12291 results for "u7buy fc points Visit Buyfc26coins.com for latest FC 26 coins news..4F5O"

in 36.45 milliseconds.

• OMKar

  The whole genome karyotype refers to the sequence of large chromosomal segments that make up an individuals genotype. karyotype analysis, which includes descriptions of aneuploidies and other rearrangements is crucial for understanding genetic risk factors, for diagnosis, treatment decisions, and genetic counseling linked to constitutional disorders. The current karyotyping standard is based on microscopic examination of chromosomes, a complex process that requires high expertise and offers Mb scale resolution. Optical Genome Mapping (OGM) technology can identify large DNA lesions in a cost-effective manner. In this paper, we developed OMKar, a method that uses OGM data to create a virtual karyotype. OMKar processes Structural (SV) and Copy Number (CN) Variants as inputs and encodes them into a compact breakpoint graph. It recomputes copy numbers using Integer Linear Programming to maintain CN balance and then identifies constrained Eulerian paths representing entire donor chromosomes. In tests using 38 whole genome simulations of constitutional disorders, OMKar reconstructed the karyotype with 88% precision and 95% recall on SV concordance and 95% Jaccard score on CN concordance. We applied OMKar to 50 prenatal, 41 postnatal, and 63 parental samples from ten different sites. OMKar reconstructed the correct karyotype in 144 out of 154 samples, covering 25 of 25 aneuploidies, 32 of 32 balanced translocations, and 72 of 82 unbalanced variations. Detected constitutional disorders included Cri-du-chat, Wolf-Hirschhorn, Prader-Willi deletions, Down, and Turner syndromes. Importantly, it identified a plausible genetic mechanism for five cases of constitutional disorder that were not detected by other technologies. Together, these results demonstrate the robustness of OMKar for OGM-based karyotyping.

  Study EGAS00001008245

• Whole transcriptome RNA sequencing as comprehensive diagnostic tool for acute myeloid leukemia.

  BackgroundAcute myeloid leukemia (AML) is characterized by uncontrolled proliferation of malignant hematopoietic cells in the bone marrow that are arrested in differentiation. In AML pathogenesis, hematopoietic progenitor cells acquire multiple genetic aberrations often occurring in the same set of genes that ultimately lead to malignant transformation. In the WHO classification 2016, six AML classes with different prognosis are identified by chromosomal translocations measured by standard cytogenetics. All balanced translocations produce fusion genes, except for t(3;3)/inv(3;3), which lead to overexpression of MECOM/EVI1 associated with poor prognosis. For accurate risk assessment of cytogenetically normal AML, four genes need to be screened to distinguish AML with mutated NPM1 in the absence of FLT3 mutations and AML with bi-allelic CEBPA mutations, which have a favorable prognosis, and AML with RUNX1 mutations which have a poor prognosis. Recently, a full genomic classification system has been proposed by Papaemmanuil et al. (NEJM 2016). In this system, the same six classes with chromosomal translocations are identified by standard cytogenetics as well as five additional classes characterized by genetic mutations in 14 different genes. AimThe aim of the study was to investigate whether whole transcriptome RNA-sequencing (RNAseq) can be used as single technology for classification of AML. MethodA panel of 100 AML were analyzed and a bio-informatics pipeline called HAMLET (Human AML Expedited Transcriptomics) was developed in which 4 modules are integrated to detect (1) fusion genes, (2) small variants in 13 recurrently mutated genes, (3) internal tandem duplications in FLT3 (FLT3-ITD) and partial tandem duplications in KMT2A (KMT2A-PTD) and (4) overexpression of MECOM/EVI1. All mutations that were called by HAMLET were validated by diagnostic data as available for all 100 AML or targeted PCR followed by Sanger or next generation sequencing on all positive AML and at least an equal number of negative cases. Results & discussionThe data showed that HAMLET accurately identified all genetic aberrations with high sensitivity and specificity. In addition, in 7 AML, fusion transcripts were detected that are not measured by standard cytogenetics including three cases that lack any class-defining lesion according to Papaemmanuil et al. Moreover, overexpression of MECOM/EVI1 was detected in two AML with inv(3) as well as in five cases without inv(3)/t(3;3), and a gene signature was measured to distinguish AML with CEBPA mutations with favorable prognosis. In conclusion, HAMLET provides a comprehensive and flexible pipeline for RNAseq analysis to retrieve all relevant information for current classification of AML as well as additional information that may improve classification in the future.

