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• Analysis of exonic somatic variants in light-chain amyloidosis (ALA) and ALA concomitant with multiple myeloma

  We aim to provide the very first insight into ALA+MM molecular profiles and compare the results with ALA and with MM. Our detailed study of ALA and ALA+MM represents an important step towards improved understanding of their genetic and transcriptomic background, which is a prerequisite for development of optimal treatment strategies in the future.

  Study EGAS00001004214

• RNA-seq following TBL1XR1 KD in human CD34+CD38- cord blood cells

  We performed RNA-seq on CD34+CD38- human cord blood (CB) cells transduced with sh_Control or sh_TBL1XR1 lentiviruses to identify differentially expressed genes. Sorted CD34+CD38- cells from 3 independent cord blood pools were transduced with lentiviruses containing BFP reporter gene. BFP+ cells were sorted 3 days post-transduction and RNA was harvested for library preparation and sequencing.

  Study EGAS00001005869

• Mutant_clone_mapping_in_oesohagus_restricted_bait

  We aim to assess the burden of mutant clones in normal human oesophagus from donors aged 65+. We will do this for a small number of genes which we know to be under positive dN/dS selection. This will allow us to assess the patient to patient variation in mutational burden as well as any changes in selection which may occur with age.

  Study EGAS00001005660

• Amplified EPOR/JAK2 genes define a unique subtype of acute erythroid leukemia

  Acute erythroid leukemia (AEL) is a unique subtype of acute myeloid leukemia characterized by prominent erythroid proliferation, whose molecular basis is poorly understood. To elucidate the underlying mechanism of erythroid proliferation, we performed comprehensive genomic study. The aim of this study is to unveil genotype phenotype correlations and the feasible molecular targets for therapy of AEL.

  Study EGAS00001005810

• whole-genome sequencing of gastric cancer

  Gastric cancer is one of the most common cancers. Genome-wide analysis of genomic signatures might reveal novel mechanisms for gastric cancer tumorigenesis. Here, we analyzed structural variations and mutational signatures via whole-genome sequencing of 168 gastric cancer samples. Our data demonstrates diverse models of complex structural variations operative in GC, which lead to high-level amplification of oncogenes.

  Study EGAS00001003512

• Finding structural variation and functional consequences from the primary leukemia cells (CLL) at the single-cell level

  In this study, we aimed to identify somatic structural variation of chronic lymphocytic leukemia (CLL) at the single-cell level and investigate its direct consequence on the nucleosome occupancy using scNOVA approach. For this purpose, we performed strand-specific single-cell sequencing of primary leukemia samples from 63-year-old female patient.

  Study EGAS00001004925

• RNA-sequencing of ex vivo exhausted human antigen-specific T cells

  Tumor-specific T cells are frequently exhausted by chronic antigenic stimulation. To explore new pathways for reinvigoration of anti-tumor immune functions, we developed a human ex vivo exhaustion model by repetitive antigenic stimulation of primary CD8 T cells. This results in T cells that resemble patient-derived T cells in tumors on a phenotypic and transcriptional level.

  Study EGAS00001006794

• Single cell RNA sequencing of bone marrow mononuclear cells

  Bone marrow aspirates were obtained from patients with relapsed/refractory large B cell lymphoma (rrLBCL), mononuclear cells isolated by ficoll density-gradient centrifugation, and loaded onto a 10X Chromium for single cell RNA-sequencing using 5’ chemistry without prior cryopreservation. Healthy donor bone marrow mononuclear cells were obtained from healthy allogeneic stem cell transplant donors and analyzed following viable cryopreservation.

  Study EGAS00001006836

• ALLoreactive T-Cell receptOr RePertoire in kidnEy tranSplantation

  A prospective study for investigating the T-cell receptor (TCR) repertoire in T-cell mediated rejection (TCMR) after kidney transplantation. The pre-formed donor-reactive TCR repertoire was tracked in blood and tissue of patients after kidney transplantation and characteristics of TCR repertoires from patients experiencing a rejection were compared to patients with no histopathological signs of rejection.

  Study EGAS00001005299

• Genomic and epigenomic study of Japanese renal cell carcinoma including WGS, RNA-seq, ATAC-seq, and methyl-seq

  We performed whole-genomic and transcriptomic sequencing of more than 100 Japanese renal cell carcinoma (RCC) cases, including clear cell RCC, papillary RCC, chromophobe RCC, and TFE3-translocated RCC. In addition, we conducted epigenome sequencing analyses: the assay for transposase-accessible chromatin sequencing (ATAC-seq) and enzymatic methyl sequencing.

  Study EGAS00001006919

• H3Africa AWI-GEN Phase 1 Phenotype

  The phenotypic data for ~12500 samples of the AWI-Gen Phase 1 Population cross-sectional study of older adults (mostly between 40 and 60 years), men and women. Six study sites in four sub-Saharan African counties including Ghana, Burkina Faso, Kenya and South Africa. Some groups are missing data for specific variables. Data includes questionnaire data (demography, health history, family health history, behaviour and infection data); anthropometry; and laboratory assays on blood and urine.

  Dataset EGAD00001006425

• Summary Statistics GWAS SSNS

  Column 1 “rsid”: SNP identifier Column 2 “chromosome”: name of chromosome on which the SNP is located Column 3: “position”: base pair position on the chromosome Column 4 “minor_test_allele”: the base that constitutes the minor allele Column 5 “major_allele”: the base that constitutes the major allele Column 6 “maf”: the frequency of the minor allele, indicated as a fraction of 1 Column 7 “allele_freq_cases”: the minor allele frequency in cases Column 8 “allele_freq_controls”: the minor allele frequency in controls Column 9 “regression_pvalue”: the p-value for the difference in allele frequency between cases and controls Column 10 “odds_ratio”: the odds ratio, as calculated using logistic regression under an additive model with adjustment for the first ten principal components of ancestry

  Dataset EGAD00001008782

• Preeclampsia InterPregGen Consortium: Whole Genome Sequencing of 100 unrelated Kazakhs (DNA samples from the Scientific Center of Obstetrics, Gynecology and Perinatology, Almaty, Kazakhstan; Gulnara Svyatova, Principal Investigator

  Whole genome sequencing of 100 unrelated Kazakhs in order to impute genotypes into PE cases and controls from Kazakhstan and to provide genetic data and infrastructure for future genetic studies in Kazakhstan and Central Asia more generally and to fill a gap in worldwide information as Central Asia is not adequately represented in available genomic data. This dataset is one component of the InterPregGen FP7 project. DNA samples for this component were collected by InterPregGen Consortium collaborators at the Scientific Center of Obstetrics, Gynecology and Perinatology, Almaty, Kazakhstan (Gulnara Svyatova, Principal Investigator)

  Dataset EGAD00001005467

• Targeted_sequencing_of_blood_DNA_from_Human_twins (2019-05-31)

  The goals of this study is to investigate the prevalence and heritability of age-related clonal haemopoeisis (ARCH) in healthy elderly individuals.We will use a bespoke bait set to pull down DNA regions of interest in whole blood samples combined with HiSeq at a deep level . By correlating findings from each individual to their respective twin we hope to elucidate whether heritable traits influence the development of ARCH. a. 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/ . This dataset contains all the data available for this study on 2019-05-31.

