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• Follow_up_for_second_tier_signals_from_the_arcOGEN_GWAS

  The arcOGEN UK nationwide consortium has been funded by the arc to undertake a genome-wide association scan in osteoarthritis. GWA genotyping has been carried out at the Sanger on the illumina 610k array. This replication study was desinged to follow-up second-tier signals from the arcOGEN GWS in 25 SNPs (1Sequenom iPLEX) with p<5x10^-6 on 6000 additional UK OA and control samples.

  Study EGAS00001001017

• Genome-wide DNA Methylation Data from Illumina HumanMethylationEPIC arrays for whole blood samples from 403 healthy individuals

  Genome-wide DNA Methylation Data from Illumina HumanMethylationEPIC arrays for whole blood samples from 403 healthy individuals. Additional associated phenotype information is available for all individuals included in this study directly from CIBMTR. Data are available under controlled access release upon reasonable request and execution of a data use agreement. Requests should be submitted to CIBMTR at info-request@mcw.edu and include the study reference IB17-04.

  Study EGAS00001006033

• The Gut Microbiome of liver transplant recipients – Cross-sectional + Longitudinal (renal and liver)

  TransplantLines is designed as a single-center, prospective cohort study and biobank including all different types of solid organ transplant recipients as well as living organ donors. In the TransplantLines gut microbiome study the gut microbiome of solid organ transplant recipients is characterized and linked to clinical phenotypes. This batch contains the cross-sectional data from liver transplant recipients and longitudinal data from renal and liver transplant recipients.

  Study EGAS00001006258

• Histological Transformation and Progression in Follicular Lymphoma: a Clonal Evolution Study

  Background: Follicular lymphoma (FL) is an indolent, yet incurable B-cell malignancy. A subset of patients experience increased mortality rate driven by two distinct clinical end points: histological transformation and early progression after immuno-chemotherapy. The nature of tumor clonal dynamics leading to these clinical endpoints is poorly understood and previously determined genetic alterations do not explain the majority of transformed cases or accurately predict early progressive disease. We contend that detailed knowledge of the expansion patterns of specific cell populations plus their associated mutations would provide insight into therapeutic strategies and disease biology over the timecourse of FL clinical histories. Methods and Findings: Using a combination of whole genome sequencing, targeted deep sequencing and digital droplet PCR on matched diagnostic and relapse specimens, we deciphered the constituent clonal populations in n=15 transformation patients and n=6 progressors, and measured their change in abundance over time. We observed widely divergent patterns of clonal dynamics in transformed cases relative to progressed cases. Transformation specimens were generally composed of clones that were rare or absent in diagnostic specimens, consistent with dramatic clonal expansions that came to dominate the transformation specimen. This pattern was independent of time to transformation and treatment modality. By contrast, progression specimens were composed of clones that were already present in the diagnostic specimens and exhibited only moderate clonal dynamics, even in the presence of immuno-chemotherapy. Analysis of somatic mutations impacting 94 genes was undertaken in a validation cohort consisting of 395 samples from 277 patients in order to decipher disrupted biology in the two clinical endpoints. We found 12 genes were more commonly mutated in transformed samples than in preceding FL, including TP53, B2M, CCND3, GNA13, S1PR2 and P2RY8. Moreover, 10 genes were more commonly mutated in diagnostic specimens of early progressors including TP53, BTG1, MKI67 and XBP1. Conclusions: Our results illuminate contrasting modes of evolution shaping the clinical histories of transformation and progression. They have implication for interpretation of evolutionary dynamics in the context of treatment-induced selective pressures, and indicate that transformation and progression will require different clinical management strategies.