  Study EGAS00001003096

• Genome-Wide Association Study of Patients with Coccidioidomycosis

  BackgroundValley Fever is typically an infection of the lungs caused by the fungi Coccidioides immitis and Coccidioides posadasii. The incidence of Coccidioidomycosis (CM), or infection with Coccidioides, has dramatically increased over the last 20 years. This is particularly true in the Southwest of the United States, where people often breathe fungal spores that arise from the soil. Reasons for increased infection rates are thought to include population growth and construction in these endemic regions, an increase in the number of people whose immune systems are compromised due to infection or treatment with drugs required for organ transplants, climate change, as well as improved testing practices and greater physician awareness. Mild CM most commonly presents itself with flu-like symptoms and rashes, which can last weeks to months. Individuals with compromised immune systems, specifically-- substantial suppression of the immune cells known as T cells, can develop severe pulmonary and disseminated disease. Infection that remains localized to the lungs is referred to as pulmonary disease, but when the infection spreads out of the lungs into other parts of the body it represents a more serious condition referred to as a disseminated disease, or disseminated CM. In nature, Coccidioides spp. exists as mold and lives in dust and soil. When the contaminated soil or dust is disturbed by human activity, animals, or weather, the Coccidiodies spores are released into the air. Airborne spores are taken up by breathing and settle in the lungs. Once in the moist and warm environment of the lung, spores transform into spherules, which divide and become filled with smaller spores, called endospores. When the spherules get large enough, they rupture and release these endospores, which can spread and disseminate to surrounding tissue. The cycle then repeats itself as these endospores develop into new spherules3. Different ethnic groups have been described to vary in their susceptibility to developing disseminated CM after initial infection with Coccidioides. For example, evidence suggests that African-American and Filipino patients suffer the disseminated disease at a greater rate than other ethnicities. The suggestion that race plays a role in the clinical expression of the disease is still a source of debate amongst the scientific community and any genetic mechanisms responsible for these differences have yet to be fully elucidated. If our genetic makeup influences our ability to limit the spread of infection, finding which DNA differences cause these variances could provide clues to how the body successfully fights infection, and provide opportunities to boost the body’s ability to do this. Further, if we are able to identify the specific genetic risk factors that correlate with the development of disseminated infection, physicians could perform genetic screenings to identify high-risk patients and provide them with preemptive antifungal therapy prior to developing disseminated disease.The genome, made up of DNA, contains all of the information needed for humans to develop and grow. Genome-wide association studies (GWAS) allow us to look for inherited differences that are more common between people who share a particular trait, for example, height or susceptibility to certain diseases, compared to those who do not share the trait. Although some traits and diseases are controlled by a single gene, the majority are influenced by contribution from several, or even many, different genes. To find evidence of genes that contribute to specific traits, GWAS typically compares genome information from large numbers of people who have a particular disease (referred to as “cases”) looking for DNA sequences that are common among these samples, and are different from DNA sequences seen in large numbers of people who lack the trait, but are as much like the cases as possible (referred to as “controls”). The DNA sequence data from each group, cases versus controls, are analyzed to see if there are specific genomic differences that tend to be associated with the disease. MethodsTwo separate GWAS approaches were taken to look for genetic differences that could be responsible for the observed differences between the different patient populations we are studying. The first method, known as genotyping, scans for differences at a set of positions across the genome, which includes both the genes that encode our proteins and the larger amount of DNA that does not. The second method, known as exome sequencing, allows us to compare the entire sequence of the portion of the genome that codes for proteins.  For this study, DNA from patients with either pulmonary or disseminated CM were genotyped and exome sequenced to look for DNA differences that are associated with one condition or the other. All patients were at least 18 years old, had no evidence of immunosuppression, and had proven or probable pulmonary coccidioidomycosis according to established diagnostic criteria. Of these patients, a subset demonstrated disseminated disease, i.e., they showed evidence of coccidioidal infection outside of the thorax by biopsy/aspiration, had radiographic imaging, and show positive coccidioidal serology. Our criteria for including patients with the pulmonary disease were that they must not require ongoing antifungal treatment or show evidence of active CM (in skin test positive patients), show no evidence of extrapulmonary dissemination, and have no evidence of ongoing pulmonary infection (pulmonary nodules are accepted) beyond six months from diagnosis.Patient DNA was purified from blood or from sputum samples by the labs of our collaborators, Drs. George Thompson (UC Davis School of Medicine) and John Galgiani (University of Arizona Health Sciences). Genome-wide association (GWAS) analysis was carried out to look for candidate loci associated with pulmonary versus disseminated disease, taking into account the population structure of the samples. Single nucleotide or insertion/deletion variants were identified from whole-exome sequences (WES) using the Picard/BWA/GATK pipeline. ResultsTable 1. Pulmonary versus Disseminated Cases of Coccidiomycosis for GWAS, Sorted by EthnicityEthnicityPulmonary CasesDisseminated CasesAsian85Black/African American1664Caucasian/White4015Filipino03Hispanic/Latino3414Indian21Mexican American1039Pacific Islander01Samoan03Vietnamese10Unknown16917More than one race02Total373134Table 1 shows the number of samples analyzed from patients with pulmonary versus disseminated disease, and patient ethnicity, where known. In all, we worked with 507 samples, including 134 samples from patients with disseminated disease and 373 samples from patients with pulmonary disease. Of these, 505 samples were genotyped using the Multi-Ethnic Global Array from Illumina Inc. In addition, we were able to generate whole-exome sequence from 498 patient samples. No significant associations were detected that differed between samples from patients with pulmonary versus disseminated disease; that is, no particular DNA sequences were found to be significantly enriched in patients with disseminated disease compared to patients with pulmonary disease. The ability to detect genetic association between specific sequences and genetically determined traits is influenced by several factors, including how many patient samples are available to compare, how many different genes contribute to the trait and how strong their contributions are. When the number of genes is small and the contribution of each gene is great, smaller numbers of patient samples are needed to detect an association. When more genes are involved, or the contribution from each gene is more modest, larger numbers of patient samples must be examined. While we were not able to detect any associated within this study, it does not mean that subsequent studies would not find this connection. Our study suggests significantly more samples should be analyzed in further studies.Whole-exome sequences were generated from 498 samples and were aligned to reference sequences to identify positions where the sequences differed from the reference. These data are being analyzed to determine if any variants are associated with pulmonary, versus disseminated, disease. 

