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• Bulk RNA sequencing of day 2 and day 7 biopsies from the tuberculin skin test in people with latent tuberculosis, and of day 2 biopsies from saline controls

  We used the tuberculin skin test (TST) as human challenge model to study the temporal evolution of the immune response to Mycobacterium tuberculosis (Mtb) antigens in vivo. Study participants comprised healthy HIV seronegative adults, 18-60 years of age. Latent tuberculosis (TB) infection was defined as immune memory for Mtb-specific antigens identified by positive peripheral blood IFNg release assays, but no clinical or radiological evidence of active TB. Two units tuberculin each were injected intradermally into the contralateral forearms of participants. After 2 days (n=216) or 7 days (n=158), 3 mm skin punch biopsies were taken from the injection sites and processed for whole genome transcriptional profiling by bulk RNA sequencing. The time points reflect maximum clinical inflammation (day 2) and maximum T cell infiltration (day 7) of the TST. TST samples were compared to skin biopsies taken two days after control injection of saline in a separate group of individuals (n=33), comprising healthy volunteers as well as patients with active or latent TB, ranging from 18-75 years of age. This dataset includes 407 samples from 256 individuals (n=33 saline, n=223 with Day 2 and/or Day 7 TST). Skin biopsies were collected into RNAlater (Qiagen) for RNA extraction. Total RNA was obtained with the Precellys Evolution homogenizer (Bertin Instruments) and Qiagen RNeasy mini kit. RNA was subjected to DNase treatment to remove contaminating genomic DNA using a TURBO DNA-free kit (Ambion, Life Technologies). The KAPA mRNA HyperPrep Kit (Roche Diagnostics) was used to construct stranded mRNA-Seq libraries from up to 500 ng intact total RNA. Paired-end sequencing was performed on the Illumina Nextseq using the Nextseq 500/550 High Output 75 cycle kit (Illumina). Runs were demultiplexed using bcl2fastq by Illumina.

  Dataset EGAD50000001208

• Single-Cell and Bulk RNA Sequencing of Oncolytic Measles Virus Treatment in Pediatric Medulloblastoma and ATRT

  This dataset includes single-cell RNA sequencing profiles from five mice bearing Group 3 medulloblastoma (CSCG) treated with oncolytic measles virus (MV-NIS) and three mice treated with MV-NIS combined with anti-PD1 therapy. Additionally, bulk RNA sequencing data were generated from 12 samples across PBS control, heat-inactivated (HI) control, MV mid-treatment, and treatment resistance conditions. Pediatric patient data were collected from the PNOC005 trial, which enrolled patients into three strata: Stratum A (focal recurrence, local MV-NIS administration post-surgical resection), Stratum B (disseminated recurrence, single MV-NIS dose via lumbar puncture), and Stratum C (disseminated recurrence, two consecutive MV-NIS doses via lumbar puncture on Day 0 and Day 7). Whole blood and PBMC samples were collected from 17 patients across all strata, generating 72 bulk RNA-seq datasets at multiple time points. All datasets are provided as raw sequencing data in FASTQ format.

  Dataset EGAD50000001197

• Clonal selection after gene therapy in sickle cell disease

  While gene therapy (GT) provides a potentially curative treatment option for patients with sickle cell disease (SCD), the occurrence of myeloid malignancies in clinical trials has prompted concern. To interrogate potential mechanisms underlying increased cancer risk, we used hematopoietic stem cell (HSC) clonal tracking by whole genome sequencing (WGS) to map the somatic mutation and clonal landscape of 2,592 gene modified as well as unmodified single stem and progenitor cells from six SCD patients undergoing gene therapy (7-26 years old, average 12.7× depth). Pre-GT phylogenetic trees in SCD were highly polyclonal and mutation burdens per cell were elevated in some, but not all, patients. Post-GT, no clonal expansions were identified. However, an increased frequency of driver mutations associated with myeloid neoplasms or clonal hematopoiesis (DNMT3A- and EZH2-mutated clones in particular) were seen in both genetically modified and unmodified cells suggested positive selection of mutant clones during gene therapy. This work sheds light on the mutation landscape and HSC clonal dynamics in gene therapy for SCD and highlights enhanced fitness of some HSCs harboring pre-existing driver mutations following gene therapy. Future studies should define the long-term fate of mutant clones including any contribution to expansions associated with myeloid neoplasms.