  Study EGAS00001001709

• EGA metadata schema

  EGA Schemas In this page, you will find information on how the European Genome-phenome Archive (EGA) manages its metadata standards using both XML Schema Definition (XSD) and JavaScript Object Notation (JSON) formats. If you are not sure what this means, you may want to explore our brief metadata introduction. This information may be of your interest if you are planning to learn more about how the EGA is built and how to wrap around it for other processes. Nevertheless, if you are a common user (e.g. submitter or requester), you would not have to worry about these schemas nor their format, since they are implemented in user-friendly ways for you. Metadata standards are rules that define how to format and structure data of metadata objects (i.e. entities), like EGA's samples or experiments in a consistent manner. These objects are the nodes of the metadata model of the EGA (Figure 1). Figure 1. Diagram of EGA's metadata model. The model's building blocks are objects (e.g. sample), which can reference each other (e.g. an experiment referencing the used samples). Once your files are uploaded, they can also be referenced by Runs and Analyses. The submission object is an object itself that compiles many others. At EGA, we inherit our metadata schemas from the European Nucleotide Archive (ENA), and we have expanded them to include bespoke objects such as "Policy", "Dataset", and "DAC" (Data Access Committee) for our specific use-case: handling sensitive human data. See below a list of all our metadata objects and some context for each. Metadata Object EGA accession Description Examples of metadata fields Study EGAS… Information about the study Study type, study title, study abstract… Sample EGAN… Information about the used samples in the experiment or analysis Taxon ID, scientific name, biological sex, phenotype… Experiment EGAX… Information about the performed experiment Used libraries, sequencing platform, reference to the used samples… Analysis EGAZ… Contains information about the analysis Type of analysis, used assembly, reference sequence… Run EGAR… The run holds information about the files containing the raw reads generated in a run of sequencing Platform, spot descriptor, raw file references… DAC EGAC… Contains information about the Data Access Committee (DAC) DAC contacts, contact emails… Policy EGAP… Contains the Data Access Agreement (DAA) and policy which its usage complies with Policy text, data use ontologies (DUO) codes… Dataset EGAD… Contains the collection of runs/analysis to be subject to controlled access Dataset type, compilation of Run's and Analysis' IDs There are two different sets of schemas, based on their formats, in which the EGA accepts metadata: XSDs (for XML files) and JSON Schemas (for JSON files). EGA's XML Schema Definition . When programmatic submissions are pushed through the European Bioinformatics Institute (EBI) system, XML format is used. The schemas that are applied for this format are defined in XML Schema Definition (XSD) files, which can be found at ENA's GitHub repository. You can find more information on how to validate and submit your data programmatically in our programmatic submission documentation. Furthermore, see at our GitHub repository some XML examples with either made-up values (similar to what you would submit) or descriptive values for each field (just for documentation). EGA's JSON Metadata Schemas . When programmatic submissions are pushed through the Centre for Genomic Regulation (CRG) system, JSON format specifications are used instead. See the full JSON specifications for further details. In conclusion, the EGA metadata schemas are crucial for maintaining the quality and consistency of submitted data. By understanding and following the rules outlined in these schemas, you can ensure that your submissions comply with the EGA's standards and contribute to a valuable and accessible genomic resource. Sample checklists Besides the standards in our schemas, we have another layer called Sample checklists. These are another system that the EGA inherits from ENA, and specifies what attributes are required or allowed for a sample object. The EGA uses these checklists to enforce that, for example, a sample object has the three mandatory attributes: subject ID, sex and phenotype of the individual the sample was taken from. When you submit to EGA, our checklist is automatically selected by default.