  Dataset EGAD00001005055

• TS and WGS data

  The dataset contains the targeted sequencing (TS) and the whole genome low pass (WGS) BAM files of the study. For the TS: Samples: TS_XXX There are normal and tumor DNA samples. Each DNA strand has been sequenced independently (PoolA and PoolB). A TruSeq Custom Amplicon panel of 20 genes frequently mutated in ILC and/or ER-positive BC in general was designed using DesignStudio from Illumina: AKT1, ARID1A, CDH1, ERBB2, ERBB3, ESR1, FOXA1, GATA3, IGF1R, JAK2, MAP2K4, MAP3K1, NF1, PIK3CA, PTEN, RB1, RUNX1, STAT3, TBX3, and, TP53. For the WGS: Samples: WGS_XXX There are normal and tumor DNA samples. Samples were sequenced to an average target coverage of 0.5X.

  Dataset EGAD00001006393

• Chracterising cellur pathways underlying CD3/CD28 activation of human CD4+ cells

  We devised an approach to disentangle the TCR and CD28 pathways upon stimulation in naive and memory primary human CD4+ T cells (Tcons) in response to defined stimulatory signals. Sorted Tcons were activated using a titration of anti-CD3 and anti-CD28 in combination as well as individually. As a control we cultured cells in the same conditions but without the stimuli. In total, we defined seven conditions from four individuals for sequencing. 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/ .

  Dataset EGAD00001006612

• Chracterising cellur pathways underlying CD3/CD28 activation of human CD4+ cells

  We devised an approach to disentangle the TCR and CD28 pathways upon stimulation in naive and memory primary human CD4+ T cells (Tcons) in response to defined stimulatory signals. Sorted Tcons were activated using a titration of anti-CD3 and anti-CD28 in combination as well as individually. As a control we cultured cells in the same conditions but without the stimuli. In total, we defined seven conditions from four individuals for sequencing. 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/.

  Dataset EGAD00001006610

• Single cell RNA sequencing data in "Three-dimensional human alveolar stem cell culture models reveal infection response to SARS-CoV-2"

  This dataset includes a total of 5 single cell RNA sequencing (scRNAseq) data of SARS-CoV-2 infected human alveolar stem cell culture models. Two scRNAseq data were obtained from SARS-CoV-2 infection with MOI of 1.0 (1 for control and 1 for infected case) and the other three were obtained from SARS-CoV-2 infection with MOI of 0.1 in the study entitled "Three-dimensional human alveolar stem cell culture models reveal infection response to SARS-CoV-2". The 10x Chromium Single Cell 3' Reagent kits were used to generate libraries.

  Dataset EGAD00001006242

• Induction Failure in Childhood and Young Adult T-cell Acute Lymphoblastic Leukemia

  We carried out WGS and RNAseq on a cohort of 48 children and young adults with induction failure in T-cell Acute Lymphoblastic Leukemia (T-ALL) to identify genomic drivers of treatment resistance. The study includes WGS for 33 tumour/normal pairs and 15 tumour-only samples. In addition, there is RNAseq data for 37 cases.

  Dataset EGAD00001009058

• Single cell sequencing of human normal luminal cells

  The dataset includes 12 paired FASTQ files (6 samples) with single cell transcriptome sequencing data of normal breast luminal cells from ducts and TDLUs derived from reduction mammoplasties from three patients. Chromium Single Cell 3’ Reagent Kit v2 or v3 (10x Genomics) were used for processing of cells, whereafter sequencing was performed using the Illumina® NextSeq500/550 High Output Kit v2. Cell Ranger was used for generating FASTQ files and files from different lanes were concatenated prior to uploading the data to EGA. "R1" files include the feature barcodes and UMIs, while "R2" files include the reads.

  Dataset EGAD00001008499

• Single-cell omics data for COVID-19 patients

  Single-cell RNA-seq, single-cell ATAC-seq, and genotypes used in the analysis for the study "Altered and allele-specific open chromatin landscape reveal epigenetic and genetic regulators of innate immunity in COVID-19". The RNA-seq and ATAC-seq are raw data in FASTQ format while the genotypes are in the VCF format which was filtered and imputed (more details are available in the main text of the study).

  Dataset EGAD00001009331

• Whole exome and transcriptome sequencing of BPDCN

  This data set contains whole exome and transcriptome data from 47 case with BPDCN. For exome data, bam files are provided (mapped against GRCh38), for transcriptome raw fastq-files (paired-end data).

  Dataset EGAD00001008692

• Immune Response Targeted Panel + B/TCR Profiling + AbSeq + Sample Tag Multiplexing

  Multiomics data for a cohort of 20 COVID-19 patients (10 patients mild, 3 patients moderate, 4 patients severe, 3 patients critical) obtained from peripheral blood mononuclear cells (PBMCs) from longitudinally sampled at hospital admission, discharge, and 1 month thereafter. The data has been obtained a multiwell-based single-cell technology (BD Rhapsody) that includes the targeted expression of BD Immune Response Targeted Panel, the TCR/BCR profiling and a set of 52 surface proteins. The samples of the different patients at different collection times were labelled using a cell hashing strategy with the BD Single-Cell Multiplexing Kit (6 samples for each run).

  Dataset EGAD00001011319

• A new subgroup of hepatocellular adenomas with sonic hedgehog pathway activation

  Using sequencing and gene expression analyses, we identified a subgroup of HCA characterized by fusion of the INHBE and GLI1 genes and activation of sonic hedgehog pathway. Molecular subtypes of HCAs associated with different patients’ risk factors for HCA, disease progression, and pathology features of tumors. This classification system might be used to select treatment strategies for patients with HCA. Related Publication: Molecular Classification of Hepatocellular Adenoma Associates With Risk Factors, Bleeding, and Malignant Transformation Nault, Jean-CharlesLaurent, Christophe et al. Gastroenterology , Volume 152 , Issue 4 , 880 - 894.e6 http://dx.doi.org/10.1053/j.gastro.2016.11.042

  Dataset EGAD00001003092

• Identification of drug resistance genes in melanoma

  Our aim is to analyze the genome of human melanoma cell lines and short term culture from human melanoma samples in order to identify genes that confer drug resistance to clinically relevant targeted therapies. We will perform whole-exome sequencing, copy number variation analysis and methylome analysis in a collection of human melanoma cell lines and short term culture that will be then screened for drug sensitivity/resistance through a library of clinically relevant drugs and drug combinations. By the combined analysis of the genomic lesion and the drug sensitivity/resistance profile of different cell lines, we will look for genes whose mutation is associated to the sensitivity or resistance to a specific drug in different samples.

  Dataset EGAD00001001124

• SCLC study George et al. - WGS data set

  Whole genome sequencing was performed with DNA extracted from fresh-frozen tumor and normal material. Short insert DNA libraries were prepared with the TruSeq DNA PCRfree sample preparation kit (Illumina) for paired-end sequencing at a minimum read length of 2x100bp. Human DNA libraries were sequenced to an average coverage of minimum 30x for both tumor and matched normal. Murine DNA libraries of tumor and matched normal were both sequenced to a coverage of 25x.

