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• The EMC-HEMA-SCN DAC for severe congenital neutropenia data

  Dac EGAC00001003590

• DAC for GWAS of Phenytoin-Induced SJS/TEN in Thailand

  Dac EGAC00001003602

• Genomic and transcriptome analysis for intrahepatic cholangiocarcinoma

  Study EGAS00001006007

• Test GWAS Data for Training and Computational Benchmarking

  Study EGAS00001007914

• Progress in Diabetes Genetics in Youth (ProDIGY) Exome Sequencing Study: SEARCH for Diabetes in Youth

  The ~52,000 sample Type 2 Diabetes Exome Sequencing project is a collaboration of six consortia with various funding mechanisms that have joined together to investigate genetic variants for type 2 diabetes (T2D) using the largest T2D case/control sample set compiled to date. This includes samples from: Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Genetics of Type 2 Diabetes (GoT2D) Exome Sequencing Project (ESP) Slim Initiative in Genomic Medicine for the Americas (SIGMA) Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCAMP) Progress in Diabetes Genetics in Youth (ProDIGY) This data generated from the SEARCH for Diabetes in Youth cohort was part of the ProDIGY (Progress in Diabetes Genetics in Youth) consortium, which is a NIDDK-funded research consortium that investigated genetic variants for childhood type 2 diabetes (T2D). This consortium involved sequencing multiethnic samples from childhood T2D cases located across the United States. The individuals were obtained from over 20 cohorts across the 6 consortia that are listed in Table 1. The strategy was to perform deep exome sequencing of individuals, 24,991 with T2D and 24,953 controls, divided among five ancestry groups: Europeans, East Asians, South Asians, American Hispanics, and African Americans. The T2D-GENES, ProDIGY and SIGMA studies, sequencing was performed at the Broad Institute using the Agilent v2 capture reagent or Illumina Rapid Capture on Illumina HiSeq machines. Please note that while we summarize the full sample list in publications and below, two of the cohorts below are not in dbGAP, due to the samples not being consented for deposition. This includes the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study and Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) study. The Exome Sequencing Project (ESP) was deposited in dbGAP as part of their initial study and the phs numbers for that project can be found here: https://esp.gs.washington.edu/drupal/dbGaP_Releases. Table 1. 52,000 sample T2D Case/Control Whole Exome Sequencing Studies table, th, td { border: 1px solid black; border-collapse: collapse; } Ancestry Consortia Study Countries of Origin # Cases # Controls African American T2D-GENES Project 1 Jackson Heart Study US 500 526 African American T2D-GENES Project 1 Wake Forest School of Medicine Study US 518 530 African American ESP Exome Sequencing Project (ESP) US 467 1374 African American T2D-GENES Follow up study BioMe Biobank Program (BioMe) US 1297 1256 East Asian T2D-GENES Project 1 Korea Association Research Project Korea 526 561 East Asian T2D-GENES Project 1& Follow up Study Singapore Diabetes Cohort Study; Singapore Prospective Study Program Singapore (Chinese) 1486 1568 East Asian T2D-GENES Follow up study Korea SNUH South Korea 450 475 East Asian T2D-GENES Follow up study Research Studies in Hong Kong (Hong Kong) Hong Kong 493 485 European T2D-GENES Project 1 Ashkenazi US, Israel 506 355 European T2D-GENES Project 1 Metabolic Syndrome in Men Study (METSIM) Finland 484 498 European GoT2D Finland-United States Investigation of NIDDM Genetics (FUSION) Study Finland 472 476 European GoT2D Kooperative Gesundheitsforschung in der Region Augsburg (KORA) Germany 97 90 European GoT2D UK Type 2 Diabetes Genetics Consortium (UKT2D) UK 322 320 European GoT2D Malmo-Botnia Study Finland, Sweden 478 443 European LuCamp Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) Denmark 997 997 European ESP Exome Sequencing Project (ESP) US 390 2843 European T2D-GENES Follow up study Genetics of Diabetes and Audit Research Tayside Study (GoDARTS) Scotland, UK 960 966 European T2D-GENES Follow up study Framingham Heart Study (FHS) US 396 596 Hispanic T2D-GENES Project 1 San Antonio Family Heart Study, San Antonio Family Diabetes/ Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study Family Component US 272 218 Hispanic T2D-GENES Project 1 & SIGMAv2 Starr County, Texas US 1762 1738 Hispanic SIGMAv1 Mexico City Diabetes Study Mexico 281 549 Hispanic SIGMAv1 & v2 Multiethnic Cohort (MEC) US 1476 1443 Hispanic SIGMAv1 & v2 UNAM/INCMNSZ Diabetes Study (UIDS) Mexico 1998 1977 Hispanic SIGMAv1 & v2 Diabetes in Mexico Study (DMS) Mexico 1522 1546 Multi ethnic ProDIGY Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) US 3097 0 Multi ethnic ProDIGY SEARCH for Diabetes in Youth (SEARCH) US 553 0 South Asian T2D-GENES Project 1 London Life Sciences Population Study (LOLIPOP) UK (Indian Asian) 551 538 South Asian T2D-GENES Project 1 & Follow up study Singapore Indian Eye Study Singapore (Indian Asian) 1640 1478 South Asian T2D-GENES Follow up study Pakistan Risk of Myocardial Infarction Study (PROMIS) Pakistan 914 932 The SEARCH study contributed 553 cases and 0 controls to 52k T2D exome sequencing study.

