16370 results for "free fc coins and points Visit Buyfc26coins.com Alennukset toistuvista ostoista..p4v2"

in 24.01 milliseconds.

• Whole-exome sequencing and microRNA profiling predicted relapse risk of stage I lung adenocarcinomas

  Despite the advance in precision therapy, about 20% of stage I non-small cell lung cancer patients suffer a relapse after surgical resection. To develop molecular signatures for relapse prediction of stage I lung adenocarcinoma (LUAD), we conduct comprehensive analyses of multiplatform molecular profiling from 113 treatment-naïve stage I Taiwanese LUAD patients. We identify 16 nonsynonymous non-sporadic point mutations (NSPOPMs) harbored at EGFR, KRAS, TP53, CTNNB1 and 6 other genes, and find that the Non-EGFR NSPOPMs were associated with early relapse in a dose-dependent manner via variant allele fraction (VAF). We define a score, maxVAF, to aggregate the VAFs of these NSPOPMs by the maximum value. MicroRNA expression analysis shows a strong correlation between miR-31 and maxVAF. Combining maxVAF with miR-31 led to a powerful risk prediction for surgically resected stage I patients (hazard ratio = 8.65, P = 0.001). Our study opens up more options for precision management of stage I LUAD patients.

  Study EGAS00001004461

• Early on-treatment changes in circulating tumor DNA fraction and response to enzalutamide or abiraterone in metastatic castration-resistant prostate cancer

  On-treatment circulating tumor DNA (ctDNA) quantification is touted as a surrogate biomarker for understanding metastatic cancer response to systemic therapy, but there is relatively little supporting data from standardized patient cohorts. Here, in a prospective observational cohort, we demonstrate that 4-week on-treatment changes in plasma ctDNA levels can accurately identify metastatic castration-resistant prostate cancer that will progress within 6 months of initiating first-line abiraterone or enzalutamide treatment. On-treatment ctDNA measurements outperformed existing clinical variables for predicting disease response and were also closely associated with differential overall survival. Although optimal timing of blood collections and thresholds for ctDNA detection will require further testing, our data highlights the potential for sequential ctDNA measurements to provide an early and reliable read-out for therapy response. The high accuracy of this information suggests that ctDNA could be useful for guiding clinical trials of therapy changes in initially non-responsive metastatic cancers.

  Study EGAS00001006856

• Study of complex rearrangements and mutational signatures in neuroblastoma heterogeneous risk groups.

  Neuroblastoma is a pediatric solid tumor characterized by strong clinical heterogeneity. Although certain complex genomic alterations, such as extrachromosomal DNA amplifications (ecDNA), are associated with adverse outcome and have been recurrently detected in neuroblastomas, the mutational processes involved in their generation remain largely unclear. By examining the topography of complex rearrangements along with mutational signatures derived from all variant classes, we identify previously unrecognized co-occurring mutational footprints, which we termed mutational scenarios. We demonstrate that clinical neuroblastoma heterogeneity is linked to differences in the processes driving these mutational scenarios. Whereas high-risk MYCN-amplified neuroblastoma genomes were characterized by signs of replication slippage and stress, homologous recombination-associated signatures defined high-risk non-MYCN-amplified patients. Non-high-risk neuroblastomas, on the other hand, were marked by footprints of chromosome missegregation and TOP1 mutational activity. This analysis provides a systematic perspective on the repertoire of mutational patterns that contribute to clinical neuroblastoma heterogeneity.

  Study EGAS00001006983

• Identification of the Global miR-130a Targetome Reveals a Novel Role for TBL1XR1 in Hematopoietic Stem Cell Self-Renewal and t(8;21) AML

