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• RNA-seq of tumor samples from clear cell renal cell carcinoma patients included in the Translational Program of the NIVOREN GETUG-AFU-26 trial

  Study on RNA-seq data from tumor tissue samples of primary tumors of advanced clear cell renal cell carcinoma patients enrolled in the NIVOREN GETUG-AFU-26 trial who consented to and participated in the translational program. The goal of this study was to assess previously published gene expression signatures and evaluate their association with the benefit and/or resistance to nivolumab, as well as their association with circulating soluble factors.

  Study EGAS50000001057

• Ribo-depleted RNA-sequencing of II.3, III.1, and III.3

  This datasets contains the Ribo-depleted RNA-sequencing of patients II.3, III.1, and III.3. Libraries were sequenced to a minimum depth of 195 million read pairs (2x75nts) on an Illumina NextSeq500 Midoutput FC.

  Dataset EGAD50000002364

• 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

• Single cell transcriptomics of human adrenal gland reveal chromosomal alterations in adrenocortical cells

  Adrenocortical cells get replenished throughout our lifetime and as such they are a cellular population of high interest in relation to adrenocortical tumours. Single cell transcriptomics of human adrenal gland were performed to identify rare subpopulations of cells, dynamics of cellular turnover and clone expansions in the adrenal cortex. The adrenal gland of this particular individual is characterized by the presence of mutant clone charecterized by aneuploidy in chromosomes 8, 9, 13 and 22, occupying the zona glomerulosa and zona fasciculata. Differential expression analysis of the mutant cells revealed upregulation of MC2R (adrenocorticotropic hormone receptor), CYP21A2, HSD3B2 genes (enzymes involved in cortisol production). Overall, this donor sample is an example of clonal tissue expansions carrying chromosomal rearrangements that likely impact organ morphology and function but are seemingly benign.

  Study EGAS00001007488

• Characterizing the cell-free transcriptome in a humanized DLBCL patient-derived tumor xenograft model for RNA-based liquid biopsy in a preclinical setting

  This study explores the cell-free transcriptome in a humanized DLBCL patient-derived tumor xenograft (PDTX) model. Blood plasma samples (n=171) derived from a DLBCL PDTX model including 27 humanized (HIS) PDTX, 8 HIS non-PDTX and 21 non-HIS PDTX NOD-scid IL2Rgnull mice were collected during humanization, xenografting, treatment, and sacrifice. The mice were treated with either R-CHOP, huCD20-Fc-AFN-CHOP, or phosphate-buffered saline (PBS). RNA was extracted using the miRNeasy serum/plasma kit and sequenced on the NovaSeq 6000 platform using the SMARTer Stranded Total RNA-seq pico v3 kit.

  Study EGAS50000000566

• Genotyping_of_additional_Inflammatory_Bowel_Disease_cases___2014

  Both internal and external funding has enabled 5000 inflammatory bowel disease cases to be whole genome sequenced (CD @4X, UC @2X). The Anderson and Barrett groups are currently generating genotypes across these samples for comparison to 4000 population controls sequenced as part of UK10K. The UK10K project has shown that imputing sequenced genetic variation into previously GWASed samples greatly increases power to detect association. Given that our study has been designed to detect association to low frequency variation (0.5% and above) these gains in power are especially important. Overall, the UKIBDGC has GWAS data for around 1800 CD samples (Affy 500K) and 3000 UC samples (Affy6), though some of these samples have also been whole genome sequenced. Here, we apply to get all remaining non-GWAS and non-WGS IBD cases in the UKIBDGC (N=X) genotyped genotyped on the Illumina Core Exome Array

  Study EGAS00001000924

• WES in muscle-invasive bladder cancer (MIBC) treated with durvalumab plus olaparib in the neoadjuvant setting: NEODURVARIB trial

  This submission contains the metadata derived from the whole exome sequencing of 70 samples from 26 patients who developed advanced urothelial carcinomas. The patients were enrolled in the Neodurvarib clinical trial, which compares the molecular profiles at diagnosis and after neoadjuvant treatment with Durvalumab and Olaparib. In this regard, the analyzed samples include: normal tissue and tumoral tissue obtained from the transurethral resection of the bladder (TURBT, a procedure performed prior to neoadjuvant therapy) from the 26 patients (52 samples), as well as tissue obtained from the radical cystectomy (a procedure performed after treatment with the aforementioned drugs; 18 samples).

  Study EGAS50000000791

• Coeliac Disease Immunochip dataset

  Illumina Immunochip genotype data for coeliac disease and control samples. Data is in PLINK binary format. Calling algorithm for genotypes is based on GenomeStudio (GenTrain), with manual clustering of selected variants (please see published manuscript). Genotypes called in this way may not be exactly identical to those generated by other algorithms - this is particularly relevant for groups wishing to utilise the control data (e.g. 1958 Birth Cohort). Please contact the investigators if you wish to discuss access to other data formats (e.g. .idat).

