22938 results for "ega"

in 30.66 milliseconds.

• WXS files for Mullighan_GL_reALL

  WXS files for Mullighan_GL_reALL paper titled "Mutational landscape and patterns of clonal evolution in relapsed pediatric acute lymphoblastic leukemia"

  Dataset EGAD00001005509

• Organoid Derivation Project TGS: Release 3

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001005958

• SPECTA Lung cancer RNA FASTQ files

  SPECTA Lung cancer RNA FASTQ files (Illumina TST170 targeted analysis)

  Dataset EGAD00001006222

• GEP-NEN Bulk-RNA-seq dataset

  This dataset entails 40 Bulk-RNA sequenced patient-derived gastro-intestinal neuroendocrine (GEP-NEN) neoplasms.

  Dataset EGAD00001006657

• Reference epigenome Islet-derived-MSC04 miRNA-Seq data generated from KEP study

  Islet-derived-MSC04 miRNA-Seq single end data

  Dataset EGAD00001007184

• Reference epigenome SMC03 input ChIP-Seq data generated from KEP study

  SMC03 input ChIP-Seq paired end data

  Dataset EGAD00001007325

• Reference epigenome SMC08 h3k4me1 ChIP-Seq data generated from KEP study

  SMC08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007346

• Reference epigenome ADMSC02 input ChIP-Seq data generated from KEP study

  ADMSC02 input ChIP-Seq paired end data

  Dataset EGAD00001007367

• Reference epigenome ADMSC03 h3k27ac ChIP-Seq data generated from KEP study

  ADMSC03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007368

• Reference epigenome ADMSC03 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007369

• Reference epigenome ADMSC03 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007370

• Reference epigenome ADMSC03 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007371

• Reference epigenome ADMSC03 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007372

• Reference epigenome ADMSC03 input ChIP-Seq data generated from KEP study

  ADMSC03 input ChIP-Seq paired end data

  Dataset EGAD00001007373

• Reference epigenome ADMSC05 h3k27Ac ChIP-Seq data generated from KEP study

  ADMSC05 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007374

• Single-cell characterization of human GBM reveals regional differences in tumor-infiltrating leukocyte activation

  Background: Glioblastoma (GBM) harbors a highly immunosuppressive tumor microenvironment (TME) which influences glioma growth. Major efforts have been undertaken to describe the TME on a single-cell level. However, human data on regional differences within the TME remain scarce. Goals: we performed single-cell RNA sequencing (scRNAseq) on paired biopsies from the tumor center, peripheral infiltration zone and blood of five patients with primary GBM Details: This EGA study includes raw data sequencing files for the scRNA-seq samples. The processed data (output from Alevin, UMI count matrix and cell metadata) are available from GEO https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE197543

  Study EGAS50000000302

• Multi-omics analysis of cocaine use disorder in postmortem brain tissue of the ventral striatum [Proteomics data to study EGAS50000000623]

  We performed a multi-omics analysis of cocaine use disorder in postmortem human brain tissue of the ventral striatum in N=41 individuals (N=20 CUD cases, N=21 non-affected individuals). From the postmortem brain tissue samples, we generated miRNA-seq, RNA-seq, proteomics, and snRNA-seq data. The miRNA-seq, RNA-seq and snRNA-seq datasets can be found in the EGA under study ID EGAS50000000623. Proteomics data is deposited under study ID EGAS00001007945 (this study). The proteomic dataset was obtained using TMT-labeling. Measurement was performed on an Orbitrap Fusion Lumos device.

  Study EGAS00001007945

• Link to the Finnish FEGA Node's service provider

  We would like to direct you to the service provider for the Finnish FEGA Node, which is CSC – IT Center for Science (https://research.csc.fi/-/fega). It's important to note that this is not a genuine DAC and does not serve as the data controller. To obtain access to data stored in FI-FEGA, it is essential to utilize the local access management system SD Apply. Please be aware that applications submitted through the EGA DAC portal will not be processed. For a seamless experience, we kindly request you to refer to the policy of your selected dataset and submit your access application through SD Apply (https://sd-apply.csc.fi/). Your cooperation is highly appreciated.