  Documentation submission/metadata/ega-schema

• CRU-Ukrainian National Research Center for Radiation Medicine Trio Study

  Data on transgenerational effects following nuclear accidents are important for understanding fully the consequences of parental exposure to ionizing radiation. Few studies to date have had adequate statistical power to detect effects of the magnitude expected based on animal data, and most have not been of low-dose, protracted exposures associated with nuclear accidents and their aftermath. Although, to date, scant use has been made of the new genomic technologies, in Chernobyl-exposed areas of Ukraine and Belarus, excess minisatellite mutations have been seen in children born after the accident. We propose a study of parent-child trios in which at least one parent was exposed to Chernobyl radiation as a clean-up worker (mean dose>=100 mGy) and/or evacuee from a contaminated area (mean >=50 mGy). The specific aims are to investigate the transgenerational and de novo mutation rates of the spectrum of genetic variants in trios, in particular looking at effects in children and mapping them to possible parental origin of the chromsoome. Together with long-term collaborators at the Research Center for Radiation Medicine (RCRM) in Kiev, epidemiologic data will be collected for up to 450 trios of parents with preconceptional doses and their unexposed offspring. We will use state-of-the-art genomic technologies to characterize the landscape of the genomes of the trios to determine whether parental radiation exposure is associated with genetic mutations transmitted to the offspring, by examining de novo mutation rates, minisatellite mutations, copy number alterations, and variations in telomere length. The analysis will be conducted in peripheral blood and/or buccal samples (when blood is not available) from complete father-mother-child trios. Doses to the gonads from the time of the accident to the time of conception will be reconstructed for all parents using existing records supplemented by interview data. Trio subjects will be selected from representative populations exposed to radiation from Chernobyl who are under active follow-up in the Clinico-Epidemiologic Registry at RCRM. To help identify specific effects of paternal and maternal radiation exposure, we will initially select sets of trio subjects in five categories: (1) exposed father, unexposed mother; (2) unexposed father, exposed mother; (3) both parents exposed; (4) both parents unexposed; and (5) a group of high dose "emergency workers" with acute radiation syndrome. All trio members will be invited to the RCRM outpatient clinic for collection of a 20 ml blood sample (or buccal cells for those who refuse phlebotomy). Both parents will be asked to complete a general questionnaire to obtain demographic and lifestyle data. Then one or both will complete detailed dosimetry questionnaires, based on forms used in previous collaborations with RCRM and administered by specially trained interviewers. Once 50 trios have been recruited (10 from each of the 5 exposure categories), we will conduct an interim evaluation of participation rates, sample collection and quality, and dose reconstruction in order to modify the protocol as needed. The analytical approach will be to correlate the extent, especially for de novo events of genetic alterations in the offspring with parental pre-conceptional radiation dose overall and by parental origin. The statistical power in relation to de novo mutations is very high, in excess of 90%, but somewhat lower for trends in minisatellite mutations. Study findings will contribute importantly to knowledge of the heritable effects of moderate- and low-dose radiation exposure in humans and to radiation risk projection. Eventually data from the Trio Study may be shared with the international community through dbGap.

