patients. Therefore, in high-risk HF patients with reduced ejection fraction and elevated uric acid levels, xanthine oxidase inhibition with allopurinol failed to improve clinical status, exercise capacity, quality of life, or LVEF at 24 weeks (Circulation. 2015 May 19; 131(20):1763-71).

  Study phs003533

• HeartShare - Extant Datasets - Harmonized Clinical Trials Collection

  Heart failure with preserved ejection fraction (HFpEF) prevalence is rising with an aging population and contributing comorbidities, such as obesity and diabetes. The Get With The Guidelines - Heart Failure (GWTG-HF) study showed that the proportion of hospitalized HFpEF patients rose from 2005 to 2010, and this trajectory continued to increase through 2020. HFpEF patients have a poor 5-year outcome with a 75% mortality rate. Taken together, these data suggest that HFpEF may be the dominant HF subtype in the future, affecting approximately 1 in 10 adults during their lifetime.In addition to its increasing prevalence, HFpEF is a heterogeneous syndrome with multiple pathophysiological processes and varied clinical presentations. There is an urgent need to improve understanding of different HFpEF phenotypes to match patients with interventions based on their unique subtype. Phenomapping studies have identified between two and six HFpEF clusters/phenogroups with shared features. Of these, three overlapping phenotypes repeatedly arise: an 'older, vascular aging' phenotype, 'metabolic, obese' phenotype, and 'relatively younger, natriuretic peptide (NP) deficiency' phenotype. Continued refinement of these HFpEF subtypes is needed to identify distinct characteristics and underlying biologic and molecular mechanisms towards therapeutic development and future precision trials with improved diagnostic criteria and treatment including prevention.The HeartShare extant datasets collection consists of selected completed trials on HFpEF which have been harmonized for the purpose identifying and defining subtypes. The goal is to identify HFpEF subtypes that have a common pathophysiology. Through the HeartShare extant datasets collection, users can request access to both the harmonized data and the individual trial datasets.To request access to this study collection, select phs003989 in the dbGaP when submitting a data access request. The following clinical trials and harmonized data is available through this collection:Study NameHandleAccessionConsent GroupsSurgical Treatment for Ischemic Heart FailureBioLINCC_STICHphs003493.v1.p1GRUHeart Failure Network Aldosterone Targeted Neurohormonal Combined with Natriuresis TherapyBioLINCC_HFN_ATHENAphs003506.v1.p1GRUHeart Failure Network - Effectiveness of Ultrafiltration in Treating People With Acute Decompensated Heart Failure and Cardiorenal SyndromeBioLINCC_HFN_CARRESSphs003510.v1.p1GRUHeart Failure Network: Diuretic Optimization Strategies Evaluation in Acute Heart FailureBioLINCC_HFN_DOSEphs003524.v1.p1GRUHeart Failure Network - Xanthine Oxidase Inhibition for Hyperuricemic Heart Failure PatientsBioLINCC_EXACT_HFphs003533.v1.p1GRUHeart Failure Network: Functional Impact of GLP-1 for Heart Failure TreatmentBioLINCC_HFN_FIGHTphs003542.v1.p1GRUHeart Failure Network - Nitrate's Effect on Activity Tolerance in Heart Failure with Preserved Ejection FractionBioLINCC_HFN_NEATphs003548.v1.p1GRUHeart Failure Network - Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Diastolic Heart FailureBioLINCC_HFN_RELAXphs003565.v1.p1GRUHeart Failure Network - Renal Optimization Strategies Evaluation in Acute Heart Failure and Reliable Evaluation of DyspneaBioLINCC_HFN_ROSEphs003589.v1.p1GRUHeart Failure: A Controlled Trial Investigating Outcomes of Exercise TrainingBioLINCC_HF_ACTIONphs003599.v1.p1HMBHeart Failure: A Controlled Trial Investigating Outcomes of Exercise TrainingBioLINCC_HF_ACTIONphs003599.v1.p1HMB-NPUGuiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart FailureBioLINCC_GUIDE_ITphs003621.v1.p1GRUSudden Cardiac Death in Heart Failure TrialBioLINCC_SCD_HeFTphs003654.v1.p1GRUTreatment of Preserved Cardiac Function Heart Failure With an Aldosterone AntagonistBioLINCC_TOPCATphs003665.v1.p1HMB-MDSHeart Failure Network - Inorganic Nitrite Delivery to Improve Exercise Capacity in HFpEFBioLINCC_HFN_INDIEphs003667.v1.p1GRUStudies of Left Ventricular DysfunctionBioLINCC_SOLVDphs003668.v1.p1GRUEvaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization EffectivenessBioLINCC_ESCAPEphs003782.v1.p1GRU

  Study phs003989

• Transcriptome atlas of brain organoid developent

  Single-cell Cut&Tag of three histone modifications and chromatin accessibility over a timecourse of brain organoid development from pluripotency. We profiled 5 time points of brain organoid development and 2 time points of retinal organoid development. We used scRNA-seq to profile transcriptome. Library preparation was carried out with the 10x genomics platform.

