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• How to use EGA Webin?

  EGA Webin EGA Webin serves as a platform for registering metadata for array based submissions, large scale sequence submission as well as for legacy EGA submission accounts (ega-box-XXXX). For large scale submitters of sequence data you have also the option to submit metadata via XMLS programmatic submission, while new submitters are advised to utilise the Submitter Portal for their submissions. 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. WEBIN actions: Register metadata for a sequence submission Register study, samples, experiments, runs, DAC, policy and dataset/s after file upload. Register components for your array-based metadata submission Register study, samples, DAC or policy before uploading files. Edit existing submission metadata Change or update previously submitted metadata. Register metadata for a sequence submission Ensure that all sequence files have been encrypted before uploading them to your submission account using the EgaCryptor. Go to the EGA Webin and log in using your submission account name (ega-box-XXX) and password. Register components of your metadata submission Study Samples Data Access Committee (DAC) Data access policy Dataset For array-base submissions: Study, Samples, Data Access Committee (DAC) and Data access policy may all be registered BEFORE file upload and dataset registration through the array-base template. Register your Study Go to the “Studies (Projects)” box Click on “Register Study” and fill in the information related to your study. Click on “Submit”, this will save the information and generate an EGA study ID. To use the study accession number in a publication, the study has to be previously released on the EGA website, 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 and “The European Genome-phenome Archive in 202 "(10.1093/nar/gkab1059)" Register your Samples Go to the “Samples” box Click on “Register Samples” Select “Download spreadsheet to register samples” and customise your template, there is a default EGA template (EGA default checklist) but more attributes can be added if required. For the EGA default checklist, there are mandatory,recommended and optional attributes. As well custom fields which can be added if required. Mandatory attributes Field Name Description tax_id Taxonomy ID of the organism as in the NCBI Taxonomy database. Entries in the NCBI Taxonomy database have integer taxon IDs. See our tips for sample taxonomy here scientific_name Scientific name of the organism as in the NCBI Taxonomy database. Scientific names typically follow the binomial nomenclature. For example, the scientific name for humans is Homo sapiens. sample_alias Unique name of the sample. If not selected system will auto generate an unique alias sample_title Title of the sample sample_description Description of the sample phenotype *** Where possible, please use the Experimental Factor Ontology (EFO) to describe your phenotypes. Recommended attributes Field Name Description subject_id Identifier for the subject where the sample has been derived from gender * Sex Optional attributes Field Name Description sex sex of the organism from which the sample was obtained disease_site Affected organ sample type Affected organ donor_id ** Identifier of the donor where the sample has been derived from *Gender should be described as 'male', 'female' or 'unknown'. If 'unknown' due to a known sex chromosome aneuploidy, please create a user defined attribute called 'Sex chromosome karyotype' and add the appropriate value, for example, 'XXY'. **Donor id (Subject id) should be a de-identified subject handle. If unknown, please add 'unknown' to the field. ***Phenotypes should, where possible, be an Experimental Factor Ontology accession. If a term cannot be found to describe your phenotype please use free text. All sample phenotypes considered important for further analysis of the data should be provided (for example, tumour type), additional phenotype attributes can be created by defining your own attributes; use the notion 'phenotype2', 'phenotype3', etc. After you have customised the fields for the sample submission, download the template and fill in the information. Example of the sample template: Finally upload the sample template to get the EGA accession IDs for the samples. Register your Data Access Committee (DAC) Further information on the role of your DAC Go to the “Data Access” box Click on “Register Dacs” Input the information about the DAC and register at least one main DAC contact. Register your Data Access 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 (DAC). Go to the “Data Access” box Click on “Register Policies” Select the DAC to which this policy will be linked to and fill in the policy information. Submitting your Runs and Analyses This section is only for sequence data submission, for array-based submission it can be skipped. Please refer to our Submitting array based metadata Runs Registration Go to the “Raw Reads (Experiments and Runs)” box Click on “Submit Reads” Select “Download spreadsheet template for Read submission” Select the template corresponding to your submission type For the templates you have the option to customise the optional fields. To check their description click on “Show Description “ Download the template and fill in the required information. Example of the runs template: We recommend that Fastq, BAM, and CRAM read files are submitted using Webin-CLI When using this interface instead of Webin-CLI, raw sequences must be uploaded in one of the supported data formats before they can be submitted. The files can be uploaded using FTP or Aspera. The study and the sequenced samples must be pre-registered before the raw reads are submitted. Please note that each individual study and sample should be registered only once. You will be asked to provide information about the sequencing libraries and instruments. Submitting your Dataset This section is only for sequence data submissions, for array based submissions it can be skipped. Please refer to our Submitting array based metadata The dataset describes the data files, defined by the run (EGARXXXXXXXXXXX) and analysis (EGAZXXXXXXXXXXX) accessions that make up the dataset and links the collection of data files to a specified Data Access Committee and Data Access Policy. As a result, you must have registered your Reads and experiments, Data Access Committee (DAC) and Data access policy before submitting your Dataset. 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. Please contact EGA Helpdesk for further assistance. Go to the “Data Access” box Click on “Register Dataset” Select the Data Access Committee (DAC) and Data access policy Register your dataset After submitting your dataset you should contact the EGA Helpdesk to provide a release date for your dataset. Datasets are automatically held (i.e. not released) unless they are affiliated to a study that has already been released. Edit/update existing submission metadata Go to the “Report” section of the object you would like to edit. Locate the object and click on the arrow under action. An option menu will be displayed. Objects can be edited through their XML or with the WEBIN menu. After an object has been edited, changes would be available on the website until the submission is released again. Please contact the EGA Helpdesk if you require further assistance.

