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• EGAD00010000624

  A new beta-globin mutation responsible of a beta-thalassemia (HbVar database ID 2928) was observed in 8 unrelated French families. The mutation carriers originated from Nord-Pas-de-Calais, a Northern French region where the chief town is Lille. 5 unrelated mutation carriers were genotyped for a set of 12 microsatellites from chromosome 11, around the beta-globin gene. Among the 5 mutation carriers, 4 were genotyped for 97 European Ancestry Informative SNPs (EAIMs).

  Dataset EGAD00010000624

• EGAD00010000626

  A new beta-globin mutation responsible of a beta-thalassemia (HbVar database ID 2928) was observed in 8 unrelated French families. The mutation carriers originated from Nord-Pas-de-Calais, a Northern French region where the chief town is Lille. 5 unrelated mutation carriers were genotyped for a set of 12 microsatellites from chromosome 11, around the beta-globin gene. Among the 5 mutation carriers, 4 were genotyped for 97 European Ancestry Informative SNPs (EAIMs).

  Dataset EGAD00010000626

• A_study_of_the_molecular_pathogenesis_of_Splenic_Marginal_Zone_and_Diffuse_Large_B_Cell_Lymphoma

  We will perform exome sequencing on selected cases of splenic marginal zone lymphoma (SMZL) and diffuse large B-cell lymphoma (DLBCL) in order to characterise their genetic makeup and identify biomarkers for prognosis and prediction of treatment response.

  Study EGAS00001000335

• TCELL_PILOT_ATAC_SEQ

  This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001000758

• Lymphocyte_RNA_profiling

  This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001000564

• Whole exome sequencing of papillary thyroid carcinoma in the Chinese population

  Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Here we submitted the sequencing results for PTC using 91 tumor-normal pairs through exome sequencing in the Chinese Han population.

  Study EGAS00001001268

• Preclinical_evolution_of_haematological_malignancies_

  This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002128

• CELM

  This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002261

• Whole-exome analysis of corticotropin-independent Cushing's syndrome

  We performed WES for 8 cases with corticotropin-independent Cushing’s syndrome, in whom paired tumor and normal DNA was obtained from fresh frozen adenomas and either normal adrenal tissues or peripheral blood, respectively.

  Study EGAS00001000661

• Characterization_of_iPSC_derived_macrophages___cardiovascular_pilot

  This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001000876

• Genetic and expression landscape of Waldenstrom macroglobulinemia

  Exome analyses for the identification of somatic mutation in Waldenstrom macroglobulinemia by comparison of tumor and CD3+(germ line control) population in 16 patients. transcription profiling of a series of Waldenstrom macroglobulinemia samples

  Study EGAS00001003603

• Genetics_and_Networks_of_Congenital_Heart_Defects

  Exome sequencing of families with Congenital Heart Defects of diverse sub-phenotypes. Comprises both parent-offspring trios for sporadic cases and multiplex families. Collaboration with David Brook, University of Nottingham. Funded by the British Heart Foundation.

  Study EGAS00001000762

• SCLC study MGH - RNAseq dataset

  RNA-seq analyses were performed on cDNA libraries prepared from PolyA+ RNA using the Illumina TruSeq protocol for mRNA. The final libraries were sequenced with a paired-end 2×75 bp protocol aiming at 8.5 Gb per sample for a 30x mean coverage of the annotated transcriptome. All sequencing reactions were conducted on an Illumina HiSeq instrument (Illumina, San Diego, CA, USA).

  Dataset EGAD00001003969

• The contribution of POT1 variants to sporadic melanoma development (2018-06-06)

  A case-control series of melanoma cases from Leeds, UK have been sequenced in the Fluidigm platform to identify genetic variants associated with sporadic melanoma development. Samples in which potentially contributing variants have been detected are being sequenced in an orthogonal platform for variant confirmation. . This dataset contains all the data available for this study on 2018-06-06.