  Dataset EGAD50000000223

• BIOMIROX

  This dataset regroups RNAseq of paired primary colorectal cancers and liver metastases extracted from 113 patients In Paris and Besançon University Hospitals. 216 FASTQ files from single-end 3' PolyA (QuantSeq 3′mRNA-Seq Kit FWD for Illumina (Lexogen)) RNAseq samples were generated On NOVASEQ 6000 (ILLUMINA).

  Dataset EGAD50000000443

• MissionBio single cell DNA and protein seq for 2 samples

  This dataset contains MissionBio Targeted single-cell DNA and Protein sequencing data from a primary sample and a patient-derived xenograft (PDX) model, which is used to investigate the clonal evolution in the FOXF1/FENDRR ALL subtype. All the 2 samples are in bam file type.

  Dataset EGAD50000001793

• Sequencing data

  WES: (7 samples, BAM files), scRNA-Seq (4 samples, BAM files), TruSight (56 samples, BAM or FASTQ files)

  Dataset EGAD00001007749

• KCL PRECSION Mutations targeted-seq

  105 Normal, DCIS and recurrences samples target-sequenced

  Dataset EGAD00001008376

• Mixture of 4

  Mixture of 4 unrelated individuals sequenced by 10x as a scRNA-seq. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile. The clustering of SNPs is submitted as the processed file. The Sequencing fastqs are submitted as unprocessed files.

  Dataset EGAD00001008724

• Mixture of 3

  Mixture of 3 unrelated individuals sequenced by 10x as a scRNA-seq. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile. The clustering of SNPs is submitted as the processed file. The Sequencing fastqs are submitted as unprocessed files.

  Dataset EGAD00001008729

• Submitting array based metadata

  Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

  Documentation submission/metadata/submission/array

• EGAD00010000584

  WTCCC2 Glaucoma samples using Illumina 670k array

  Dataset EGAD00010000584

• Average_5mC_DIFF_Roadmap

  Average methylation difference 12 months vs 0 months at Roadmap Epigenomics chromatin state annotations from different cell types using nanopolish. Data from 8 individuals.

  Dataset EGAD00010002410

• postQC Genotypes

  postQC genotype data from the Affymetrix AxiomTM HGCoV2 1 array in plink binary format. QC was carried out using PLINK v1.9

  Dataset EGAD00010002436

• HRR gene mutation analysis

  Mutation analysis of cfDNA from urine (stored at various storage conditions) and plasma using the QIAseq HRR panel

  Dataset EGAD50000001586

• Cord blood sequencing data

  SmMIP libraries using cord blood DNA were generated in replicates and were sequenced on the NovaSeq SP platform (Illumina)

