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• Host pathogen interaction long read transcriptome

  Our incomplete knowledge of the human transcriptome impairs the detection of disease-causing variants, in particular in transcripts only expressed under certain conditions. These transcripts are often lacking from reference transcript sets, such as Ensembl/GENCODE and RefSeq, and could be relevant for establishing genetic diagnoses. We present SUsPECT (Solving Unsolved Patient Exomes/gEnomes using Custom Transcriptomes), a pipeline based on the Ensembl Variant Effect Predictor (VEP) to predict variant impact on custom transcript sets, such as those generated by long-read RNA-sequencing, for downstream prioritization. Our pipeline predicts the functional consequence and likely deleteriousness scores for missense variants in the context of novel open reading frames predicted from any transcriptome. We demonstrate the utility of SUsPECT by uncovering potential mutational mechanisms of pathogenic variants in ClinVar that are predicted to be benign using the reference transcript annotation. In further support of SUsPECT’s utility, we identified an enrichment of immune-related variants predicted to have a more severe molecular consequence when annotating with a newly generated transcriptome from stimulated immune cells instead of the reference transcriptome. Our pipeline outputs crucial information for further prioritization of potentially disease-causing variants for any disease and will become increasingly useful as more long-read RNA sequencing datasets become available.

  Study EGAS00001006779

• T cell responses of ALS patients

  In this project, we aimed to understand how T cell responses contribute to the disease progression of amyotrophic lateral sclerosis (ALS). The present data is on single-cell sequencing isolated from human cerebrospinal fluid (CSF) cells from both ALS patients (n=5) and controls (n=4). This analysis was conducted as part of a bigger project which is summarized in the section below. We used flow cytometry to define T cell subsets and phenotypes in blood and CSF samples collected at the time of diagnosis on a cohort of 89 newly diagnosed ALS patients in Stockholm, Sweden. High frequency of CD4+FOXP3- effector T cells in blood and CSF was associated with poor survival whereas high frequency of activated regulatory T (Treg) cells and high ratio between activated and resting Treg cells in blood was associated with better survival. T cell profiles also predicted disease progression rate. On an independent cohort of cases and controls, we used single cell transcriptomics data to demonstrate that ALS patients had altered T cell gene expression patterns and clonally expanded CD4+ and CD8+ T cells in CSF. In summary, T cell responses contribute to disease progression of ALS, supporting modulation of adaptive immunity as a viable therapeutic option.

  Study EGAS00001006675

• Longitudinal evaluation of serum microRNAs as biomarkers for neuroblastoma burden and therapeutic p53 reactivation

  Accurate assessment of treatment response and residual disease is indispensable for the evaluation of cancer treatment efficacy. However, performing tissue biopsies for longitudinal follow-up poses a major challenge in the management of solid tumours like neuroblastoma. In the present study, we evaluated whether circulating miRNAs are suitable to monitor neuroblastoma tumour burden and whether treatment-induced changes of miRNA abundance in the tumour are detectable in serum. We performed small RNA sequencing on longitudinally collected serum samples from mice carrying orthotopic neuroblastoma xenografts that were exposed to treatment with idasanutlin or temsirolimus. We identified 57 serum miRNAs to be differentially expressed upon xenograft tumour manifestation, out of which 21 were also found specifically expressed in the serum of human high-risk neuroblastoma patients. The murine serum levels of these 57 miRNAs correlated with tumour tissue expression and tumour volume, suggesting potential utility for monitoring tumour burden. In addition, we describe serum miRNAs that dynamically respond to p53 activation following treatment of engrafted mice with idasanutlin. We identified idasanutlin-induced serum miRNA expression changes upon one day and 11 days of treatment. By limiting to miRNAs with a tumour-related induction, we put forward hsa-miR-34a-5p as a potential pharmacodynamic biomarker of p53 activation in serum.