  Study phs002170

• 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

• Data Access Commitee for the project : Methylome profiling of human mesenchymal chondrosarcoma

  Dac EGAC00001003217

• Data Access Commitee for single-cell analysis of multiple myeloma and precursors

  Dac EGAC00001002920

• Data Access Committee for the Metastatic Breast Cancer Whole-exome sequencing study

  Dac EGAC00001000434

• DAC for Direct Detection of Early-stage Cancers Using Circulating Tumor DNA

  Dac EGAC00001000701

• Data Access Committee for desmoplastic small round cell tumor (DSRCT) RNAseq data.

  Dac EGAC00001000851

• DAC for Oncogenic fate conversion by PRDM16s causes acute myeloid leukemia

  Dac EGAC00001001023

• EMBL genome biology research group for structural variation (Strand-seq application)

  Dac EGAC00001001091

• DAC for Integrated Genomic, Epigenetic, and Expression Analyses of Ovarian Cancer Cell Lines

  Dac EGAC00001001101

• Stanford Data Access Committee for Multi-Region WES of Metastatic Colorectal Cancer

  Dac EGAC00001001164

• DAC for the study of tumor-derived somatic mutation detection in cfDNA

  Dac EGAC00001001569

• Nicola Murray Centre for Ovarian Cancer Research Data Access Committee

  Dac EGAC00001001588

• Cancer Clinical Research Trust DAC for WES of HER2+ metastatic cancer samples

  Dac EGAC00001001632

• Data Access Commitee of the Princess Maxima Center for Pediatric Oncology

  Dac EGAC00001001948

• Data access committee for Gut microbiome of BA patients and normal controls

  Dac EGAC00001002146

• "DAC for RNAseq from regions of insitu and invasive human mammary ductal disease"

  Dac EGAC00001002538

• Chromatin Profiles in PRAD - Englander Institute for Precision Medicine - Weill Cornell Medicine

  Dac EGAC00001002559

• Single cell multi-omics group for somatic structural variation in human blood lineages

  Dac EGAC00001002852

• DAC for Tuebingen-Stuttgart cohort

  Dac EGAC00001000317

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Jackson Heart Study (JHS)

  Study phs002907

• DAC for Clonal driver neoantigen loss under EGFR TKI and immune selection pressures

  Dac EGAC00001003522

• SGMedical Data Acess Committee

  DAC for SG Medical Inc. of South Korea

  Dac EGAC50000000636

• Quantitative analysis of a novel DNA hypermethylation panel for lung cancer diagnosis

  Study EGAS00001007008

• A multi-center genome-wide association study for nasopharyngeal carcinoma

  Study EGAS00001006062

• bed_files

  Bed files as whitelist for Tapestri Insights analysis

  Dataset EGAD00010002560

• irish_fineSTRUCTURE

  SNP data for 991 Irish individuals

  Dataset EGAD00010001479

• DNA Methylation data for EGAS00001002592

  Islet_HumanMethylation450K_ThurnerEtAl

  Dataset EGAD00010001695

• EGAD00010000831

  BLUEPRINT EpiMatch: harnessing epigenetics for haematopoietic stem cell transplantation

  Dataset EGAD00010000831

• DAC for patient-derived cell line samples

  Dac EGAC00001000594

• 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

• The Genomics and Randomized Trials Network (GARNET) Vitamin Intervention Stroke Prevention (VISP) Trial

  The VISP trial (PI Jim Toole, M.D., Wake Forest University School of Medicine) was a multi-center, double-blind, randomized, controlled clinical trial that enrolled patients aged 35 or older with homocysteine (Hcy) levels above the 25th percentile at screening and a non-disabling cerebral infarction (NDCI) within 120 days of randomization (Toole, 2002, PMID: 12417369). The trial was designed to determine if daily intake of a multivitamin tablet with high dose folic acid, vitamin B6 and vitamin B12 reduced recurrent cerebral infarction (primary endpoint), and nonfatal myocardial infarction (MI) or mortality (secondary endpoints). Subjects were randomly assigned to receive daily doses of the high-dose formulation (n=1,827), containing 25mg pyridoxine (B6), 0.4mg cobalamin (B12), and 2.5mg folic acid; or the low-dose formulation (n=1,853), containing 200mcg pyridoxine, 6mcg cobalamin and 20mcg folic acid. Enrollment in VISP began in August 1997, and was completed in December 2001, with 3,680 participants enrolled. Within the trial, 2,164 participants from 46 clinic sites provided DNA and agreed for it to be shared for use in a genetic subset study of VISP. This study is part of Genomics and Randomized Trials Network (GARNET), funded by the National Human Genome Research Institute (NHGRI). The overarching goal is to identify novel genetic factors that contribute to stroke through large-scale genome-wide association studies of treatment response in randomized clinical trials. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were performed at the GARNET Coordinating Center at the University of Washington. The data of the VISP trial have been released to dbGaP users in several segments: Version 1 (phs000343.v1.p1), consisted of n=4 phenotype datasets, and all raw, cleaned and imputed genotype data. Version 2 (phs000343.v2.p1) included n=14 additional phenotype datasets (plus pedigree, consent, and sample-mapping data), and increased the available data to a total of n=970 phenotype variables. Version 3 (phs000343.v3.p1) included all n=36 phenotype datasets (plus pedigree, consent, and sample-mapping data), and increased the available data to a total of n=1918 phenotype variables.Version 4 (phs000343.v4.p1), the current release, includes all previous phenotypic datasets, includes one new phenotypic dataset for the untargeted metabolomics data of 50 individuals, includes DNA methylation profiles for n=180 individuals, and sequencing data for n=179 individuals.