  Dataset EGAD00001001273

• Tam-Seq of HGSOC samples for fixative comparison study

  Targeted amplicon sequencing of samples as part of the study "Methanol-based fixation is superior to buffered formalin for next-generation sequencing of DNA from clinical cancer samples. The amplicon panel consists of 48 amplicons in TP53, PTEN, EGFR, PIK3CA, KRAS and BRAF genes as described previously [Forshew, STM 2012]. All libraries were pooled and quantify using DNA 1000 kit on Agilent 2100 Bioanalyzer and KAPA SYBR FAST ABI Prism qPCR Kit (KAPA Biosystems) on 7900HT Fast Real-Time PCR System (Applied Biosystems) according to the supplier's recommendations. Reads were aligned using bwa-mem v0.7.12-r1039 to the 1000 genomes version of human genome build GRCh37, retaining duplicate reads.

  Dataset EGAD00001001937

• RNA-Seq data for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

  RNA-Seq data for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

  Dataset EGAD00001003433

• Oxford Nanopore targeted RNA-based amplicon data of 12 classical HLA genes

  The dataset consists of Oxford Nanopore targeted RNA-based amplicon data of 12 classical HLA genes (HLA-A, -B, -C, -DRA, -DRB1, -DRB3, -DRB4, -DRB5, -DQA1, -DQB1, -DPA1, and DPB1) of 50 healthy individuals. The 12 classical genes were sequenced in two separate gene pools on R9.4 flowcells using MinION sequencer. Per individual, gene pool 1 contains HLA-A, -B, -C, -DRB1, -DRB3, -DRB4, -DRB5, and -DPB1 and gene pool 2 HLA-DRA, -DQA1, -DQB1, and -DPA1. The dataset includes 100 fastq files of Oxford Nanopore 2D reads (50 for gene pool 1 and 50 for gene pool 2).

  Dataset EGAD00001006814

• A108768B

  Whole genome sequencing for single cells for library A108768B 1126 cells; filetype=bam

  Dataset EGAD00001007089

• cis-eQTL mapping of TB-T2D comorbidity in a five-way admixed SA cohort

  The aim of the study was to identify ancestry-specific genetic variants associated with differentially expressed genes in TB-T2D comorbid patients compared to T2D patients, as well as between TB patients and healthy controls. Both global and local ancestry was incorporated in regression models to account for the genetic heterogeneity in our complex multi-way admixed population.

  Study EGAS00001007059

• Three-dimensional patient-derived models of glioblastoma retain intra-tumoral heterogeneity

  The intra- and inter- tumoral heterogeneity of glioblastoma represents a significant therapeutic challenge, as well as difficulty in generating reliable models for in vitro studies. Here, we quantify the tumor cell composition from matched patient-derived neurospheres (PDN) and organoids (PDO) established from the same primary tumor using a series of multi-omic interrogations, relative to primary tissue

  Study EGAS00001008023

• The Primary Open-Angle Glaucoma Genes and Environment (GLAUGEN) Study

  Background Information: Primary open-angle glaucoma (POAG) is an age-related, intraocular pressure (IOP)-dependent progressive optic neuropathy that ultimately leads to irreversible blindness. Vision loss from POAG is a condition of public health significance. Current evidence suggests that POAG is a polygenetic disease modified by environmental influences. Despite the fact that a positive family history of disease is an important risk factor for POAG, conventional linkage and candidate gene approaches have revealed less than 5% of the genetic component of the disease. Furthermore, there is no consensus on environment risk factors for POAG. Elevated IOP is the only modifiable risk factor for POAG; yet, lowering IOP slows, but does not halt the disease process. Study Objectives: The overall goal of our research is to elucidate the pathogenesis of POAG so that cost-effective disease detection and primary prevention strategies can be implemented. The primary aim of the Glaucoma Gene Environment Initiative (GLAUGEN), funded by the Human Genome Research Institute (NHGRI), is to discover genetic loci associated with POAG. The secondary aim of GLAUGEN, funded by NHGRI and the National Eye Institute, is to discover gene environment interactions in POAG. Methods and study populations: For this study, we have assembled cases and controls from three studies: the Nurses' Health Study (NHS), the Health Professionals Follow-up Study (HPFS) and the Genetic Etiologies of POAG (GEP) project based at Massachusetts Eye and Ear Infirmary (MEEI). This case-control group includes 1057 unrelated cases and 1272 controls. Members of the NHS and HPFS also have repeated environmental exposure data collected prior to a diagnosis of POAG. NHS - The Nurses Health Study started in 1976 under the direction of Dr. Frank E. Speizer. With funding from the NIH, registered nurses from 11 US states were invited to complete a detailed questionnaire regarding lifestyle and health biennially. Initially, 121,000 women responded to the baseline questionnaire. Currently, Dr. Susan Hankinson serves as the program director for the NHS. HPFS - The Health Professionals Follow-up Study began in 1986 under the direction of Drs. Walter Willett and Meir Stampfer. Under the auspices of the NIH, they enlisted 51,529 male health professionals from throughout the US to complete similarly designed biennial questionnaires. Beginning in 1990, questions regarding ocular health were added to biennial questionaires completed by health professionals participating in the NHS and HPFS. This allowed us to formulate (PI: S. Hankinson; NEI) and maintain (PI: L. Pasquale; NEI) a cohort at risk for POAG derived from the respective general cohorts who were under ophthalmic care. We then developed a definition of POAG that allowed us to identify cases from a population that was geographically dispersed. The centerpiece of this definition is the presence of reproducible visual field loss consistent with nerve fiber layer (NFL) dropout (the NFL contains the axons that comprise the optic nerve) on reliable tests. Reproducible visual field loss occurred in the context of anterior segment findings that did not suggest a secondary cause of elevated IOP and posterior segment findings that did not suggest a secondary cause of visual field loss. We selected controls from the cohort at risk for POAG on the basis of age, gender and time period when cases were identified. GEP - The Genetic Etiologies of POAG was initiated in 1996 with funding from the National Eye Institute under the direction of Dr. Janey Wiggs. The purpose of this work was to discover novel genetic loci associated with POAG. In the GEP, cases were derived predominantly from the Glaucoma Service at MEEI. The majority of cases had an examination by a glaucoma specialist and met the definition for POAG used in NHS and HPFS. Cases with only one reliable visual field consistent with NFL dropout were included if there was a cup-disc ratio of 0.7 or more. The majority of controls were patients who presented to the MEEI comprehensive ophthalmology service for routine eye examination or from spouses of MEEI patients with secondary forms of glaucoma. Other controls were identified from regional glaucoma screenings held throughout Massachusetts. Members of GEP have detailed ocular phenotype data but limited information on environmental exposures. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to primary open-angle glaucoma through large-scale genome-wide association studies of three well-characterized cohorts of cases and controls, some in matched pairs. Genotyping was performed at the Broad Institute of MIT and Harvard, a GENEVA genotyping center. Data cleaning and harmonization were performed at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000308