  Study phs001511

• Slim Initiative in Genomic Medicine for the Americas (SIGMA): Diabetes in Mexico Study (DMS)

  The ~52,000 sample Type 2 Diabetes Exome Sequencing project is a collaboration of six consortia with various funding mechanisms that have joined together to investigate genetic variants for type 2 diabetes (T2D) using the largest T2D case/control sample set compiled to date. This includes samples from: Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Genetics of Type 2 Diabetes (GoT2D) Exome Sequencing Project (ESP) Slim Initiative in Genomic Medicine for the Americas: Type 2 Diabetes (SIGMA T2D) Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCAMP) Progress in Diabetes Genetics in Youth (ProDIGY) This data generated from the SIGMA Diabetes in Mexico Study (DMS) was part of the Slim Initiative in Genomic Medicine for the Americas: Type 2 Diabetes (SIGMA T2D), which is an international research consortium funded by the Carlos Slim Foundation that seeks to identify the genetic risk factors for type 2 diabetes (T2D) in Mexico and Latin America and translate those findings into improved treatment and prevention of diabetes. The SIGMA T2D project has sequenced and genotyped more than 13,000 samples from Mexican and Mexican Americans. The individuals were obtained from over 20 cohorts across the 6 consortia that are listed in Table 1. The strategy was to perform deep exome sequencing of individuals, 24,991 with T2D and 24,953 controls, divided among five ancestry groups: Europeans, East Asians, South Asians, American Hispanics, and African Americans. The T2D-GENES, ProDIGY and SIGMA studies, sequencing was performed at the Broad Institute using the Agilent v2 capture reagent or Illumina Rapid Capture on Illumina HiSeq machines. Please note that while we summarize the full sample list in publications and below, two of the cohorts below are not in dbGaP, due to the samples not being consented for deposition. This includes the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study and Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) study. The Exome Sequencing Project (ESP) was deposited in dbGaP as part of their initial study and the phs numbers for that project can be found here: https://esp.gs.washington.edu/drupal/dbGaP_Releases. Table 1. 52,000 sample T2D Case/Control Whole Exome Sequencing Studies Ancestry Consortia Study Countries of Origin # Cases # Controls African American T2D-GENES Project 1 Jackson Heart Study US 500 526 African American T2D-GENES Project 1 Wake Forest School of Medicine Study US 518 530 African American ESP Exome Sequencing Project (ESP) US 467 1374 African American T2D-GENES Follow up study BioMe Biobank Program (BioMe) US 1297 1256 East Asian T2D-GENES Project 1 Korea Association Research Project Korea 526 561 East Asian T2D-GENES Project 1& Follow up Study Singapore Diabetes Cohort Study; Singapore Prospective Study Program Singapore (Chinese) 1486 1568 East Asian T2D-GENES Follow up study Korea SNUH South Korea 450 475 East Asian T2D-GENES Follow up study Research Studies in Hong Kong (Hong Kong) Hong Kong 493 485 European T2D-GENES Project 1 Ashkenazi US, Israel 506 355 European T2D-GENES Project 1 Metabolic Syndrome in Men Study (METSIM) Finland 484 498 European GoT2D Finland-United States Investigation of NIDDM Genetics (FUSION) Study Finland 472 476 European GoT2D Kooperative Gesundheitsforschung in der Region Augsburg (KORA) Germany 97 90 European GoT2D UK Type 2 Diabetes Genetics Consortium (UKT2D) UK 322 320 European GoT2D Malmo-Botnia Study Finland, Sweden 478 443 European LuCamp Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) Denmark 997 997 European ESP Exome Sequencing Project (ESP) US 390 2843 European T2D-GENES Follow up study Genetics of Diabetes and Audit Research Tayside Study (GoDARTS) Scotland, UK 960 966 European T2D-GENES Follow up study Framingham Heart Study (FHS) US 396 596 Hispanic T2D-GENES Project 1 San Antonio Family Heart Study, San Antonio Family Diabetes/ Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study Family Component US 272 218 Hispanic T2D-GENES Project 1 & SIGMAv2 Starr County, Texas US 1762 1738 Hispanic SIGMAv1 Mexico City Diabetes Study Mexico 281 549 Hispanic SIGMAv1 & v2 Multiethnic Cohort (MEC) US 1476 1443 Hispanic SIGMAv1 & v2 UNAM/INCMNSZ Diabetes Study (UIDS) Mexico 1998 1977 Hispanic SIGMAv1 & v2 Diabetes in Mexico Study (DMS) Mexico 1522 1546 Multi ethnic ProDIGY Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) US 3097 0 Multi ethnic ProDIGY SEARCH for Diabetes in Youth (SEARCH) US 553 0 South Asian T2D-GENES Project 1 London Life Sciences Population Study (LOLIPOP) UK (Indian Asian) 531 538 South Asian T2D-GENES Project 1 & Follow up study Singapore Indian Eye Study Singapore (Indian Asian) 1640 1478 South Asian T2D-GENES Follow up study Pakistan Genomic Resource (PGR) Pakistan 914 932 The Diabetes in Mexico Study (DMS) study contributed 1,522 cases and 1,546 controls to the 52k T2D exome sequencing study.