  Study phs000343

• 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

• 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

• GWAS for Genetic Determinants of Bone Fragility in European-American Premenopausal Women

  Osteoporotic fractures are largely due to an increased propensity to fall with aging and a reduction in bone strength. Although skeletal architecture contributes to fracture risk, bone mineral density (BMD) is the most important determinant of bone strength and fracture risk. Between 60 and 80% of the variance of BMD of adult Caucasian women is due to heritable factors. Final BMD is a function of peak bone mass attained during young adulthood and the subsequent rate of bone loss, which occurs as a result of both post-menopausal estrogen loss and aging. The evidence for a genetic contribution to rate of loss in BMD is substantially weaker than that for peak BMD. Therefore, we have focused our sample collection on the recruitment of premenopausal women, in whom we have sought to identify the genes influencing peak BMD at the spine and hip, the two major skeletal sites of osteoporotic fracture. The primary goal of this study is to identify genes that affect peak BMD in premenopausal women. Identification of these genes may: 1) lead to molecular tests that predict risk of osteoporosis and allow institution of early preventive measures; 2) provide insight into basic bone cell biology and other factors that affect peak BMD; and 3) provide molecular targets for therapeutic agents to increase BMD.

  Study phs000138

• 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

• Investigating Host Genetic Risk Factors for Tuberculosis in Highly Endemic South African Populations

  It is apparent from our recent population genetic and admixture mapping work 1–3 that a substantial part of the ancestral input into the South African Coloured (SAC) population is from the San or Khoe groups, which are not well represented in publicly available genetic databases. It is a reasonable assumption that the SAC population in the Western Cape may have derived more genetic input from the present KhoeSan population in the Northern Cape than the San residing in Namibia. It was concluded by our group that it is indeed the southern African KhoeSan group, the ≠Khomani, that best represent the KhoeSan contribution seen in the SAC 4. More recently, it has been shown that there are ancestry related increases in TB susceptibility, especially with increased Bantu-speaking African and KhoeSan ancestry 5. We aim to elucidate the epidemiological and human-host genetic risk factors for TB and the immunological pathways modulating TB infection. The proposed sub-study will enroll people evaluated for TB at Northern Cape community health clinics and their contacts from their households and/or community. We will conduct a demographic interview, collect saliva, which will be used for genetic analysis, and blood to determine latent TB infection status and capture immunological responses to mycobacterial infections at the cellular level and RNA sequencing.

  Study EGAS00001007850

• A Cancer Cell-Line Titration Series for Evaluating Somatic Classification

  Accurate detection of somatic single nucleotide variants and small insertions and deletions from DNA sequencing experiments of tumour-normal pairs is a challenging task. Tumour samples are often contaminated with normal cells confounding the available evidence for the somatic variants. Furthermore, tumours are heterogeneous so sub-clonal variants are observed at reduced allele frequencies. We present here a cell-line titration series dataset that can be used to evaluate somatic variant calling pipelines with the goal of reliably calling true somatic mutations at low allele frequencies. Cell-line DNA was mixed with matched normal DNA at 8 different ratios to generate samples with known tumour cellularities, and exome sequenced on Illumina HiSeq to depths of >300x. The data was processed with several different variant calling pipelines and verification experiments were performed to assay >1,500 somatic variant candidates using Ion Torrent PGM as an orthogonal technology. By examining the variants called at varying cellularities and depths of coverage, we show that the best performing pipelines are able to maintain a high level of precision at any cellularity. In addition, we estimate the number of true somatic variants undetected as cellularity and coverage decrease. Our cell-line titration series dataset, along with the associated verification results, is effective for this evaluation and will serve as a valuable dataset for future somatic calling algorithm development.

  Study EGAS00001001016

• 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

• Genomic Sequencing of Pediatric Rhaboid Cancers

  In this study, we sequenced the exomes of 35 rhabdoid tumors, highly aggressive cancers of early childhood. This study is part of a larger effort to characterize pediatric cancers as part of the Slim Initiative for Genomic Medicine (SIGMA) project.

  Study phs000508

• Blood Handling and Leukocyte Isolation Methods Impact the Global Transcriptome of Immune Cells

  In order to characterize the effects of upstream sample handling on the transcriptome of isolated leukocyte populations, we simulated various sample handling methods on whole blood prior to leukocyte isolation for low-input RNA-sequencing.