  Dataset EGAD00001004151

• RNA-Seq files for St. Jude Clinical Pilot

  This dataset contains whole genome sequencing BAM files for 78 tumor-normal pairs (a total of 156 samples) used in the St. Jude Clinical Pilot. Mapping was performed using BWA. This dataset accompanies the paper "Clinical Cancer Genomic Profiling by Three-Platform Sequencing of Whole Genome, Whole Exome and Transcriptome"

  Dataset EGAD00001004280

• Whole exome sequencing files for St. Jude Clinical Pilot

  This dataset contains whole exome sequencing BAM files for 78 tumor-normal pairs (a total of 156 samples) used in the St. Jude Clinical Pilot. Mapping was performed using BWA. This dataset accompanies the paper "Clinical Cancer Genomic Profiling by Three-Platform Sequencing of Whole Genome, Whole Exome and Transcriptome"

  Dataset EGAD00001004287

• Whole genome sequencing files for St. Jude Clinical Pilot

  This dataset contains whole genome sequencing BAM files for 78 tumor-normal pairs (a total of 156 samples) used in the St. Jude Clinical Pilot. Mapping was performed using BWA. This dataset accompanies the paper "Clinical Cancer Genomic Profiling by Three-Platform Sequencing of Whole Genome, Whole Exome and Transcriptome"

  Dataset EGAD00001004290

• LifeLines-NEXT pilot study

  The pilot study from the LifeLines-NEXT (NEXT) cohort consisting of 30 mother-infant pairs with longitudinal sampling from pregnancy till 3 months after birth. Shotgun data is available for virome and microbiome annotation.

  Study EGAS00001005969

• Whole-transcriptome characterization of cell-free RNA (cfRNA) in cancer and non-cancer patients

  Whole-transcriptome characterization of cfRNA in cancer (stage III breast [n=46], lung [n=30]) and non-cancer (n=89) participants from the Circulating Cell-free Genome Atlas (NCT02889978). Dataset includes collapsed BAM files for plasma cfRNA from each patient, as well as collapsed BAM files for RNA from matched tumor tissue (when available).

  Dataset EGAD00001006484

• BLUEPRINT release January 2015, Bisulfite-Seq for erythroblast

  Bisulfite-Seq data for 2 erythroblast sample(s). 35 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001133

• BLUEPRINT release January 2015, Bisulfite-Seq for inflammatory macrophage

  Bisulfite-Seq data for 3 inflammatory macrophage sample(s). 38 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001139

• BLUEPRINT release January 2015, Bisulfite-Seq for macrophage

  Bisulfite-Seq data for 5 macrophage sample(s). 72 run(s), 5 experiment(s), 5 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001192

• BLUEPRINT release January 2015, RNA-Seq for common myeloid progenitor

  RNA-Seq data for 3 common myeloid progenitor sample(s). 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001169

• PROP1_study

  The dataset for the PROP1 study consists of samples of patients with combined pituitary hormone deficiency due to two most prevalent mutations in the PROP1 gene (c.301_302delGA and c.150delA) and healthy relatives and controls. All subjects were genotyped for 21 single nucleotide polymorphisms surrounding the PROP1 gene in order to assess the potential ancestral origin of the respective mutations. The genotype data are displayed in the vcf format.

  Dataset EGAD00001001303

• BLUEPRINT release August 2014, Bisulfite-Seq for inflammatory macrophage

  Bisulfite-Seq data for 3 inflammatory macrophage sample(s). 38 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000914

• BLUEPRINT release August 2014, RNA-Seq for common myeloid progenitor

  RNA-Seq data for 3 common myeloid progenitor sample(s). 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000907

• Blueprint: A human variation panel of genetic influences on epigenomes and transcriptomes in three immune cells, (ChIP-Seq_H3K4me3 for mature neutrophil, on genome GRCh37)

  ChIP-Seq_H3K4me3 data for 133 mature neutrophil sample(s). 208 run(s), 136 experiment(s), 136 analysis(s) on human genome GRCh37. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/blueprint_Epivar/protocols/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002711

• Blueprint: A human variation panel of genetic influences on epigenomes and transcriptomes in three immune cells, (ChIP-Seq_H3K27me3 for mature neutrophil, on genome GRCh37)

  ChIP-Seq_H3K27me3 data for 131 mature neutrophil sample(s). 321 run(s), 134 experiment(s), 134 analysis(s) on human genome GRCh37. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/blueprint_Epivar/protocols/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002712

• Whole Genome Sequencing of INTERVAL (2019-06-12)

  15x Whole Genome Sequencing of 15,000 individuals from the INTERVAL study cohort, phase II. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-06-12.