  Study phs001388

• Slim Initiative in Genomic Medicine for the Americas (SIGMA): Mexico City Diabetes Study (MCDS)

  The ~52,000 sample Type 2 Diabetes Exome Sequencing project is a collaboration of six consortia with various funding mechanisms that have joined together to investigate genetic variants for type 2 diabetes (T2D) using the largest T2D case/control sample set compiled to date. This includes samples from: Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Genetics of Type 2 Diabetes (GoT2D) Exome Sequencing Project (ESP) Slim Initiative in Genomic Medicine for the Americas: Type 2 Diabetes (SIGMA T2D) Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCAMP) Progress in Diabetes Genetics in Youth (ProDIGY) This data generated from the SIGMA Mexico City Diabetes Study was part of the Slim Initiative in Genomic Medicine for the Americas: Type 2 Diabetes (SIGMA T2D), which is an international research consortium funded by the Carlos Slim Foundation that seeks to identify the genetic risk factors for type 2 diabetes (T2D) in Mexico and Latin America and translate those findings into improved treatment and prevention of diabetes. The SIGMA T2D project has sequenced and genotyped more than 13,000 samples from Mexican and Mexican Americans. The individuals were obtained from over 20 cohorts across the 6 consortia that are listed in Table 1. The strategy was to perform deep exome sequencing of individuals, 24,991 with T2D and 24,953 controls, divided among five ancestry groups: Europeans, East Asians, South Asians, American Hispanics, and African Americans. The T2D-GENES, ProDIGY and SIGMA studies, sequencing was performed at the Broad Institute using the Agilent v2 capture reagent or Illumina Rapid Capture on Illumina HiSeq machines. Please note that while we summarize the full sample list in publications and below, two of the cohorts below are not in dbGaP, due to the samples not being consented for deposition. This includes the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study and Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) study. The Exome Sequencing Project (ESP) was deposited in dbGaP as part of their initial study and the phs numbers for that project can be found here: https://esp.gs.washington.edu/drupal/dbGaP_Releases Table 1. 52,000 sample T2D Case/Control Whole Exome Sequencing Studies Ancestry Consortia Study Countries of Origin # Cases # Controls African American T2D-GENES Project 1 Jackson Heart Study US 500 526 African American T2D-GENES Project 1 Wake Forest School of Medicine Study US 518 530 African American ESP Exome Sequencing Project (ESP) US 467 1374 African American T2D-GENES Follow up study BioMe Biobank Program (BioMe) US 1297 1256 East Asian T2D-GENES Project 1 Korea Association Research Project Korea 526 561 East Asian T2D-GENES Project 1& Follow up Study Singapore Diabetes Cohort Study; Singapore Prospective Study Program Singapore (Chinese) 1486 1568 East Asian T2D-GENES Follow up study Korea SNUH South Korea 450 475 East Asian T2D-GENES Follow up study Research Studies in Hong Kong (Hong Kong) Hong Kong 493 485 European T2D-GENES Project 1 Ashkenazi US, Israel 506 355 European T2D-GENES Project 1 Metabolic Syndrome in Men Study (METSIM) Finland 484 498 European GoT2D Finland-United States Investigation of NIDDM Genetics (FUSION) Study Finland 472 476 European GoT2D Kooperative Gesundheitsforschung in der Region Augsburg (KORA) Germany 97 90 European GoT2D UK Type 2 Diabetes Genetics Consortium (UKT2D) UK 322 320 European GoT2D Malmo-Botnia Study Finland, Sweden 478 443 European LuCamp Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) Denmark 997 997 European ESP Exome Sequencing Project (ESP) US 390 2843 European T2D-GENES Follow up study Genetics of Diabetes and Audit Research Tayside Study (GoDARTS) Scotland, UK 960 966 European T2D-GENES Follow up study Framingham Heart Study (FHS) US 396 596 Hispanic T2D-GENES Project 1 San Antonio Family Heart Study, San Antonio Family Diabetes/ Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study Family Component US 272 218 Hispanic T2D-GENES Project 1 & SIGMAv2 Starr County, Texas US 1762 1738 Hispanic SIGMAv1 Mexico City Diabetes Study Mexico 281 549 Hispanic SIGMAv1 & v2 Multiethnic Cohort (MEC) US 1476 1443 Hispanic SIGMAv1 & v2 UNAM/INCMNSZ Diabetes Study (UIDS) Mexico 1998 1977 Hispanic SIGMAv1 & v2 Diabetes in Mexico Study (DMS) Mexico 1522 1546 Multi ethnic ProDIGY Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) US 3097 0 Multi ethnic ProDIGY SEARCH for Diabetes in Youth (SEARCH) US 553 0 South Asian T2D-GENES Project 1 London Life Sciences Population Study (LOLIPOP) UK (Indian Asian) 531 538 South Asian T2D-GENES Project 1 & Follow up study Singapore Indian Eye Study Singapore (Indian Asian) 1640 1478 South Asian T2D-GENES Follow up study Pakistan Genomic Resource (PGR) Pakistan 914 932

