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• PELICAN45 RNAseq Dataset

  To better understand the biology of prostate cancer, we performed RNAseq in cancerous (primary and metastatic) and noncancerous tissues in the PELICAN45 cohort. The PELICAN45 RNAseq samples include androgen-deprived metastasis samples from the PELICAN33 consenting rapid autopsy study participants, whose identifiers are A1, A2, etc. The PELICAN45 RNAseq dataset also includes data from non-androgen deprived metastases in an additional 12 men. These samples are identified as PB50, PB133, etc.

  Study EGAS00001006959

• sWGS of Pap test smears from healthy donors and HGSOC patients and matched tumor tissue

  Shallow whole genome sequencing was performed on Pap test smears considering at least 10 M reads per sample. The cohort consists of 77 samples from healthy women, i.e. no tumor diagnosis, and 65 samples from women with a diagnosis of high-grade serous ovarian cancer (HGSOC) taken months or years before diagnosis. 44 primary tumor samples matched to the respective HGSOC patients were sequenced in the same manner as the Pap test smears.

  Study EGAS00001007084

• Genetic scoring guide for personalized risk assessment in pediatric B-cell precursor Acute Lymphoblastic Leukemia

  In this study a next-generation sequencing based method was applied to comprehensively screen for recurrent, disease-relevant copy number aberrations in a cohort of Hungarian patients. Diagnostic bone marrow samples from 260 children with B-cell acute lymphoblastic leukemia and were investigated by digital multiplex ligation-dependent probe amplification using the disease-specific D007 probemix. Whole chromosome gains and losses, as well as subchromosomal copy number aberrations were simultaneously profiled.

  Study EGAS00001007239

• Possible DNA damage after paternal exposure to ionizing radiation in radar technicians

  Effects of accidental and prolonged radiation exposure on the human germline remain a topic of medical interest. Transgenerational signatures of ionizing radiation exposure are primarily implicated to be clustered de-novo mutations (cDNMs, multiple de-novo mutations within 20bp). We sequenced the whole genome of 110 children from 70 families (Radar cohort). The father in this cohort were exposed to 0-353mSv of ionizing radiation and serve as exposed samples.

  Study EGAS00001007321

• Combination Immune Checkpoint Inhibition in Australian Rare Cancers_WES

  Combination immune checkpoint inhibition (ICI) with anti-CTLA-4 and anti-PD-1 blockade has demonstrated significant clinical activity across tumour types. Rare cancer patients have limited treatment options due to the scarcity of studies of novel treatment options, so they are often offered chemotherapies untested in their disease. ONJ2016-001/CA209-538 is a prospective, multicentre clinical trial of combination ICI (ipilimumab and nivolumab) in patients with advanced rare cancers, including biliary tract, adrenocortical, rare gynaecological and neuroendocrine cancers (https://clinicaltrials.gov/ct2/show/NCT02923934). The primary endpoint is clinical benefit rate (complete response + partial response + stable disease >3 months) by RECIST 1.1. This study involves generation and analysis of pre-treatment formalin fixed paraffin embedded (FFPE) tumour DNA sequencing data, paired with germline DNA sequencing data, to potentially identify genomic biomarkers that may associate with clinical benefit or other clinical efficacy / toxicity metrics.