• Programmatic submission based on XML

  Programmatic submissions (XML based) For further information please check our Submission FAQs, submission quickguide as well as submission terms! Introduction Besides the Submitter Portal tool, EGA supports programmatic sequence and clinical data metadata submissions. If you are not sure what this means, you may want to explore our brief metadata introduction. Programmatic submissions are recommended for array-based submission. Moreove, it may be of help if your submission is recurrent or it is difficult to manage manually due to its sheer size. Otherwise, we highly recommend using the Submitter Portal to perform submissions. In this page we will guide you through the required steps to programmatically submit data to the EGA. Programmatic submissions require your metadata to be structured for an easy and straightforward validation and archival. It basically consists in formatting your metadata as Extensible markup language (XML) files and submitting them to the EGA using the WEBIN Before submitting metadata to the EGA, it is important to ensure that the information in your XML files is compliant with our standards. You can see further details on how these standards are maintained at EGA at our EGA Schemas documentation page. Using WEBIN, you can validate your XML files against EGA's schemas to ensure that your metadata is compliant before submission. WEBIN services WEBIN production service WEBIN test service We advise you to submit your metadata to the test service when submitting to the production service for the first time. The test service is identical to the production service except that all submissions will be discarded in the following 24 hours. This allows you to learn about the submission process without having to worry about data being submitted. Authentication Authentication is required each time a submission is made. The submission service uses HTTPS protocol for metadata encryption and identification to provide a secure submission environment. Data file upload Both Runs and Analyses reference files (e.g. FASTQ need to be uploaded to the EGA before these metadata objects are submitted. In other words, if you submit a Run that references a file that we cannot find associated with your account, the metadata submission will fail. See further details on how to upload your files in our File Upload documentation. Metadata model of the EGA Our metadata model is formed by multiple metadata objects. Check further details in our documentation at our EGA Schema documentation page. Working with EGA XMLs files Now that the basic concepts of the EGA metadata have been described, you can start preparing your programmatic submission through XML. Here you will find the guidance on how to prepare the XML files. Programmatic Submission Tutorial Video Take a look at the Programmatic Submission Tutorial Video, which explains the workflow of a programmatic submission and goes over an example metadata submission. Programmatic Submission Tutorial Video. When building your XML files, we recommend using text editors (e.g.Sublime Text or VisualStudio) that allow you to visualise the structure of the XML with ease. Furthermore, these editors constantly check the consistency of the XML structure. Alternatively, and if the submission consists of a big number of objects (specially analyses), you may find the tool star2xml handy. This tool allows for a direct conversion between metadata in a tabular format (e.g. a spreadsheet) into XMLs. Identifying objects: Aliases and center names Every EGA object must be uniquely identified within the submission account using their alias attribute. The aliases can be used in submissions to make references between EGA objects. Let us dig into EGA's use of aliases and center names: alias: every object should have a name that is unique within your submission account. Once submitted successfully, every alias will be assigned a unique and permanent accession (EGA ID). refname: when an object references another by its alias, the alias of the referenced object goes into the "refname" attribute of the referencing object. For example, if a sample has the alias "sample1", and an experiment uses this sample, then the experiment's "EXPERIMENT/SAMPLE/refname" attribute should be "sample1". center_name: The "center_name" attribute is required within the submission XML and, if not provided when the object is submitted, it will be automatically filled using your default EGA account center_name. This element is the "controlled vocabulary acronym or abbreviation that is provided to the account holder when the account is first generated". If the submitter is brokering a submission for another institute, the submitter should use their special broker account name in broker_name while the data centre acronym remains in center_name. Log-in details should have been provided when you requested a submission account. Please contact our Helpdesk team if you have any questions. run_center: Many submitting centers contract out the actual sample sequencing to another center. In these cases, the sequencing center should be acknowledged in the run_center attribute. Again, this is controlled vocabulary and the acronym should be sought from EGA helpdesk before submitting. Please contact our Helpdesk team if you have any questions. Prepare your XMLs The goal of this section is to provide sufficient information to be able to create the metadata XML documents required for programmatic submissions. Please note, the EGA utilises the XML schemas maintained at the European Nucleotide Archive (ENA). It is important due to the fact that by using a similar system, some pieces of documentation from the ENA's programmatic submission can also help you with your programmatic submission to the EGA. For example, you can submit programmatically without using a Submission XML by following the steps at Submission actions without submission XML. A submission does not have to contain all different types of XMLs. For example, it is possible to submit only a few samples; or a study that is later to be referenced. You can submit each object one by one, or submit all in a batch: you choose what method of submission works best for you. We do recommend, nevertheless, that you submit the objects to be referenced (e.g. samples or studies) first, and the objects that reference these (e.g. experiments or datasets) afterwards. You can see a graphical view of these objects and their relationships at our EGA Schemas page. Independently of the submission scenario, you will always require a Dataset XML. The entity of a dataset is what is used to control access to the given data, in the form of runs or analyses. In other words, when a requester is granted access, it is through the dataset and the objects (e.g. runs or analyses) that the dataset contains, granting access to them in one go. Given the nature of the EGA, a dataset XML will always be required for the data access. First, we will differentiate between submissions of "raw" and "processed" data: Runs and Analyses, respectively. Run data submissions Raw data derives from instruments "as is". For example, a plain sequence file (e.g. FASTQ or unaligned BAM files) would be considered raw data. A typical raw (unaligned) sequence read submission consists of 8 XMLs: Submission Study Sample Experiment Run DAC Policy Dataset When technical reads (e.g. barcodes, adaptors or linkers) are included in the submitted raw sequences, a spot descriptor must be submitted to describe the position of the technical reads so that they can be removed. The following data files can be submitted without providing spot descriptor information in the experiment/run XML: BAM files (single reads) SFF files (single reads without barcodes) FastQ files (single reads without any technical reads) Complete Genomics files Analysis data submissions Processed data is, in some way, refined raw data. This includes raw data that has been processed by some form of analysis method (e.g. alignment, noise reduction, etc.). For example, an aligned sequence (e.g. BAM file), that was created using raw FASTQ files, would be a processed file. This category includes most types of data: sequence alignment files (e.g. BAM or CRAM), clinical data (e.g. phenopackets), sequence variation files (e.g. VCF), sequence annotation, etc. A typical EGA analysis data submission consists of 7 EGA XML: Submission Study Sample Analysis DAC Policy Dataset We accept three different types of analysis data submissions: BAM files (for multiple read alignments) VCF files (for sequence variations) Phenotype files (in any format) In anycase, keep in mind that samples must be created in order to be referenced in the analyses. In other words, the provenance of the information within the BAM, VCF and phenotype files Example XMLs Below you can find a non-extensive list of example XMLs with descriptive fields (i.e. explaining what to provide in each field). Furthermore, you can also find real examples (i.e. the true value of the provided fields) in our GitHub repository. Submission XML The submission XML is used to validate, submit or update any number of other objects. The submission XML refers to other XMLs. New submissions use the ADD action to submit new objects. Object updates are done using the MODIFY action and objects can be validated using the VERIFY action. Descriptive submission XML example True values submission XML example Study XML The study XML is used to describe the study containing a title, a study type and abstract as it would appear in a publication. Descriptive study XML example True values study XML example Please use the following notation within the property "STUDY_LINKS" when including PubMed citations in the Study XML: <STUDY_LINKS> <STUDY_LINK> <XREF_LINK> <DB>PUBMED</DB> <ID>18987735</ID> </XREF_LINK> </STUDY_LINK> </STUDY_LINKS> Sample XML The sample XML is used to describe the samples used to obtain the data, whether they were sequenced, measured in any other way, or have an associated phenotype. The mandatory fields include information about the taxonomy of the sample, sex, subject ID and phenotype. For example, the mandatory attribute fields for each sample would look like these, within the array of "SAMPLE_ATTRIBUTES": <SAMPLE_ATTRIBUTES> <SAMPLE_ATTRIBUTE> <TAG>subject_id</TAG> <VALUE>free text!