  Study phs001375

• Distribution of data using Live Distribution

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

  Documentation access/download/files/live-outbox

• Successful Clinical Response in Pneumonia Therapy (SCRIPT)

  The goal of this study is to iteratively identify, validate and refine biomarkers to predict clinical failure, and identify novel targets for therapy, beyond traditional antibiotics, for patients with severe pneumonia requiring intubation, including severe community-acquired pneumonia (CAP), hospital-acquired pneumonia, and ventilator-associated pneumonia (HAP/VAP). We hypothesize that factors within the alveolar microenvironment in many patients with pneumonia prevent the active process of pneumonia resolution.The major emphasis will be on HAP. Mechanically ventilated CAP patients are a very high-risk group for subsequent VAP, and therefore CAP samples will be included to determine baseline bacteria, microbiome, and host defense patterns for those that do or do not develop HAP/VAP. Hospital-acquired pneumonia is the leading cause of death from nosocomial infections. Even in patients receiving pathogen-appropriate therapy, reported clinical failure rates are increasing, approaching 50% in recent clinical trials. Current approaches to improve outcomes in patients with pneumonia almost exclusively focus on the development of antibiotics to address the problem of resistance. However, the prevalence of antibiotic-resistant pathogens is insufficient to explain the high rates of clinical failure. Instead, we hypothesize that factors within the alveolar microenvironment in many patients with pneumonia prevent the active process of pneumonia resolution. These factors include pathogen-specific adaptations to the injured lung that modulate the innate and adaptive immune response, as well as changes to the alveolar microbiome, driven by selective pressure within our modern intensive care units. Understanding this complex adaptive system requires the integration of clinical information with unbiased measures of the host, pathogen, and microbiome changes within the alveolar microenvironment. These measures must be repeated over time to identify factors that distinguish successful from failed therapy for pneumonia sufficiently early during the clinical course to allow for effective interventions. To address this challenge, we have assembled a talented group of investigators in the Successful Clinical Response In Pneumonia Therapy (SCRIPT) Systems Biology Center. We will leverage our routine clinical practice of safe alveolar sampling in mechanically ventilated patients with pneumonia with repeated non-bronchoscopic bronchoalveolar lavage (NBBAL) sampling over the course of pneumonia. From this fluid, we will combine flow cytometry with multi-omic technologies (cell population-specific transcriptomics, epigenomics, shotgun microbiome sequencing, and pathogen-specific sequencing). Our systems scientists will integrate these genomic data with robust clinical -omics with the goal of identifying biomarkers that can be prospectively tested in patients and causally evaluated in humanized mouse models.The addition of a pooled sample artificially increases the number of automatically computed subjects by one.