  Study EGAS00001006678

• T cell landscape definition by multi-omics identifies galectin-9 as novel immunotherapy target in chronic lymphocytic leukemia (CLL)

  Failure of immunotherapy after applying checkpoint inhibitors or CAR-T cells is linked to T cell exhaustion. Here, we explored the T cell landscape in chronic lymphocytic leukemia (CLL) by single-cell omics analyses of blood, bone marrow and lymph node samples of patients and spleen samples of a CLL mouse model. By single-cell RNA- sequencing, mass cytometry (CyTOF), and multiplex image analysis of tissue microarrays, we defined the spectrum of phenotypes and transcriptional programs of T cells and and their differentiation state trajectories. We identified disease-specific accumulation of distinct regulatory T cell subsets and T cells harboring an exhausted phenotype exclusively in the CLL lymph node tissue. Integration of TCR data revealed a clonal expansion of CD8+ precursor exhausted T cells, suggesting their reactivity for CLL cells. Interactome analyses identified the TIM3 ligand Galectin-9 as novel immunoregulatory molecule in CLL. Blocking of Galectin-9 in CLL-bearing mice slowed down disease development and reduced the number of TIM3 expressing T cells. Galectin-9 expression correlated with shorter survival of CLL patients. Thus, Galectin-9 contributes to immune escape in CLL and represents a novel target for immunotherapy.

  Study EGAS00001006864

• Targeting TRIP13 in Wilms Tumor with Nuclear Export Inhibitors

  Wilms tumor (WT) is the most common renal malignancy of childhood. Despite improvements in the overall survival, relapse occurs in ~15% of patients with favorable histology WT (FHWT). Half of these patients will succumb to their disease. Identifying novel targeted therapies in a systematic manner remains challenging in part due to the lack of faithful preclinical in vitro models. We established ten short-term patient-derived WT cell lines and characterized these models using low-coverage whole genome sequencing, whole exome sequencing and RNA-sequencing, which demonstrated that these ex-vivo models faithfully recapitulate WT biology. We then performed targeted RNAi and CRISPR-Cas9 loss-of-function screens and identified the nuclear export genes (XPO1 and KPNB1) as strong vulnerabilities. We observed that these models are sensitive to nuclear export inhibition using the FDA approved therapeutic agent, selinexor (KPT-330). Selinexor treatment of FHWT suppressed TRIP13 expression, which was required for survival. We further identified in vitro and in vivo synergy between selinexor and doxorubicin, a chemotherapy used in high risk FHWT. Taken together, we identified XPO1 inhibition with selinexor as a potential therapeutic option to treat FHWTs and in combination with doxorubicin, leads to durable remissions in vivo.

  Study EGAS00001007389

• Diverse_outcomes_of_controlled_human_malaria_infection_originate_from_host_intrinsic_immune_variation_and_not_var_gene_switching

  Falciparum malaria is clinically heterogeneous and the relative contribution of parasite and host factors in shaping disease severity remains unclear. We set out to explore the interaction between host inflammation and parasite variant surface antigen (VSA) expression, asking whether this relationship underpins the variation observed in controlled human malaria infection. We uncovered marked heterogeneity in the response of naive hosts to blood challenge with some volunteers maintaining a state of immune quiescence, others triggering interferon-stimulated inflammation and a small group showing transcriptional evidence of myeloid cell suppression. Significantly, only inflammatory volunteers experienced hallmark symptoms of clinical malaria. When we then tracked temporal changes in parasite VSA expression to ask whether variants associated with severe disease preferentially expand in naive hosts (as predicted by current theory) we found that var gene profiles were essentially unchanged after 10-days of blood-stage infection. These data therefore show that the diverse clinical outcomes of CHMI largely originate from host-intrinsic variation and there is no evidence for switching or selection of var genes in naïve hosts. 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 EGAS00001003766

• Single-cell level characterization of B cell depletion and repopulation following rituximab in systemic lupus erythematosus