  In this study, we identified miR-130a as a regulator of HSC self-renewal and differentiation. To characterize gene expression changes following enforced expression of miR-130a OE, we performed RNA-seq in CD34+ cord blood (CB) cells transduced with control and miR-130a OE lentiviruses. To capture miRNA targets in an unbiased, transcriptome-wide manner, we perfomed enhanced CLIPseq procol in 2 replicates of CD34+ CB cells and Kasumi-1 cell line, which represent a model system for t(8;21) AML. We chose this cell line, as we found miR-130a to be highly expressed in this AML subtype where it is critical for maintaining the oncogenic molecular program mediated by AML1-ETO. Chimeric Ago2 eCLIPseq in CD34+ CB cells combined with Mass Spectrometry data analysis identified TBL1XR1 as a principal target of miR-130a. To elucidate gene expression changes associated with TBL1XR1 loss of function, we performed RNA-seq in CD34+CD38- CB cells transduced with control and shRNA targeted against TBL1XR1. To determine the functional significance of high miR-130a expression levels in Kasumi-1 cells on the molecular network controlled by AML1-ETO, we performed CUT&RUN assay and RNA-seq in Kasumi-1 cells following miR-130a knock-down (KD). Collectively, our findings reveal a unique role of miR-130a in regulating normal hematopoietic stem cell self-renewal and how elevated levels of miR-130a in t(8;21) AML contribute to the leukemogenesis of this AML subtype.

  Dataset EGAD00001008412

• Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - ExomeNanoSeq

  Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.

  Dataset EGAD00001016058

• Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - PTA_DNAHyb

  Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.

  Dataset EGAD00001016062

• Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - LCMB_WGS

  Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.

  Dataset EGAD00001016064

• Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - TargetedNanoSeq

  Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.

  Dataset EGAD00001016059

• Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - TargetedEMSeq

  Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.

  Dataset EGAD00001016060

• Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - PTA_WGS

  Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.

  Dataset EGAD00001016061

• Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - PTA_RNA

  Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.

  Dataset EGAD00001016063

• Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - LCMB_WES

  Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.

  Dataset EGAD00001016065

• 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

• Pediatric Investigation for Genetic Factors Linked with Renal Progression (PediGFR)

  The Pediatric Investigation for Genetic Factors Associated with Renal Progression (PediGFR) (RO1-DK082394) is an international collaborative study among three large prospective cohort studies of children with chronic kidney disease. The participating parent cohort studies are the Chronic Kidney Disease in Children (CKiD), the Effect of Strict Blood Pressure Control and ACE Inhibition on CRF Progression in Pediatric Patients (ESCAPE), and the Cardiovascular Comorbidity in Children with Chronic Kidney Disease (4C) study. In these cohorts, pediatric subjects with CKD have been prospectively followed with standardized measurements for renal progression. The dbGaP data upload will utilize the structure of sub-studies to include the genotype and baseline phenotype for the three cohorts, as well as the RBC trait and anemia data for a sub-study under PI Susan Furth: Role of Genetic Variation in the Anemia of Chronic Kidney Disease (K24DK078737). In brief PediGFR is a prospective study of children with chronic kidney disease (CKD) to determine genetic factors associated with kidney function measured by the estimated glomerular filtration rate (eGFR) by Schwartz equation. The PediGFR_v2 Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs000842 PediGFR_v2 Cohort. phs000843 4C phs000650 CKiD phs000844 ESCAPE

  Study phs000842

• Rare Cancer Tumors Project

  A large proportion of common cancers affecting patients around the world have been selected for comprehensive cancer genome studies. Further efforts will be needed to tackle the remaining tumor types, including the rare forms of cancers. Although rare, these cancers tend to be more aggressive and fast growing with an early recurrence following initial chemotherapy and poor prognosis. Besides, patients diagnosed with rare cancers may have difficulty finding a physician knowledgeable in treating their type of cancer. While sample collection is a major challenge, the integrated genomic analyses would identify novel causative genes in these rare cancers, shed new light on the biology of the rare cancers, as well as guide novel targeted cancer therapies. Through efficient collaboration, the Human Genome Sequencing Center (HGSC) at Baylor College of Medicine (BCM) has collected/is expected to collect 20 different types of rare cancers, 15-30 cases each. Whole-exome sequencing and high-resolution SNP array analysis were/will be performed for all cases and whole-genome sequencing was designed for a selected subset of the cases. The Rare Cancer Tumors Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in this sub-study, please click on the following sub-study below or in the "Sub-studies" section of this top-level study page phs000725 Rare Cancer Tumors Cohort. phs000754 Intracranial Germ Cell Tumors phs000861 Craniopharyngioma Tumors phs000859 Sezary Syndrome Genomic Analysis