  Study EGAS00000000053

• Long-read data (PacBio)

  The goal of this project was to perform long-read RNA sequencing (LR-seq, PacBio) in combination with short-read RNA-seq for systematic characterization of the isoform diversity in primary breast tumor samples. We sequenced the full-length transcriptomes of 26 breast tumors and 4 normal breast samples.

  Dataset EGAD00001006597

• CSF and PBMC scRNAseq RRMS patients at diagnostic n=5

  To get insights about immune cells infiltrating cells the CSF of RRMS patients (n=5), we proceed to 10X scRNAseq of paired CSF and blood circulating cells (about 20000 cells each) sampled at diagnostic.

  Dataset EGAD50000000445

• Epigenome analysis of human trophoblast stem cells

  Trophoblast cells play an essential role in the interactions between the fetus and mother. Mouse trophoblast stem (TS) cells have been derived and used as the best in vitro model for molecular and functional analysis of mouse trophoblast lineages. However, attempts to derive human TS cells have so far been unsuccessful. In this study, we derived human TS cells from human blastocysts. Epigenome analysis revealed that the human TS cells maintained epigenome profiles similar to those of primary trophoblast cells. JGA submission accessions: JGA000122,JGA00000000122

  Study JGAS000112

• Ballett

  TSO500 study: The project, called "BALLETT" (Belgian Approach of Local Laboratory Extensive Tumor Testing), has a double goal: (1) show the relevance of broad molecular profiling to improve oncological patients care, (2) demonstrate that broad molecular testing can be performed in a decentralized setting by local diagnostics laboratories in a fully standardized and uniform way while complying with the highest quality standards. NCT05058937 The publication number is: Your manuscript ID is bcr-2023-256124. Title: CUP and BRAF V600E meeting the BEACON combination.

  Study EGAS50000000478

• Non-coding mutations reveal cancer driver cistromes in luminal breast cancer

  A dataset of ER+PR+ breast tumor samples that were analyzed in order to identify mutation enrichment in cis-regulatory elements and cistrome. The repository includes sequencing data from 88 patients. 26/88 were sequenced using ATAC-seq

  Dataset EGAD00001007650

• MCF10A 12h IL6 classical signaling

  Gene expression after cell culture, 12h IL6+sgp130-Fc stimulated

  Dataset EGAD00010001970

• MCF10A 24h IL6 classical signaling

  Gene expression after cell culture, 24h IL6+sgp130-Fc stimulated

  Dataset EGAD00010001963

• Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES)

  The T2D-GENES/GoT2D 13K exome sequencing study includes ~13,000 samples, half T2D cases and half T2D controls, from five ancestries (~5K Europeans, ~2K each of African-American, East-Asian, South-Asian, and Hispanic). Samples underwent deep exome sequencing, with SNVs and INDEls called according to GATK best practices; variant sites were then filtered according to the GATK best practices, and then samples and variants underwent further filtering based on aggregate genotype quality as described in Fuchsberger et al. (e.g. low call rate, excess heterozygosity for samples, low call rate or coverage for variants). Please note that one of the samples in the T2D-GENES vcf does not have phenotype data.

  Dataset EGAD00001002246

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

  DNA methylation changes are associated with cancer the best predictor of the presence of cancer. In order to detect and classify gynecological cancer, we performed MeD-seq to generate DNA methylation profiles of vulvar, cervical, endometrial, fallopian tube and ovarian cancers and their associated healthy tissues. DNA methylation profiles were used to find general cancer, regional cancer and specific cancer biomarkers that can be used for cancer detection, classification and treatment monitoring.

  Study EGAS50000000417

• Center for Cancer Genomics (CCG) Cancers of Unknown Primary Project (CUPP)

  The Cancers of Unknown Primary Project (CUPP) aims to characterize difficult tumor types with the goal of determining future best treatment practices. NCI will utilize whole exome sequencing, copy-number variation, DNA methylation in conjunction with transcriptome sequencing to provide a comprehensive genomic landscape of Cancers of Unknown Primary (CUP). The samples will be processed and submitted for genomic characterization using pipelines and procedures established within the Center for Cancer Genomics (CCG).

  Study phs001801

• Single-cell RNA and ATAC sequencing data analysis of human postmenopausal fallopian tube and ovary

  In this study, we used a combination of technologies, including Drop-seq, 10X single-cell RNA-seq (scRNA-seq), and single-cell assay on transposase accessible chromatin (scATAC-seq) to generate  a high-quality cell atlas that consists of four tissue sites from the reproductive tract of postmenopausal women. This atlas, by far the most comprehensive map of the postmenopausal female reproductive system to date, will pave the way to a better understanding of the pathophysiology of many diseases affecting older women.