  Dac EGAC50000000022

• singel cell RNAseq dataset for the study "Composition and functional status of T and NK cells in Extramedullary myeloma tumor microenvironment""

  Dataset contains all newly generated single cell RNAseq data generated for the study. It includes 5 EMM soft tissue samples, 5 EMM bone marrow samples (EMM_BM) and 6 RRMM BM samples without EMM (RRMM_BM). Samples that are sequenced twice are marked by _1 or _2 in the sample name. The sample name mentioned corresponds to the sample name used in the publication. The rest of the EMM single cell RNAseq samples mentioned int the publication, EMM_03_1, EMM_11, EMM_14_1, EMM_14_2, EMM_16, and EMM_15_1 are already uploaded in EGA archive – dataset id EGAD50000000053.

  Dataset EGAD50000001511

• The study provides comprehensive access to the set of EGA studies which may be useful as controls.

  The subjects studies in these datasets were selected due to being healthy control individuals or otherwise provide well characterised phenotype or exclude certain disease phenotype categories such that they may be useful as control population, such as to derive allele frequencies from a known reference population.

  Study EGAS00001000646

• Single cell sequencing of human normal luminal cells

  The dataset includes 12 paired FASTQ files (6 samples) with single cell transcriptome sequencing data of normal breast luminal cells from ducts and TDLUs derived from reduction mammoplasties from three patients. Chromium Single Cell 3’ Reagent Kit v2 or v3 (10x Genomics) were used for processing of cells, whereafter sequencing was performed using the Illumina® NextSeq500/550 High Output Kit v2. Cell Ranger was used for generating FASTQ files and files from different lanes were concatenated prior to uploading the data to EGA. "R1" files include the feature barcodes and UMIs, while "R2" files include the reads.

  Dataset EGAD00001008499

• 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

• International Multiple Sclerosis Genetics (IMSGC) Data Access Committee

  Dac EGAC00001001662

• UCSF Brain Tumor Bioinformatics DAC

  Dac EGAC00001001858

• EGAD00010000730

  WTCCC2 Psychosis Endophenotype samples from UK, Germany, Holland, Spain and Australia using the Affymetrix 6.0 array

  Dataset EGAD00010000730

• EGAD00010000246

  Coeliac disease cases and control samples. (1958BC samples excluded)

  Dataset EGAD00010000246

• EGAD00010000248

  1958BC control samples

  Dataset EGAD00010000248

• EGAD00010000558

  SNP 6.0 arrays of small cell lung cancer

  Dataset EGAD00010000558

• Paroxysmal neurological disorders

  Paroxysmal neurological disorders

  Dataset EGAD00001000021

• Paroxysmal neurological disorders

  Paroxysmal neurological disorders

  Dataset EGAD00001000108

• A Comprehensive Catalogue of Somatic Mutations from a Human Cancer Genome

  A Comprehensive Catalogue of Somatic Mutations from a Human Cancer Genome

  Dataset EGAD00001000149

• UK10K_RARE_FIND REL-2012-01-13

  UK10K_RARE_FIND REL-2012-01-13

  Dataset EGAD00001000171

• UK10K_RARE_FIND REL-2012-02-22

  UK10K_RARE_FIND REL-2012-02-22

  Dataset EGAD00001000190

• UK10K_RARE_FIND REL-2012-07-05

  UK10K_RARE_FIND REL-2012-07-05

  Dataset EGAD00001000209

• UK10K_RARE_FIND REL-2012-11-27

  UK10K_RARE_FIND REL-2012-11-27

  Dataset EGAD00001000297

• ICGC MMML-seq Data Freeze March 2013 transcriptome sequencing

  ICGC MMML-seq Data Freeze March 2013 transcriptome sequencing

  Dataset EGAD00001000356

• SYSCOL Human colorectal cancer project pilot RNA-seq and germline genotyping

  Genotyping by array and Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001000880

• Whole genome MeDIP-seq of DNA in whole blood samples from 5yr old Swedish children (the ABIS study)