  Dataset EGAD00001010914

• OpACIN-neo – A Multicenter Phase 2 Study to identify the Optimal neo-Adjuvant Combination scheme of Ipilimumab and Nivolumab – Whole exome sequencing

  The outcome of patients with macroscopic stage III melanoma is poor. Neoadjuvant treatment with ipilimumab plus nivolumab at the standard dosing schedule induced pathological responses in a high proportion of patients in two small independent early-phase trials. However, toxicity of the standard ipilimumab plus nivolumab dosing schedule was high, preventing its broader clinical use. The aim of the OpACIN-neo study (NCT02977052) to identify a dosing schedule of ipilimumab plus nivolumab that is less toxic but equally effective. 86 patients with macroscopic stage III melanoma were randomised to one of three dosing schedules: arm A, two cycles of ipilimumab 3 mg/kg plus nivolumab 1 mg/kg once every 3 weeks; arm B, two cycles of ipilimumab 1 mg/kg plus nivolumab 3 mg/kg once every 3 weeks; or arm C, two cycles of ipilimumab 3 mg/kg once every 3 weeks directly followed by two cycles of nivolumab 3mg/kg once every 2 weeks. Within the first 12 weeks, grade 3–4 immune-related adverse events were observed in 12 (40%) of 30 patients in group A, six (20%) of 30 in group B, and 13 (50%) of 26 in group C. Pathological responses occurred in 24 (80%) patients in group A, 23 (77%) in group B, and 17 (65%) in group C. The 2-year estimated relapse-free survival was 84% for all patients, 97% for patients achieving a pathologic response and 36% for nonresponders (p<0.001). High tumor mutational burden (TMB) and high interferon-gamma-related gene expression signature score (IFN-γ score) were associated with pathologic response and low risk of relapse; pathologic response rate (pRR) was 100% in patients with high IFN-γ score/high TMB, patients with high IFN-γ score/low TMB or low IFN-γ score/high TMB had pRRs of 91% and 88%; while patients with low IFN-γ score/low TMB had a pRR of only 39%. These data demonstrate long-term benefit in patients with a pathologic response and show the predictive potential of TMB and IFN-γ score.

  Study EGAS00001004832

• OpACIN-neo – A Multicenter Phase 2 Study to identify the Optimal neo-Adjuvant Combination scheme of Ipilimumab and Nivolumab – RNA sequencing

  The outcome of patients with macroscopic stage III melanoma is poor. Neoadjuvant treatment with ipilimumab plus nivolumab at the standard dosing schedule induced pathological responses in a high proportion of patients in two small independent early-phase trials. However, toxicity of the standard ipilimumab plus nivolumab dosing schedule was high, preventing its broader clinical use. The aim of the OpACIN-neo study (NCT02977052) to identify a dosing schedule of ipilimumab plus nivolumab that is less toxic but equally effective. 86 patients with macroscopic stage III melanoma were randomised to one of three dosing schedules: arm A, two cycles of ipilimumab 3 mg/kg plus nivolumab 1 mg/kg once every 3 weeks; arm B, two cycles of ipilimumab 1 mg/kg plus nivolumab 3 mg/kg once every 3 weeks; or arm C, two cycles of ipilimumab 3 mg/kg once every 3 weeks directly followed by two cycles of nivolumab 3mg/kg once every 2 weeks. Within the first 12 weeks, grade 3–4 immune-related adverse events were observed in 12 (40%) of 30 patients in group A, six (20%) of 30 in group B, and 13 (50%) of 26 in group C. Pathological responses occurred in 24 (80%) patients in group A, 23 (77%) in group B, and 17 (65%) in group C. The 2-year estimated relapse-free survival was 84% for all patients, 97% for patients achieving a pathologic response and 36% for nonresponders (p<0.001). High tumor mutational burden (TMB) and high interferon-gamma-related gene expression signature score (IFN-γ score) were associated with pathologic response and low risk of relapse; pathologic response rate (pRR) was 100% in patients with high IFN-γ score/high TMB, patients with high IFN-γ score/low TMB or low IFN-γ score/high TMB had pRRs of 91% and 88%; while patients with low IFN-γ score/low TMB had a pRR of only 39%. These data demonstrate long-term benefit in patients with a pathologic response and show the predictive potential of TMB and IFN-γ score.