  Study phs001563

• Ultrasensitive Profiling of UV Mutations in Facial Tumors in TSC

  In this study we searched for mutations in the TSC2 and TP53 genes in sample sets from individuals with Tuberous Sclerosis Complex (TSC) and from subjects without TSC. The study was focused on the analysis of TSC facial angiofibroma.

  Study phs002914

• Whole Exome Sequencing of Parathyroid Cancer Identifies Candidate Driver Mutations and Core Pathways

  Here we performed genomic characterization of twenty-nine sporadic parathyroid cancers using next generation whole exome sequencing. Thirty-three novel candidate driver genes for sporadic parathyroid cancer were proposed, in addition to previously known driver genes CDC73 and MEN1.

  Study phs001765

• The Normal Human Tissue Sequencing Project: Non-Diseased, Non-Malignant Tissues

  The Normal Human Tissue Sequencing Project provides RNA expression and DNA methylation data from normal (non-diseased) human tissues. This data set from a variety of source tissue is a valuable resource for human disease studies.

  Study phs000819

• Systematic Analysis of Coding and Non-coding Elements in Developmental Pathways Implicated in Holoprosencephaly Pathogenesis

  We applied targeted capture to examine 153 genes representative of all the major vertebrate developmental pathways among 340 probands and 285 of their unaffected parents to rank their relative significance as causes for holoprosencephaly (HPE).

  Study phs001653

• Germline Genetic Variants in Cancer-Susceptibility Genes in Patients with Osteosarcoma

  We conducted whole-exome sequencing of germline DNA from patients with osteosarcoma to estimate the frequency of pathogenic/likely pathogenic germline genetic variants in known cancer-susceptibility genes in a large population of osteosarcoma patients unselected for family history.

  Study phs002444

• An Omics View of Asthma through Monozygotic Twins

  The etiology of asthma involves both genetic and non-genetic factors. We performed whole transcriptome sequencing on monozygotic twins discordant or concordant for asthma (with concordant healthy twins as controls), and identified multiple potential transcriptomic profiles associated with the asthma phenotype.

  Study phs000886

• Single Cell ATAC-Seq of MELAS

  This dataset includes combined single Assay for Transposase-Accessible Chromatin, using sequencing (ATAC-seq), and mitochondrial DNA sequencing of peripheral blood mononuclear cells from three individuals with m.3243A&gt;G associated MELAS (Mitochondrial Encephalopathy, Lactic Acidosis, and Stroke-like episodes).

  Study phs002217

• Kids First: Genetics of Kidney and Urinary Tract Malformations

  Congenital defects of the kidney and urinary tract are a common cause of kidney failure in children and adults and elucidation of the genetics of these disorders will provide new opportunities for diagnosis, risk stratification and prevention of complications.

  Study phs002162

• Comprehensive sequencing analyses of uterine and ovarian carcinosarcoma

  We collected 109 primary tumor and mached normal samples of uterine and ovarian carcinosarcoma. The purpose of this study is to find novel therapeutic options for carcinosarcoma as well as a clue to understand the molecular histogenic mechanism.

  Study JGAS000172

• Comprehensive molecular and clinicopathological profiling of desmoid tumors

  Previous studies have not clearly identified a prognostic factor for desmoid tumors (DT). Whole-exome sequencing and/or RNA sequencing were performed in 64 cases of DT to investigate the molecular profiles in combination with the clinicopathological characteristics.

  Study JGAS000270

• Arkansas Children’s Research Institute (ACRI) Data Access Committee – Kelly Research Group

  The Arkansas Children’s Research Institute (ACRI) Data Access Committee – Kelly Research Group oversees requests for access to controlled human genomic and transcriptomic data generated by the Kelly Research Group. The committee ensures that all data access requests are consistent with participants’ informed consent, institutional review board (IRB) approvals, and applicable regulatory requirements. All approved users must agree to use the data solely for biomedical research purposes, maintain data confidentiality, and acknowledge the data source in resulting publications.

  Dac EGAC50000000819

• Whole exome sequencing of virus-associated HCC

  To understand comprehensive combinations of molecular alterations and seek for potential therapeutic targets/pathways in virus-associated HCC, we have conducted a whole exome sequencing (in-solution capturing all human coding exons) of 150 pairs of HCC cases.

  Study EGAS00001000389

• Liquid biopsy to identify taxane resistance in castration-resistant prostate cancer patients

  Taxanes are life-prolonging treatments for patients with advanced prostate cancer. However, treatment resistance and lethal disease invariably develops. We here used liquid biopsies to identify and characterize resistance to cabazitaxel using a targeted sequencing panel (PCF-SELECT).

  Study EGAS50000001292

• smallRNA-seq of isolated pancreatic islets

  In this study we performed smallRNA-sequencing, a sequencing method designed for small noncoding RNAs, to investigate the miRNA expression profile of human islets isolated from pancreata of donors without diabetes mellitus (n=3) and donors with type 2 diabetes (n=3).