  Study phs002300

• Multiomic Landscape of Multiple Myeloma Precursor and Relapsed Disease

  Multiple myeloma (MM) is a treatable, though incurable, plasma cell malignancy. The molecular mechanisms driving disease onset and the emergence of drug resistance remain elusive. To better characterize the mutational, transcriptional, and epigenetic alterations that accompany MM disease evolution, we accessed molecular data from MM patients treated at Moffitt Cancer Center, with bone marrow biopsies collected between 2011 and 2023.We performed scMultiome (single-cell, paired RNA/ATAC-Seq, 10x Genomics) on 18 CD138+-selected bone marrow aspirate samples, including: 2 healthy donors, 3 patients with monoclonal gammopathy of undetermined significance (MGUS), 4 with smoldering myeloma (SMM), 3 with newly diagnosed MM (NDMM), 1 with early relapse MM (ERMM, 1 to 3 lines of therapy), and 5 with late relapse MM (LRMM, 4 or more lines of therapy). Our dataset includes a sequential sample: a patient with a premalignant condition ("scMultiome_SMM_4") that progressed to active myeloma ("scMultiome_NDMM_3"). These data allow for the assessment of transcriptional and epigenetic dysregulation at the cellular level. Our findings illustrate the dynamic epigenetic and transcriptional landscapes that accompany MM disease progression.We also conducted CUT&#38;Tag analysis on 4 NDMM and 4 LRMM samples to map histone modifications, specifically H3K27ac, to investigate their role in transcriptional reprogramming observed in advanced stages of MM. This analysis revealed sample heterogeneity in terms of super-enhancer-regulated genes associated with active transcription.This cohort also includes samples from the clinical trial NCT04151667, &#8220;Daratumumab-Based Response-Adaptive Therapy for Older Adults With Newly Diagnosed Multiple Myeloma.&#8221; We performed scRNAseq (single-cell RNA sequencing, 10x Genomics) on 30 CD138+-selected bone marrow aspirate samples and 31 pre-sort samples featuring bone marrow mononuclear cells of various cell types from 5 healthy donors and 11 patients with newly diagnosed MM (NDMM). Our dataset includes sequential samples for all 11 NDMM patients, both prior to induction therapy (denoted by &#8216;a') with Daratumumab and Dexamethasone, and at cycle 2 day 22 (C2D22, i.e., 60 days after induction therapy start, denoted by &#8216;b'). For three NDMM patients, we also have samples at relapse (denoted by &#8216;c'). These data allow for the assessment of transcriptional dysregulation at the single-cell level in both the tumor and immune-tumor microenvironment, associated with Daratumumab treatment in NDMM patients.In addition to the molecular data, demographic, and phenotypic information for all samples is provided.

  Study phs003892

• Virtual Growing Child 5-Dimensional Functional Models for Treating Respiratory Anomalies (dMRI-VGC)

  Objectives We are sharing a database of dynamic magnetic resonance imaging (dMRI) scans of normal children, which can serve as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The database can also be useful to advance future AI-based research on image-based object segmentation and analysis. Background In pediatric patients with respiratory abnormalities, it is important to understand the alterations in regional dynamics of the lungs and other thoracoabdominal components, which in turn requires a quantitative understanding of what is considered as normal in healthy children. Currently, such a normative database of regional respiratory structure and function in healthy children does not exist. Participants 200 normal children (ages 6-18 years) participated in our research study related to this dataset. DesignThe shared open-source normative database is from our ongoing virtual growing child (VGC) project, which includes 4D dMRI images representing one breathing cycle for each normal child and also segmentations of 10 objects at end expiration (EE) and end inspiration (EI) phases of the respiratory cycle in the 4D image. The lung volumes at EE and EI as well as the excursion volumes of chest wall and diaphragm from EE to EI, left and right sides separately, are also reported. The database has thus 4,000 3D segmentations from 200 normal children in total. The database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. All dMRI scans are acquired from normal children during free-breathing. The dMRI acquisition protocol was as follows: 3T MRI scanner (Verio, Siemens, Erlangen, Germany), true-FISP bright-blood sequence, TR=3.82 ms, TE=1.91 ms, voxel size ~1&#215;1&#215;6 mm3, 320&#215;320 matrix, bandwidth 258 Hz, and flip angle 76o. With recent advances, for each sagittal location across the thorax and abdomen, we acquired 40 2D slices over several tidal breathing cycles at ~480 ms/slice. On average, 35 sagittal locations are imaged, yielding a total of ~1400 2D MRI slices, with a resulting total scan time of 11-13 minutes for any particular study participant.The collected dMRI scan data then went through the procedure of 4D image construction, image processing, object segmentation, and volumetric measurements from segmentations. 4D image construction: For the acquired dMRI scans, we utilized an automated 4D image construction approach to form one 4D image over one breathing cycle (consisting of typically 5-8 respiratory phases) from each acquired dMRI scan to represent the whole dynamic thoraco-abdominal body region. The algorithm selects 175-280 slices (35 sagittal locations &#215; 5-8 respiratory phases) from the 1400 acquired slices in an optimal manner using an optical flux method. Image processing: Intensity standardization is performed on every time point/3D volume of the 4D image so that image values have the same tissue-specific meaning across all subjects. Object segmentation: For each subject, there are 10 objects segmented at both EE and EI time points in this database. They include the thoracoabdominal skin outer boundary, left and right lungs, liver, spleen, left and right kidneys, diaphragm, and left and right hemi-diaphragms. All dMRI scans utilize large field of view images, which include the full thorax and abdomen to the inferior aspect of the kidneys in the sagittal plane. We used a pretrained U-Net based deep learning network to first segment all objects, and then all auto-segmentation results were visually checked and manually refined as needed, under the supervision of a radiologist with over 25 years of expertise in MRI and thoracoabdominal radiology. Manual segmentations have been performed for all objects in all datasets. Volumetric measurements based on object segmentations for lung volumes (left and right separately) at EE and EI, as well as for chest wall and diaphragm excursion volumes (left and right separately) are reported. ConclusionsThe provided database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. The database has 4,000 3D segmentations from 200 normal children, which to our knowledge is the largest and only such dMRI dataset to date. All images and object segmentations are saved in DICOM. All DICOM files (176,574 in total) have been anonymized, and PHI has been removed. The database can be used as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The large amount of object segmentations can potentially benefit AI-based research on image-based object segmentation and analysis.