  Dataset EGAD00001005085

• Single Cell Targeted DNA sequencing for Variant Calling in T-ALL

  This dataset contains the raw fastq-files and the VCF files of single cell targeted DNA sequening with the MissionBio Tapestri platform. This was performed on 8 male pediatric T-ALL cases: X09-XB37-XB47-XD83-XF91-XF97-XF100-XF121. For some patients we have timepoints during treatment: XF100 and XG121. XD83 is a patient that relapsed twice.

  Dataset EGAD00001006167

• Metagenome for FINRISK2002

  The dataset contains data for n=7211 FINRISK 2002 participants who underwent fecal sampling. Demultiplexed shallow shotgun metagenomic sequences were quality filtered and adapter trimmed using Atropos (Didion et al., 2017), and human filtered using Bowtie2 (Langmead and Salzberg, 2012). The files are in FASTQ format.

  Dataset EGAD00001007035

• Whole genome sequencing of adult glioblastoma nuclei

  Three primary adult glioblastoma specimens were dissociated and nuclei were extracted. A portion of the nuclei was used for single-cell ATAC seq and the remainder were submitted for whole genome sequencing, to provide orthogonal validation of copy number variations in the samples compared to single-cell ATAC seq. Samples were sequenced on the Illumina NovaSeq 6000 in paired-end mode.

  Dataset EGAD00001007651

• CRUK Accelerator: Pancreatic neuroendocrine tumours (panNETs) whole exome and RNAseq raw sequencing data

  Whole exome and RNASeq raw sequencing data for an individual with multiple pancreatic neuroendocrine tumours (panNETs). Age at diagnosis was 51. Tumour tissue and PBMCs were used for sequencing. This data was generated as part of a study funded by a Cancer Research UK Centres Network Accelerator Award Grant (A21998).

  Dataset EGAD00001009685

• Whole genome sequencing data for five Japanese subjects

  WGS was performed for five Japanese subjects. DNA samples isolated from whole blood were sequenced at Macrogen Japan Corporation. All libraries were constructed using the TruSeq DNA PCR-Free Library Preparation Kit according to the manufacturer’s protocols. Libraries were sequenced on HiSeqX (Illumina, San Diego, CA, USA) or Novaseq6000 (Illumina, San Diego, CA, USA).

  Dataset EGAD00001010075

• Pediatric Obesity Transcriptomics Data Access Committee

  The Pediatric Obesity Transcriptomics Data Access Committee is responsible for reviewing and approving requests for access to controlled-access transcriptomic and associated phenotypic data generated within the study "Circulating mRNA and miRNA signatures used as predictive markers of lifestyle modification outcomes in pediatric obesity management". Access requests are evaluated with regard to scientific merit, ethical compliance, consistency with participant consent, data protection regulations, and proposed data security measures.

  Dac EGAC50000001050

• Transcriptomic Data for Manuscript with title: Comprehensive genomic profiling in esophageal adenocarcinoma unmasks potential precision therapies

  Transcriptomic Data for Manuscript with title: Comprehensive genomic profiling in esophageal adenocarcinoma unmasks potential precision therapies

  Dataset EGAD00001015476

• DAC for achalasia risk consortium (arc)

  Dac EGAC00001000394

• DAC for aflatoxin project Qidong patient samples

  Dac EGAC00001000679

• DAC for the cfSort study (tissue deconvolution)

  Dac EGAC00001003222

• DAC for seminoma genomic data

  Dac EGAC00001000231

• DAC for study WES of pituitary tumors

  Dac EGAC00001000443

• Data Access Committee for Hui Lab

  Dac EGAC00001000454

• INRP - Italian Network for Research on Psychoses

  Dac EGAC00001000576

• DAC for MOMA, Short QT Exome data

  Dac EGAC00001000730

• DAC for Dissecting Tumor Microenvironment with Tumorgrafts

  Dac EGAC00001000809

• Data Access Committee for data from EGAS00001002840

  Dac EGAC00001000834

• DAC for human single nuclei RNAseq of Epilepsy

  Dac EGAC00001000860

• Data Access Committee for the CentralAfricanCMC_Pemberton dataset

  Dac EGAC00001000896

• Laboratory for Neurobiology (VIB-KU Leuven)

  Dac EGAC00001001282

• DAC for HSTCL study. National Cancer Centre Singapore.

