464 results for "GitHub+primate+acoustics+baboon+Zenodo+10.5281+Gui*"

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• Spatial transcriptomics analysis of triple negative breast cancers

  Spatial transcriptomics analysis of triple negative breast cancers Both bulk sequencing and ST were performed Counts, images etc are available at https://zenodo.org/doi/10.5281/zenodo.8135721

  Dataset EGAD50000000686

• Contribution of systemic and somatic factors to clinical response and resistance in urothelial cancer: an exploratory multi-omic analysis

  Patients with metastatic urothelial cancer (n=29) were treated with atezolizumab (anti-PD-L1). All 29 patients had T-cell receptor sequencing (TCR-seq) of the pre-treatment blood; 24 also had TCR-seq on at least one post-treatment blood sample. 24 patients had TCR-seq of their pre-treatment tumors. TCR-seq data can be found at http://doi.org/10.5281/zenodo.546110. 26 patients also had RNAseq of their tumors and whole exome sequencing (WES) of their tumors and matched normal blood. WES results for one sample were excluded from our analysis after failing to meet coverage requirements.

  Study phs001743

• RNA sequencing of end-stage kidney disease patients with COVID-19

  This dataset contains paired RNA sequencing data for end-stage kidney disease (ESKD) patients on dialysis. There are two cohorts. The first includes 179 samples from 51 COVID-19 patients recruited during the initial phase of the COVID-19 pandemic (April-May 2020) and 55 non-infected ESKD patients as controls. 17 patients initially recruited as controls as part of the Wave 1 cohort were later infected with COVID-19 in January-March 2021. We acquired a total of 90 samples during the acute infection and convalescent samples for 12 of the 17 patients following the acute COVID-19 episode. RNA-seq counts and full clinical metadata for these cohorts are available without restriction from Zenodo (https://doi.org/10.5281/zenodo.6497251).

  Dataset EGAD00001009752

• scRNAseq of aplastic anemia and healthy immune cells co-cultured with autologous HSPCs

  This dataset contains 8 batches of bone marrow or peripheral blood cells of patients with aplastic anemia and bone marrow cells of healthy controls. The samples from different experimental conditions have been multiplexed with Totalseq-C antibodies (feature barcoding). To demultiplex the samples, see instructions in 10.5281/zenodo.2590196 and https://github.com/janihuuh/aa_manu. Each sample is analysed with Chromium V(D)J and 5' Gene Expression Platform v2 (10X Genomics). The raw data includes fastq files for Gene expression, feature barcodes and V(D)J Expression. The processed data have been deposited in the ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress).

  Dataset EGAD00001012120

• Spatially resolved antigen receptor and gene expression data from breast cancer patients

  The dataset includes spatially-resolved and single-cell antigen receptor, as well as gene expression, data from two different HER2+ breast cancer patients. The tumor piece obtained during surgery from each patient was divided into several regions and tissue sections were used for spatial transcriptomics (Visium, 10x genomics). As indicated, some tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. In parallel, tissue pieces from the same tumor were dissociated for single-cell gene expression analysis (10x genomics GEX, VDJ, and feature barcoding/Hash Tag Oligonucleotide). The deposited data is in the form of fastq files. All processed data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are publicly available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Cell Ranger, Seurat, Space Ranger, and STutility pipelines.

  Dataset EGAD00001011061

• Spatially resolved antigen receptor and gene expression data from human tonsil tissue

  The dataset includes spatially-resolved gene expression and antigen receptor data from two Tonsil samples (1 and 2). Tissue sections from the tonsil samples were used for spatial transcriptomics (Visium, 10x genomics). Tonsil 2 tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. Nearby or adjacent tissue sections (from Tonsil2) were also analyzed by a bulk antigen receptor sequencing approach (amplicon sequencing), by a method also newly developed by us in the same publication (Bulk SS3 VDJ). For Visium, the data were anonymized (all SNPs removed) using Bamboozle (Ziegenhain and Sandberg, Nature Communications 2021). The deposited data is in the form of fastq files. All remaining data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Seurat, Space Ranger, and STutility pipelines.