  Rituximab, a CD20+ B cell depletion therapy, is frequently used in the treatment of systemic lupus erythematosus (SLE). However, variability in patient response highlights the need for a deeper understanding of the underlying immune cell dynamics of B cell depletion and repopulation. In this study, we conducted longitudinal single-cell profiling of nine SLE patients treated with rituximab from pretreatment to up to 15 months post-treatment. These were compared to eight healthy controls. We profiled PBMCs via 10X Genomics single-cell RNA, surface protein (CITE-seq), B cell receptor (BCR), and T cell receptor (TCR) sequencing and sequenced bulk BCR repertoires in parallel. For single cell sequencing, 10 pools were created with an equal number of cells from 4 samples each. Samples collected at different timepoints from the same patient were distributed across different pools to allow demultiplexing of individuals using genotypes captured from the scRNA-seq data. Libraries were pooled using a ratio of GEX:CITE:TCR:BCR=9:2:1:1 and were sequenced across two lanes of the NovaSeq 6000. For bulk BCR sequencing, individual samples were demultiplexed using primer barcodes and paired-end BCR amplicon reads were merged prior to submission; data are provided as unmapped single-end reads.

  Study EGAS00001006798

• Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer's disease and Longitudinal follow-up of Genotype-Phenotype Associations in Alzheimer's disease and Neuroimaging component of Genotype-Phenotype Associations in Alzheimer's disease

  GenADA is a multi-site collaborative study, involving GlaxoSmithKline Inc and nine medical centres in Canada, to develop a dataset containing 1000 Alzheimer's disease patients and 1000 ethnically-matched controls in order to associate DNA sequence (allelic) variations in candidate genes with Alzheimer's disease phenotypes. The study consists of both retrospective and prospective components, that is, patients with an existing diagnosis of Alzheimer's disease as well as newly diagnosed patients were enrolled in the study. Thus, clinical data was retrospectively or prospectively obtained on Day 1 of entry into GenADA. Where possible, biological relatives with Alzheimer's (up to third degree relationship such as cousins) and unaffected siblings of AD cases were also recruited. Note that recruitment numbers for biological relatives were lower than expected and genotypic data has not been submitted to dbGap for these subjects. The purpose of this study is: To identify DNA sequence variations (genotype) in candidate genes that are associated with the clinical symptoms and behavioural features of Alzheimer's disease (phenotype), which differ between study participants with and without the disease. To identify other genotype-phenotype associations in cognitively impaired study participants such as age of onset, family history, rate of cognitive decline, patterns of behavioural/psychiatric non-cognitive symptoms factors, response to treatment co-morbid conditions, and risks/exposure. The final subject recruitment for this study included 875 Alzheimer's disease patients, 850 ethnically-matched controls, and 37 family members. GenADA LONG is a longitudinal assessment to the original GenADA study. Eligible subjects were recruited from five of the nine memory clinics that participated in the GenADA study. Mild to moderate AD participants, a matched subset of controls, and biological related siblings (both affected and unaffected) or other blood relatives affected with AD, were initially examined a minimum of 12 months from recruitment into the original GenADA study, then at two further intervals of 12 and 18 months after time of entry into GenADA LONG. This enables an evaluation of the disease progression in AD patients and a determination of whether controls show evidence of cognitive decline. The overall goal of this extension study is to identify genetic differences and environmental influences that modulate the age of onset of the disease, the course of the disease, and/or biomarkers for neurodegenerative processes. Three of the five memory clinics that participated in the GenADA LONG study recruited eligible patients into GenADA Imaging. A concurrent neuroimaging sub-study was conducted at three of the five memory clinics participating in GenADA LONG. Eligible AD cases with mild to moderate AD, who were recruited into the original GenADA study and participated in the GenADA LONG extension study, were enrolled. Additionally, controls that showed signs of cognitive decline, as part of the assessment in GenADA LONG, were imaged at baseline, 12 and 18 month scanning intervals. The objective of this study is to find genes that: affect changes in AD brain volume measure by magnetic resonance in order to investigate how well change in brain volume predicts other key clinical measures in AD, such as neurodegenerative scales; that correlate changes in brain volume for other genotype-phenotype associations in cognitively impaired study participants; and that correlate with other clinically applicable magnetic resonance measures of pathology that can be conducted at the same time as structural volume measures, and are complementary to the volume measures. The ultimate aim of this research is to obtain a better understanding and definition of Alzheimer's Disease in order to develop new improved medicines.