  Study EGAS00001004833

• Nala TAS-LRS PGx Study

  Sequencing was performed on the PromethION Flow Cell R10 (M version) using the P2 Solo Sequencer (MinION release 24.02.16). The library was divided into three portions and loaded in a tapered manner across three time points. At the first time point, the initial portion of the library was loaded, and sequencing began in whole-genome mode (without adaptive sampling), to monitor QC (N50 within the expected range). After one hour, adaptive sampling was activated. A custom BED file was used to define the target regions (N=326). Each region was extended by 20 kb upstream and downstream, and overlapping regions were merged into non-redundant intervals using bedtools43, covering a total of 1.3% of the human genome. At the second time point (20–24 hours after sequencing began, or when fewer than 2000 pores remained active), the flow cell was washed following ONT’s Flow Cell Wash Kit protocol, and the second portion of the library was loaded. At the third time point (40–48 hours), the second portion of the library was retrieved, and merged with the third portion. The flow cell was washed and reloaded with the mixed library. POD5 files were basecalled using Dorado v0.8.1 in super-accuracy (SUP) mode with the dna_r10.4.1_e8.2_400bps_SUP@v5.0.0. Reads from the initial 1 h Whole Genome Sequencing (WGS) and subsequent 72 h adaptive sampling were filtered for Q-scores >10. Passing reads were demultiplexed using Dorado’s demux function, then combined per sample and mapped to the GRCh38 reference genome using Minimap2 v2.22. Files were converted to CRAM using samtools.

  Dataset EGAD50000001609

• Efficacy of a Therapeutic Treatment Trial in Angelman Syndrome

  This study has been designed to evaluate the possible therapeutic benefits of L-5-methyltetrahydrofolate (Metafolin), vitamin B12, creatine and betaine in children with Angelman Syndrome (AS). This study is based on the hypotheses 1) that dietary manipulation may increase global DNA methylation; 2) that increased methylation of the paternal chromosome in AS participants may increase expression of the Angelman gene causing clinical benefit; and 3) that dietary interventions with Metafolin in conjunction with creatine, betaine, and vitamin B12 would represent little risk, but some chance for benefit. The dietary regimen is felt to be quite benign and safe. The clinical response will be measured by recording: changes in seizure activity, changes in communication skills, changes in developmental skills and changes in behavioral patterns as indicated by formal developmental assessments and clinical evaluations. We will assess the progress of the participants by monitoring their changes from baseline throughout their participation in the study and measure their improvement in particular in the developmental parameters. As a secondary aim, we plan to measure the biochemical response to these compounds by obtaining levels of homocysteine, methionine, S-adenosyl-methionine (SAM), and S-adenosyl-homocysteine (SAH). The molecular response will be measured by global DNA methylation analysis. As an exploratory aim, we hope to use control data we have previously obtained from a double blind placebo-controlled trial done in the past using betaine and folic acid to compare the development of those children who have and those who have not received medications. More specifically, we wish to compare differences in the rate of development for those children who have never received study medications (from the first trial) with those who have received medications from the present trial. Developmental parameters used in the previous trial will be consistent with those used in the present trial in order to permit these comparisons. The previous trial was conducted at Baylor College of Medicine, Rady San Diego Children's Hospital and Boston Children's Hospital whereas a group of AS participants received folic acid and betaine or placebo in a randomized double-blind basis. All the participants for the study will include patients who have a documented molecular diagnosis of AS. The participants will be seen at Baylor College of Medicine, Rady Children's Hospital of San Diego, Boston Children's Hospital and the Greenwood Genetics Center at 0 months and at 12 months for follow-up. Clinical and relevant historical data will be collected during the clinical visit and examination and also by reviewing medical and laboratory records. This information will then be entered into a HIPAA compliant clinical research database.