  Dataset EGAD00001011062

• CRUK-ICGC Prostate Cancer Group Study

  Prostate cancer somatic genomic sequencing data generated from 2011 onwards under auspices of the International Cancer Genome Consortium Prostate Cancer UK consortium (CRUK-ICGC Prostate Group), co-led by Colin Cooper and Ros Eeles, with other Principal Investigators (Brewer, Neal, Bova, McDermott, Wedge, Lynch, Massie, and Foster) and others as listed in study publications. Funded by Cancer Research UK and other funders as listed in study publications. Contents and publications related to each dataset are described with each dataset (EGAD) entry. Our study was funded with the ambition of collecting Whole Genome DNA sequence data from 250 prostate cancers, with matching transcriptome and methylome data. The original aims of the project were: 1.To understand the significance of multifocal prostate cancer. 2.To understand the clinical heterogeneity of prostate cancer to devise markers for predicting outcome and for drug targeting. 3.To understand the molecular basis of development and spread of castration-resistant and metastatic disease 4.To understand the aetiology of prostate cancer particularly the large variation in incidences that occur in different populations and ethnic groups. Please see https://doi.org/10.5281/zenodo.4022439 for links between sample IDs reported in literature and EGA IDs.

  Study EGAS00001000262

• Psoriasis Patient PBMC scRNA-seq data

  This dataset contains raw and processed data of four scRNA-seq PBMC samples from four Psoriasis patients (Pso3 = male, Pso4 = female, Pso7 = female, Pso8 = male). Raw sample libraries were prepared with 10x 5'-kit v2 multiplexing the individual samples using TrueSeq barcoded antibodies to stain the cells. Resulting sequencing data consists of four FASTQs (2 x antibody multiplex to demultiplex the samples + 2 x transcriptome data containing the typical cell x UMI barcode data for count matrix generation). The raw sequencing data was processed with cellranger 6.0.1 with the GRCh38 3.0.0 reference and cell to sample assignment was subsequently done with couhto 1.1.0 (https://github.com/dmalzl/counhto) employing the same algorithm as cellranger multi (couldn't be used here though as multi does not handle 5'-chemistry data). Processed data contains all main outputs of cellranger count in raw text format or in h5 formats including the used feature reference for (i.e. antibody barcode to sample assignment) as well as the computed cell to sample assignment computed by counhto. For further details including all used code in the analysis of the data please consult https://doi.org/10.5281/zenodo.13846873

  Dataset EGAD50000001102

• DAC-2023-07-05-Ritz (DAC-007) - Diagnosis of tuberculosis infection in children with a novel skin test and the traditional tuberculin skin test: an observational study

  Diagnosis of tuberculosis infection in children with a novel skin test and the traditional tuberculin skin test: an observational study This is only the location for the raw data and any check if the dataset may be relevant please refer to the MF-DAC Zenodo community (https://zenodo.org/records/11237107).

  Study EGAS50000000780

• Beacon v2

  Beacon v2: a tool for data discovery Motivation In the era of data-driven health research and personalised medicine, human genomic data has become extremely valuable. These are also identifiable data, as they carry information pointing to a specific individual as well as their own family; and as such, they must be protected. This makes data discovery particularly challenging: this is where “Beacon” comes in. A “Beacon” is an API aiming to enable the search of genomic variants and associated information without jeopardising the privacy of the dataset. Here, we refer to its current version, namely version 2 (v2). Definition Beacon v2 is a term that can refer to different aspects. The EGA is playing a central role in the following aspects: The Beacon v2 protocol is a Global Alliance for Health and Genomics standard. The Beacon v2 Reference Implementation (B2RI) is an “out-of-the-box” Beacon instance developed with ELIXIR, which facilitates Beacon deployment. The EGA Beacon(s) are Beacons following the v2 standard and using the B2RI, deployed on top of data hosted at the EGA and allowing for their discovery. Resources Depending on whether you are visiting us a stakeholder (you need more general information about Beacon), a deployer /implementer (you want to have your own Beacon instance), or an EGA user (you want to query Beacon and start browsing data), you will be interested in the following resources: Your role Beacon aspect Documentation type Stakeholder Beacon v2 protocol Beacon website Beacon page on the GA4GH website Deployer/Implementer Beacon v2 protocol Read the docs: Beacon v2 standard technical description GitHub repository Beacon v2 standard Beacon v2 Reference Implementation Read the docs: B2RI technical description GitHub repository B2RI Guide to deploy Beacon using B2RI EGA user EGA Beacon(s) API in construction UI in construction

  Documentation about/projects-and-funders/beacon

• ST lobular manuscript dataset

  Spatial transcriptomics analysis for 43 breast cancer lobular samples. Note that there are 2 runs per sample Images and read counts will be made availalble on Zenodo upon publication.