  Study phs000219

• Bogalusa Heart Study (BHS-BioLINCC)

  Objectives: To investigate the early natural history of cardiovascular disease in a cohort of children and young adults in a biracial, semirural community.Background: The Bogalusa Heart Study has been a long-term epidemiologic study. The investigators have identified and followed black and white participants for nearly 40 years, and have described the incidence and prevalence of biologic and behavioral cardiovascular disease risk factors from childhood through adulthood. Their participation has enabled the study to not only document differences between males and females, but also between blacks and whites. The results from the Bogalusa Heart Study have clearly documented that the genesis of atherosclerosis has its basis in childhood, and that prevention can and must begin at the early ages.The Bogalusa Heart Study had been funded over the years by the Specialized Centers of Research (SCOR) Program. The SCOR program was initiated by NHLBI in 1970 to expedite the development and application of new knowledge essential for improved diagnosis, treatment, and prevention of arteriosclerosis, hypertension, pulmonary disease, and thrombosis. In 1984 a Demonstration and Education Component was added to the parent SCOR of the Bogalusa Heart Study in order to translate the experience gained in epidemiological studies into an intervention study designed to retard the development of cardiovascular risk factors in children. Beginning in 1997, the study was supported by the cooperative agreement mechanism. Participants: The Bogalusa dataset includes 11,796 participants that attended at least one of seven cross-sectional pediatric exams and/or the 1995-96 adult examination. Subjects ranged in age from 3 to 20 years at the pediatric exams and 20-37 at the time of the adult exam. Approximately 6,000 have more than one examination constituting a dynamic cohort. Design: The initial survey in 1973-1974 was restricted to children ages 2 1/2 to 14. A physical examination was conducted and information was collected on anthropometric data, hemoglobin, blood pressure, serum lipids, and health history. Over 3,500 children participated. The second cross-sectional survey of 1976-1977 and subsequent surveys expanded the eligible population to include all children ages 5-17 years. The second survey of over 4,000 children also included information on salt intake, smoking, health beliefs, and attitudes, and for girls ages 8-17, menstrual history and oral contraceptive use. The third survey of over 3,500 participants in 1978-1979 also collected anthropometric measurements on skinfold thickness and two measurements of heart rate. The fourth survey of over 3,300 participants in 1981-1982 added data on alcohol use, Type A behavior, peer networks and dieting habits.The Bogalusa Heart Study continued to use a cross-sectional and longitudinal design with the general cross-sectional survey of approximately 3,700 Bogalusa children ages five to seventeen in 1988-1989 in the sixth screen and additional longitudinal studies to recall children in defined subgroups for more intensive evaluation. Half of the 12,000 participants screened since 1973 had been studied three or more times. The Post High School Study examined young adults ages 21 through 30 who previously were examined as children ages five through fourteen in the first Bogalusa Heart Study screening in 1973-1974. The population included approximately 4,603 young adults originally screened and any other children or adolescents examined for the first time in any subsequent surveys. The cardiovascular phenotypes include obesity, blood pressure, lipids, lipoproteins, apoproteins, homocysteine, glucose-insulin, fibrinogen, plasminogen activator inhibitor-1 and von Willebrand Factor. Environmental risk factors consist of sociodemographic characteristics, tobacco and alcohol use, oral contraception, physical activity, cognitive and physical function, and quality of sleep and diet. Subclinical morbidity includes echo-Doppler measurements of cardiac-carotid structure and function.

