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• Whole-Genome Sequences from five human populations of Sudan

  This dataset provides the first high-coverage (~30X) whole-genome sequencing (WGS) data from Sudan, encompassing 125 individuals from five ethnolinguistic groups: Copts, Beja, Fulani, Fur, and Mahas. These populations represent three major linguistic families: Afro-Asiatic, Nilo-Saharan, and Niger-Congo, offering a detailed view of the country’s genetic diversity.

  Dataset EGAD00001015636

• scRNA-seq dataset of A cycling, progenitor-like cell population at the root of atypical teratoid rhabdoid tumor subtype differentiation trajectories

  Study describing the differentiation trajectory...

  Dataset EGAD00001015545

• scATAC-seq dataset of A cycling, progenitor-like cell population at the root of atypical teratoid rhabdoid tumor subtype differentiation trajectories

  Study describing the differentiation trajectory...

  Dataset EGAD00001015546

• PyEGA3 download client

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

  Documentation access/download/files/pyega3

• Detection of rare mutations, copy number variation, and DNA methylation in the same template DNA molecules

  The analysis of cell-free DNA (cfDNA) from plasma offers great promise for the earlier detection of cancer. At present, changes in DNA sequence, methylation, or copy number are the most sensitive ways to detect the presence of cancer. To further increase the sensitivity of such assays with limited amounts of sample, it would be useful to be able to evaluate the same template molecules for all these changes. Here we report an approach, called MethylSaferSeqS, that achieves this goal, and can be applied to any standard library preparation method suitable for massively parallel sequencing. The innovative step was to copy both strands of each DNA-barcoded molecule with a primer that allows the subsequent separation of the original strands (retaining their 5-methylcytosine residues) from the copied strands (in which the 5-methylcytosine residues are replaced with unmodified cytosine residues). The epigenetic and genetic alterations present in the DNA molecules can then be obtained from the original and copied strands, respectively. We applied this approach to plasma from 265 individuals, including 198 with cancers of the pancreas, ovary, lung and colon, and found the expected patterns of mutations, copy number alterations, and methylation. Furthermore, we could determine which original template DNA molecules were methylated and/or mutated. MethylSaferSeqS should be useful for addressing a variety of questions relating genetics and epigenetics in the future.

  Study EGAS00001006839

• Genetic and Phenotypic Analysis of Multiple Sclerosis in Hispanics

  We sought to identify MS susceptibility variants specific to the Major Histocompatibility Complex and to assess the effect of ancestral risk modification within 2652 Latinx and Hispanic individuals, as well as 2435 Black and African American individuals ascertained in collaboration with the Alliance for Research in Hispanic MS (ARHMS). All DNA samples were obtained through whole blood extraction and were genotyped on the MS Chip, an Illumina Infinium custom genotyping array that contains targeted and dense coverage of the extended MHC, specifically designed for imputation. Classical HLA alleles, SNPs, and amino acid residues were imputed simultaneously from genotyped SNPs using HLA-TAPAS and a multi-ethnic reference panel of 2504 samples from the 1000 Genomes Project. We have identified several novel susceptibility alleles which are rare in European populations including HLA-B*53:01, and we have identified an independent role for HLA-DRB1*15:01 and HLA-DQB1*06:02 on MS risk. We found a decrease in Native American ancestry in MS cases vs controls across the MHC, peaking near the previously identified MICB locus; and we have identified several susceptibility variants for which MS risk is modified by global or local ancestry. We are making data on all genotyped and imputed SNPs, classical HLA alleles, and amino acid residues available through dbGaP for the Hispanic and African American samples utilized in these analyses.

  Study phs003105

• 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

• NHLBI TOPMed: Pharmacogenomics of Hydroxyurea in Sickle Cell Disease (PharmHU)

  Sickle cell disease (SCD) is characterized by the presence of sickle hemoglobin (HbS) within circulating erythrocytes resulting in hemolytic anemia, vascular occlusion, and end organ damage due to alterations in the shape and deformability of the cell membrane. The disease is inherited in an autosomal recessive pattern, and is most commonly caused by a single nucleotide substitution in the hemoglobin subunit beta (HBB) gene located on chromosome 11. Participants in this study include children with SCD treated with hydroxyurea to pharmacologically increase fetal hemoglobin (HbF) levels and reduce disease severity. Therefore, the primary phenotype of interest in this study is the change in HbF levels in response to hydroxyurea treatment. Genetic factors have been shown to influence inter-individual variation in drug response, and identification of novel genes and variants associated with clinical outcomes in SCD will be achieved through collaboration between Baylor College of Medicine, Augusta University, Columbia University Medical Center, Emory University School of Medicine and Children's Healthcare of Atlanta, and St. Jude Children's Research Hospital. The NHLBI TOPMed Program is designed to generate scientific resources to enhance understanding of fundamental biological processes that underlie heart, lung, blood and sleep disorders (HLBS). It is part of a broader Precision Medicine Initiative, which aims to provide disease treatments that are tailored to an individual's unique genes and environment.

  Study phs001466

• Fixed single-cell transcriptomic characterization of human radial glial diversity

  The human neocortex is created from diverse intermixed progenitors in the prenatal germinal zones. These progenitors have been difficult to characterize since progenitors - particularly radial glia (RG) - are rare, and are defined by a combination of intracellular markers, position and morphology. To circumvent these problems we developed a method called FRISCR (Fixed and Recovered Intact Single Cell RNA) for transcriptome profiling of individual fixed, stained and sorted cells. We developed and validated FRISCR on human embryonic stem cells. We then profiled primary human RG (96 - 132 days post conception) that constitute only 1% of the mid-gestation cortex. These RG could be classified into ventricular zone-enriched RG (vRG) that express ANXA1 and CRYAB, and outer subventricular zone-localized RG (oRG) that express HOPX. Our study identifies the first markers and molecular profiles of vRG and oRG cells, and provides an essential step for understanding molecular networks driving the lineage of human neocortical progenitors. Reprinted from Thomsen et. al. Nature Methods (2015), with permission from Nature Publishing. Human embryonic stem cell data may be obtained through NCBI's GEO database, using accession number GSE71858. Raw data from one human sample that was not consented to be released to dbGaP may be obtained directly from the authors of Thomsen et. al., 2015.

  Study phs001016

• 3/7 Psychiatric Genomics Consortium: Finding Actionable Variation

  The goal of this study was to generate new therapeutic hypotheses by targeted sequencing of 161 genes most likely to harbor schizophrenia (SCZ) causal variants in multi-ancestry schizophrenia case-control cohorts. The gene panel was intended to contain putative SCZ risk genes, from within which, the majority of new discoveries from additional WES/WGS would come. To this end, we integrated current knowledge of the genetic architecture of SCZ (results from The Schizophrenia Exome Sequencing Meta-Analysis Consortium, or SCHEMA) and gene membership in gene sets that have been implicated in SCZ using a Bayesian framework. A total of 23,352 individuals selected to be non-overlapping with SCHEMA, as well as other previous and ongoing sequencing efforts in the field, were identified from over 20 individual study sites. Sequencing of the exonic regions of 161 prioritized genes was performed on the Ion Torrent platform in between June 2018 and April 2019. Sequencing plates were matched with respect to ancestry and case/control composition whenever possible. The average sequencing depth across all samples was 224x. Single sample calling was performed using Torrent Variant Caller v5.8.0, which is specially optimized to exploit the underlying flow signal information generated by the Ion Torrent sequencing. The schizophrenia case-control status was obtained from each original study site.

  Study phs003138