  Dataset EGAD00001001221

• Exome chip analysis file

  This data set includes the following summary level data file used for the exome chip analysis: exome_chip.sv.assoc.txt: results from single variant association analysis in exome chip

  Dataset EGAD00010001187

• RNA-seq and WGS data of MB-0152

  Identification of fusion transcripts by RNA-sequencing and Whole genome sequencing of a breast cancer patient sample (METABRIC ID MB-0152)

  Dataset EGAD00001003342

• Exome-seq data from study of juvenile myelomonocytic leukemia (JMML)

  Whole exome seqeuncing from primary human JMML samples

  Dataset EGAD00001003419

• Immunochip_genotypes

  Intensity files for Immunochip genotypes from blood

  Dataset EGAD00010001495

• MS-Twins-EPIC-PBMCs

  Genome-wide DNA methylation profiles of MZ twins clinically discordant for MS generated using Illumina’s Infinium MethylationEPIC BeadChip assay (EPIC array)

  Dataset EGAD00010001618

• GR@ACE_StageI.v02

  Binary Plink files for post-GWAS quality control in 7409 samples genotyped using Axiom 815K Spanish Biobank array (Thermo Fisher)

  Dataset EGAD00010001653

• RP3_Freeze_1

  Freeze 1 of the RP3 project

  Dataset EGAD00010000887

• REL-2014-11_2

  HipSci - Healthy Normals - Genotyping Array - November 2014

  Dataset EGAD00010000773

• APCDR AGV Kalenjin

  APCDR AGV Project: Array data from 100 Kalenjin. Raw data, intensity files and post-QC Plink files.

  Dataset EGAD00010001052

• APCDR AGV Sotho

  APCDR AGV Project: Array data from 86 Sotho. Raw data, intensity files and post-QC Plink files.

  Dataset EGAD00010001046

• APCDR AGV Wolof

  APCDR AGV Project: Array data from 78 Wolof. Raw data, intensity files and post-QC Plink files.

  Dataset EGAD00010001050

• APCDR AGV Mandinka

  APCDR AGV Project: Array data from 88 Mandinka. Raw data, intensity files and post-QC Plink files.

  Dataset EGAD00010001057

• APCDR AGV Jola

  APCDR AGV Project: Array data from 79 Jola. Raw data, intensity files and post-QC Plink files.

  Dataset EGAD00010001048

• APCDR AGV Kikuyu

  APCDR AGV Project: Array data from 99 Kikuyu. Raw data, intensity files and post-QC Plink files.

  Dataset EGAD00010001049

• APCDR AGV Igbo

  APCDR AGV Project: Array data from 99 Igbo. Raw data, intensity files and post-QC Plink files.

  Dataset EGAD00010001045

• APCDR AGV Barundi

  APCDR AGV Project: Array data from 97 Barundi. Raw data, intensity files and post-QC Plink files.

  Dataset EGAD00010001051

• Methylation_Both

  Epigenome wide DNA methylation assay of OSCC-GB using Illumina methylation array

  Dataset EGAD00010001850

• Identifying the role of ID3 in DNA repair and maintenance of genome integrity (RNA-seq)

  Dataset EGAD00001008198

• PIAMA DNA methylation dataset

  Nasal DNA methylation at three CpG sites predicts childhood allergic disease

  Dataset EGAD00010002263

• Average_5hmC_TF_sites

  Average hydroxymethylation levels on transcription factor binding sites obtained from ENCODE (ChIP-sequencing of GM12878 lymphoblastoid cell line). Data from 6 individuals at different time points.

  Dataset EGAD00010002421

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - pilot normal and pre/post-chemo

  Dataset EGAD00001000704

• uganda_autosomes

  Genotype data (in binary PLINK format) and imputed data (with merged 1000Gp3 and AGVP reference panel in GEMMA BIMBAM dosage format) for 1391 individuals from EMaBS in Entebbe, Uganda.