  Study EGAS50000000865

• scD&D Multiome of GATA1 regulatory network dynamics during erythropoiesis

  Ex vivo differentiation experiments of human CD34+ hematopoietic stem and progenitor cells and profiled cells along the erythropoietic trajectory at multiple timepoints (differentiation days 0, 5, 8, 18, and 24), for simultaneous assessment of transcriptome, chromatin accessibility and GATA1 binding

  Study EGAS50000001606

• Cohort B tumor exome sequencing

  Cohort B includes IDH-wild type, EGFR amplified, surgically derived glioblastoma tissue samples from male and female individuals. Tumor DNA was isolated from FFPE tissue. Both tumor and germline DNA are present for each sample in the overall dataset.

  Study EGAS50000000955

• WNT7B-reporter organoids sorted

  Transcriptomic data from pancreatic ductal adenocarcinoma (PDAC) organoids to investigate the role of WNT7B. This dataset includes: - Bulk mRNA sequencing of WNT7B-reporter pancreatic ductal adenocarcinoma (PDAC) organoids sorted for cells displaying low, medium and high expression of the reporter

  Study EGAS50000001543

• Spatially resolved transcriptomics reveals profound subclonal heterogeneity and T cell dysfunction in extramedullary myeloma

  Investigating the spatial architecture and microenvironment of EMD lesions in Multiple Myeloma patients using spatial whole transcriptome sequencing (10X Visium), tomography for spatially resolved transcriptomics (TOMO-seq), and single-cell RNA sequencing (scRNAseq)

  Study EGAS50000000227

• Dataset of 10 WES from bladders tumors and PBMC of 4 non-muscle invasive bladder cancer patients

  WES (FASTQ files) of paired tumor and PBMC samples of non-muscle-invasive bladder cancer patients. All CTRL samples originate from PBMC. For patient UC2, DNA from 3 different synchronous tumors were extracted. DNA from tumors was extracted from fresh frozen tumors,except for patient UC4 (FFPE). NB : patients aliases are the following :ROXY : UC1, OMAD : UC2, MAKI : UC3, HAPE : UC4

  Dataset EGAD50000002008

• Sequence data for "An allele-resolved nanopore-guided tour of the human placental methylome" (Kindlova et al 2025)

  Sequence data generated for the study "An allele-resolved nanopore-guided tour of the human placental methylome" includes Illumina WGS, Illumina RNA-seq, Illumina EM-seq, and Oxford Nanopore Technologies PromethION sequence data.

  Dataset EGAD50000001850

• 10X snMultiome (ATAC+GEX) for the study of "SPEN loss drives extra-follicular diffuse large B cell lymphoma with female-specific lethality and TLR pathway therapeutic vulnerabilities"

  10X snMultiome (ATAC+GEX) sequencing of 1 human reactive tonsil sample and 1 DLBCL sample for the study of "SPEN loss drives extra-follicular diffuse large B cell lymphoma with female-specific lethality and TLR pathway therapeutic vulnerabilities"

  Dataset EGAD50000002277

• 10x Genomics BCR Sequencing

  BCR sequencing done with 10x Genomics V(D)J kit + platform for the paper: Integrated single cell analysis reveals co-evolution of malignant B cells and tumor micro-environment in transformed follicular lymphoma. Dataset contains paired FASTQ files for 35 samples, each was processed with the 10x Genomics V(D)J kit to obtain the sequence of the B-cell receptor sequence.

  Dataset EGAD50000001373

• miRNAseq of paired FL and tFL samples

  This dataset contains miRNA-seq of paired FL and tFL samples (n=10 pairs) sequenced by Illumina NextSeq500. Libraries used in this miRNA-seq dataset were prepared with NEBNext Multiplex Small RNA Library Prep Set for Illumina (NEB,Catalog number: E7560S). RNAs used for library preparation were isolated from FFPE tissues using High Pure miRNA Isolation Kit (Roche,Catalog number: 05080576001). Data are uploaded as FASTQ format.

  Dataset EGAD50000001385

• SCIMAP PILOT D21

  This dataset includes the RNA-seq and ATAC-seq fastq files, as well as the Cellranger ARC count table, for the first experiment with multimodal sequencing in the SCI-MAP project (1 sample). Here, human induced pluripotent stem cells underwent differentiation to chondrocytes for 21 days following an established protocol. Nuclei were isolated and sequenced in two modalities simultaneously; RNA and ATAC.

  Dataset EGAD50000001516

• HighIGHG1 CRISPR-Cas9 edited alleles in IGHUND COH-DHL1 HGBCL2-DH-BCL2 cells

  High-throughput sequencing of IGHG1 alleles amplified from genomic DNA and the IGG1 (IGVH-CG1) functional transcript, flanking the gRNA-targeted region, from IGHUND HGBCL-DH-BCL2 COH-DHL1 cell line. IGHG1 targeting gRNAs were cloned into lentiCRISPRv2-Puro lentiviral vector (Addgene #98290). COH-DHL1 transduced cells were selected in Puromycin for 7 days, and expanded for further 17 days in the absence of any antibiotic selection.

  Dataset EGAD50000001525

• Targeted Sequencing Data for RESOLVE Clinical Trial

  This is a Phase II clinical trial. This research study is studying a combination of targeted therapies as a possible treatment for estrogen-receptor positive (ER+) endometrial cancer and low-grade serous ovarian cancer. The drugs involved in this study are: - Abemaciclib (also known as Verzenio™) - Letrozole (also known as Femara®) - Metformin (also known as Glucophage®) - Zotatifin (also known as eFT226) - Gedatolisib (also known as PF-05212384)

  Dataset EGAD50000001711

• Whole genome sequencing data from paired tumor and normal tissue in a cohort of NSCLC patients.