  Study phs000725

• Genomic Analysis of Pediatric Low Grade Gliomas

  Pediatric low-grade gliomas (PLGGs) are among the most common solid tumors in children but, apart from mutations or duplications in the BRAF kinase in specific subclasses, few genetic driver events are known. Diffuse PLGGs compose a set of uncommon subtypes that exhibit invasive growth and are therefore especially challenging clinically. These tumors are particularly poorly understood. We performed high-resolution copy-number analysis of 44 diffuse PLGGs to identify recurrent alterations. Diffuse PLGGs exhibited fewer such alterations than adult low-grade gliomas, but we identified several significantly recurrent events. The most significant event, 8q13.1 gains, was observed in 28% of diffuse astrocytoma WHO grade II (DA2) and resulted in partial duplication of the transcription factor MYBL1 with truncation of its C-terminal negative-regulatory domain. A similar recurrent deletion-truncation breakpoint was identified in two angiocentric gliomas in the related gene MYB on 6q23.3. Whole genome sequencing of a MYBL1-rearranged diffuse astrocytoma grade II demonstrated MYBL1 tandem duplication and few other events. Two truncated MYBL1 transcripts identified in this tumor induced anchorage-independent growth when expressed in 3T3 cells and tumor formation in nude mice. Truncated transcripts were also expressed in two additional tumors with MYBL1 partial duplication. Our results define clinically relevant molecular subclasses of diffuse PLGGs and highlight a potential role for the MYB family in the biology of low-grade gliomas. "Reprinted from www.pnas.org/cgi/doi/10.1073/pnas.1300252110 with permission from PNAS."

  Study phs000614

• Genome-Wide Association Study in Systemic Sclerosis

  The Scleroderma Family Registry and DNA Repository (Registry) was initially developed as a registry and bio-specimen repository of patients with systemic sclerosis (scleroderma), family members and unaffected healthy controls. A case-control design was later adopted due to the lack of availability of many parents in this adult-onset disease, which precluded a linkage approach. In addition to collecting demographic data, the registry included the collection of disease-pertinent, cross-sectional, clinical information from medical records of affected participants. Registry participants were &#62;/= 18 years of age at enrollment. On the basis of medical record review, all cases were verified by the principal investigator to meet the eligibility criteria. 800+ blood samples from verified affected cases of European ancestry were selected for a Genome Wide Association Study (GWAS). These genetic data, as well as phenotypic data, are available in dbGaP. DNA, serum and plasma from Registry participants, which are currently being stored in the University of Texas Health Science Center - Houston, Scleroderma Registry Genetic Repository, are available for research purposes. However, not all of the 800+ registry participants who submitted a blood sample currently have DNA available due to depletion of some samples. In addition to including SNP data from the genome-wide (first study release) and immunochip (second study release) scan on the 800+ samples, the dbGaP database provides a set of data tables with phenotypic information collected from the participants in the registry.

  Study phs000357

• Molecular Characterization of Hemimegalencephaly

  Somatic mutations, in which a fraction of the cells in the body have a deleterious mutation, are well recognized in cancer but only recently appreciated in neurological disease. Given a somatic mutation in a population of progenitor cells, all daughter cells inherit the mutation and are able to express the resultant phenotype as a function of the differentiation program. We recently identified the first de novo somatic mutations in the developing brain in the condition hemimegalencephaly (HME), a catastrophic focal epilepsy condition associated with a malformation of cerebral cortical development (MCD). HME is one of the most severe MCD syndromes, characterized by massive hamartomatous overgrowth of either of the two cerebral hemispheres. Cerebral hemispherectomy is a frequent treatment for refractory epilepsy, allowing the sampling of diseased tissue. By comparing DNA from a diseased brain with DNA from blood/saliva, we identified de novo somatic mutations in PIK3CA, AKT3 or MTOR, part of the mTOR pathway. Mutations were present in 8-40% of sequenced alleles in various brain regions sampled during surgery and in some codons known to activate proteins.&#8226; Population informationPatient samples were collected from multiple centers. The population is Caucasian and Hispanic&#8226; Molecular technologies employedWhole-exome sequencing&#8226; Principal findings of the studyWe were the first to describe mTOR-related and non-mTOR mutations causing hemimegalencephaly, and we described two-hit mutational models to explain the genetic pathogenesis of hemimegalencephaly