  Study phs000701

• Youth-GEMs, the project using data to define the mental health trajectories of young people

  Mental health trajectories are shaped by genetic, cognitive, social, and environmental factors that are particularly influential during human development. How exactly the interplay between these factors is impacting mental health, however, is yet unknown. The project “Gene Environment interactions in Mental health trajectories of Youth (Youth-GEMs)”, funded by the European Commission under the Horizon Europe programme, aims to help reducing mental suffering among European youth. How do Youth-GEMs contribute to bettering mental health in young people? Youth-GEMs aims to identify specific neurological, (epi)genetic and environmental drivers on mental health trajectories of young people. To do so, the project is working on a public knowledge base on functional genomics of the developing human brain, between birth and 24 years old. Reliable predictive models for trajectories of onset and course of mental illness in young people of the general population in Europe will be developed. Additionally, a secure and FAIR data infrastructure in mental health is being produced. Engaging and empowering young people Youth-GEMs is also working on the engagement of young people in the scientific field of Psychiatry. For that, the project is developing initiatives such as Science Café, intended to create a learning community where ideas and stories are shared. The 10th Science Café will take place today, with the participation of Lauren Fromont from the EGA. How do the EGA contribute to Youth-GEMs? The contributions require careful data management and harmonisation: we are dealing with an extraordinary amount of data types, sources, and time points (since the studies are longitudinal). To analyse them together and to determine which factors and interactions are truly key to account for mental health trajectories, the variables need to be transformed in a common “language”, i.e., harmonised under a common data model. Further, to guarantee FAIRness of data storage and sharing for future projects that will further advance our knowledge in the mental health field, we are ensuring that data are processed securely but can also be discovered easily. Find out more information about the project in the Youth-GEMs website.

  Blog youth-gems-mental-health

• PyEGA3 download client

  PyEGA3 Checks prior to download - Is the dataset available for download? Most datasets are available using the PyEGA3 Download client, except for specific legacy datasets. For these ones, refer to Live Outbox distribution, an alternative way to download these datasets.If the dataset you've been granted access to begins with the ID EGAD5XXXXXXX, please refer to the Live Outbox distribution. If the dataset you have been granted access to contains encrypted data (filename ending in .gpg), contact EGA Helpdesk for detailed information on how to download these files.General Information The PyEGA3 download client is available at its GitHub repository. In its README you will find a step by step guide on how to use it to download files you have been granted access to. PyEGA3 implements the GA4GH-compliant HTSGET protocol supporting requests over genomic ranges. This enables the download of specific regions of interest rather than the entire file. HTSGET is not possible for all the files held by EGA: if the dataset does not contain index files (e.g. filename ending in *.crai), then genomic range requests cannot be performed. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Check out our video tutorial! Frequently Asked Questions General questions How do I download the datasets to which I have been granted access? After setting up your EGA download account, you can proceed to download using the EGA download client – PyEGA3. The pyEGA3 download client is a python-based tool for viewing and downloading files from authorised EGA datasets. This download client is continuously being developed for more user-friendly download experiences. Can I download the dataset’s metadata via the download client? No, the download client cannot be used to download metadata. Registered EGA users can download metadata of an authorised dataset by logging into the EGA webpage and navigating to the dataset of choice. Approximately two thirds down the page you will find the option to download the metadata as a zip file. How do I download large datasets as quickly as possible? The main EGA download client is pyEGA3 download client, a python-based tool for viewing and downloading files from authorised EGA datasets.We could exceptionally consider the option of an Aspera download for users experiencing very slow download rates or facing particular issues.Contact the EGA Helpdesk for further information. Pre-requirement and Installation What are the major requirements for the PyEGA3 download client installation and usage? The PyEGA3 client is compatible with any OS with Python 3.6+ installed. The client requires a connection to the internet, sufficient space on the destination drive, and the EGA download account credentials. Where can I find the PyEGA3 download client installation and download instructions? We strongly recommend checking our video tutorial demonstrating the usage of PyEGA3 from installation to file download. More detailed information is available at PyEGA3 GitHub. What ports are required for the PyEGA3 download client to function? PyEGA3 makes HTTPS calls to the EGA Data API (https://ega.ebi.ac.uk:8443). This port 8443 must be reachable from the location where PyEGA3 is executed to avoid timeouts. PyEGA3 download client How to update the PyEGA3 download client to be on the latest version? Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 --upgrade I do not have an active EGA account. Can I test the download with the PyEGA3 download client? An EGA download test account (ega-test-data@ebi.ac.uk) has been created for troubleshooting and training purposes. The test account does not require an EGA username and password because it contains publicly accessible files from the 1000 Genomes Project. More information about the use of this account and log in details are available at pyEGA3. I lost my connection while the download was in progress. Does this mean that I have to download the file again from the start? The PyEGA3 download client supports automatic resumption of downloads. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the resume feature, downloads will automatically resume if you encounter any errors or if the connection is interrupted. How can I improve the overall download speed of the PyEGA3 download client? Download speeds can be optimised using the --connections parameter which will parallelise download at the file level. If the --connections parameter is provided, all files >100Mb will be downloaded using the specified number of parallel connections. It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Why is my file taking a long time to be saved? Please note that when a file is being saved, it goes through two processes. First, the downloaded file "chunks" are pieced back together to reconstruct the original file. Secondly, PyEGA3 calculates the checksum of the file to confirm that the file was downloaded successfully. Larger files will take more time to reconstruct and validate the checksum. Why is the HTSGET protocol not working with my dataset of interest? PyEGA3 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file, saving both time and storage space. Please note that in order for the genomic range requests to work for a BAM, CRAM or VCF file there must be an associated BAI, CRAI or TBI file, respectively. If the dataset does not contain these index files then genomic range requests cannot be performed. After download Do I need to decrypt data files downloaded through the pyEGA3 download client? Files are transferred over secure HTTPS connections and received unencrypted, thus there is no need for decryption after data download. What is the purpose of an MD5 file? After the download completes, file integrity is verified using checksums. The PyEGA3 download client automatically verifies each file’s unencrypted md5 after download to ensure that the file was downloaded correctly from the EGA. Errors Why do I get a “400 Client Error” while trying to access the download client? Ensure that your credentials are formatted correctly. Please contact the EGA Helpdesk if you are unsure about your account’s username (i.e. your email address). Why do I get a “500 Server Error”? It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Otherwise, 500 server errors are internal server errors from EGA’s end. Retry after some time and, if the 500 error persists: check our homepage or Twitter for any outage information or report it to EGA Helpdesk. Why do I get a “slice error”? This type of error occurs when one slice download was prematurely interrupted. Please re-start the same download. The download client will pick up any partial download and download any missing slices. Why do I get the error “Dataset' EGADXXXXXXXXXXX' is not in the list of your authorised datasets”? Please note that the data access was given to the email (username) provided to the Data Access Committee (DAC) in your official request. Therefore, you shall check that the account that you are using is the same one that you sent to the DAC. Why do I get “ERROR:root:Failed to obtain IP address”? This might mean that the connection ports are blocked. Check that the port 8443 is reachable.In case the port is reachable and the error persists, please try to modify the file pyega3.py on line 44 in the python module installation: Replace endpoint = 'https://ipinfo.io/json' with endpoint = 'https://api.myip.com' Why do I get the error “Invalid username, password or secret key - please check and retry”? Make sure that you are using the correct credentials (username and password) and there is no typo. Contact EGA Helpdesk if the problem persists.