  Dac EGAC00001001302

• DAC for MEITL study. National Cancer Centre Singapore.

  Dac EGAC00001001323

• DAC for Phylogenetic reconstruction of breast cancer

  Dac EGAC00001001572

• Center for Technology Licensing at Cornell University

  Dac EGAC00001001587

• Data Access Commitee for single-cell analysis

  Dac EGAC00001001600

• DAC for Stuttgart Breast Cancer Cohort

  Dac EGAC00001001693

• Data Access Committee for data from EGAS00001004619

  Dac EGAC00001001712

• DAC for 16 year brca case report

  Dac EGAC00001001714

• Data Access Committee for data from EGAS00001004666

  Dac EGAC00001001756

• Data Access Committee for data from EGAS00001004455

  Dac EGAC00001001798

• Genentech DAC for IMmotion151

  Dac EGAC00001001813

• Single cell multi-omics (scNOVA) group for AML

  Dac EGAC00001001866

• Data Access Committee for IPF Collaboration

  Dac EGAC00001001878

• Center for Medical Genetics Data Access Commitee

  Dac EGAC00001001892

• DAC for cell free DNA TAPS study

  Dac EGAC00001001903

• Data access committee for lung cancer

  Dac EGAC00001001942

• Data Access Committee for the MPNST project

  Dac EGAC00001001987

• DAC for single cell RNAseq in FL project

  Dac EGAC00001002114

• DAC-for-UniExome-EGA-upload-2021

  Dac EGAC00001002168

• DAC for Germline biallelic mutation

  Dac EGAC00001002423

• DAC for Identification of Germline Monoallelic Mutations

  Dac EGAC00001002461

• DAC for studying human normal breast cells

  Dac EGAC00001002481

• DAC for studying human normal breast cells

  Dac EGAC00001002498

• Annie Huang and the Hospital for Sick Children

  Dac EGAC01000000015

• Data Access Committee for the Berardi group

  Dac EGAC00001003392

• DAC for the study EGASXXX

  Dac EGAC00001002746

• DAC for Castleman. National Cancer Centre Singapore.