  Documentation submission/metadata/submission/EGA_webin

• Hermansky-Pudlak syndrome type 1 causes impaired anti-microbial immunity through a pathogenic lipid metabolism-mTOR circuit

  Mendelian diseases that present with immune-mediated disorders can provide insights into the molecular mechanisms that drive inflammation. Hermansky-Pudlak syndrome (HPS) types 1 and 4 are caused by defective vesicle trafficking involving the BLOC-3 complex. The presence of inflammatory complications such as Crohn’s disease-like inflammation and lung fibrosis in these patients remains enigmatic. Using mass cytometry we observe an augmented inflammatory monocyte compartment in HPS1 patient peripheral blood that may be associated with a TNF - and IL-1α-dominated cytokine dysregulation. HPS1 patient monocyte-derived macrophages express an inflammatory TNF-OSM mRNA gene signature and changes in lipid metabolism. Using stimulation experiments and lysosomal proteomics we show that defective lipid metabolism drives RAB32-dependent mTOR signaling, facilitated by the accumulation of mTOR on lysosomes. This pathogenic circuit translates into aberrant bacterial clearance, which can be rescued with mTORC1 inhibition. We reveal that a pathogenic lipid-mTOR signaling circuit acts as a metabolic checkpoint for defective anti-microbial activity. This mechanism may be relevant to the complex pathology of HPS1 patients featuring macrophage lipid accumulation, granuloma formation, defective anti-microbial activity and tissue inflammation. Lastly, this circuit may be present in a wider group of disorders with defective lipid metabolism and cholesterol accumulation.

  Study EGAS00001005053

• Hermansky-Pudlak syndrome type 1 causes impaired anti-microbial immunity through a pathogenic lipid metabolism-mTOR circuit - 10x Genomics scRNAseq

  Mendelian diseases that present with immune-mediated disorders can provide insights into the molecular mechanisms that drive inflammation. Hermansky-Pudlak syndrome (HPS) types 1 and 4 are caused by defective vesicle trafficking involving the BLOC-3 complex. The presence of inflammatory complications such as Crohn’s disease-like inflammation and lung fibrosis in these patients remains enigmatic. Using mass cytometry we observe an augmented inflammatory monocyte compartment in HPS1 patient peripheral blood that may be associated with a TNF - and IL-1α-dominated cytokine dysregulation. HPS1 patient monocyte-derived macrophages express an inflammatory TNF-OSM mRNA gene signature and changes in lipid metabolism. Using stimulation experiments and lysosomal proteomics we show that defective lipid metabolism drives RAB32-dependent mTOR signaling, facilitated by the accumulation of mTOR on lysosomes. This pathogenic circuit translates into aberrant bacterial clearance, which can be rescued with mTORC1 inhibition. We reveal that a pathogenic lipid-mTOR signaling circuit acts as a metabolic checkpoint for defective anti-microbial activity. This mechanism may be relevant to the complex pathology of HPS1 patients featuring macrophage lipid accumulation, granuloma formation, defective anti-microbial activity and tissue inflammation. Lastly, this circuit may be present in a wider group of disorders with defective lipid metabolism and cholesterol accumulation.