  Study phs002156

• Collagen XVII Promotes Pancreatic Cancer Through Regulation of PIK3R5

  Pancreatic ductal adenocarcinoma (PDAC) contains a desmoplastic stroma that promotes the aggressive biology of this disease. While the varied mechanisms by which fibrillar collagens contribute to PDAC have been defined, little is known about the contribution of transmembrane collagens. Among this class of collagens, Collagen XVII uniquely contains an extensive intracellular domain that connects intracellular cytoplasmic components with the extracellular matrix. Collagen XVII is best characterized as the defining component of the type I hemidesmosome, an anchoring complex that attaches epithelial cells to the underlying basement membrane. In this study, we characterize the expression of collagen XVII in PDAC and the mechanisms by which it contributes to PDAC biology. We demonstrate that crosstalk between PDAC cells and cancer associated fibroblasts drive collagen XVII expression in vitro and xenograft tumor models in vivo. Genetic depletion of collagen XVII in low-passage patient derived cell lines diminishes their metastatic potential and ability to form tumors in mice. Whole transcriptome sequencing of tumors formed by cells with diminished collagen XVII levels revealed dramatically lower levels of PIK3R5, a regulatory subunit of class IB phosphatidylinositol-3-kinase (PI3K) that interacts with G protein-coupled receptors. Consistent with this, collagen XVII-deficient tumors exhibited lower levels of phosphorylated AKT, a downstream effector of PI3K. The studies highlight the importance of collagen XVII signaling in PDAC and define a novel mechanism by which collagen XVII mediates activation of PI3K.

  Study phs003641

• Epigenetic age deceleration reflects fitness improvements following a six-month endurance exercise intervention

  Epigenetic clocks are emerging as promising biomarkers of biological aging, yet their sensitivity to short-term interventions remains unclear. This pilot study investigates whether the GrimAge clock can capture the effects of a six-month cycling-based endurance exercise training intervention, with cardiorespiratory fitness (VO₂ max) and body composition as primary outcomes. We enrolled 42 adults aged 35-65, of whom 38 completed the study and 33 adhered to the protocol (&gt;66% adherence). Participants demonstrated significant improvements in VO₂ max (+20%, P &lt; 0.001) and body composition (P &lt; 0.001). High-quality epigenetic data preprocessing yielded highly reproducible GrimAge estimates (&lt; 2 months measurement error), which strongly correlated with chronological age (R² = 0.86, P &lt; 0.001). On average, GrimAge decreased by 7.44 months relative to the expected trajectory (P = 0.012), reflecting improvements in VO₂ max (R² = 0.27, P = 0.002) but not body composition changes. Notably, GrimAge changes strongly correlated with fluctuations in leukocyte composition, particularly neutrophil fraction (R² = 0.74, P &lt; 0.001). Adjusting for leukocyte composition improved consistency in GrimAge changes, aligning them with additional intervention outcomes and explaining up to 81% of variance. These findings demonstrate that GrimAge is responsive to short-term endurance training, serving as a meaningful biomarker of improved cardiorespiratory fitness, while also capturing immune system variability. This study supports the use of GrimAge in evaluating longevity interventions and highlights the importance of accounting for leukocyte composition in epigenetic aging research.

  Study EGAS00001008221

• Cisplatin increases sensitivity to FGFR inhibition in patient-derived xenograft models of lung squamous cell carcinoma

  Lung squamous cell carcinoma (SqCC) is a molecularly complex and genomically unstable disease. No targeted therapy is currently approved for lung SqCC, although potential oncogenic drivers of SqCC have been identified, including amplification of the fibroblast growth factor receptor 1 (FGFR1). Reports from a recently completed clinical trial indicate low response rates in patients treated with FGFR tyrosine kinase inhibitors, suggesting inadequacy of FGFR1 amplification as a biomarker of response, or the need for combination treatment. We aimed to develop accurate models of lung SqCC and determine improved targeted therapies for these tumors. We show that detection of FGFR1 mRNA by RNA in situ hybridisation is a better predictor of response to FGFR inhibition than FGFR1 gene amplification using clinically relevant patient-derived xenograft (PDX) models of lung SqCC. FGFR1-overexpressing tumors were observed in all histological subtypes of non-small cell lung cancers (NSCLC) as assessed on a tissue microarray, indicating a broader range of tumors that may respond to FGFR inhibitors. In FGFR1-overexpressing PDX tumors, we observed increased differentiation and reduced proliferation following FGFR inhibition. Combination therapy with cisplatin was able to increase tumor cell death, and dramatically prolonged animal survival compared to single agent treatment. Our data suggest that FGFR tyrosine kinase inhibitors can benefit NSCLC patients with FGFR1-overexpressing tumors and provides a rationale for clinical trials combining cisplatin with FGFR inhibitors.