  Study phs001163

• PAK4 inhibition improves PD-1 blockade immunotherapy

  Introduction with rationale and aims for study Immune checkpoint blockade therapies have significantly altered the current landscape of cancer treatment. However, this immunotherapy still fails more often than it succeeds. There are now evidences that the lack of tumour infiltration by immune cells is the main mechanism of primary resistance to PD‐1 blockade therapies for cancer. It has been postulated that cancer cell‐intrinsic mechanisms may actively exclude T cells from tumours, suggesting that the finding of actionable molecules that could be inhibited to increase T cell infiltration may synergize with checkpoint inhibitor immunotherapy. With the idea of finding potential drivers of immune exclusion, we performed RNA sequencing analysis of biopsies from melanoma patients and compared the transcriptomic differences of samples that where infiltrated with those that did not have immune infiltration. Methods RNAseq analysis of gene expressions on biopsies from melanoma patients treated with checkpoint blockade were analysed. We focused on the variable of "infiltration: yes or no,", e.g. tumors that were well infiltrated by immune cells with those that kept immune cells out. Sequencing revealed a list of genes whose expression differed between the infiltrated and noninfiltrated tumors. The kinase PAK4 stood out as a good candidate for inhibition treatment in the future as it was consistently enriched in the samples without immune infiltration. To test whether immunotherapy would work better if we deleted PAK4, we first knocked out PAK4, using CRISPR‐Cas9, in the ‐resistant melanoma cell line, B16. Then they injected the melanoma cells into mice and observed that PAK4 KO tumours now responded to PD‐1 blockade. In order to elucidate whether pharmacological inhibition of PAK4 could recapitulate the results observed in the B16 PAK4 KO tumours, we obtained the PAK4 inhibitor, KPT-9274, from Karyopharm Therapeutics. Indeed, inhibiting PAK4 in combination with anti‐PD‐1 immunotherapy significantly slowed the growth of the B16 melanomas more than either drug alone. Results and Conclusions Transcriptomic analysis of melanoma tumors that were well infiltrated by immune cells with those that kept immune cells out revealed a list of genes whose expression differed between the infiltrated and non-infiltrated tumours. The result suggests that p21 activated kinase 4 (PAK4) is enriched in non-responding tumour biopsies with low T cell and dendritic cell infiltration. In addition, PAK4 decreased WNT activity, a signalling pathway that has previously been involved in immune exclusion. In mouse models, genetic deletion of PAK4 increased T cell infiltration and reversed resistance to PD‐1 blockade in a CD8 T cell-dependent manner. Furthermore, combination of with the PAK4 inhibitor, KPT-9274, improved anti‐tumour responses compared to anti‐PD‐1 alone. Therefore, high PAK4 expression is correlated with low T cell and dendritic cell infiltration and lack of response to PD‐1 blockade, which could be reversed with PAK4 inhibition. The data establish a rationale for targeting this kinase with inhibitors in combination with immune checkpoint inhibitors for patients. Future work and data that we can anticipate for this study Oncogenic signalling pathways and specially the WNT signalling pathway, have been associated with lack of immune infiltration and resistance to PD‐1 blockade therapies. Here, we show that PAK4 overcome resistance to PD‐1 blockade while significantly decreases WNT signalling activity. Therefore, it constitutes the first potential druggable target that is able to reverse oncogenic driven immune cell exclusion. However, the fully mechanism whereby PAK4 sensitizes tumours to immunotherapy remains to be fully elucidated. It is necessary to address whether PAK4 inhibition overcomes resistance through WNT signalling inhibition or if other signalling pathways are involved in the observed phenotype.

  Study phs001919

• Acute Respiratory Distress Network Early Versus Delayed Enteral Feeding to Treat People with Acute Lung Injury or Acute Respiratory Distress Syndrome (ARDSNet EDEN-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Biospecimens: Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from ARDSNet-EDEN include bronchial lavage, plasma, DNA, and urine. Please note that use of biospecimens in genetic and non-genetic research is subject to a tiered consent. Objectives: To determine if initial lower-volume trophic enteral feeding would increase ventilator-free days (VFDs) and decrease gastrointestinal intolerances compared with initial full enteral feeding. Background: Mechanically ventilated patients cannot eat normally and if not fed for long periods become malnourished. Because malnutrition is associated with poor outcomes in critically ill patients, artificial nutrition is often provided, especially in those with acute lung injury (ALI) and with expected longer duration of mechanical ventilation. When feasible, enteral nutrition targeting full caloric needs has been advocated over parenteral nutrition. However, feeding intolerance and common care practices often serve as practical barriers to reaching recommended goals. Although confounded by indication and severity of illness, several observational studies have shown improved clinical outcomes, including fewer infections, shorter duration of mechanical ventilation, and lower mortality for patients receiving a higher percentage of calculated caloric needs. Nonetheless, the best timing, formulation, and amount of enteral nutrition remain unknown. Participants: A total of 1,000 participants were enrolled. Design: Participants were randomized, stratified by site and presence of shock at enrollment, to receive either trophic or full enteral feeding for the first 6 days of mechanical ventilation. The initial 272 patients were also simultaneously randomized to a separate trial (the OMEGA study) comparing a nutritional supplement containing omega-3 fatty acids and antioxidants with an isocaloric, isovolemic control in a 2 × 2 factorial design.The designated feeding strategy was initiated within 6 hours of randomization and continued until death, extubation, or day 6. The care of mechanically ventilated patients still receiving enteral feedings after day 6 was managed according to the full feeding strategy in both groups. In extubated patients who then required reintubation, enteral nutrition was restarted and managed according to the study protocol.In the full-feeding group, enteral nutrition was initiated at 25 mL/h and advanced to goal rates as quickly as possible. Gastric residual volumes were checked every 6 hours while enteral feeding was increased. Patients randomized to the initial trophic-feeding group had enteral nutrition initiated at 10 mL/h (10-20 kcal/h) for the first 272 patients who also received the omega-3 or control supplement (240 mL volume per day). After the data and safety monitoring board stopped the OMEGA portion of the factorial design, the initial trophic feeding rate was changed to 20 kcal/h to approximate the calories that had been delivered in the OMEGA study. Gastric Residual Volumes (GRVs) were checked every 12 hours during trophic feeding. In patients randomized to trophic feeding, enteral nutrition was advanced to full-energy feeding rates following the same protocol used for the full-feeding group if they were still receiving mechanical ventilation at 144 hours. Conclusions: There was no difference between groups with regard to the primary end point, VFDs to day 28. There also were no differences in 60-day mortality, organ failure−free days, ICU-free days, or the incidence of infection between groups. Similarly, there were no differences between groups in VFDs or survival when analyzed by body mass index category or when subsets of patients with shock or more severe lung injury (acute respiratory distress syndrome) were examined.JAMA. 2012 Feb 22;307(8):795-803