  Dataset EGAD50000001467

• DAC-2023-07-05-Ritz (DAC-007)

  Full description of the dataset and all necessary information on metadata may be found under (https://zenodo.org/records/11237107): DAC-2023-07-05-Ritz (DAC-007), raw data in EGA, metadata in Zenodo (https://zenodo.org/records/11237107)

  Dataset EGAD50000001147

• Construction of Thousands of Single Cell Genome Sequencing Libraries Using Combinatorial Indexing

  Single-cell genome sequencing has proven valuable for the detection of somatic variation, particularly in the context of tumor evolution. Current technologies suffer from high library construction costs which restrict the number of cells that can be assessed and thus impose limitations on the ability to measure heterogeneity within a tissue. Here, we present Single cell Combinatorial Indexed Sequencing (SCI-seq) as a means of simultaneously generating thousands of low-pass single cell libraries for somatic copy number variant detection. We constructed libraries for 16,698 single cells from a combination of cultured cell lines, primate frontal cortex tissue, and two human adenocarcinomas, including a detailed assessment of subclonal variation within a pancreatic tumor. "Reprinted from Vitak, S.A., et. al. 2017 with permission from Nature Methods."

  Study phs001268

• Dataset Rdata segmentations

  This data set contains an .Rdata file for all the processed segmentation profiles from 81 lpWGS samples included used in downstream analyses from Github repository Evo_history_CACRC. Lastly, there is an .Rdata object with 50 segmentations for 50 total samples from 25 sporadic SNP adenomas used in the comparison with colitis samples.

  Dataset EGAD00010001586

• Human CD4+ T cells regulate peripheral immune responses in rheumatoid arthritis via IGFL2

  To elucidate the role of the immunoregulatory mechanisms of human CD4+ T cells, we analyzed CD4+ T cells in PBMC, synovium and synovial fluid using single-cell RNA sequencing (scRNA-seq). We identified insulin-like growth factor (IGF) like family member 2 (IGFL2), an evolutionary novel paralogous inflammatory factor found exclusively in primates. ScRNA-seq of RA synovium showed that IGFL2 is specifically expressed by CD4+ T cells, predominantly within peripheral helper T (Tph) cells, with upregulation correlating to disease severity. IGFL2 promotes TGF-��-induced CXCL13 production in CD4+ T cells. In addition, IGFL2 activates NF-��B signaling and induces monocyte gene signatures associated with pathogenic macrophages from RA synovial tissue, including CXCL10 and CXCL9. Notably, blood IGFL2 protein levels significantly correlate with RA disease severity and effectively discriminate RA patients. These findings highlight the substantial involvement of IGFL2 in RA pathogenesis, emphasizing how human CD4+ T cells exert pleiotropic regulation of local immune responses via this novel primate-specific soluble factor.

  Study JGAS000727

• Origins and functional consequence of somatic mitochondrial DNA mutations

  Recent sequencing studies have extensively explored the somatic alterations present in the nuclear genomes of cancers. Although mitochondria control energy metabolism and apoptosis, the origins and impact of cancer-associated mutations in mitochondrial DNA (mtDNA) are unclear. Here, we analysed somatic alterations in mtDNA from 1,675 tumors across 31 histologies. We identified 1,907 somatic substitutions, which exhibited dramatic replicative strand bias, predominantly C>T and A>G on the mitochondrial heavy strand. This strand-asymmetric signature differs from those found in nuclear cancer genomes but matches the inferred germline process shaping primate mtDNA sequence content. Numbers of mtDNA mutations showed considerable heterogeneity across tumor types. Missense mutations were selectively neutral and often gradually drifted towards homoplasmy over time. In contrast, mutations resulting in protein truncation undergo negative selection and were almost exclusively heteroplasmic. Our findings indicate that the endogenous mutational mechanism has far greater impact than any other external mutagens in mitochondria, and is fundamentally linked to mtDNA replication.

  Study EGAS00001000968

• Single-cell bam files and RNA sequencing of viral RNA stocks

  244 infected single-cell alveolar bam files, 48 empty well bam files, and 52 RNA sequencing of amplicons (4 SARS-CoV-2 variants with 12 batches and 4 viral variants pool samples). 244 alveolar single cells were captured over 12 experimental batches and experimental condition is written in metadata uploaded as "infected_cells_final_revision.csv". on github (https://github.com/twkim-0510/SARS-CoV-2_viral_competition). Each bam file name corresponds to the sample_name column of the metadata.