  Dac EGAC00001003328

• Data access committee for the HOMOCULTURGEN dataset

  Dac EGAC00001003485

• DAC for the study EGAS00001007247

  Dac EGAC00001003262

• PDiamond. DAC

  DAC for pdiamond project

  Dac EGAC50000000377

• DAC - Institute for Pharmacology and Toxicology

  Dac EGAC00001003385

• DAC_iCope

  DAC for the iCope submission

  Dac EGAC50000000696

• DAC for ALSPAC whole exome sequencing data

  Dac EGAC00001003496

• DAC for NMIBC study - UCLouvain

  Dac EGAC50000000789

• Data Access Committee for data from EGAS00001004454

  Dac EGAC00001002077

• LUMC Department Pediatrics, Laboratory for Pediatric Immunology

  Dac EGAC50000000822

• Data Access Committee for data from EGAS00001006662

  Dac EGAC00001003009

• Data Access Committee for data from EGAS00001005944

  Dac EGAC00001002583

• Tumor educated platelets for BrCA

  Study EGAS00001006821

• RNASeq for BMI GL study

  Study EGAS00001004127

• EGAD00010000606

  SNP6 data for matched normal samples

  Dataset EGAD00010000606

• How to upload GPG files

  Uploading files Users that holds an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. In order to manage the available resources, we enforce a limit of 10Tb per submission account at any one time. Please do not exceed this limit. INBOX FTP The FTP is only compatible with files encrypted using the EGACryptor tool Before uploading Once your submission files have been prepared using the EGAryptor, the resulting encrypted files and associated md5sum files can be uploaded to your submission account using Aspera or FTP. The EGA is a shared, public service with limited resources. In order to manage the available resources, EGA submission boxes should not exceed 8Tb in size, and cannot exceed 12Tb. If you are approaching this limit please contact contact EGA Helpdesk so that we can advise on how to register the associated metadata and trigger the archiving of files, so that you can continue with your submission. If we note that your submission account increases above 10Tb on a consistent base your password will be changed until metadata is associated. Aspera Download Aspera Using Aspera FTP FTP windows FTP Linux / Unix FTP client (Filezilla) FTP and TLS Troubleshooting Troubleshooting Aspera Download Aspera is a commercial file transfer protocol that may provide faster transfer speeds than ftp especially over longer distances. The Aspera ascp command line client. Please select Aspera Connect. The ascp command line client is distributed as part of the aspera connect highperformance transfer browser plugin and is free to use, without registration. The minimum required version of the IBM Aspera Cli is V4. Further instructions. Using the Aspera ascp command line program The location of the ascp program in the filesystem: Mac: on the desktop go cd /Applications/Aspera\ Connect.app/Contents/Resources/ there you'll see the command line utilities where you're going to use ascp. Windows: the downloaded files are a bit hidden. For instance, in Windows 7 the ascp.exe is located in the users home directory in: AppData\Local\Programs\Aspera\Aspera Connect\bin\ascp.exe Linux: should be in your user's home directory, cd /home/username/.aspera/connect/bin/ there you'll see the command line utilities where you're going to use ascp. Your command should look similar to this: ascp -P33001 -O33001 -QT -l300M -L- /path/file ega-box-N@fasp.ega.ebi.ac.uk:/path If you wish to upload several files without being requested the password, please use the below command : ASPERA_SCP_PASS=ega-box-password ascp -P33001 -O33001 -QT -l300M /path/file ega-box-N@fasp.ega.ebi.ac.uk:/path/ Explanation of parameters l300M option sets the upload speed limit to 30MB/s. You may wish to lower this value to increase the reliability of the transfer. L option is for printing logs out while transferring files to upload can be a file mask (e.g. '/homes/submitter/*.srf) or a list of files. ega-box-N is your submission account login. Add k2 switch for transfer restarts Check the command line transfer usage for more configuration details. Using FTP to upload your prepared files Use your preferred ftp client. For example, lftp is a popular choice for Linux and Mac users. Use binary mode for file transfers. Use ftp.ega.ebi.ac.uk as the target host. Login with your ega-box username and password. Upload files to your private ega-box upload area. Depending on your network setting you might wish to start FTP in passive or active mode. Using default FTP command line client in Windows Start the command line interpreter: press WinR, type cmd, hit enter Enter ftp ftp.ega.ebi.ac.uk Enter your submission username Enter your submission password Type binary to enter binary mode for transfer To see a list of available ftp commands type help. Type ls command to check the content of your submission account. Type prompt to switch off confirmation for each file uploaded. Use mput command to upload files: mput *.bam* Use bye command to exit the ftp client. Use exit command to exit the command line interpreter. Using default FTP command line client in Linux / Unix Open a terminal and type ftp ftp.ega.ebi.ac.uk Enter your submission username Enter your submission password Type binary to enter binary mode for transfer To see a list of available ftp commands type help. Type ls command to check the content of your submission account. Type prompt to switch off confirmation for each file uploaded. Use mput command to upload files: mput *.bam* Use bye command to exit the ftp client. Using FTP client FileZilla We recommend the use of FileZilla, a free FTP client . FileZilla is open source software distributed free of charge under the terms of the GNU General Public License. Use the following connection details (File - Site Manager) and add yoursubmission account username and password: Using FTP client FileZilla Select the files you wish to upload and then select upload: Using FTP client Filezilla Using LFTP with TLS We recommend the following to force the use of a secure connection. lftp > set ftp:ssl-force yes We also recommend setting the following for not encrypting the bulk data itself for performance reasons (theauthentication will still be encrypted): lftp > ftp:ssl-protect-data no In order to verify the certificate,the recommended way would be to use the CA certificates from your machine. To do that use this command in lftp adjusting the path to your ca-certificates location. lftp > ssl:ca-file "/etc/ssl/certs/ca-certificates.crt" If that is not possible or certificates are old and you can't update them, you can download the certificates needed from Quo Vadis Digital Repository The two certificates to download in PEM format are: QuoVadis Root CA2 G3 QuoVadis EV SSL ICA G3 Then you can concatenate them in a file one after the other and save it as lftp-certificates.pem Once this is done you have to point the ssl:ca-file variable to the path lftp > set ssl:ca-file "/path/to/lftp-certificates.pem" Also note that you can save this configuration at ~/.lftp/rc Another option is to download the certificates and add them to the ca-certificates of your machine. For example: In RHEL7 and cenots and others box the process to add the certificates globally is: Download the two certificates (in PEM or DER format, doesn't matter) and save them to "/etc/pki/ca-trust/source/anchors/" Run "update-ca-trust extract" Another less secure option is to turn off certificate verification with the following command: lftp > set ssl:verify-certificate false Troubleshooting If you are having problems with Aspera connection timeouts, it can be down to either one of the following. Transfers cannot start the connection fails instantly. Ensure that TCP traffic on port 33001 is allowed (open) for outbound connections through your computer's firewall and network's firewall. The connection is made, transfers are started, but 0 bytes (0%) are uploaded for each file. Ensure that UDP traffic on port 33001 is allowed (open) for outbound connections through your computer's firewall and network's firewal