  Dataset EGAD00010002578

• Multi-site tumor sampling highlights molecular intra-tumor heterogeneity in malignant pleural mesothelioma

  Dataset EGAD00001007762

• The CCA Genome Core is responsible for archiving the sequencing data generated by the Cancer Centre Amsterdam (CCA).

  Dac EGAC00001000181

• Knoll et al Identification of drug candidates targeting monocyte reprogramming in people living with HIV PBMC drug in vitro

  PBMC RNAseq drug in vitro

  Dataset EGAD50000000070

• Whole genome bisulfite sequencing of prostate cancer samples upon oral pimonidazole administration

  Whole genome bisulfite sequencing of prostate cancer samples upon oral pimonidazole administration

  Dataset EGAD50000000099

• The Genomic Map of Polad in Open Access

  Part of the population based Cohort of Polish inviduals POPULOUS (bbmri-eric:ID:PL_BLUL:collection:POPULOUS_BLUL)

  Study EGAS50000000094

• Single-Cell RNA Sequencing of Primary High Grade Serous Carcinoma

  CD45- Single Cell RNA Sequencing data on 8 High Grade Serous Carcinoma Primary Tumors

  Dataset EGAD50000000098

• tRCC UTSW 2024 Cohort

  This submission contains the WES and RNA-Seq raw data for the study titled 'Comparative Genomics of a Translocation Renal Cell Carcinoma Mouse Model Reveals Molecular Mechanisms of Tumorigenesis.

  Dac EGAC50000000105

• WGS of 78 FL tumour normal pairs

  This dataset is the second batch of WGS uploaded from FL GenomeCanada data. The other batch is in EGAD00001011343

  Dataset EGAD50000000253

• Repeated Sampling experiment

  Repeated Sampling Experiment containing 135 fastq files of RNA sequencing. These represent time series of different organ areas sampled during autopsies.

  Dataset EGAD50000000330

• WES data for Heterogeneity of IKZF1 genomic alterations and risk of relapse in childhood BALL

  Whole exome sequencing data for 425 samples with B-cell acute lymphoblastic leukemia

  Dataset EGAD50000000150

• Long read microbiome whole metagenome sequencing

  Long read microbiome whole metagenome sequencing of pediatric GI samples.

  Dataset EGAD50000000486

• bfast CohortD OSPL

  ctDNA sample data (one sample per line) for BFAST Cohort D including sample-level summaries like tumor fraction (cTF).

  Dataset EGAD50000000147

• ASD WGS DAC

  This data access committee will oversee the request for applications to whole genome sequencing data (WGS) generated at The Centre for Applied Genomics (TCAG), as part of our Autism Genome Project.

  Dac EGAC50000000279

• Walter and Eliza Hall Institute - University of Melbourne

  Data Access Committee for Gocuk, Lancaster et al. 2024: nanopore sequencing data of patients with choroideremia and X-linked retinitis pigmentosa, to determine X inactivation skew.

  Dac EGAC50000000301

• First in vivo investigation of the impact of PARP inhibition with rucaparib alone and in combination with atezolizumab: results of the phase Ib COUPLET clinical study in advanced gynecologic and triple-negative breast cancers

  Study EGAS00001006100

• Whole exome sequencing data for two samples

  Whole exome sequencing of two human samples run on the Illumina HiSeq2500 platform. It contains two BAM files aligned to the refrence genoeme GRCh38.

  Dataset EGAD50000000825

• RCRF release 2

  RNA sequencing of a Chromophobe renal cell carcinoma, Non-clear cell chromophobe renal cell carcinoma, Neuroendocrine PDAC and clear cell sarcoma

  Dataset EGAD50000000872

• WES dataset for cfDNA cohort

  Whole exome sequencing data for 457 samples, including diagnosis and germline sample, across multiple cancer types for cfDNA cohort

  Dataset EGAD50000000103

• WGS data for cfDNA cohort

  Whole genome sequencing data for 423 samples, including diagnosis and germline, across multiple cancer types for cfDNA cohort

  Dataset EGAD50000000102

• resistance mechanims to targeted therapies from DNA sequencing

  WES of tumor tissue biopsies collected at resistance to molecular targeted agents

  Study EGAS50000000486

• Whole Genome Sequencing of Insulinomas

  Whole Genome Sequencing of Insulinomas and paired blood controls from the same patients.