</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>sex</TAG> <VALUE>female/male/unknown</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>phenotype</TAG> <VALUE>Free text, EFO terms (e.g. EFO:0000574) are recommended</VALUE> </SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTES> Sample is one of the most important objects to be described biologically, it is highly recommended that “TAG-VALUE” pairs are generated as SAMPLE_ATTRIBUTES to describe the sample in as much detail as possible. For example, were we to give the population ancestry of the sample, we could add a new attribute to the array, in which, for example, we would indicate that the sample derives from an individual of "Mende in Sierra Leone" (MSL), with an african ancestry: <SAMPLE_ATTRIBUTE> <TAG>Population</TAG> <VALUE>MSL</VALUE> </SAMPLE_ATTRIBUTE> Given that VALUE and TAG are free text, the combinations are limitless in order to give you full flexibility on the information you want to provide. We recommend you use the Experimental Factor Ontology (EFO) to describe the phenotypes of your samples. You can provide more than one phenotype by adding more items to the array of SAMPLE_ATTRIBUTES. Phenotypes considered essential for understanding the data submission should be provided. Each phenotype described should be listed as a separate sample attribute <SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTE>. There is no limit to the number of phenotypes that can be submitted. If a suitable EFO accession cannot be found for your phenotype attribute, please consider using another controlled ontology database (e.g. HPO, MONDO, etc.) before using free text. Descriptive sample XML example True values sample XML example Experiment XML The experiment XML is used to describe the experimental setup, including instrument platform and model details, library preparation details, and any additional information required to correctly interpret the submitted data. Where any of these values differ between runs, a new experiment object must exist, since runs are grouped by experiments. Each experiment references a study and a sample by alias, or if previously-submitted, by accession. Pooled data must be demultiplexed by barcode for submission. Descriptive experiment ( Illumina paired read ) XML example True values experiment ( Illumina paired read ) XML example Run XML The run XML is used to associate data files with experiments and typically comprises a single data file (e.g. a FASTQ file). Please note that pooled samples should be de-multiplexed prior submission and submitted as different runs. Descriptive run XML example True values run XML example Analysis XML Given that an analysis can be used to submit any type of processed data to the EGA, we will list below an example of each of the three most common types of analysis XMLs submitted to the EGA: sequence alignments (e.g. BAM files); sequence variation (e.g. VCF files); and clinical metadata or phenotypes (e.g. phenopackets). Regardless of the type of processed data submitted in the analysis, the analysis must be associated with a Study and can reference multiple types of other objects, from samples to experiments, if they are available at the EGA. Just like with Runs, whenever a file is submitted to the EGA through an analysis object, the file MD5 checksums must be present, in order for the EGA to validate file integrity upon transfer. This also includes index files when applicable (e.g. .bai.md5 files). Ideally, any analysis that uses a reference sequence for some kind of alignment (e.g. BAM, CRAM or VCF files), would contain metadata about the alignment, such as INSDC reference assemblies and sequences, by either using accessions (e.g. CM000663.1) or common labels (e.g. GRCh37). Read alignment (BAM) Analysis XML The Analysis can be used to submit BAM alignments to EGA. Only one BAM file can be submitted in each analysis and the samples used within the BAM read groups must be associated with Samples. Descriptive bam alignments XML example True values bam alignments XML example Sequence variation (VCF) Analysis XML The Analysis can be used to submit VCF files to EGA. Only one VCF file can be submitted in each analysis and the samples used within the VCF files must be associated with Samples. Download analysis XML (VCF) Phenotype files The Analysis XML can be used to submit phenotype files to the EGA. Only one phenotype file can be submitted in each analysis and the samples used within the phenotype files must be associated with EGA Samples. Download analysis XML (Phenotype) DAC XML The DAC XML describes the Data Access Committee (DAC) affiliated to the data submission. The DAC may consist of a group or a single individual and is responsible for the data access decisions based on the application procedure described in the POLICY.XML. As with any other object, if it was already submitted to the EGA, there is no need to submit it again: you can reference an existing object within the EGA. Hence, A DAC XML does not need to be provided if your submission is affiliated to an existing EGA DAC.. Further information on DACs can be found here, and you can always contact our Helpdesk team if you have further inquiries. Descriptive dac XML example True values dac XML example Policy XML The Policy XML describes the Data Access Agreement (DAA) to be affiliated to the named Data Access Committee. Descriptive policy XML example True values study XML example Dataset XML The dataset XML describes the data files, defined by the Run.XML and Analysis.XML, that make up the dataset and links the collection of data files to a specified Policy. The dataset xml is commonly the last metadata object to be submitted, since it references multiple other entities. Please consider the number of datasets that your submission consists of. For example, a case-control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Descriptive dataset XML example True values dataset XML example Validating and submitting your EGA Validating EGA's XMLs through Webin After you have ensured that the XMLs are properly formatted and contain all the required information. You can proceed to validate and submit your data. Use the curl command to validate your XML file: Once you have prepared your XML file and asserted you have access to Webin, you can validate your XML file programmatically against EGA's schemas using the curl command. There are multiple ways in which you can validate your XMLs. This variety has to do with the fact that: (1) there are 2 instances of Webin (test and production); and (2) that validation is a default step during submission. In other words, any time that you submit your data through Webin, it will be validated automatically before being accepted. This allows for 4 possible routes of validation, all having the same validation result: validating or submitting to either the production service or the test service of Webin. For example, directly validating a "study" object XML in the testing service (wwwdev…) would look like the following: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" In this command, you would need to replace <USERNAME> and <PASSWORD> with your EGA account username and password, respectively. You would also replace <INPUT_FILE> with the path to your XML file. A mock example would look like the following: curl -u ega-test-data@ebi.ac.uk:egarocks -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" The validation attempt can have different results depending on the given arguments: If your XML file is valid according to EGA's schemas, you will see a message indicating that your XML file is compliant. For example, see below for our mock example, where the "success" was "true" (i.e. no validation errors found). Nevertheless, notice how the "<STUDY accession=" is empty: it is because we were simply validating, so the study did not get an accession or ID. <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:19:28.850+01:00" submissionFile="submission-EBI-TEST_1681222768850.xml" success="true"> <STUDY accession="" alias="Mock example" status="PRIVATE"/> <SUBMISSION accession="" alias="SUBMISSION-11-04-2023-15:19:28:840"/> <MESSAGES> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If there are any errors or warnings, the tool will display them, allowing you to correct them before submitting your data to EGA. For example, in the following response, it is said that the object we were trying to submit was already existing, and therefore the "success" was "false". <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:12:35.609+01:00" submissionFile="submission-EBI-TEST_1681222355609.xml" success="false"> <STUDY alias="Example!_Human Microbiome Project SP56J" status="PRIVATE" holdUntilDate="2023-03-11Z"/> <SUBMISSION alias="SUBMISSION-11-04-2023-15:12:35:576"/> <MESSAGES> <ERROR>In study, alias: "Example!_Human Microbiome Project SP56J". The object being added already exists in the submission account with accession: "ERP127584".</ERROR> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If the curl command retrieves no response at all, please double check if your username and password are correctly provided. Also notice the "ACTION=..." argument passed to the Curl command. This specifies the action to take during the call to Webin, so we do not need a "Submission" XML just for a validation attempt. See more at submission actions without submission XML. Furthermore, validation of multiple files or objects (e.g. sample, experiment, study…) can be done in a single command by adding more arguments (i.e. '-F'). For example: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" -F "SAMPLE=@sample.xml" -F "DATASET=@dataset.xml" As mentioned above, beside "validate" action in the test environment, you can also validate your metadata by three other methods: "Validate" in the production server. From our example above, you simply need to take the "dev" away from the URL. curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the development server. From our example above, you would simply need to replace the action: from "validate" to "add". Whatever is submitted to this service will be discarded in 24h, so whether something gets submitted or not would not matter in the long run. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the productionserver. A combination of the previous two methods, which would render this attempt into a submission. This path is just to be taken when you are sure your metadata is compliant and what you want to submit. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" What happens after the submission of a dataset XML? Once you have completed the registration of your dataset/s please contact our Helpdesk Team to provide a release date for your study. Please note that all datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitter or DAC contact contact the EGA Helpdesk for the study to be released. We strongly advise you not to delete your data until EGA Helpdesk confirms that your data has been successfully archived.