  Study phs004002

• Evaluation of Hybridization Capture versus Amplicon-based Methods for Whole Exome Sequencing

  Next generation sequencing has aided characterization of genomic variation. While whole genome sequencing may capture all possible mutations, whole exome sequencing is more cost-effective and captures most phenotype-altering mutations. Initial strategies for exome enrichment utilized a hybridization-based capture approach. Recently, amplicon-based methods were designed to simplify preparation and utilize smaller DNA inputs. We appraised two hybridization capture-based and two amplicon-based whole exome sequencing methods, utilizing both Illumina and Ion Torrent sequencers, comparing on-target alignment, uniformity, and variant calling. While the amplicon methods had higher on-target rates, the hybridization capture-based approaches showed better uniformity. All methods identified many of the same single nucleotide variants, but each amplicon-based method missed variants detected by the other three methods and reported additional variants discordant with all three other technologies. Many of these potential false positives or negatives appear to result from limited coverage, low variant frequency, vicinity to read starts/ends, or the need for platform-specific variant calling algorithms. All methods demonstrated effective copy number variant calling when compared against a single nucleotide polymorphism array. This study illustrates some differences between various whole exome sequencing approaches, highlights the need for selecting appropriate variant calling based on capture method, and will aid laboratories in selecting their preferred approach.

  Study phs000938

• BrainCloud: Data from human postmortem brain procurement for the neuropathology section

  This study explores the temporal dynamics and genetic control of transcription and DNA methylation in the human dorsolateral prefrontal cortex in postmortem tissue. This study examines 269 subjects for gene expression (version 1) and 108 subjects for DNA methylation (version 2). The subjects are normal controls without neuropathological and neuropsychiatric diagnosis and range in age from fetal weeks 14-20 through old age (&gt;80). We discover fast changes in gene expression occurring during early brain development. Later in life, the changes are considerably slower. Many genes reverse pattern of expression between fetal and early postnatal development. We identify thousands of strong associations of SNPs with gene expression. We examine DNA methylation in ~14,500 genes at ~27,000 CpG loci focused on 5' promoter regions. The fastest changes in DNA methylation also occur during the prenatal period, slow down markedly after birth and continue to slow further with aging. DNA methylation is strongly associated with genotypic variants and correlates with expression of a subset of genes. DNA for genotyping was obtained from the cerebella and applied to either Illumina 650K or 1 million BeadArrays - only genotypes common to both platforms are analyzed here. Genotypes were called using BeadExpress software. Doi: 10.1038/nature10524 Nature, 478:519-524, 2011; doi:10.1016/j.ajhg.2011.12.020, AJHG 90, 1-13, Feb 10, 2012. The methylation data can be downloaded at: BrainCloud.

  Study phs000417

• NHLBI TOPMed: Novel Risk Factors for the Development of Atrial Fibrillation in Women

  The Women's Genome Health Study (WGHS) is a prospective cohort comprised of over 25,000 initially healthy female health professionals enrolled in the Women's Health Study, which began in 1992-1994. All participants in WGHS provided baseline blood samples and extensive survey data. Women who reported atrial fibrillation during the course of the study were asked to report diagnoses of AF at baseline, 48 months, and then annually thereafter. Participants enrolled in the continued observational follow-up who reported an incident AF event on at least one yearly questionnaire were sent an additional questionnaire to confirm the episode and to collect additional information. They were also asked for permission to review their medical records, particularly available ECGs, rhythm strips, 24-hour ECGs, and information on cardiac structure and function. For all deceased participants who reported AF during the trial and extended follow-up period, family members were contacted to obtain consent and additional relevant information. An end-point committee of physicians reviewed medical records for reported events according to predefined criteria. An incident AF event was confirmed if there was ECG evidence of AF or if a medical report clearly indicated a personal history of AF. The earliest date in the medical records when documentation was believed to have occurred was set as the date of onset of AF.