  Dataset EGAD00001009711

• Star2xml: metadata converter into XML

  Star2xml: metadata converter into XML Welcome to the landing page for star2xml - a tool developed by the European Genome-phenome Archive (EGA) that enables users to convert metadata into XML format that is compliant with EGA's programmatic submission system. For starters, if you are not sure what metadata is in this context, you may want to explore our brief metadata introduction. . Star2xml is useful for users who have already submitted their data to the EGA and want to also submit their metadata programmatically without having to create the XML files manually. The need for these XMLs containing metadata is explained in the EGA's metadata schemas and programmatic submission guidelines. In summary, star2xml allows for users to deposit metadata in a tabular format (e.g. excel spreadsheet) and transform it into a compliant XML format. This saves time in large submissions, where the sheer size of metadata makes it difficult to manage. For smaller submissions, it is recommended to use EGA's Submitter Portal (SP) instead. The installation of the tool is straightforward, and it contains detailed documentation on how to use it in its GitHub repository . While star2xml can help users convert metadata formats, it does not validate the resulting metadata against EGA's schemas. Therefore, after creating your XMLs, you should still validate your metadata before submitting it to EGA, as described in the documentation for programmatic metadata validation.

  Documentation tools/star2xml

• Spatial transcriptomics analysis of triple negative breast cancers

  While triple negative breast cancer (TNBC) is known to be heterogeneous at the genomic and transcriptomic levels, spatial information on tumor organization and cell composition is still lacking. Here, we investigated TNBC tumor architecture including its microenvironment using spatial transcriptomics on a series of 92 patients. We performed an in-depth characterization of tumor and stroma organization and composition using an integrative approach combining histomorphological and spatial transcriptomics. Furthermore, a detailed molecular characterization of tertiary lymphoid structures led to identify a gene signature strongly associated to disease outcome and response to immunotherapy in several tumor types beyond TNBC. A stepwise clustering analysis identified nine TNBC ecotypes, further validated in external datasets. Several ecotypes were associated with disease outcome and characterized by potentially actionable features. In this work, we provideThis is the largest TNBC series providing a comprehensive insight into the complexity of TNBC ecosystem with potential clinical relevance, opening avenues for treatment tailoring including immunotherapy. All the processed data needed to replicate the results from the publication (counts, images, clinical data, etc) are available on Zenodo: https://zenodo.org/records/14204217

  Study EGAS50000000475

• PCA Atlas Visium spatial transcriptomics data (FASTQs, spaceranger BAMs and Visium mapping tables)

  This dataset contains Visium spatial transcriptomics data for the PCA Atlas study EGAS00001008332. It includes: - Raw Visium FASTQ runs (EGAR00004172272–EGAR00004172283) for all libraries. - spaceranger alignment BAM analyses for those libraries where BAM output was generated. - visium.csv: a slide-level mapping table linking Visium slide samples to EGA sample accessions, sequencing runs/experiments and spaceranger BAM analyses. - visium_tma.csv: a TMA mapping table describing how mini-TMA slides relate slide-level Visium libraries to tissue-level samples/cores. Together these objects provide the raw and aligned Visium data and the technical/biological mappings needed to understand and reuse the spatial transcriptomics component of the PCA Atlas. High resolution histology images are available from the following Zenodo repository, along with additional spaceranger outs for each slide: - https://zenodo.org/records/17411292

  Dataset EGAD00001015796

• EGA QuickView

  EGA QuickView Secure and Remote Access to EGA Files What is EGA QuickView? EGA QuickView is a FUSE file system that allows secure and remote access to EGA files. It is a hybrid of sshfs and crypt4ghfs, which means it uses ssh to connect to the EGA distribution servers, downloads data in Crypt4GH format, and transparently decrypts those files. It is compatible with Linux and macOS 12. Why use EGA QuickView? EGA QuickView is a highly useful and convenient tool for anyone who needs to access EGA files remotely and securely. The software offers a quick and convenient way to browse through EGA files without the need for complete downloads, thus saving bandwidth and time. Whether you are a medical professional requiring access to sensitive patient data or a research scientist working on a project that involves EGA files, EGA QuickView provides a highly useful solution. The software boasts advanced security features, ensuring that your data is safe and protected at all times. Get started with EGA QuickView To get started with EGA QuickView, visit the EGA QuickView GitHub repository for more information and installation instructions.