  Documentation submission/metadata/submission/programmatic-submission-xml

• Exome chip analysis file

  This data set includes the following summary level data file used for the exome chip analysis: exome_chip.sv.assoc.txt: results from single variant association analysis in exome chip

  Dataset EGAD00010001187

• Immunochip_genotypes

  Intensity files for Immunochip genotypes from blood

  Dataset EGAD00010001495

• MS-Twins-EPIC-PBMCs

  Genome-wide DNA methylation profiles of MZ twins clinically discordant for MS generated using Illumina’s Infinium MethylationEPIC BeadChip assay (EPIC array)

  Dataset EGAD00010001618

• GR@ACE_StageI.v02

  Binary Plink files for post-GWAS quality control in 7409 samples genotyped using Axiom 815K Spanish Biobank array (Thermo Fisher)

  Dataset EGAD00010001653

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - pilot normal and pre/post-chemo

  Dataset EGAD00001000704

• uganda_autosomes

  Genotype data (in binary PLINK format) and imputed data (with merged 1000Gp3 and AGVP reference panel in GEMMA BIMBAM dosage format) for 1391 individuals from EMaBS in Entebbe, Uganda.

  Dataset EGAD00010002578

• The CCA Genome Core is responsible for archiving the sequencing data generated by the Cancer Centre Amsterdam (CCA).

  Dac EGAC00001000181

• tRCC UTSW 2024 Cohort

  This submission contains the WES and RNA-Seq raw data for the study titled 'Comparative Genomics of a Translocation Renal Cell Carcinoma Mouse Model Reveals Molecular Mechanisms of Tumorigenesis.

  Dac EGAC50000000105

• bfast CohortD OSPL

  ctDNA sample data (one sample per line) for BFAST Cohort D including sample-level summaries like tumor fraction (cTF).

  Dataset EGAD50000000147

• Walter and Eliza Hall Institute - University of Melbourne

  Data Access Committee for Gocuk, Lancaster et al. 2024: nanopore sequencing data of patients with choroideremia and X-linked retinitis pigmentosa, to determine X inactivation skew.

  Dac EGAC50000000301

• Whole exome sequencing data for two samples

  Whole exome sequencing of two human samples run on the Illumina HiSeq2500 platform. It contains two BAM files aligned to the refrence genoeme GRCh38.

  Dataset EGAD50000000825

• SMPaeds PanelSeq of cfDNA at relapse with UMIs

  Deep sequencing of a targeted panel of 67 paediatric cancer related genes for cfDNA with UMIs at approximately 1500xUMI coverage.

  Dataset EGAD50000000781

• Psoriasis PBMCs

  This DAC is for the purpose of controlling access to Psoriasis PBMC scRNA-seq data of the study Spermidine/spermine N1-acetyltransferase controls tissue-specific regulatory T cell function in chronic inflammation

  Dac EGAC50000000470

• HUG-CELL ASD Data Access Committee

  Data Access Committee for data generated by projects involving ASD and associated phenotypes by the Human Genome and Stem Cell Research Center (HUG-CELL), University of São Paulo

  Dac EGAC50000000475

• Picuris Pueblo Genomic Project – Ancient Dataset

  Ancient individual from Picuris Pueblo. Please refer to paper for more information.

  Dataset EGAD50000001246

• APOBEC Breast Cancer Whole Genome

  Five tumor-normal human breast cancer cases driven by APOBEC mutagenesis, including normal tissue, primary, and metastatic samples for each patient.

  Dataset EGAD50000001275

• DLP+ Single Cell Genomic Library A98288

  DLP+ Single Cell Genomic Library A98288 for Follicular lymphoma patient sample TFRIPAIR2_FL

  Dataset EGAD00001010247

• Reconstructed BCRs

  Computationally reconstructed B-cell receptor sequences (using BraCeR) from scRNA-seq data for all cells passing quality control.

  Dataset EGAD00001008456

• scRNA PDX

  Single cell RNA-seq for PT1-derived PDX samples

  Dataset EGAD00001006134

• Reference epigenome KNIH005 mRNA-seq data generated from KEP study

  A KNIH005 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002171

• Reference epigenome KNIH001 miRNA-seq data generated from KEP study

  A KNIH001 miRNA-seq single end data for islet cells

  Dataset EGAD00001002760

• Leeds Paired Primary and Recurrent GBM RNAseq

  Whole transcriptome, strand-directional RNAseq data for paired primary dn recurrent samples from patients with glioblastoma

  Dataset EGAD00001005224

• ATAC-Seq of human stimulated and cultured CD4+ Treg cells

  This dataset includes whole genome sequence data for ATAC-seq (42 samples) of human stimulated and cultured CD4+ Treg cells.

  Dataset EGAD00001004829

• TLR7 variants in human lupus patients

  Set of 2 bam files from patients affected with Lupus. Fastq alignments for exonic variants present in TLR7 gene.

  Dataset EGAD00001008534

• Reference epigenome IPS02_N_NPC_mRNA-Seq data generated from KEP study

  A IPS02_N_NPC_mRNA-Seq paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003489

• Reference epigenome IPS03_N_ENeuron_smRNA-Seq data generated from KEP study

  A IPS03_N_ENeuron_smRNA-Seq single end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003503

• Reference epigenome IPS02_N_NPC_smRNA-Seq data generated from KEP study

  A IPS02_N_NPC_smRNA-Seq single end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003502

• Reference epigenome KNIH001 mRNA-seq data generated from KEP study

  A KNIH001 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002167

• Reference epigenome IPS05_X_NPC_mRNA-Seq data generated from KEP study

  A IPS05_X_NPC_mRNA-Seq paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003492

• Oxford Human islet ATAC-seq dataset of 5 human islets

  ATAC-seq data for 5 non-diabetic human pancreatic islet samples

  Dataset EGAD00001003759

• RNA-seq data of LMS tumors

  This dataset contains paired fastq files for LMS tumor samples

  Dataset EGAD00001003828

• Additional Sanger Sequencing and qPCR data to PSCNL samples

  Sanger sequencing and RT-qPCR data for validation used in Primary lymphomas of the central nervous system (PCNSL).