  Study phs004168

• Korea Epigenome Project(KEP), Korea National Research Institute of Health(KNIH)

  The KEP was launched in 2012 to produce 50 epigenomic sets on Korean chronic diseases related target cells with the participation on Internationsl Human Epigenome Consortium(IHEC) of which focus is constructing 1000 reference epigenome map on 250 human cell type. The objective of the KEP was to provide a comprehensive epigenomic map of Korean chronic disease-related target tissues. Single cells were isolated from the pancreas, fat and kidney. Islet, ductal, acinar, and beta cells from the pancreas were purified into a single homogenous cell type using flow-assisted cell sorting technique. Adipocytes and pre-adipocytes were purified from fat, using Percoll gradient centrifugation technique. Mesangial, distal tubule, proximal tubule, podocytes, and collecting duct cells from the kidney were collected using mechanical sieving and immunostaining methods. Each cell type was derived from normal tissue or those with diabetes, obesity, or chronic kidney disease. Sixty reference epigenome datasets of pancreatic, fat, kidney cells were produced. Cell types such as the islets, and beta from the pancreas, adipocytes, pre-adipocytes from fat tissue, podocyte, mesangial, tubules from kidney were used for primary target to produce a Korean metabolic reference epigenome. And the bisulfite converted-whole genome sequencing, Infinium 450k DNA methylation bead array, mRNA, miRNA-Sequencing were performed to produce a comprehensive epigenomic map. The aim of roadmap reports is preparation of Long-term and Mid-term Technology Roadmaps for Korean Human Epigenome Project towards a competitive execution of International Epigenome Project and successful expansion of the research outcomes. Roadmap reports reviewed on recent technological development of DNA methylation and histone modification analysis, emerging new trends in epigenome medicine, plans for hardware and software analysis systems aimed to epigenome analysis, medical expert opinion on target cells and isolation scheme, bioinformatics expert opinion on analytical tools, new ideas for a putative epigenome studies, trends in single cell epigenome analysis. In conclusion, roadmap report suggested several projects for the future KNIH epigenome research in collaboration with epigenome research group. These are including refocusing the goals of KNIH-driven research in identification of disease mechanism, development of new studies on GWAS-EWA, higher priority on the epigenomic studies of human primary cells, establishment of collaborative relationship with tissue banking centers, establishment of mapping centers and data analysis portals, establishment of Korean Epigenome Research Consortium, establishment of steering committee governing both research and funding, launching of additional funding for epigenomic researches.