  Dataset EGAD00001015465

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

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

  Study EGAS00001006033

• Distribution of data using Live Distribution

  Live Distribution Welcome to the documentation for using the Live Distribution feature for distributing data files securely through the EGA platform. This guide will walk you through the process of downloading encrypted files and decrypting them using Crypt4GH. Please follow the steps below to ensure a smooth experience. Before Downloading Create an EGA user. Make sure that you have the permissions to download the dataset of interest. In case you don’t have access, request access to the dataset. Add your Crypt4GH-compatible public key to your EGA account. Please allow a few hours for your public key to be synced with your profile. Afterwards, you will be able to connect to your EGA outbox using the SFTP protocol. Download Graphical User Interface (GUI) You can use any GUI that supports SFTP connections, such as FileZilla, an open-source FTP client. For Filezilla as your GUI, follow these steps to download files: Open FileZilla and access Site Manager (File > Site Manager). Create a new connection with the following settings (Figure 1): Protocol: SFTP - SSH File Transfer Protocol Host: __EGA_OUTBOX_DOMAIN__ Logon Type: Key file User: your EGA username Key file: path/to/your/private_key Figure 1: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the EGA outbox, ensuring seamless data transfers. Click Connect to access your Outbox. This folder serves as your storage space within the EGA cloud, containing files accessible for download in a secure way. Browse the remote directory on the right side of the FileZilla screen. Select the files you wish to download, right-click, and choose Download (Figure 2). Figure 2: Step-by-step process of manually downñoad files from __EGA_OUTBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can downñoad data from the EGA outbox to their local storage by following the depicted steps SFTP command line You can also use the SFTP command line to securely download files from the EGA Outbox. Using SFTP command line client in Linux/Unix Open a terminal window Enter the following command to connect: sftp username@hostname Enter your EGA password To see a list of available sftp commands type help sftp> put – Upload file sftp> get – Download file sftp> cd path – Change remote directory to ‘path’ sftp> pwd – Display remote working directory sftp> lcd path – Change the local directory to ‘path’ sftp> lpwd – Display local working directory sftp> ls – Display the contents of the remote working directory sftp> lls – Display the contents of the local working directory Type get command to download files. For example: get encrypted_file.c4gh Use the bye command to close the connection (SFTP session). Convenient SSH settings Include the following settings in your SSH config file, located in ~/.ssh/config Host __EGA_OUTBOX_DOMAIN__ EGA-outbox hostname __EGA_OUTBOX_DOMAIN__ User username IdentityFile path/to/the/private/key Replace username and path/to/the/private/key with the appropriate settings, and you will be able to connect to the __EGA_OUTBOX_DOMAIN__ simply using sftp EGA-outbox. How to decrypt Files archived at the EGA are encrypted based on Crypt4GH. Hence, to decrypt the files you need to install 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 After installing Crypt4GH, decrypt files using the following command: crypt4gh decrypt --sk /path/private/key < encrypted_file.c4gh > decrypted_filename The command reads the encrypted file from stdin (with <) and output the decrypted version to stdout (with >). Replace encrypted_file.c4gh and decrypted_filename 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. Frequently Asked Questions What username should I use to log in to my outbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username, not your email address. Therefore, if you registered a username different from your email address when creating your EGA account, you must use that username to log in. If you have forgotten your registered username, please, contact our Helpdesk team for assistance. I see that some files in my dataset have 'unavailable' as extension. What should I do? Within your Outbox, you'll find a list of all the datasets available for download. Occasionally, certain files may be marked as "unavailable". These unavailable files can be identified by the "unavailable" extension added to their filenames (e.g. filename.fastq.gz.unavailable.c4gh). If you encounter an unavailable file that you need, please reach out to our Helpdesk. We'll promptly work on making the file accessible for download as soon as possible. Specific to using keys Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

  Documentation access/download/files/live-outbox

• Pilot study Pilocytic Astrocytoma ICGC PedBrain, whole genome sequencing of 5 tumors and matched blood

  Pilot study Pilocytic Astrocytoma ICGC PedBrain, whole genome sequencing of 5 tumors and matched blood

  Dataset EGAD00001000271

• Cancer Genome Scanning in Plasma: Detection of Tumor-Associated Copy Number Aberrations, Single-Nucleotide Variants, and Tumoral Heterogeneity by Massively Parallel Sequencing

  Cancer Genome Scanning in Plasma: Detection of Tumor-Associated Copy Number Aberrations, Single-Nucleotide Variants, and Tumoral Heterogeneity by Massively Parallel Sequencing

  Dataset EGAD00001000308

• Oncoscan SNP arrays for study "Molecular and functional profiling of plasmablastic lymphoma"

  1 cell line and 82 Oncoscan SNP tumor initial samples, zipped Affymetrix CEL file types, Oncoscan CNV FFPE Assay Kit, Thermo Fisher Scientific GeneChipTM Scanner 3000 7G

  Dataset EGAD00010001978

• med-pchic-dac

  The data are accessible under the terms of the following Data Access Agreement that needs to be filled in, signed (by yourself and your institution’s official contact) and returned by email to med-pchic-dac@lists.cam.ac.uk

  Dac EGAC00001000523

• Sequence-Based Analysis of Human Breast Tumors

  The goal of this study is to study gene expression patterns in tumors, normal tissues, and cell lines.