  Dataset EGAD00001008583

• Melanoma post mortem analysis

  We have been applying whole genome and transcriptome sequencing across metastases collected during post mortem. Herein we show the findings for the first such patient.

  Dataset EGAD00001005072

• Reference epigenome KNIH002 mRNA-seq data generated from KEP study

  A KNIH002 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002168

• Reference epigenome KNIH003 mRNA-seq data generated from KEP study

  A KNIH003 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002169

• Benchmark and validation of whole exome sequencing of a trio and singleton for mobile element detection.

  Illumina HiSeq paired-end exome sequencing of a trio and singleton.

  Dataset EGAD00001000883

• Clinical data

  Clinical data for KATHERINE: Clinical data include Treatment Arm, Invasive Disease Free Survival (IDFS), Clinical Stage at Presentation, Hormone Receptor Status, Preoperative HER2 Directed Therapy, Pathological Node.

  Dataset EGAD00001008785

• Reference epigenome KNIH002 miRNA-seq data generated from KEP study

  A KNIH002 miRNA-seq single end data for islet cells

  Dataset EGAD00001002761

• Reference epigenome KNIH005 miRNA-seq data generated from KEP study

  A KNIH005 miRNA-seq single end data for islet cells

  Dataset EGAD00001002764

• Reference epigenome KNIH003 miRNA-seq data generated from KEP study

  A KNIH003 miRNA-seq single end data for islet cells

  Dataset EGAD00001002762

• Reference epigenome KNIH004 miRNA-seq data generated from KEP study

  A KNIH004 miRNA-seq single end data for islet cells

  Dataset EGAD00001002763

• Illumina HiSeq X Ten

  Three files of patients 10, 11 and 13 with WGS done on Illumina HiSeq X Ten. For research purpose and authorised user only.

  Dataset EGAD00001003439

• ITS amplicon sequencing dataset

  This dataset contains raw ITS amplicon sequencing data for 719 sputum samples from individuals in Guangdong province, China.

  Dataset EGAD00001009817

• Reference epigenome IPS03_N_ENeuron_mRNA-Seq data generated from KEP study

  A IPS03_N_ENeuron_mRNA-Seq paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003490

• Reference epigenome IPS06_X_ENeuron_mRNA-Seq data generated from KEP study

  A IPS06_X_ENeuron_mRNA-Seq paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003493

• DLP+ Single Cell Genomic Library A98221A

  DLP+ Single Cell Genomic Library A98221A for Follicular lymphoma patient sample TFRIPAIR5_FL

  Dataset EGAD00001010245

• Whole Genome Bi-Sulfite Sequencing files for RMS.

  WGBS files accompanying the paper titled "Identification of Therapeutic Targets in Rhabdomyosarcoma Through Integrated Genomic, Epigenomic, and Proteomic Analyses".

  Dataset EGAD00001004315

• Reference epigenome KNIH006 mRNA-seq data generated from KEP study

  A KNIH006 mRNA-seq paired end data for beta cells

  Dataset EGAD00001002172

• COMET TCRseq raw data

  This is the raw TCRseq data for the manuscript T cell receptor repertoire sequencing reveals chemotherapy-driven clonal expansion in colorectal liver metastases.

  Dataset EGAD00001010269

• Reference epigenome KNIH004 mRNA-seq data generated from KEP study

  A KNIH004 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002170

• HIV exome pilot, exome data hs37d5

  exome sequence data for 57 HIV elite long term non-progressors and rapid progressors. Complete dataset of improved BAMs mapped to hs37d5 and including phenotype information.

  Dataset EGAD00001003345

• Reference epigenome IPS05_X_NPC_smRNA-Seq data generated from KEP study

  A IPS05_X_NPC_smRNA-Seq single end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003505

• Reference epigenome IPS06_X_ENeuron_smRNA-Seq data generated from KEP study

  A IPS06_X_ENeuron_smRNA-Seq single end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003506

• Melanoma - Exome sequencing

  whole exome sequencing of tumor- as well as PBMC-derived DNA of five melanoma patients for identification of naturally presented patient-specific neoepitopes