  Documentation access/download/visualisation/ega-quickview

• EGA FUSE Client

  EGA FUSE Client What is EGA FUSE Client? The EGA FUSE Client is a Java Native Runtime (JNR) based Filesystem in Userspace (FUSE) client that allows users to access authorised EGA files by presenting them in a virtual directory. This means that users can use authorised EGA files like regular files without having to download them first. The EGA FUSE Client is a useful tool for researchers who need to access and work with large amounts of EGA data. Why use EGA FUSE Client? The EGA FUSE Client provides several benefits for researchers working with EGA data: Efficient use of storage space: With the EGA FUSE Client, users can access authorised EGA files without having to download them first. This means that users can save storage space on their local machines. Faster access to data: Since users can access authorised EGA files directly through the EGA FUSE Client, they can work with the data more quickly than if they had to download the files first. Simplified workflow: The EGA FUSE Client presents authorised EGA files in a virtual directory, allowing users to work with the files like regular files. This simplifies the workflow for researchers who need to access and work with large amounts of EGA data. Get started with EGA FUSE Client To get started with EGA FUSE Client visit the EGA FUSE Client GitHub repository for more information and installation instructions.

  Documentation access/download/visualisation/fuse-client

• PyEGA3 download client

  PyEGA3 Checks prior to download - Is the dataset available for download? Most datasets are available using the PyEGA3 Download client, except for specific legacy datasets. For these ones, refer to Live Outbox distribution, an alternative way to download these datasets.If the dataset you've been granted access to begins with the ID EGAD5XXXXXXX, please refer to the Live Outbox distribution. If the dataset you have been granted access to contains encrypted data (filename ending in .gpg), contact EGA Helpdesk for detailed information on how to download these files.General Information The PyEGA3 download client is available at its GitHub repository. In its README you will find a step by step guide on how to use it to download files you have been granted access to. PyEGA3 implements the GA4GH-compliant HTSGET protocol supporting requests over genomic ranges. This enables the download of specific regions of interest rather than the entire file. HTSGET is not possible for all the files held by EGA: if the dataset does not contain index files (e.g. filename ending in *.crai), then genomic range requests cannot be performed. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Check out our video tutorial! Frequently Asked Questions General questions How do I download the datasets to which I have been granted access? After setting up your EGA download account, you can proceed to download using the EGA download client – PyEGA3. The pyEGA3 download client is a python-based tool for viewing and downloading files from authorised EGA datasets. This download client is continuously being developed for more user-friendly download experiences. Can I download the dataset’s metadata via the download client? No, the download client cannot be used to download metadata. Registered EGA users can download metadata of an authorised dataset by logging into the EGA webpage and navigating to the dataset of choice. Approximately two thirds down the page you will find the option to download the metadata as a zip file. How do I download large datasets as quickly as possible? The main EGA download client is pyEGA3 download client, a python-based tool for viewing and downloading files from authorised EGA datasets.We could exceptionally consider the option of an Aspera download for users experiencing very slow download rates or facing particular issues.Contact the EGA Helpdesk for further information. Pre-requirement and Installation What are the major requirements for the PyEGA3 download client installation and usage? The PyEGA3 client is compatible with any OS with Python 3.6+ installed. The client requires a connection to the internet, sufficient space on the destination drive, and the EGA download account credentials. Where can I find the PyEGA3 download client installation and download instructions? We strongly recommend checking our video tutorial demonstrating the usage of PyEGA3 from installation to file download. More detailed information is available at PyEGA3 GitHub. What ports are required for the PyEGA3 download client to function? PyEGA3 makes HTTPS calls to the EGA Data API (https://ega.ebi.ac.uk:8443). This port 8443 must be reachable from the location where PyEGA3 is executed to avoid timeouts. PyEGA3 download client How to update the PyEGA3 download client to be on the latest version? Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 --upgrade I do not have an active EGA account. Can I test the download with the PyEGA3 download client? An EGA download test account (ega-test-data@ebi.ac.uk) has been created for troubleshooting and training purposes. The test account does not require an EGA username and password because it contains publicly accessible files from the 1000 Genomes Project. More information about the use of this account and log in details are available at pyEGA3. I lost my connection while the download was in progress. Does this mean that I have to download the file again from the start? The PyEGA3 download client supports automatic resumption of downloads. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the resume feature, downloads will automatically resume if you encounter any errors or if the connection is interrupted. How can I improve the overall download speed of the PyEGA3 download client? Download speeds can be optimised using the --connections parameter which will parallelise download at the file level. If the --connections parameter is provided, all files >100Mb will be downloaded using the specified number of parallel connections. It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Why is my file taking a long time to be saved? Please note that when a file is being saved, it goes through two processes. First, the downloaded file "chunks" are pieced back together to reconstruct the original file. Secondly, PyEGA3 calculates the checksum of the file to confirm that the file was downloaded successfully. Larger files will take more time to reconstruct and validate the checksum. Why is the HTSGET protocol not working with my dataset of interest? PyEGA3 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file, saving both time and storage space. Please note that in order for the genomic range requests to work for a BAM, CRAM or VCF file there must be an associated BAI, CRAI or TBI file, respectively. If the dataset does not contain these index files then genomic range requests cannot be performed. After download Do I need to decrypt data files downloaded through the pyEGA3 download client? Files are transferred over secure HTTPS connections and received unencrypted, thus there is no need for decryption after data download. What is the purpose of an MD5 file? After the download completes, file integrity is verified using checksums. The PyEGA3 download client automatically verifies each file’s unencrypted md5 after download to ensure that the file was downloaded correctly from the EGA. Errors Why do I get a “400 Client Error” while trying to access the download client? Ensure that your credentials are formatted correctly. Please contact the EGA Helpdesk if you are unsure about your account’s username (i.e. your email address). Why do I get a “500 Server Error”? It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Otherwise, 500 server errors are internal server errors from EGA’s end. Retry after some time and, if the 500 error persists: check our homepage or Twitter for any outage information or report it to EGA Helpdesk. Why do I get a “slice error”? This type of error occurs when one slice download was prematurely interrupted. Please re-start the same download. The download client will pick up any partial download and download any missing slices. Why do I get the error “Dataset' EGADXXXXXXXXXXX' is not in the list of your authorised datasets”? Please note that the data access was given to the email (username) provided to the Data Access Committee (DAC) in your official request. Therefore, you shall check that the account that you are using is the same one that you sent to the DAC. Why do I get “ERROR:root:Failed to obtain IP address”? This might mean that the connection ports are blocked. Check that the port 8443 is reachable.In case the port is reachable and the error persists, please try to modify the file pyega3.py on line 44 in the python module installation: Replace endpoint = 'https://ipinfo.io/json' with endpoint = 'https://api.myip.com' Why do I get the error “Invalid username, password or secret key - please check and retry”? Make sure that you are using the correct credentials (username and password) and there is no typo. Contact EGA Helpdesk if the problem persists.