  Study phs000676

• T-ALL and Thymus 10x genomics immune profiling

  10x Genomics immune profiling including sequenced libraries of: 5'-end mRNA transcriptomics Beta and alpha chain VDJ transcripts Cell surface expression by 204 DNA barcoded antibodies

  Dataset EGAD50000000533

• MBD4 targeted sequencing

  We sequenced a gene of interest, MBD4, in both germline (n=1099) and tumor (n=192) sample from Uveal Melanoma patients.

  Study EGAS00001005012

• scRNA and spatial-seq of human skin wound healing

  Human skin acute wound healing contains 12 samples of scRNA-seq and 16 samples of spatial transcriptomics, Human skin chronic wound data contains 9 samples scRNA-seq.

  Dataset EGAD50000000813

• 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

• H3Africa ACEGID Array Phenotype

  Phenotype data for Lassa Fever cases and population controls from Nigeria and Sierra Leone associated with genotype data generated using Illumina H3Africa array version 1.

  Dataset EGAD00001010923

• TRACERx NSCLC - whole genome sequencing for ctDNA study

  An ultra-sensitive and specific ctDNA assay provides novel pre-operative disease stratification in early stage lung adenocarcinoma

  Study EGAS50000000313

• WGBS of patients from six tumor types

  This dataset contains healthy and tumor samples from patients covering breast adenocarcinoma, colon adenocarcinoma, kidney renal clear cell carcinoma, hepatocellular carcinoma, lung adenocarcinoma and pancreas adenocarcinoma.

  Dataset EGAD50000001318

• Identify disease-related genes

  Raw data (idat file) analyzed by Illumina SNP array for patients in the 1st cohort and the 2nd cohort (51 diseases).

  Study JGAS000703

• Whole-exome-sequencing in Frontotemporal dementia (FTD)

  To identify the genetic cause and modifiers of Frontotemporal dementia (FTD), we performed Whole-exome-sequencing of FTD patients.

  Study JGAS000374

• Human non-malignant plasma cfRNA study - raw data

  mRNA capture sequencing on plasma sample from 88 patients with a non-malignant disease and from 37 healthy donors.

  Study EGAS50000001264

• Sensitive gene analysis of hereditary cardiovascular disease

  This research aims to identify disease sensitive genes and their gene variants by genome analysis for hereditary cardiovascular diseases.

  Study JGAS000295

• Massachusetts General Hospital Cancer Center DAC for single cell RNA-seq data from small cell lung cancer primary tumors and CTCs

  single cell RNA-seq data from 10 small cell lung cancer primary tumors and circulating tumor cells of three small cell lung cancer patients

  Dac EGAC50000000808

• BCAC TIIC data

  TIIC dataset generated by BCAST project in BCAC from tumor cores from 12,285 breast cancer patients. Includes automated scores (multiply imputed) for CD8, CD20, CD163 and FOXP3 in all tissue, stromal and tumour tissue separately. Linked clinical patient data.

  Dataset EGAD50000002125

• WGS and RNA-Seq data from a GBM patient PT-DS9789

  WGS and RNA-Seq data from a GBM patient PT-DS9789

  Dataset EGAD00001004233

• Gain of Function Mutations in RPA1

  Exome sequencing and amplicon-based single-cell sequencing dataset on the patients and family members that were analyzed in this study.

  Dataset EGAD00001008329

• TGL49_HBC CHARM panel

  Targeted panel sequencing of hereditary cancer syndrome-associated genes (TP53, BRCA1, BRCA2, PALB2, MLH1, MSH2, MSH6, PMS2, EPCAM, and APC) in plasma and buffy coat samples from healthy control patients.

  Dataset EGAD00001010001

• WGS and RNA-Seq data from a GBM patient PT-GJ3716

  WGS and RNA-Seq data from a GBM patient PT-GJ3716

  Dataset EGAD00001004240

• WGS and RNA-Seq data from a GBM patient PT-KM5291

  WGS and RNA-Seq data from a GBM patient PT-KM5291

  Dataset EGAD00001004249

• Sequencing files for 7 melanoma patients

  Files from whole exome sequencing of matched normals and multiple tumors from 7 melanoma patients. The tumors include primary tumors and distant metastases.