  Study phs001040

• RNA-sequencing of human skin samples obtained from SSc patients and healthy controls.

  Patient information SSc patients were recruited at the Department of Rheumatology of the Leiden University Medical Center (Leiden, The Netherlands). All patients met the American Rheumatism Association classification criteria for SSc (Subcommittee for scleroderma criteria 1980), and were classified according to LeRoy and Medsger criteria as either limited or diffuse cutaneous disease (LeRoy EC, Black C, Fleischmajer R, Jablonska S, Krieg T, Medsger TA Jr, Rowell N 1988). Institutional review board approval and written informed consent was obtained before patients entered this study. Two 4 mm skin biopsies were taken from a standardized location on the most proximal part of the lower arm, distal from the elbow. In 10 patients the skin biopsy came from a clinically affected area and in 4 patients the skin was locally unaffected. One sample was used for RNA sequencing and one sample was used for immunohistochemistry. Skin biopsies from healthy individuals were commercially sourced (Tissue Solutions, UK) and collected from donors undergoing skin resection surgery and after informed consent. To match the healthy skin with patients as much as possible, skin biopsies from healthy controls were also taken from a similar position (the under-arm (for 4 controls) and leg (for 2 controls)). Healthy skin donors were selected to match the age and sex of the SSc patient cohort. Biopsies from patients and controls were equally treated and were both stored at -80°C until RNA isolation was performed. RNA from frozen skin biopsies was isolated using RNeasy kit from fibrous tissue (Qiagen, the Netherlands). RNA quantity was determined by using SimplyNano 2000 and quality was assessed on Tapestation (Agilent, the Netherlands). All samples included in the study had a RIN score above 7.0. Transcriptome characterisation and analysis RNA sequencing was performed using polyA selection and a stranded protocol using Ion Torrent next generation sequencing technology (Service XS, The Netherlands). The Ion PI Template OT2 200 Kit v3 and Ion PI Sequencing 200 Kit v3 were used according to the manufacturer’s instructions. 20 samples were run on 11 PI chips. PI chip analyses, base calling and quality checks were performed using the Torrent Server Suite. An average of 42 million 100 bp reads was generated per sample. Following quality control, reads were aligned to the human genome (Homo sapiens GRh38.78) using Bowtie2 and STAR (Dobin et al. 2013; Langmead and Salzberg 2012). Reads were first aligned with STAR. For the unmapped reads from STAR, a second alignment step was performed using bowtie2 (local very sensitive options)

  Dataset EGAD00001003832

• Single cell transcriptional characterization of human megakaryocyte lineage commitment and maturation

  Single cell RNA-sequencing of sternal bone marrow reciding Hematopoietic Stem Cells (HSCs) and Megakaryocytes (MKs) from individuals undergoing elective open heart valve replacement. HSCs were defined as Lineage-, CD34+, CD38-, CD45RA-, CD90+, CD49f+ cells. MKs where CD41a+, CD42b+ and ploidy was determined with Hoechst. A sternal bone marrow scraping was taken directly following median sternotomy using a Volkmann’s spoon. The sample was collected into an EDTA Vacutainer tube containing 1.8mg/ml EDTA. 4mL of Dulbecco’s phosphate buffered saline (PBS, Sigma) containing 10% human serum albumin (HSA, Gemini Bio Products) was added and the whole volume was resuspended by pipetting 2-3 times. The sample was then put on metallic thermal beads (ThermoFisher Scientific) at a temperature between 0-4°C and transported to the University of Cambridge for further processing. For HSC isolation the cells were stained with the following antibody cocktail: PECy5 conjugated anti-lineage specific antibodies: CD2 (BD), CD3 (BD), CD10 (BD), CD11b (BD), CD11c (BD), CD19 (BD), CD20 (BD), CD56 (BD), biotinylated CD42b (Pab5, NHS Blood and Transplant, International Blood Group Reference Laboratory [IBGRL]), biotinylated GP6 (Pab5, NHS Blood and Transplant, International Blood Group Reference Laboratory [IBGRL]) used in combination with PECy5 conjugated streptavidin (Biolegend). Alexa Fluor 700 conjugated anti-CD34 (BD), PerCP-Cy5.5 conjugated anti-CD38 (BD), Pacific Blue conjugated anti-CD45RA (Invitrogen), PECy7 conjugated anti-CD90 (BD),PE conjugated anti-CD49f (BD). After staining cells were kept at 4°C before sorting using a FACS Aria Fusion flow sorter (BD). Single HSCs defined as Lineage-, CD34+, CD38-, CD45RA-, CD90+, CD49f+ cells were sorted by FACS directly into individual wells of a 96-well plate. Index sort data was collected for each single cell. For MK isolation the cells were stained for surface MK markers with mouse anti-human CD41a APC conjugated antibody (BD) and mouse anti-human CD42b PE conjugated antibody (BD) and for ploidy analysis with 1ug/ml Hoechst 33342 (Invitrogen). After incubation at 37°C for 30 minutes, the cells were kept at 4°C before sorting using a FACS Aria Fusion flow sorter (BD). Single cells and MK pools of 20 cells were sorted by FACS according to ploidy level using a 100uM nozzle directly into individual wells of a 96-well plate. cDNA synthesis and poly(A) enrichment was performed following the G&amp;T-seq protocol (Macaulay et al. 2015), a variation of the Smart-seq2 protocol1. ERCC spike-in RNA (Ambion) was added to the lysis buffer in a dilution of 1:4,000,000.