  WGS data for paired tumor and normal tissue from 260 patients in the TRACERx ctDNA study. The patient cohort is comprised of NSCLC patients undergoing surgical resection of their tumour. The patients were then followed up for a median of ~5 years after surgery, and serial blood samples were collected. The WGS data here was generated to inform the manufacture of custom ctDNA panels.

  Dataset EGAD50000001693

• Low-input RNA-seq libraries from FFPE samples using TaKaRa SMARTer kit

  Using the TaKaRa SMARTer Stranded Total RNA-Seq Kit v2, RNA-seq libraries were generated from 5 ng of RNA extracted from FFPE tissue. A low-input workflow was applied with 5 PCR cycles for the first amplification and 16 for the second. Library quality was assessed by Bioanalyzer and Qubit, and libraries were sequenced (2×150 bp) on the Illumina NextSeq 550 platform.

  Dataset EGAD50000001552

• ChIP-seq for 10 samples

  This dataset contains ChIP-seq data generated from two patient-derived xenograft (PDX) models. For each model, sequencing data are provided from input control (n=1), IgG control (n=2), and FOXF1 immunoprecipitation (n=2). All the 10 samples are in bam file type.

  Dataset EGAD50000001786

• RNA fastq files and mutation annotation files

  This dataset contains the results of both RNA sequencing and DNA sequencing. For RNA sequencing, it includes data from 12 samples derived from 9 patients, provided as raw sequencing files in FASTQ format. For DNA sequencing, it contains mutation information obtained from targeted panel DNA sequencing, including data from 35 individuals in the mutation annotation file (MAF) format.

  Dataset EGAD50000001768

• Emirati Phased Diploid Trio-Assemblies (27 Individuals, 54 Assemblies)

  This dataset comprises 27 phased diploid genome assemblies (i.e., 54 haplotypes) from Emirati family trios, provided in FASTA format. Sequencing used PacBio HiFi reads generated on the Revio platform at ≥25× coverage for each parent and offspring. Assemblies were produced with hifiasm (trio mode), then scaffolded with NTLink and reference-guided merging via RagTag against CHM13v2. The collection offers haplotype-resolved references suitable for downstream variant discovery and pangenome analyses.

  Dataset EGAD50000001755

• IMPRESS_all

  Dataset containing tumor, normal and blood sample data, for various tissue and tumor types. Data is targeted methylation data, using smMIP probes to target informative CpG sites in the genome. Sample type can be inferred from the name, with 'W' being normal samples and 'TC' tumor samples. Dataset contains 259 samples in duplicate, with one half of the runs being cut by MSRE's, and the other not. For every sample paired fastq files are present.

  Dataset EGAD50000000882

• scRNA-seq of Patient-derived tumor fragments (PDTFs)

  scRNA-seq of Patient-derived tumor fragments (PDTFs) which were treated with either Isotype (DP47), Isotype-IL2v (DP47-IL2v), anti-PD1 (PembroPGLALA) or PD1-IL2v (PembroPGLALA-IL2v) for 48 hours. Dataset content: 10 samples from 3 donors File type: paired-end fastq files Protocol: Chromium Single Cell 3' Reagent Kits User Guide (Dual Index) with Feature Barcoding technology for Cell Surface Protein (CG000317) Technology: Illumina sequencing Experimentation: scRNA-seq using the 10X technology

  Dataset EGAD50000000584

• paired-end FASTQ files from the study: Identification of the cause of juvenile parkinsonism in a case_SYNJ1

  The dataset consists of three samples: two controls and one case of juvenile Parkinsonism. Whole Exome Sequencing (WXS) was performed on these samples using hybrid selection for library preparation. Sequencing was carried out on the Illumina NextSeq 500 platform. For each sample, two FASTQ files containing paired-end reads (R1 and R2) were generated. The data deposited consists of the corresponding FASTQ files.

  Dataset EGAD50000000413

• scRNAseq and scTCRseq of three tumor lesions derived from one patient receiving adoptive TIL therapy

  scRNA sequencing and scTCR sequencing on three tumor lesions derived from one patient receiving adoptive tumor-infiltrating lymphocyte (TIL) therapy. Expanded metastasis was used for TIL expansion and yielded the TIL product which also was used for scTCR sequencing. 10 weeks after transfer of the TIL product the regressing metastasis was resected. After the patient progressed the progressing metastasis was removed (61 weeks).

  Dataset EGAD50000000406

• SNP array

  The whole study comprises of two patient cohorts. Screening cohort: 40 patients of Germany; validation cohort: 40 patients from Asia. Further, bile duct and CCA cell lines have been analyzed.&#10;This dataset contains SNP array data for tumor/normal pairs for a subset of 36 patients from the screening cohort plus an additional relapse tumor of one of those 36 patients. Data was generated on a OmniExpress-24 v1.1 Bead Array (Illumina) and is stored in IDAT file format.