  Documentation access/download/files/pyega3

• 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

• How to encrypt files with Crypt4gh

  File preparation Due to the processes used at the EGA for file archival the use of non-alphanumeric characters in a filename will cause issues in archival. By convention whitespaces in filenames are to be avoided and should be replaced with the underscore character (_). Before encrypting your files please make sure that any files that will be uploaded to EGA do not use special characters such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | & Crypt4gh EGACryptor Files encrypted with Crypt4gh must be uploaded via INBOX Before uploading If you are not a registered EGA user, you will first need an EGA user account. Please note that it may take a few days for your account to be activated, as it needs to be vouched for by the EGA Helpdesk. Once your account is validated you will be able to request a submitter role. Meanwhile, you can create and add your public key to your EGA account profile. This option is not available for old submission accounts (e.g. ega-box-NNN). As soon as you have been granted a submitter role, you will be able to connect with your username and password to the EGA inbox using the SFTP protocol. If you have also registered a public key in your profile, you can also connect using this key. Encrypting your files Please note that you can also encrypt your files by uploading them directly to the "to-encrypt" folder in your upload area If your connection is unstable, please encrypt them using Crypt4gh The EGA encryption of this inbox is based on Crypt4GH. You can install a python implementation of it, with pip install crypt4gh or directly from the Github repository pip install git+https://github.com/EGA-archive/crypt4gh.git Save now the following Crypt4GH public key, into a file, say ingestion.pubkey. __EGA_INGESTION_PUBKEY__ Encrypt a given file with the following command: crypt4gh encrypt --recipient_pk ingestion.pubkey < file_to_encrypt > encrypted_file.c4gh The command reads the file from stdin (with < ) and output the encrypted version to stdout (with > ). Replace file_to_encrypt and encrypted_file.c4gh with the appropriate filenames but make sure to not use the same filename for both reading and writing because your SHELL would then truncate both files before you even read or write.

  Documentation submission/data/file-preparation/crypt4gh