  Study EGAS00001001774

• Submission FAQ

  Submission FAQ Before Submission Is the EGA the right archive for my data? The most suitable archive for your data is dependent on the type of data you are wishing to submit and whether the data require public or controlled access. Public access is defined as complete and open access to all submitted data. On the contrary, controlled access, exerted by the EGA, requires formal applications to be made to access the submitted data files and metadata. EGA only accepts human-derived data subject to controlled access. If your submission contains other types of data, please choose the appropriate repository for it (see image below): ENA, EVA, ArrayExpress, BioSD and GWAS catalog.  Should your submission be subjected to controlled access? Data access conditions are normally defined in the original informed consent agreements signed by the participants involved in your study. All data submitted to the EGA is subject to controlled access. These consents prevent the derived data files, potentially identifiable, from being dispersed by open and public access. Controlled access data often refers to human data derived from medical research and consortium projects. If in doubt, consult the informed consent agreements that apply to your study The EGA enables you to hold a submission before publication. What data types can be submitted to the EGA? Data types accepted by the EGA can be split into three categories: Sequences: both in generic and platform-specific formats. Array-based: from raw signal files to processed matrices. Phenotypes: all possible phenotype formats are accepted. All manufacturer-specific raw data formats derived from major next generation sequencing platforms are accepted. Also generic sequence formats: flat reads in a FASTQ file, aligned sequences (BAM or CRAM files) as well as sequence variation files in VCF format. All array-based technologies are accepted, including raw data, intensity and analysis files, without any restriction on data formats accepted. We also accept and distribute phenotype data (associated to the samples) in almost any format: from an image to a README file. How long does a submission take? Submissions to EGA come in a variety of formats and sizes, thus it is difficult for us to exactly predict how long a submission will take. We, therefore, advise all of our submitters to allow as much time as possible to make a submission. Based on previous records, we anticipate that the submission process may take at least one month. The submitter’s familiarity with the procedures, possible technical issues that may arise during submission and the amount of data that is being submitted are the main factors that will affect the length of the submission process. However, each step of a regular submission should be considered when estimating the time it would take: Encryption of the files Upload of the files Metadata submission Archival of the files Release of the study and datasets to EGA webpage For example, the upload of the files depends on the submission size, while metadata submission mainly relies on each submitter’s expertise. Further, some steps (e.g. answering to inquiries) depend on the EGA Helpdesk team, which may take some days to be processed during busy times Is data deposited in the EGA secure? The EGA set-up consists of a secure computing facility for data processing, a shared EBI set-up for data submissions and distribution of data via data requests made through the EGA website. Data is also copied in the Barcelona Supercomputing Center (BSC) infrastructure, where all stored and distributed data is encrypted Data is encrypted along the submission process and stored securely, granting its access to authorised users exclusively. During the download process, through our Python Client or Aspera, all requested data is downloaded over secure https connections. All data at the EGA is encrypted, and only accessible (for log-in and download) through secure protocols. For further information please, visit our security overview. What documentation do I need to provide? All submissions require policy’s documentation: 'Data Access Agreement (DAA)', 'Data Processing Agreement (DPA)' and 'Authorized Submitters Formulary'. The data processors (EGA) and the data owners will also sign the DPA. Will all metadata be public? Among the submitted metadata we need to make the distinction between identifiable and unidentifiable metadata: (1) the former may allow the identification of the human the sample derived from (e.g. detailed geographical providence, personal name, family ancestry…); (2) while the latter can be used to interpret the data without compromising the anonymity of the patients. The majority of the metadata submitted to the EGA corresponds to the unidentifiable category (e.g. sequencer's model). This type of metadata is publicly available on the EGA website and other EBI resources/partners’ websites. On the other hand, some parts samples’ metadata are subject to being identifiable, and thus only accessible by authorized data requesters, with the exception of: 5 submitter-defined attributes of the sample: alias, title, subject_id, gender and phenotype. It is the submitter’s responsibility not to submit sensitive metadata in these public fields. 