  Study phs004173

• How to use EGA Webin?

  EGA Webin EGA Webin serves as a platform for registering metadata for array based submissions, large scale sequence submission as well as for legacy EGA submission accounts (ega-box-XXXX). For large scale submitters of sequence data you have also the option to submit metadata via XMLS programmatic submission, while new submitters are advised to utilise the Submitter Portal for their submissions. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. WEBIN actions: Register metadata for a sequence submission Register study, samples, experiments, runs, DAC, policy and dataset/s after file upload. Register components for your array-based metadata submission Register study, samples, DAC or policy before uploading files. Edit existing submission metadata Change or update previously submitted metadata. Register metadata for a sequence submission Ensure that all sequence files have been encrypted before uploading them to your submission account using the EgaCryptor. Go to the EGA Webin and log in using your submission account name (ega-box-XXX) and password. Register components of your metadata submission Study Samples Data Access Committee (DAC) Data access policy Dataset For array-base submissions: Study, Samples, Data Access Committee (DAC) and Data access policy may all be registered BEFORE file upload and dataset registration through the array-base template. Register your Study Go to the “Studies (Projects)” box Click on “Register Study” and fill in the information related to your study. Click on “Submit”, this will save the information and generate an EGA study ID. To use the study accession number in a publication, the study has to be previously released on the EGA website, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX.Further information about EGA can be found on https://ega-archive.org and “The European Genome-phenome Archive in 202 "(10.1093/nar/gkab1059)" Register your Samples Go to the “Samples” box Click on “Register Samples” Select “Download spreadsheet to register samples” and customise your template, there is a default EGA template (EGA default checklist) but more attributes can be added if required. For the EGA default checklist, there are mandatory,recommended and optional attributes. As well custom fields which can be added if required. Mandatory attributes Field Name Description tax_id Taxonomy ID of the organism as in the NCBI Taxonomy database. Entries in the NCBI Taxonomy database have integer taxon IDs. See our tips for sample taxonomy here scientific_name Scientific name of the organism as in the NCBI Taxonomy database. Scientific names typically follow the binomial nomenclature. For example, the scientific name for humans is Homo sapiens. sample_alias Unique name of the sample. If not selected system will auto generate an unique alias sample_title Title of the sample sample_description Description of the sample phenotype *** Where possible, please use the Experimental Factor Ontology (EFO) to describe your phenotypes. Recommended attributes Field Name Description subject_id Identifier for the subject where the sample has been derived from gender * Sex Optional attributes Field Name Description sex sex of the organism from which the sample was obtained disease_site Affected organ sample type Affected organ donor_id ** Identifier of the donor where the sample has been derived from *Gender should be described as 'male', 'female' or 'unknown'. If 'unknown' due to a known sex chromosome aneuploidy, please create a user defined attribute called 'Sex chromosome karyotype' and add the appropriate value, for example, 'XXY'. **Donor id (Subject id) should be a de-identified subject handle. If unknown, please add 'unknown' to the field. ***Phenotypes should, where possible, be an Experimental Factor Ontology accession. If a term cannot be found to describe your phenotype please use free text. All sample phenotypes considered important for further analysis of the data should be provided (for example, tumour type), additional phenotype attributes can be created by defining your own attributes; use the notion 'phenotype2', 'phenotype3', etc. After you have customised the fields for the sample submission, download the template and fill in the information. Example of the sample template: Finally upload the sample template to get the EGA accession IDs for the samples. Register your Data Access Committee (DAC) Further information on the role of your DAC Go to the “Data Access” box Click on “Register Dacs” Input the information about the DAC and register at least one main DAC contact. Register your Data Access Policy Your Data Access Policy provides the terms and conditions of data use, this is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAC). Go to the “Data Access” box Click on “Register Policies” Select the DAC to which this policy will be linked to and fill in the policy information. Submitting your Runs and Analyses This section is only for sequence data submission, for array-based submission it can be skipped. Please refer to our Submitting array based metadata Runs Registration Go to the “Raw Reads (Experiments and Runs)” box Click on “Submit Reads” Select “Download spreadsheet template for Read submission” Select the template corresponding to your submission type For the templates you have the option to customise the optional fields. To check their description click on “Show Description “ Download the template and fill in the required information. Example of the runs template: We recommend that Fastq, BAM, and CRAM read files are submitted using Webin-CLI When using this interface instead of Webin-CLI, raw sequences must be uploaded in one of the supported data formats before they can be submitted. The files can be uploaded using FTP or Aspera. The study and the sequenced samples must be pre-registered before the raw reads are submitted. Please note that each individual study and sample should be registered only once. You will be asked to provide information about the sequencing libraries and instruments. Submitting your Dataset This section is only for sequence data submissions, for array based submissions it can be skipped. Please refer to our Submitting array based metadata The dataset describes the data files, defined by the run (EGARXXXXXXXXXXX) and analysis (EGAZXXXXXXXXXXX) accessions that make up the dataset and links the collection of data files to a specified Data Access Committee and Data Access Policy. As a result, you must have registered your Reads and experiments, Data Access Committee (DAC) and Data access policy before submitting your Dataset. Please consider the number of datasets that your submission consists of, for example, a case control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Please contact EGA Helpdesk for further assistance. Go to the “Data Access” box Click on “Register Dataset” Select the Data Access Committee (DAC) and Data access policy Register your dataset After submitting your dataset you should contact the EGA Helpdesk to provide a release date for your dataset. Datasets are automatically held (i.e. not released) unless they are affiliated to a study that has already been released. Edit/update existing submission metadata Go to the “Report” section of the object you would like to edit. Locate the object and click on the arrow under action. An option menu will be displayed. Objects can be edited through their XML or with the WEBIN menu. After an object has been edited, changes would be available on the website until the submission is released again. Please contact the EGA Helpdesk if you require further assistance.