  Study EGAS50000000321

• Tumor Profiler AML Study

  Multi-omics data generated from 22 samples from the Tumor Profiler AML cohort

  Study EGAS50000000577

• SMPaeds PanelSeq of cfDNA at relapse with UMIs

  Deep sequencing of a targeted panel of 67 paediatric cancer related genes for cfDNA with UMIs at approximately 1500xUMI coverage.

  Dataset EGAD50000000781

• Psoriasis PBMCs

  This DAC is for the purpose of controlling access to Psoriasis PBMC scRNA-seq data of the study Spermidine/spermine N1-acetyltransferase controls tissue-specific regulatory T cell function in chronic inflammation

  Dac EGAC50000000470

• HUG-CELL ASD Data Access Committee

  Data Access Committee for data generated by projects involving ASD and associated phenotypes by the Human Genome and Stem Cell Research Center (HUG-CELL), University of São Paulo

  Dac EGAC50000000475

• Picuris Pueblo Genomic Project – Ancient Dataset

  Ancient individual from Picuris Pueblo. Please refer to paper for more information.

  Dataset EGAD50000001246

• Nanopore whole-genome sequencing of human osteosarcomas

  We performed single and multi-region nanopore whole-genome sequencing on human osteosarcoma samples from adult and paediatric patients.

  Dataset EGAD50000000925

• APOBEC Breast Cancer Whole Genome

  Five tumor-normal human breast cancer cases driven by APOBEC mutagenesis, including normal tissue, primary, and metastatic samples for each patient.

  Dataset EGAD50000001275

• Nanopore whole genome sequencing data of HG002

  Data generated from single cell, 10 cells, and bulk samples of HG002.

  Study EGAS50000000552

• RNA-Seq of kidney cancer organoid biobank

  Dataset EGAD00001005318

• Raw RNA sequencing of hepatoblastoma PDX cell line HB-303-LEF

  Raw RNA sequencing of hepatoblastoma PDX cell line HB-303-LEF

  Study EGAS50000000928

• ADARIO / Targeting ADAR1 in Immuno-Oncology

  RNA editing analyses of BRCA-isogenic cell lines and patient-derived xenografts (PDX)

  Study EGAS50000000518

• Single-nucleus RNA-sequencing of normal adrenal cortex and adrenocortical tumors

  BAM generated using Cell Ranger v3.1.0 and Space Ranger v1.3.1 and anonymized using BAMBoozle

  Dataset EGAD50000000835

• Human embryonic stem cells dopaminergic neurons

  These are two biological replicates at timepoint day 17 and day 28 of dopaminergic neuron differentiation of human embryonic stem cells. Ásgrimsdottir et al. (Arenas Lab, Karolinska Institutet)

  Dac EGAC50000000652

• Whole genome sequencing of colon cancer data

  We performed longitudinal genomic analysis of samples from patients with colorectal-derived neoplasias from different stages through metastasis and treatment.

  Dataset EGAD50000002141

• GLASS-NL whole exome sequencing (WES) normal samples

  Whole exome sequencing (WES) data from 45 normal samples of 45 patients in the GLASS-NL trial.

  Dataset EGAD50000000582

• EGAD00010000566

  HipSci - Healthy Normals - Genotyping Array - May 2014

  Dataset EGAD00010000566

• Whole Genome sequencing of adult T-cell leukemia/lymphoma

  Whole Genome sequencing of a single adult T-cell leukemia/lymphoma case

  Study EGAS00001001210

• MDS_Sequential_Treatment_Validation

  Validations of variants identified by exome sequencing in sequential samples derived after treatment cycle with AZA.

  Study EGAS00001000703

• V2_Colorectal_panel_test

  Targeted gene screen of cell line tumour samples for testing the new V2 Colorectal gene panel.

  Study EGAS00001001806