3 anonymised fields that pinpoint the sample record in archivals: sample’s EGA stable ID (EGAN…), BioSample ID (SAMEA…) and submitter’s center name. During Submission Are there any sample specific requirements for EGA? All samples submitted to the EGA must include the attributes of biological sex, subject ID (anonymised individual identifier) and phenotype information. These are critical for data findability and its analysis, and we highly recommend using controlled ontology terms where applicable. For example: defining tumour and non-tumour samples and/or defining disease state. The EGA recommends using the Experimental Factor Ontology Database to find ontologized terms that describe your sample phenotypes. How do I get an accession number to use in my publication? You will receive your study accession number (EGAS…) upon complete your submission, either: Programmatically. As soon as the metadata is submitted and validated your study will be assigned an accession number that will be given in the submission’s response. Manually registering your study and relevant metadata using the online metadata submission tool: the EGA submitter Portal. How are files uploaded to the EGA? Data files are uploaded into private submission drop boxes (i.e. environments to which you are granted access and where you can transfer your files) using INBOX or FTP. These spaces are provided as part of the submission procedure. Before uploading any file, you must encrypt your files, . Only encrypted files shall be uploaded to the drop boxes. Why does data need to to encrypted for my submitted files? It is one of the security steps the EGA has implemented. In case of a security breach, people without the proper encryption key will not be able to read or use the information that could have been leaked. This measure is essential when working with sensitive data, such as controlled access human data. All submitters must use crypt4gh to create EGA compliant files prior to uploading them. This encryption is GPG-based, using EGA’s public key. Why are my files not available if I see them in the INBOX? There exists a time window between the data upload and the availability of such files via the Submitter Portal. For this reason, some metadata (run and analysis objects) cannot be registered until at least 24 hours after the files have been uploaded to your box. Why are MD5 sum values generated for my submitted files? We require pre- and post- encryption MD5 (message-digest) checksum values to be provided for all submitted files. These 128-bit values are computed using the content of each file, creating unique sequences that allow us to ensure that file integrity has been maintained during the transfer process. In other words, if the MD5 checksums we generate and those you generated match, we infer that the content of the transferred files is correct (i.e. files are not corrupted or truncated). MD5 checksums are computed automatically using the crypt4gh tool provided. Your submission will not be accepted and may be significantly delayed if you do not provide MD5 checksum values for all data files in the required format. How can I check if my files are correctly uploaded to the inbox? It is important to check the status pf your file so you know whether your files are in the inbox, being processed, or if there is any issue with one of them. In order to check this, you should: Look for the file locally. Drag and drop it to the file table. Then bars will appear on the table, which means that we are processing it. Green: The files checksum are correct and your file will move to “ingested files”. No further actions are needed from your end.. Red: The files checksum does not match and your file needs to be re-uploaded. Please re-upload relevant files to your inbox using the same path. After Submission How do I use my accession number in my publication? We suggest the use of the below template, using your study accession ID (EGAS…) : 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 at https://ega-archive.org and "The European Genome-phenome Archive of human data consented for biomedical research" Your study ID will be the one that groups your whole submission, and thus its usage is recommended as such. Nevertheless, all metadata submitted to EGA hold a unique and persistent identifier (starting with EGA…) that can be used to identify specific records. For example, you could reference a specific dataset (EGAD…) or sample (EGAN…) in your publications (see full list of identifiers). How do I make my data searchable? Once you have finalized your submission, you can schedule the data release. Please take into account that the release process needs time for the files to be archived in our system, and for the Helpdesk team to validate your submission. Can I withdraw (meta)data from the EGA? We have methods in place for the secure removal of deposited (meta)data. Contact EGA-helpdesk for further details. EGA complies with FAIRness of (meta)data, and thus, even when the data is removed we keep an entry for their identifiers in our system. In other words, we execute a soft delete on canceled objects (e.g. a study): metadata is still stored in our systems, but it loses all links, cannot be queried and data files cannot be retrieved anymore. The reason behind this behaviour is so that queries using withdrawn data properly respond back (see example of a canceled study). What happens to the data once it has been submitted to the EGA? When the data is submitted, the submitter can choose either keep their data private or schedule the release of their data.