  Documentation submission/metadata/submission/EGA_webin

• 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

• Data access committee for study - The subclonal architecture of metastatic breast cancer: Results from a prospective community-based rapid autopsy program "CASCADE".

  Dac EGAC00001000574

• EGAD00010000389

  Cambridge control samples using a 24k expression array from Illumina

  Dataset EGAD00010000389

• Determining the quality and complexity of NGS data without a reference genome

  Dataset EGAD00001000759

• EGA_PBC_phased

  A cohort of 2886 participants of the Japan PBC-GWAS Study

  Dataset EGAD00010001533

• Array_data_psychosis

  Array data from a family with high prevalence of psychosis

  Dataset EGAD00010002028

• Array_data

  Array data from a family with high prevalence of psychosis

  Dataset EGAD00010002030

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from Dublin, Ireland.

  Study EGAS00001005844

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from Umea, Sweden.

  Study EGAS00001005842

• Whole Exome Sequencing of Bipolar cases and controls performed at the Broad Institute on a cohort from Cardiff, UK (Craddock)

  Study EGAS00001005845

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from Cambridge, UK

  Study EGAS00001005854

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from London, UK

  Study EGAS00001005851

• A single cell view on host immune transcriptional response to in vivo BCG-induced trained immunity

  Study EGAS00001006990

• Assessment of genomic copy number alterations in breast cancer

  The aim of this study was to assess genomic copy number alterations in a panel of breast cancer cell lines. These data were used to identify common aberrations associated with breast cancer, and also to identify aberrations associated with response to therapeutic compounds.

  Study EGAS00000000059

• SC_DDD-G-5

  Post-QC (pre-imputation) genotype data for N=2,166, a subset of trios described in EGAD00010001602. These data form N=722 complete trios in which the proband has a neurodevelopmental phenotype (Niemi et al. Nature 2018). Includes HPO phenotype terms for patients. Samples were genotyped on the Illumina HumanOmniExpress BeadChip

  Dataset EGAD00010001606