  Documentation submission/data/uploading-files/ftp

• Coronary Artery Risk Development in Young Adults (CARDIA-BioLINCC)

  Data Access NOTE: Please refer to the &#8220;Authorized Access&#8221; section below for information about how access to the data from this accession differs from many other dbGaP accessions.Related Studies: Other CARDIA data available include:phs000236.v2.p2 PAGE: CALiCo: Coronary Artery Risk Development in Young Adults(CARDIA) phs000309.v3.p2 The CARDIA-GENEVA Study phs000399.v1.p2 NHLBI GO-ESP: Heart Cohorts Exome Sequencing Project (CARDIA) phs000613.v1.p2 NHLBI CARDIA Candidate Gene Association Resource (CARe) phs003675.v1.p1 CARDIA Multi-omics Obesity &#38; CVD Substudy &#8211; Year 20 Untargeted Metabolomics Data phs001612.v3.p2 NHLBI TOPMed: CARDIA (Coronary Artery Risk Development in Young Adults) phs003045.v1.p1 Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Coronary Artery Risk Development in Young Adults Study (CARDIA) phs000285.v4.p3 CARDIA_Cohort Available Data: Available data for request include exam data through Year 30 and outcomes and death data through 2022. Additionally, data from 28 ancillary studies are available for request.Objectives: The original objectives of CARDIA were to document levels of risk factors for coronary artery disease and potential determinants of these risk factors in young adults; to study the interrelationships of risk factors and lifestyles and to document behavioral and environmental changes during the transition from young adulthood to middle age; to compare cross-sectional and longitudinal data on age-related trends in cardiovascular disease risk factors; and to compare levels and evolution of risk factors between men and women, blacks and whites, and in groups of differing socioeconomic status. Goals of the study have evolved to emphasize understanding determinants of left ventricular mass, emerging obesity and hypertension, and sequelae of hypertension in pregnancy.Background: CARDIA is designed to increase understanding of contributors to changes in cardiovascular disease (CVD) risk factors during the critical years of transition from young adulthood to middle age. CARDIA was funded initially in 1983 for a five-year cycle that included two rounds of examinations. Contract renewals have allowed for subsequent re-examinations.Participants: Black and white men and women; ages 18-30 years at entry with a range of attained education; original sample size: 5,115.Design: CARDIA is a population-based observational study of 5,115 participants aged 18-30 years recruited in 1985-1986. The sample was designed to achieve approximately balanced subgroups of race, gender, education (high school or less and more than high school) and age (18-24 and 25-30). Forty percent of the cohort had no more than a high school education. A total of nine examinations have been completed in the cohort with examination cycles at year 2 of the project and years 5, 7, 10 , 15, 20, 25, and 30.In addition to standard measurements of blood pressure, anthropometry, blood lipids, smoking behavior, physical activity, diet, pulmonary function, and many psychological factors, CARDIA has other included measurements (in subsets or in the full cohort) to obtain unique information on other aspects of risk factor development and early morbidity. These have included: graded exercise treadmill testing; echocardiography, particularly for measurement of left ventricular mass; cardiovascular reactivity; serum cotinine; Lp(a), apoE phenotype, apolipoprotein A1 and B; homocysteine; skin reflectance; body composition by dual X-ray absorptiometry; glucose tolerance testing; vascular resistance and compliance; and plasma renin activity and sympathetic nervous system activity.

