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• A dataset compiled as a resource a for orthogonal assessment of exon CNV calling in NGS data

  The dataset includes high-quality sequencing data from a targeted NGS assay (the TruSight Cancer Panel) together with Multiplex Ligation-dependent Probe Amplification (MLPA) results for 96 independent samples. 66 samples contain at least one validated exon CNV and 30 samples have validated negative results for exon CNVs in 26 genes. The dataset includes 46 exon CNVs in BRCA1, BRCA2, TP53, MLH1, MSH2, MSH6, PMS2, EPCAM and PTEN, giving excellent representation of the cancer predisposition genes most frequently tested in clinical practice. Moreover, the validated exon CNVs include 25 single exon CNVs the most difficult exon CNV to detect. The full dataset includes FASTQ files, target BED file, and MLPA results.

  Study EGAS00001002428

• Mitochondrial DNA sequencing of iPSC, parental cells, and iPSC derived cardiomyocytes

  The aim of this work was to analyze to which extent mitochondrial DNA (mtDNA) variants in human induced pluripotent stem cells (iPSC) originate from their parental cells or from de novo mutagenesis, and whether dynamics in variant heteroplasmy levels during prolonged expansion culture of iPSC are caused by inter- or intra-cellular selection or genetic drift. Hence, mtDNA of 26 early passage iPSC clones from, in total, 10 donors and the corresponding parental cell populations (here endothelial cells) were sequenced. Furthermore, 7 iPSC clones were expanded up to passage 50 and the change in variant heteroplasmy levels was monitored. Lastly, the effect of differentiation on the variant heteroplasmy levels was investigated during directed cardiomyocyte differentiation of 4 iPSC clones.

  Study EGAS00001005560

• DNA methylation database for gynecological cancer detection, classification and assay development

  MeD-seq data (fastq files) from gynecological cancers and associated healthy tissues. In total 292 fastq files generated by MeD-seq are deposited consisting of: healthy tissues (vulva n=11, cervix n=15, endometrium n=13, fallopian tube n=18 and ovary n=13), precursor lesions of cancer (vulva n=23 and cervix n=46) and cancer (vulva n=21, cervix n=45, endometrium n=26, fallopian tube n=8 and ovary n=33)

  Dataset EGAD50000000611

• Regulatory T cell transcriptomic reprogramming characterizes adverse events by checkpoint inhibitors in solid tumors

  Tregs were sorted as CD4+CD25+CD127- cells from peripheral blood of patients with advanced metastatic melanoma, stage III(B-D)-IV, who were receiving treatment with anti-PD1 (n =26); and patients with kidney, non-small cell lung, liver and bladder cancer who were receiving treatment with anti-PD1. RNA was extracted and polyA libraries were prepared using the Illumina Truseq sample preparation kit v.2. Single-end sequencing was performed on NextSeq500.

  Dataset EGAD00001006415

• BLUEPRINT release August 2016, ChIP-Seq for band form neutrophil, on genome GRCh38

  ChIP-Seq data for 4 band form neutrophil sample(s). 26 run(s), 23 experiment(s), 23 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002454

• Mitochondrial DNA sequencing of human iPSC, parental cells, and iPSC derived cardiomyocytes

  Dataset contains whole mitochondrial DNA sequencing data in fastq format (Illumina MiSeq paired-end) of 62 samples, in total. Those samples include sequencing data of the endothelial cell populations of 10 different donors and of 26 early-passage iPSC clones derived thereof. Moreover, the dataset contains the data of 7 of those iPSC clones sequenced additionally in passage 30 and 50, each. Lastly, 4 iPSC clones were sequenced during directed cardiomyocyte differentiation, each at day 0, 5, and 15 of differentiation.

  Dataset EGAD00001008021

• Synergy study: "Tissue resident CD8+ T cell clonal expansion in advanced triple negative breast cancer is associated with response to chemoimmunotherapy"

  Dataset of 10x Genomics 5'prime single-cell RNA sequencing of 40 tumor biopsy samples or triple negative breast cancer. Samples are taken before and after immuno checkpoint blockade treatment. Processed according the 10X genomics protocol, sequenced on Immumina NovaSeq6000. Raw sequencing reads are further processed with Cellranger 3.0 to get gene-expression matrices.

  Study EGAS50000000527

• Integrating molecular imaging and transcriptomic profiling in advanced HER2-positive breast cancer receiving trastuzumab emtansine (T-DM1): an analysis of the ZEPHIR clinical trial

  Purpose: The ZEPHIR clinical trial evaluated the role of [89Zr]trastuzumab-PET/CT (HER2-PET/CT) and 2-[18F]fluoro-2-deoxy-D-glucose PET/CT ([18F]FDG-PET/CT) in predicting outcomes in patients with advanced HER2-positive breast cancer treated with trastuzumab emtansine (T-DM1). Here, we combined molecular/metabolic imaging and transcriptomic data to investigate the biological processes associated with [89Zr]trastuzumab and [18F]FDG uptake, and to dissect the mechanisms involved in T-DM1 resistance. Experimental design: RNA was extracted from metastasis biopsies obtained at study inclusion in the ZEPHIR trial. HER2-PET/CT and [18F]FDG-PET/CT imaging data of biopsied lesions were integrated with RNA sequencing data. Lesions were compared based on the level of [89Zr]trastuzumab uptake (dichotomized into high/low classes) as well as on the presence/absence of metabolic response, defined comparing baseline and on-treatment [18F]FDG-PET/CT. Results: We analyzed matched transcriptomic and molecular/metabolic imaging data for 24 metastases (one lesion/patient). Pathways involved in extracellular matrix (ECM) organization, glycosylphosphatidylinositol synthesis, and collagen formation were enriched in lesions presenting low [89Zr]trastuzumab uptake. [18F]FDG uptake at baseline correlated with proliferation and immune-related processes. Hypoxia and ECM-related processes were enriched in lesions that showed no metabolic response to T-DM1. Interestingly, immune-related processes (e.g., Fc gamma receptor activation, complement cascade) were associated with high [89Zr]trastuzumab uptake and metabolic response. Conclusions: To our knowledge, this study represents the first correlative analysis between [89Zr]trastuzumab tumor uptake and gene expression profiling in humans. Our findings point toward a role of ECM in impairing [89Zr]trastuzumab tumor uptake and T-DM1 metabolic response in advanced HER2-positive breast cancer, and highlight the potential of molecular imaging to depict tumor microenvironment features.

  Study EGAS50000000470

• Tissue Site

  Tissue site for RNAsequencing data. Tissue site is associated with the clinical biomarker data and be linked to the biomarker data using the SAMPLE and PAT identifiers.

  Dataset EGAD50000000931

• 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