  Study EGAS00001006780

• Single-cell targeted DNA-sequencing and protein sequencing

  We performed single-cell targeted DNA sequencing (MissionBio) on 13 high hyperdiploid B-ALL patients at diagnosis to study the genetic heterogeneity. For a number of patients single-cell surface protein antibody sequencing was added (DAB-seq) to better characterize the different cell types. Furthermore, additional samples during treatment were collected and analysed for some patients. In this way we want to study the clonal heterogeneity of these B-ALL samples, based both on mutations and CNVs, and identify resistant clones that could be targeted after relapse.

  Study EGAS50000000580

• Long-read and short-read isoform sequencing in breast cancer

  The goal of this project was to perform long-read RNA sequencing (LR-seq, PacBio) in combination with short-read RNA-seq for systematic characterization of the isoform diversity in primary breast tumor samples. We sequenced the full-length transcriptomes of 26 breast tumors and 4 normal breast samples.

  Study EGAS00001004819

• Immunoprotective mechanisms and microbiota interplay in Salmonella Typhi infection

  Vaccine development against Salmonella enterica serovar Typhi (S. Typhi) requires a better understanding of interaction between human host and the resident microbial consortia in gastrointestinal tract. Healthy adult volunteers received either Ty21a, M01ZH09 or placebo, and underwent challenges with wt S. Typhi. Stool samples were collected at the screening interview (Baseline 1), prior to the first vaccination visit (Baseline 2), during vaccination (Day -28 and -26 for placebo and M01ZH09 groups; Day -32, -30, -28, -26 for Ty21a group), prior to challenge with S. Typhi (Day 0), and after challenge (day 0 12h, day 1, day 3, day 7, and day 10). 16S rRNA and messenger RNA were extracted from stool and sequenced on the Illumina Miseq and HiSeq 2000 platforms, respectively.

  Study phs001521

• Supraphysiologic MDM2 Expression Impacts P53-Independent Chromatin Networks and Therapeutic Responses in Sarcoma

  We collected multiple liposarcoma samples from different patients, including different subtypes (DD, WD) and normal adipose tissue as control. This study provides the biggest liposarcoma cohort to date, and examined the molecular regulatory circuits associated with MDM2 overexpression. To this end, we obtained not only RNA-seq data, but also ChIP-seq from Jun, RUNX, MDM2, P53 and H3K27ac, as well as hiChIP from tumor samples and cell lines. We found that MDM2 binds to thousands of genes involved in cellular growth and stress response in a P53-independent manner. Furthermore, we described these active genes engaged in multi-way topological interactions that resemble transcriptional hubs.

  Study phs003272

• Nala GSI GSAv3 PGx Study

  Background: Microarrays are a well-established and widely adopted technology capable of interrogating hundreds of thousands of loci across the human genome. Combined with imputation to cover common variants not included in the chip design, they offer a cost-effective solution for large-scale genetic studies. Beyond research applications, this technology can be applied for testing pharmacogenomics, nutrigenetics, and complex disease risk prediction. However, establishing clinical reporting workflows requires a thorough evaluation of the assay's performance, which is achieved through validation studies. In this study, we performed pre-clinical validation of a genetic testing workflow based on the Illumina Global Screening Array for 25 pharmacogenomic-related genes. Methods: To evaluate the accuracy of our workflow, we conducted multiple pre-clinical validation studies. Here, we present the results of accuracy and precision assessments, involving a total of 73 cell lines. These assessments encompass reference materials from the Genome-In-A-Bottle (GIAB), the Genetic Testing Reference Material Coordination Program (GeT-RM) projects, as well as additional samples from the 1000 Genomes project (1KGP). We conducted an accuracy assessment of genotype calls for target loci in each indication against established truth sets. Results: In our per-sample analysis, we observed a mean analytical sensitivity of 99.39% and specificity 99.98%. We further assessed the accuracy of star-allele calls by relying on established diplotypes in the GeT-RM catalogue or calls made based on 1KGP genotyping. On average, we detected a diplotype concordance rate of 96.47% across 14 pharmacogenomic-related genes with star allele-calls. Lastly, we evaluated the reproducibility of our findings across replicates and observed 99.48% diplotype and 100 % phenotype inter-run concordance. Conclusion: Our comprehensive validation study demonstrates the robustness and reliability of the developed workflow, supporting its readiness for further development for applied testing.

  Study EGAS00001007710

• High titers and low fucosylation of early phase anti-SARS-CoV-2 IgG promote hyper-inflammation by alveolar macrophages

  We show that early phase anti-Spike IgG in serum of critically ill COVID-19 patients induces hyper-inflammatory responses by human alveolar macrophages. We identified that this excessive inflammatory response is dependent on two antibody features that are specific for severe COVID-19. First, inflammation is driven by high titers of anti-Spike IgG, a hallmark of severe disease. Second, we found that anti-Spike IgG from severely ill patients is intrinsically more pro-inflammatory because of different glycosylation of the Fc tail, particularly by low fucosylation. This dataset is linked with the following ArrayExpress Experiment: E-MTAB-10431 - 2020_COVID19_FCGR_MIL10_IgGfucosylation

  Study EGAS00001005206