  Dataset EGAD00001006677

• Data Access Commitee for schizophrenia and control cases sequencing data

  Dac EGAC00001002810

• The data access committee for TIGIT in MCL with CART

  Dac EGAC00001003167

• DAC for study characterisation of CpG islands in human tissues

  Dac EGAC00001000068

• DAC for the MDC-LSR-SAHLSIS ischemic stroke study

  Dac EGAC00001000226

• DAC for integrated genomics of metastatic prostate cancer

  Dac EGAC00001000230

• English Longitudinal Study for Ageing (ELSA) Genetic Data Access

  Dac EGAC00001000270

• Data access committee for the head and neck project

  Dac EGAC00001000374

• DAC for the Singapore Integrating Omics Study

  Dac EGAC00001000685

• DAC for AA HCC patient from Chang Gung Memorial Hospital

  Dac EGAC00001000707

• European Bank for induced pluripotent Stem Cells (EBiSC)

  Dac EGAC00001000768

• DAC for the project on epigenetic dysregulation in tuberculosis

  Dac EGAC00001000909

• Data Access Committee members for NETWork! project

  Dac EGAC00001000930

• Ludwig Data Access Committee for the Study of Oesophageal Cancer

  Dac EGAC00001000978

• Fecal Microbiota Transplantation for refractory immune checkpoint inhibitor-associated colitis

  Dac EGAC00001001021

• Search for bacteria in neural tissue from amyotrophic lateral sclerosis

  Dac EGAC00001001058

• DAC for WGS data Romanians and Roma/Rroma (Romania)

  Dac EGAC00001001184

• DAC for Analysis of mechanisms of CD19- relapse in CARPALL study

  Dac EGAC00001001257

• DAC for Genome-Wide Scans of Signals of Selection

  Dac EGAC00001001351

• Tytgat Institute for Liver and Intestinal Research Data Access Committee

  Dac EGAC00001001483

• Tytgat Institute for Liver and Intestinal Research Data Access Committee

  Dac EGAC00001001485

• Tytgat Institute for Liver and Intestinal Research Data Access Committee

  Dac EGAC00001001615

• Epichaperome - Englander Institute for Precision Medicine - Weill Cornell Medicine DAC

  Dac EGAC00001001890

• DAC for the study EGAS00001005773

  Dac EGAC00001002411

• Tytgat Institute for Liver and Intestinal Research Data Access Committee

  Dac EGAC00001002437

• DAC for COPD human sputum 16s rRNA gene sequencing data

  Dac EGAC00001002779

• Data access committee for sequencing data at Kyoto University

  Dac EGAC50000000045

• Data access committee for neural retina and retinal organoid data

  Dac EGAC50000000019

• DAC for access to array genotypes from the PREGO biobank.

  Dac EGAC00001003484

• Centre for Drug Repurposing and Medicines Research Data Access Committee

  Dac EGAC00001002764

• XClone for analyzing somatic copy number alterations Data Access Committee

  Dac EGAC00001003492

• Single cell multi-omics committee for CK-AML

  Dac EGAC00001003353

• Developmental and Stem Cell Biology department - Hospital for Sick Children

  Dac EGAC00001002951

• Data access committee for sequencing data generated by Wyatt Lab

  Dac EGAC50000000538

• University of Melbourne Centre for Cancer Research (UMCCR) Data Access Committee

  Dac EGAC00001003567