  Documentation access/download/files/pyega3

• Acute Respiratory Distress Syndrome Clinical Network: Fluid and Catheter Treatment Trial (ARDSNet FACTT-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions.Biospecimens: Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from ARDSNet-FACTT include plasma, DNA, and serum. Please note that use of biospecimens in genetic research is subject to a tiered consent. Objectives: This study evaluated the benefits and risks of Pulmonary Artery Catheters (PACs) in patients with established acute lung injury in a trial comparing hemodynamic management guided by a PAC with hemodynamic management guided by a central venous catheter (CVC) using an explicit management protocol.Background: Optimal fluid management in patients with acute lung injury is unknown. Diuresis or fluid restriction may improve lung function but could jeopardize extrapulmonary organ perfusion.Participants: This randomized study compared a conservative and a liberal strategy of fluid management using explicit protocols applied for seven days in 1000 patients with acute lung injury. The primary end point was death at 60 days. Secondary end points included the number of ventilator-free days and organ-failure-free days and measures of lung physiology. Design: Participants were randomly assigned to a strategy involving either conservative or liberal use of fluids with concealed allocation in permuted blocks of eight with the use of an automated system. Participants were simultaneously and randomly assigned to receive either a pulmonary-artery catheter or a central venous catheter in a two-by-two factorial design.Conclusions: There was no significant difference in the primary outcome of 60-day mortality; however, the conservative strategy of fluid management was associated with improved lung function and shortened the duration of mechanical ventilation and intensive care without increasing non-pulmonary organ failures. These results support the use of a conservative strategy of fluid management in patients with acute lung injury. (NEJM June 15, 2006; Vol 354, No. 24, pp 2564-75; NEJM May 25, 2006; Vol 354, No. 21, pp 2213-24)

  Study phs004165