  Documentation submission/metadata/submission/FAQ

• TB-DAR Genotyping Study

  Active TB patients (sputum smear-positive and GeneXpert-positive) recruited at the Temeke District Hospital in Dar es Salaam, Tanzania, as part of a prospective study that ran between November 2013 and June 2022.

  Dataset EGAD00010002507

• Whole Genome - Uveal Melanoma

  Whole genome sequencing on 1 uveal melanoma and corresponding germline sample. Libraries were prepared using the Kapa HyperPrep kit (Roche, 07962363001). Paired-end libraries (2 x 150 bp) were sequenced on a NovaSeq 6000 instrument (Illumina).

  Dataset EGAD50000000764

• PacBio data: Rapid brain tumor classification from sparse epigenomic data

  This dataset contains 16 samples sequenced in pools on SMRT Cells 25M on a PacBio Revio instrument. One unaligned BAM file per sample is provided.

  Dataset EGAD50000000798

• WGS of PDO in depleted media

  Whole genome sequencing profiling of 7 PDAC patient-derived organoids (PDO) grown in a culture medium lacking both WNT3A and RSPO1- WGS unmapped reads, sequenced using NovaSeq 6000, 15x coverage 160 million reads per sample.

  Dataset EGAD50000000282

• pediatric AML genomic sequences

  This dataset contains whole sequencing data from 30 patients with pediatric AML. For each patient, there is one dataset derived from tumor cells and one dataset derived from a matched non-malignant control.

  Dataset EGAD50000001572

• WGS FASTQ files studied in Allogeneic induced pluripotent stem (iPS) cell-derived cartilage transplantation for articular cartilage damage in knee joints: a phase I, first-in-human study

  This data set contains 5 paired fastq files (WGS).

  Dataset EGAD50000001666

• Spatially resolved targetted sequencing of colorectal cancers

  Sequencing of laser-capture micro dissected colorectal tumour glands using a targeted capture panel. Tumours were previously subjected to WGS in the parent EPICC study. UMI-resolved aligned bam files, aligned to hg38.

  Dataset EGAD50000001650

• mFAST-SeqS

  mFAST‑SeqS, or modified Fast Aneuploidy Screening Test‑Sequencing System, is a streamlined and cost-effective approach to estimate the fraction of ctDNA in blood samples by detecting genome-wide aneuploidy in cfDNA

  Dataset EGAD50000001670

• A distinct monocyte cellular state links systemic immune dysregulation to pulmonary impairment in long COVID

  single cell multiome dataset (snRNA-seq + snATAC-seq) from PBMC of patients with long COVID disease.

  Dataset EGAD50000001729

• single-nuclei RNA-seq of primary pineal parenchymal tumors

  Raw fastq files for 10x Chromium snRNA-seq of pineal parenchymal tumors profiled in the study "A tumor-associated photoreceptor signature unifies distinct central nervous system malignancies".

  Dataset EGAD50000002101

• Nanopore Telomere Sequencing of II.3, II.4, and III.4

  This datasets contains the putative telomeric sequences isolated by TARPON in ubam format after using a Duplex Capture Based Enrichment Protocol for patients II.2, II.4, and III.4.

  Dataset EGAD50000002363

• Genome sequencing of HCC from a Chinese cohort

  project aims to understand somatic variants, differential expressed genes and signaling pathways in HBV- and NASH related HCC tumors.

  Study EGAS00001001783

• GWAS in a dengue Thai cohort

  Around 700,000 SNPs were genotyped in 290 controls, 252 dengue fever patients and 159 dengue shock syndrome patients from Thailand.

  Study EGAS00001002756

• HV31 - 10x linked-read sequencing

  10x linked-read sequencing data for individual HV31 generated using DNA from CD14+ monocytes, to a sequencing depth of ~40×. Sequencing was performed at Bart’s and the London Genome Centre on the Illumina HiSeq platform.

  Dataset EGAD00001007046

• miR-17-92 organoids

  Organoid cultures derived from colorectal adenomas were transduced with a miR-17-92 expressing vector. RNA from miR17-92-overexpressing organoids and respective non-transduced organoids (controls) was isolated for expression analysis.

  Dataset EGAD00001008480