  Study phs003739

• Targeted_sequencing_of_genes_recurrently_mutated_in_AML

  Background Massively parallel sequencing technology has transformed cancer genomics. It is now feasible, in a clinically relevant time-frame, for a clinically manageable cost, to screen DNA from patient tumours for mutations essentially genome-wide. The challenge for personalised medicine will be to increase the sample size to thousands or tens of thousands of well-characterised cases in order to attain sufficient statistical power to stratify patients accurately across the complexity and genomic heterogeneity expected for most of the common tumour types. Currently, whole genome sequencing on this scale is not feasible, and targeted sequencing of relevant portions of the genome will be required. Pilot data We have developed protocols for large-scale, multiplexed sequencing of 100-200 genes in thousands of samples. Essentially, using robotic technology, genomic DNA from the cancer specimen is processed into sequencing libraries with unique DNA barcodes, thereby allowing sequencing reads to be attributed to the sample they derive from. Currently, these sequencing libraries can be generated in a 96-well format using fully automated protocols, and we are exploring methods to expand this to a 384-well format. The sequencing libraries are pooled and hybridized to custom sets of RNA baits representing the genomic regions of interest. Sequencing of the pulled-down libraries is done in pools of 48-96 samples per lane of an Illumina Hi-Seq. This protocol is already implemented at the Sanger Institute. We have published proof that somatic mutations in novel cancer genes can be identified from exome-wide sequencing. In unpublished pilot data, we have established the feasibility of robotic library production, custom pull-down, and multiplexed sequencing of barcoded libraries for 100 known myeloid cancer genes across 760 myelodysplasia samples. Highlights of the data thus far analysed reveal that the coverage is remarkably even between samples; when 96 samples are run, average coverage per lane of sequencing is ~250, with 90-95% of targeted exons covered by &gt;25 reads; known mutations can be discovered in the data set; and the protocol is amenable to whole genome amplified DNA. The bioinformatic algorithms for identification of substitutions and indels in pull-down data are well-established; we have pilot data proving that copy number changes, LOH and genomic rearrangements in specific regions of interest can also be identified by tiling of baits across the relevant loci. Proposal We propose to apply this methodology to 10000 samples from patients with AML enrolled in clinical trials over the last 10-20 years. Oncogenic point mutations and potentially genomic rearrangements will be identified, and linked to clinical outcome data, with a view to undertaking the following sorts of analyses: • Identification of co-occurrence, mutual exclusivity and clusters of driver mutations. • Correlation of prognosis with driver mutations and potentially gene-gene interactions • Exploration of genomic markers of drug response Ultimately, we would like to be in a position to release the mutation data together with matched clinical outcome data to genuine medical researchers via a controlled access approach, possibly within the COSMIC framework (www.sanger.ac.uk/genetics/CGP/cosmic/). The vision here is to generate a portal whereby a clinician faced with an AML patient and his / her mutational profile can obtain a ‘personalised’ prediction of outcome, together with a fair assessment of the uncertainty of the estimate. With a sufficient sample size, there would also be the potential to develop decision support algorithms for therapeutic choices based on such data.

  Study EGAS00001000408

• DAC for human liver NPC single cell project

  Dac EGAC00001003207

• Data Access Commitee for skin scRNA-seq

  Dac EGAC00001003129

• DAC for Rare Disease Studies from the Broad Institute

  Dac EGAC00001000566

• DAC for study PTP and Vel Exome Sequencing

  Dac EGAC00001000013

• DAC for Cell Line Data Test of TraIT

  Dac EGAC00001000436

• DAC for Department of Haematology, Aalborg University Hospital

  Dac EGAC00001000543

• DAC for Sardinia Leukocytes polyA RNAseq 624 project

  Dac EGAC00001000561