  Dataset EGAD00010002597

• Quantitative analysis of a novel DNA hypermethylation panel using bronchial specimen for lung cancer diagnosis

  The dataset includes IDAT raw files for 10 samples and the analyzed DMP file which describes the differential methylation positions based on Illumina Infinium MethylationEPIC BeadChip. All samples (5 lung cancer cases vs. 5 benign lung disease controls) were obtained from bronchial washings at the site of the lesion under bronchoscopy manipulation. The histological type of the five lung cancer cases is adenocarcinoma and squamous cell carcinoma.

  Dataset EGAD00010002465

• InterPregGen-GWAS-UZB-1

  GWAS genotype data for maternal and fetal (baby) cases of preeclampsia and controls from Uzbekistan. This dataset is a component of the InterPregGen FP7 project. DNA samples for this component were collected by InterPregGen Consortium members in Tashkent, Uzbekistan at the Institute of Immunology, Uzbek Academy of Sciences and at the Republic Specialized Scientific Practical Medical Centre of Obstetrics and Gynecology

  Dataset EGAD00010001918

• Large deletion predisposes to familial melanoma

  Mutations in CDKN2A and CDK4 account for ~40% of multi-case melanoma families, with another ~10% attributable to BAP1 and genes associated with telomere maintenance. Thus, a substantial proportion of the genetic risk underlying familial melanoma remains unexplained.

  Study EGAS50000001496

• BGISEQ-500 Cancer Dataset - WGS tumour/normal pairs

  This study contains whole genome sequencing data for 3 matched tumour / normal pairs from Mesothelioma patients. The sequencing is 50bp paired-end generated using the BGISEQ-500 and is provided as 6 aligned and duplicate-marked BAM files.

  Study EGAS00001002298

• Evaluation_of_size_selection_on_cancer_specific_sequencing_libraries

  A couple of previously characterized and sequenced libraries will be repeated using a couple of differing size selection criteria and skim sequenced using an Illumina HiSeq. The resulting sequence will be analyzed to determine the optimal DNA library size for our specific downstream analysis.

  Study EGAS00001000293

• GWAS data (Illumina 2.5 M SNPs) in Cuban cohorts of dengue disease

  We will have 274 individuals typed for the Illumina Human Omni 2.5 chip. The individuals are from two locations in Cuba (Havana and Guantanamo) and from four phenotype classes (asymptomatic, control dengue fever and dengue hemorrhagic fever).

  Study EGAS00001002276

• Integrated genetic and epigenetic analysis of myxofibrosarcoma

  Myxofibrosarcoma (MFS) is a common histological subtype of adult soft tissue sarcoma characterized by an infiltrative growth pattern and a high propensity for local recurrence. We underwent whole exome sequencing, RNA sequencing, and methylation analysis of MFS.

  Study EGAS00001002889

• Parallel Detections of Somatic Gene Mutations in Surgically Resected Tumor tissues and Matched Plasma Specimens in Early-Stages of Primary Breast Cancer

  We developed two panel successively, contain 68 and 136 genes respectively. Combination with ultrasound or mammography, it could be used for breast cancer early detection and avoided unnecessary surgery or other invasive detection.

  Study EGAS00001003075

• All available datasets of DEEP

  All available datasets of DEEP. There are several Reference Epigenomes available. For metadata check out www.deep.dkfz.de One reference epigenome represents a set of 6 ChIP-Seqs, WGBS, RNA-Seq, smallRNA-Seq and sometimes DNase1 and/or NOME-Seq.

  Study EGAS00001001608

• HCA_Heart_Disease_BHF_DZHK_RNA_

  Cell Atlas of the diseased human heart. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001004566

• Single cell RNA sequencing of lung adenocarcinoma.

  We performed single cell RNA sequencing (scRNA-seq) for 208,506 cells derived from 58 lung adenocarcinomas from 44 patients, which covers primary tumour, lymph node and brain metastases, and pleural effusion in addition to normal lung tissues and lymph nodes.

  Study EGAS00001003681

• Isotype_resolved_sequencing_of_B_cell_receptor_in_measles_virus_infection

  Isotype-resolved sequencing of B cell receptor in measles virus infection 1) This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002635

• SPECTA__NGS_Screening_Program_for_Efficient_Clinical_Trial_Access

  SPECTA comprises a network of participating European clinical sites and NGS screening platforms that can screen individual patients for multiple molecular targets and potentially allow the design of trials that will match the specific biology of the diseases affecting specific patients with cancer.

  Study EGAS00001000728

• Transcriptome_analysis_of_LCM_samples_

  We aim to set up a RNA pipeline for LCM samples to study transcriptome landscape of tumor and normal tissues. RNA was extracted from Lazer capture micro-dissection tissues and cDNA libraries were prepared with SMARTseq2 protocol. Libraries are pooled to be sequenced.

  Study EGAS00001003862

• STRATAA_RNAseq

  Transcriptome signatures of acute typhoid infection This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001003967

• Mutational_burden_in_skin_following_UV_treatment_Nanoseq

  We wish to assess the changes in mutational burden in patients under going UV treatment for psoriasis. Using duplex sequencing we will assess mutational burden in traditionally sun exposed (arm) and non-sun exposed (buttock) skin epidermis both pre and post UV treatment.

  Study EGAS00001007681