  Study EGAS00001005098

• University of Alabama at Birmingham (Xenotransplantation Project) - DAC

  This is a Data Access Committee (DAC) developed to govern access to sequence level data produced by the Porrett Lab, University of Alabama at Birmingham (Xenotransplantation Project), and headed by Dr. Jayme Locke, and Dr. Porrett M. Paige, Principal, and Co-Principal Investigators respectively. These data were largely used in the generation of the manuscript: “Spatiotemporal immune atlas of the first clinical-grade gene-edited pig-to-human kidney xenotransplant”. Access to these data will be granted to qualified investigators for appropriate use following data use approval from members of the DAC.

  Dac EGAC50000000188

• Bulk Iso-Seq from brain tissue and exosomes isolated from brain tissue using long-read PacBio sequencing of poly-adenylated transcripts

  Human brain sEVs were isolated from postmortem human brain tissue. RNA was extracted from both the source brain tissue and sEVs and prepared for long-read sequencing. cDNA was sequenced on the PacBio Sequel II. The data provided have undergone processing using the tool ccs (v6.0.0) to generate circula consensus reads. For further processing, it is recommended to follow the guidelines at https://isoseq.how/, starting with using lima to remove cDNA primers.

  Dataset EGAD50000000043

• Long Read sequencing data from 4 tumor/germline samples with non muscle invasive bladder cancer

  A better understanding of the molecular landscape of non-muscle-invasive bladder cancer (NMIBC) is essential to improve risk assessment and identify potential therapeutic targets. Here, we perform a comprehensive genomic analysis of patients diagnosed with NMIBC based on whole-exome- (n=438), shallow whole-genome- (n=362), and total RNA-sequencing (n=414). Additionally, tumor/germline samples from 4 patients were sequenced using Oxford Nanopore technology. This dataset contains 8 BAM files corresponding to the Long read sequencing data.

  Dataset EGAD50000000731

• RnaSeq data from 414 tumor samples with non muscle invasive bladder cancer

  A better understanding of the molecular landscape of non-muscle-invasive bladder cancer (NMIBC) is essential to improve risk assessment and identify potential therapeutic targets. Here, we perform a comprehensive genomic analysis of patients diagnosed with NMIBC based on whole-exome- (n=438), shallow whole-genome- (n=362), and total RNA-sequencing (n=414). This dataset contains 414 BAM files corresponding to the full total RNA-sequencing dataset.

  Dataset EGAD50000000733

• RNA-Seq data from study: Molecular underpinnings of dedifferentiation and aggressiveness in chromophobe renal cell carcinoma

  The ChRCC study RNA-Seq dataset contains raw whole transcriptome sequencing data of 16 tumor and 6 adjacent normal samples from 7 UTSW patients, who have consented to depositing their genomic data to public repository. RNA-Seq was performed using 50bp single-end on a HiSeq2500 platform (Illumina, San Diego, CA, USA). 50M reads per sample on average. The raw data is in fastq format.

  Dataset EGAD50000000416

• De novo modeling of chromoplectic Ewing sarcoma tumors from patient-derived mesenchymal stem cells

  Ewing sarcoma is characterized by pathognomonic translocations fusing most frequently EWSR1 with FLI1 (EF1). In addition, Ewing sarcoma can also display alterations in STAG2, TP53 and CDKN2A (SPC). Starting from Ewing sarcoma derived human mesenchymal stem cells (MSCpat), we recapitulated this translocation and SPC alterations using a CRISPR/cas9 approach and generated a bona fide Ewing sarcoma model (EWIma1) displaying transcriptomic (RNA-seq) and epigenetic (ChIP-seq) hallmarks of EwS.

  Dataset EGAD50000001673

• cfDNA sWGS BAM — Healthy donors

  Paired-end shallow whole-genome sequencing (sWGS) of plasma cfDNA from healthy donors recruited at EFS Montpellier (France). Libraries: DSP (NEBNext Ultra II) and/or SSP (IDT/Swift Accel-NGS 1S Plus). Sequencing by IntegraGen (France, 2022–2024). Reads aligned to GRCh38 (hg38 canonical) with BWA-MEM; BAMs coordinate-sorted and indexed; submission contains primary mapped reads only (unmapped/secondary/supplementary/duplicates removed; typical filter “-F 3332”, MAPQ threshold as noted in analysis records). Each BAM/BAI pair is linked to its Sample and Experiment. Data Use: controlled access.

  Dataset EGAD50000001880