  Dataset EGAD00001007789

• How to upload GPG files

  Uploading files Users that holds an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. In order to manage the available resources, we enforce a limit of 10Tb per submission account at any one time. Please do not exceed this limit. INBOX FTP The FTP is only compatible with files encrypted using the EGACryptor tool Before uploading Once your submission files have been prepared using the EGAryptor, the resulting encrypted files and associated md5sum files can be uploaded to your submission account using Aspera or FTP. The EGA is a shared, public service with limited resources. In order to manage the available resources, EGA submission boxes should not exceed 8Tb in size, and cannot exceed 12Tb. If you are approaching this limit please contact contact EGA Helpdesk so that we can advise on how to register the associated metadata and trigger the archiving of files, so that you can continue with your submission. If we note that your submission account increases above 10Tb on a consistent base your password will be changed until metadata is associated. Aspera Download Aspera Using Aspera FTP FTP windows FTP Linux / Unix FTP client (Filezilla) FTP and TLS Troubleshooting Troubleshooting Aspera Download Aspera is a commercial file transfer protocol that may provide faster transfer speeds than ftp especially over longer distances. The Aspera ascp command line client. Please select Aspera Connect. The ascp command line client is distributed as part of the aspera connect highperformance transfer browser plugin and is free to use, without registration. The minimum required version of the IBM Aspera Cli is V4. Further instructions. Using the Aspera ascp command line program The location of the ascp program in the filesystem: Mac: on the desktop go cd /Applications/Aspera\ Connect.app/Contents/Resources/ there you'll see the command line utilities where you're going to use ascp. Windows: the downloaded files are a bit hidden. For instance, in Windows 7 the ascp.exe is located in the users home directory in: AppData\Local\Programs\Aspera\Aspera Connect\bin\ascp.exe Linux: should be in your user's home directory, cd /home/username/.aspera/connect/bin/ there you'll see the command line utilities where you're going to use ascp. Your command should look similar to this: ascp -P33001 -O33001 -QT -l300M -L- /path/file ega-box-N@fasp.ega.ebi.ac.uk:/path If you wish to upload several files without being requested the password, please use the below command : ASPERA_SCP_PASS=ega-box-password ascp -P33001 -O33001 -QT -l300M /path/file ega-box-N@fasp.ega.ebi.ac.uk:/path/ Explanation of parameters l300M option sets the upload speed limit to 30MB/s. You may wish to lower this value to increase the reliability of the transfer. L option is for printing logs out while transferring files to upload can be a file mask (e.g. '/homes/submitter/*.srf) or a list of files. ega-box-N is your submission account login. Add k2 switch for transfer restarts Check the command line transfer usage for more configuration details. Using FTP to upload your prepared files Use your preferred ftp client. For example, lftp is a popular choice for Linux and Mac users. Use binary mode for file transfers. Use ftp.ega.ebi.ac.uk as the target host. Login with your ega-box username and password. Upload files to your private ega-box upload area. Depending on your network setting you might wish to start FTP in passive or active mode. Using default FTP command line client in Windows Start the command line interpreter: press WinR, type cmd, hit enter Enter ftp ftp.ega.ebi.ac.uk Enter your submission username Enter your submission password Type binary to enter binary mode for transfer To see a list of available ftp commands type help. Type ls command to check the content of your submission account. Type prompt to switch off confirmation for each file uploaded. Use mput command to upload files: mput *.bam* Use bye command to exit the ftp client. Use exit command to exit the command line interpreter. Using default FTP command line client in Linux / Unix Open a terminal and type ftp ftp.ega.ebi.ac.uk Enter your submission username Enter your submission password Type binary to enter binary mode for transfer To see a list of available ftp commands type help. Type ls command to check the content of your submission account. Type prompt to switch off confirmation for each file uploaded. Use mput command to upload files: mput *.bam* Use bye command to exit the ftp client. Using FTP client FileZilla We recommend the use of FileZilla, a free FTP client . FileZilla is open source software distributed free of charge under the terms of the GNU General Public License. Use the following connection details (File - Site Manager) and add yoursubmission account username and password: Using FTP client FileZilla Select the files you wish to upload and then select upload: Using FTP client Filezilla Using LFTP with TLS We recommend the following to force the use of a secure connection. lftp > set ftp:ssl-force yes We also recommend setting the following for not encrypting the bulk data itself for performance reasons (theauthentication will still be encrypted): lftp > ftp:ssl-protect-data no In order to verify the certificate,the recommended way would be to use the CA certificates from your machine. To do that use this command in lftp adjusting the path to your ca-certificates location. lftp > ssl:ca-file "/etc/ssl/certs/ca-certificates.crt" If that is not possible or certificates are old and you can't update them, you can download the certificates needed from Quo Vadis Digital Repository The two certificates to download in PEM format are: QuoVadis Root CA2 G3 QuoVadis EV SSL ICA G3 Then you can concatenate them in a file one after the other and save it as lftp-certificates.pem Once this is done you have to point the ssl:ca-file variable to the path lftp > set ssl:ca-file "/path/to/lftp-certificates.pem" Also note that you can save this configuration at ~/.lftp/rc Another option is to download the certificates and add them to the ca-certificates of your machine. For example: In RHEL7 and cenots and others box the process to add the certificates globally is: Download the two certificates (in PEM or DER format, doesn't matter) and save them to "/etc/pki/ca-trust/source/anchors/" Run "update-ca-trust extract" Another less secure option is to turn off certificate verification with the following command: lftp > set ssl:verify-certificate false Troubleshooting If you are having problems with Aspera connection timeouts, it can be down to either one of the following. Transfers cannot start the connection fails instantly. Ensure that TCP traffic on port 33001 is allowed (open) for outbound connections through your computer's firewall and network's firewall. The connection is made, transfers are started, but 0 bytes (0%) are uploaded for each file. Ensure that UDP traffic on port 33001 is allowed (open) for outbound connections through your computer's firewall and network's firewal

  Documentation submission/data/uploading-files/ftp