  Dataset EGAD00001006410

• WGS and RNA-Seq data from a GBM patient PT-JW6420

  WGS and RNA-Seq data from a GBM patient PT-JW6420

  Dataset EGAD00001004248

• RNA Sequencing of mCRC xenografts under cetuximab treatment.

  RNA sequencing on cetuximab treated, untreated and release samples of one metastatic colorectal xenograft. 3 cetuximab treated samples, 3 placebo and 3 release - paired fastq.

  Dataset EGAD00001005185

• WGS and RNA-Seq data from a GBM patient PT-SK0976

  WGS and RNA-Seq data from a GBM patient PT-SK0976

  Dataset EGAD00001004260

• BIG MS Pilot

  Testing logistics and infrastructure of molecular screening program. Core biopsies taken from invasive recurrent or metastatic breast cancer to evaluate and identify molecular traits rendering them suitable for clinical trials

  Dataset EGAD00001000870

• WGS and RNA-Seq data from a GBM patient PT-LC6372

  WGS and RNA-Seq data from a GBM patient PT-LC6372

  Dataset EGAD00001008522

• PCa-LINES: rRNA-minus RNA-seq of PCa cell-lines (VCaP &amp; PC346c) and 5 additional patient samples

  PCa-LINES: rRNA-minus RNA-seq of PCa cell-lines (VCaP &amp; PC346c) and 4 additional patient samples

  Dataset EGAD00001006366

• Ribodepletion RNA-Seq of 8 samples and polyA RNA-Seq of 22 samples

  Ribodepletion RNA-Seq of 8 samples and polyA RNA-Seq of 22 samples

  Dataset EGAD00001004993

• WES data from optic atrophy study

  The dataset includes Fastq files from WES experiments performed on a proband presenting with syndromic optic atrophy and his healthy parents. Exons were captured by hybridization and sequenced on an Illumina platform

  Dataset EGAD00001005321

• WGS and RNA-Seq data from a GBM patient PT-PD6881

  WGS and RNA-Seq data from a GBM patient PT-PD6881

  Dataset EGAD00001004255

• WGS and RNA-Seq data from a GBM patient PT-LC3356

  WGS and RNA-Seq data from a GBM patient PT-LC3356

  Dataset EGAD00001004250

• Whole genome sequencing of AML samples at presentation, remission, and relapse

  Whole genome sequencing of AML blood or bone marrow at presentation and remission for 5 patients. Relapse samples are included for 2 patients, totaling 12 WGS BAM files.

  Dataset EGAD00001005120

• RNA-seq of frontal post-mortem human brain tissue

  RNA-sequencing dataset of post-mortem human brain tissue of FTD patients with mutations in GRN, MAPT and C9orf72 and healthy controls.

  Dataset EGAD00001008014

• 184-hTERT-L2, SA501X3F, and SA501X4F bulk genomes

  Bulk whole-genome BAM files for 184-hTERT-L2, SA501X3F, and SA501X4F.

  Dataset EGAD00001003151

• Set of Structural Variants and Indels for GoNL samples

  These files contain indels and structural variants on 769 GoNL samples (SV release 6, 2016-05-25).

  Dataset EGAD00001002261

• Single-cell RNA sequencing of 43 BALF samples from CAPA and COVID-19 patients

  Single-cell RNA sequencing of 43 bronchoalveolar lavage fluid (BALF) samples from 15 CAPA and 22 COVID-19 mechanically ventilated patients.

  Dataset EGAD00001011342

• There are 80 Brain cancer cases in this study and belong to GBM-CN project.

  There are 80 Brain cancer cases （160 samples）in this study and belong to GBM-CN project.

  Dataset EGAD00001003218

• Gastric cancer RNA-seq data

  strand-specific RNA-seq data from 19 gastric tumors and their adjacent normal tissues, plus 16 gastric cancer cell lines, one normal gastric cell line, and 3 normal stomach RNAs

  Dataset EGAD00001003448

• Exome - MBD4-deficient AML

  Exome sequencing for WEHI-AML-1 and WEHI-AML-2. Exome capture was performed with the Human All Exon v5_UTR Capture Library and the Agilent Technologies SureSelectXT2 Target Enrichment System, with sequencing on an Illumina HiSeq2500.

  Dataset EGAD00001003570

• WGS and RNA-Seq data from a GBM patient PT-EV3071

  WGS and RNA-Seq data from a GBM patient PT-EV3071

  Dataset EGAD00001004234