  Study EGAS00001002423

• Low-coverage whole genome sequencing for a highly selective cohort of severe COVID-19 patients

  Background Despite advances in identification of genetic markers associated to severe COVID-19, the full genetic characterisation of the disease remains elusive. Imputation of low-coverage whole genome sequencing (lcWGS) has emerged as a competitive method to study such disease-related genetic markers as they enable genotyping of most common genetic variants used for genome wide association studies. This study aims at exploring the potential use of imputation in lcWGS for a highly selected severe COVID-19 patient cohort. Findings We generated an imputed dataset of 79 variant call format (VCF) patient files using the GLIMPSE1 tool, each containing, on average, 9.5 million single nucleotide variants. The validation assessment of imputation accuracy yielded a squared Pearson correlation of approximately 0.97 across sequencing platforms, showing that GLIMPSE1 can be used to confidently impute variants with minor allele frequency up to approximately 2% in Spanish ancestry individuals. We conducted a comprehensive analysis on the patient cohort, examining hospitalisation and intensive care utilisation, sex and age-based differences, and clinical phenotypes using a standardised set of medical terms specifically developed to characterise severe COVID-19 symptoms for this cohort. Conclusion This dataset highlights the utility and accuracy of lcWGS imputation in the study of COVID-19 severity, setting a precedent for other applications in resource-constrained environments. The methods and findings presented here may be leveraged in future genomic projects, providing vital insights for health challenges like COVID-19.

  Study EGAS00001007573

• Atypical 3q26/MECOM rearrangements genocopy inv(3)/t(3;3) in acute myeloid leukemia

  Acute myeloid leukemia (AML) with inv(3)/t(3;3)(q21q26) is a distinct WHO recognized entity, characterized by its aggressive course and poor prognosis. In this subtype of AML, the translocation of a GATA2 enhancer (3q21) to MECOM (3q26) results in overexpression of the MECOM isoform EVI1 and monoallelic expression of GATA2 from the unaffected allele. The full-length MECOM transcript, MDS1-EVI1, is not expressed as the result of the 3q26 rearrangement. Besides the classical inv(3)/t(3;3), a number of other 3q26/MECOM rearrangements with poor treatment response have been reported in AML. Here we demonstrate, in a group of 33 AML patients with atypical 3q26 rearrangements, MECOM involvement with EVI1 overexpression, but no or low MDS1-EVI1 levels. Moreover, the 3q26 translocations in these AML patients often involve super-enhancers of genes active in myeloid development (e.g. CD164, PROM1, CDK6 or MYC). In more than 50% of these cases allele specific GATA2 expression was observed, either by copy number loss or by an unexplained allelic imbalance. Altogether, atypical 3q26 recapitulate the main leukemic mechanism of inv(3)/t(3;3) AML, namely EVI1 overexpression driven by enhancer hijacking, absent MDS1-EVI1 expression and potential GATA2 involvement. Therefore, we conclude that both atypical 3q26/MECOM and inv(3)/t(3;3) can be classified as a single entity of 3q26-rearranged AMLs. Routine analyses determining MECOM rearrangements, EVI1 and MDS1-EVI1 expression are required to recognize 3q-rearranged AML cases.

  Study EGAS00001004325

• Cancer Genome Scanning in Plasma: Detection of Tumor-Associated Copy Number Aberrations, Single-Nucleotide Variants, and Tumoral Heterogeneity by Massively Parallel Sequencing

  Cancer Genome Scanning in Plasma: Detection of Tumor-Associated Copy Number Aberrations, Single-Nucleotide Variants, and Tumoral Heterogeneity by Massively Parallel Sequencing

  Dataset EGAD00001000284

• Cancer Genome Scanning in Plasma: Detection of Tumor-Associated Copy Number Aberrations, Single-Nucleotide Variants, and Tumoral Heterogeneity by Massively Parallel Sequencing

  Cancer Genome Scanning in Plasma: Detection of Tumor-Associated Copy Number Aberrations, Single-Nucleotide Variants, and Tumoral Heterogeneity by Massively Parallel Sequencing

  Dataset EGAD00001000290