  Dataset EGAD00001002731

• Reference epigenome KNIH006 miRNA-seq data generated from KEP study

  A KNIH006 miRNA-seq single end data for beta cells

  Dataset EGAD00001002765

• Demographically Diverse Substance Use Disorder Cohorts of Dr. Stanley H. Weiss

  The Demographically Diverse Substance Use Disorder Cohorts of Dr. Stanley H. Weiss, which constitute the Epidemiology of the Weiss Cohort Projects, consist of a series of inter-connected projects, building upon a set of cohort projects of various groups, mainly drug users from medication-assisted treatment programs, that Dr. Stanley H. Weiss first developed in the 1980&#8217;s plus several newer initiatives, each with an array of collaborators. Beginning in the 1980&#8217;s, Dr. Stanley H. Weiss started several long-term studies of persons who inject drugs (PWID) across the United States, ultimately enrolling over 10,000 participants through the early 1990&#8217;s with an average age then in their 30&#8217;s. About a quarter were enrolled from sites in New Jersey (NJ). These studies included the first testing of PWID for the human immunodeficiency virus (HIV) and the human T-cell lymphotropic viruses (HTLV I and HTLV II). Cumulative past support (initiation thru ~ 1999) for these cohort studies included ~ $20 million from intramural resources from the National Cancer Institute (NCI) and the National Institute on Drug Abuse (NIDA), plus multiple grants and in-kind support from the New Jersey Department of Health (NJDOH) totaling ~ $1 million. The Weiss Cohort Projects include the first large AIDS-era cohorts to include women at high risk for HIV. A high percentage of subjects in these studies are black or Latino. Thus, this is an ethnically diverse US cohort, with a high proportion of women included. These subjects are at high risk of parenteral and sexual infection from both drug use and sexual practices. Samples from other studies conducted by Dr. Weiss, in which detailed interviews were conducted, are included as controls (persons documented by us not to have a history of opioid drug use). As one of our groups of subjects have many persons of Haitian ancestry, we specifically included some Haitians who had never used opioids as controls. Our documentation includes such ancestry. These cohorts demonstrated high rates of HIV and HTLV-II infection in PWIDs, including one study initiated in 1981 with confirmation in the later cohorts. In the first two decades of these studies, among numerous publications was the first study showing a very high rate of hepatitis C infection among PWIDs. An example of how the studies&#8217; long-time horizon proved essential was that it first became possible to test whether a person had ever been infected with hepatitis C virus (HCV), as well as how much HCV was in each person&#8217;s blood, many years after the specimens were collected. This allowed HCV amounts in blood to be compared for subjects who had died of liver disease early in the study versus those who survived. Then a sequence of published papers culminated in demonstrating, using a nested case-control design, that a high baseline HCV titer was predictive of early progression to death from end-stage liver failure. Outcomes related to HCV (end stage liver disease and hepatocellular carcinoma) remain under study. In the original cohort studies, the mean age at enrollment was ~ 33 years old, so that those still alive in 2022 are mainly now ~ 60 - 75 years old. Many participants have already died. The tincture of time has led to subjects reaching ages when many more are dying from a wide array of outcomes, including from many chronic diseases (including cancer) as well as from infectious agents (especially HIV, HCV) or drug overdose. Renewed collaboration with local drug treatment programs has led to new field-based studies, including examination of some currently evolving problems among drug users. Dr. Weiss joined the National Institute on Drug Abuse (NIDA) Genetics Consortium (NGC) in 2017, and through the NIDA project officer has had access to NGC contract resources (see below). NIH Certificate of Confidentiality, CC-DA-16-214 (attached) protects these studies. Past arrangements related to data on our subjects leads to restrictions on the use of data emanating from our study, such as potential commercialization and restrictions on whom may access and use these data. NIDA Genetics Consortium (NGC) resources further support these endeavors and will be used as part of the NGC analyses studying the genetics of substance use. Study participants signed informed consent for the information collected from them to be used with no time limit and for biologic specimens collected from them to be used without restriction in future research. Serum samples were collected from participants, and from many also plasma, white blood cells and/or urine samples. About 100,000 vials were stored. All specimens have been continuously preserved at sufficiently cold temperatures to prevent deterioration, and many subjects separated white blood cells were processed and frozen in such a way as to maintain viability. Detailed data from the participants has been accumulated over time, and in general, linkage has been retained in each sub-study in accordance with the consent forms and protocols. For some participants, specimens were collected at multiple times (that is, sequential specimens). Multiple specimens from a single person exist in this database, and efforts at de-duplication remain ongoing. Dr. Weiss should be contacted if an investigator requires unique individuals since: &#8226; Multiple phases of enrollment occurred, and as our prospective follow-up continues; Dr. Weiss may identify new instances of multiple enrollment. &#8226; Some persons are related to each other. &#8226; In general, in this dataset for dbGaP, only a single specimen/record form a given person is included. Advances in laboratory testing techniques now permit innovative new uses for our linked research biospecimen repository. The ongoing focus of an interdisciplinary research program based on these cohorts relates subjects&#8217; diseases, behaviors, medical history, and outcomes with biological and exposure markers. Participants&#8217; use of various substances was ascertained on study enrollments, many serially over time. Quantitative frequency of use data, also sometimes sequential over time, were ascertained. Active ascertainment of outcomes is being conducted, including matching to mortality and cancer databases. Investigators interested in collaborations on specific outcomes (which is not part of this dbGaP dataset) or in the use of our stored specimens are encouraged to contact the principal investigator, Dr. Weiss. The processing of the genomic data was done in conjunction with NIDA, and in accordance with some longstanding data cleaning steps used by NIDA in the NIDA Genetics Consortium (NGC), a group to which we shall be contributing these data for collaborative analyses. Since there is the potential for these steps to introduce certain types of potential biases, we summarize these here. Under contract from NIDA, cryopreserved sera or plasma (-80 C) or cells (in liquid nitrogen) were used, with most stored having been stored for 30 to 40 years in our biorepository. In the case of serum or plasma, in which only (largely) cell-free DNA fragments were available, DNA was extracted and restored prior to amplification. Industry standard DNA amplification techniques were done on all samples prior to genotyping in accord with established protocols of the NIDA Genetics Consortium. Our genotype data were run and processed on the Illumina Infinium OmniExpress_v_1.3 array. This array has 714,238 SNPs, and was designed many years ago. There were 628 SNPs on the array that do not correspond to any chromosome position, and these were removed. Genotype data were submitted by NIDA&#8217;s contracted genotyping laboratory in six batches over time to NIDA&#8217;s contracted dbGaP data management group, which conducted quality control (QC) analyses. QC analysis included an assessment of batch effects on for five of the six batches. (One of the batches, with only 12 samples, was too small for QC analysis of batch effects.) Standard NIDA Genetic Consortium cleaning was performed. Samples with a call rate &lt;.85 were removed. Only one sample per person was retained. When more than one specimen was genotyped from one subject, only the sample with the higher call rate was retained (provided, of course, that that call rate was &#8805; 0.85). We have retained some people we know are related, including some found to have been related through genotyping; the pedigree file describes those relationships. In summary, key cleaning steps include: 1. Using PLINK to check gender discrepancy. 2. Using PREST-PLUS and KING (Kinship-based Inference for GWAS) to check relatedness. 3. Using PEDCHECK and PLINK to check/zero-out Mendelian error. 4. Using PLINK to perform sample QC, SNP QC, along with KING to perform chromosome X and chromosome Y QC. 5. SNP-QC: Batch-effect: 5 Batches were compared (one batch, with few samples, was not). These five batches were compared to each other in all ten possible pairs, one batch vs. another batch, examining SNP allele frequency discrepancies by population (from GRAF), Fisher Exact Allelic test, with the criterion of p&lt;5e-8 for removal. 6. SNP-QC: discordant SNPs in QC duplicates. Compared 25 QC duplicated samples with call rate &gt; 0.95, removed SNPs with 3+ discordance. 7. There were 1,056 SNPs that were monomorphic; these have been retained so they can be included in analyses in which our dbGaP data are combined with those from other cohorts (in the latter of which those SNPs may not be monomorphic). The final cleaned dataset submitted has 8,898 samples and 606,793 SNPs.

  Study phs002140

• National Cancer Institute (NCI) Genome Wide Association Study (GWAS) of Lung Cancer in Never Smokers

  A genomewide study of lung cancer in never smokers Abstract and specific aims In the United States, lung cancer incidence and mortality rates have been steadily declining over the past decade, following decline in the prevalence of tobacco smoking. However, lung cancer remains the leading cause of cancer death, killing more patients than breast, colon, and prostate cancers combined. Although tobacco smoke is the predominant risk factor for development of lung cancer, some patients develop the disease without a history of tobacco smoking. About 10 - 15% of all lung cancers occur in lilfetime never smokers. This figure will increase as the proportion of never smokers increases in the population. Even at present rates, lung cancer in never smokers, if considered a separate disease, is 6th to 8th top cause of cancer death. The growing number of never smokers in the USA and other countries emphasizes the importance of understanding the epidemiology and biology underlying lung cancer in this group. Genetic polymorphisms associated with the risk of lung cancer in never smokers are expected to overlap with those associated with the risk of lung cancer in ever smokers only partially. Epidemiological, molecular and clinical data suggest that molecular mechanisms of LC may differ in smokers and non-smokers, implying that lung cancer in never smokers is a different disease compared to the lung cancer in smokers. One can expect that there should be stronger genetic component in the control lung cancer in never smokers because effects of the genetic factors in never smokers are unmasked by the lack of tobacco smoke exposure. The genetic epidemiology of lung cancer in never smokers has not been well explored, largely because of difficulties in accruing the needed sample size for association studies. We propose a multicenter (total 14 sites from the US and Europe) genomewide association study of lung cancer in never smokers with the following specific aims: Aim 1: To identify candidate SNPs influencing risk for lung cancer in never smokers using Discovery sample. In the Discovery phase we will genotype 1256 Caucasian cases and 1365 age- and gender-matched never smoker controls using the Illumina Human660W-Quad platform. In addition, we will include in the analysis 284 cases and 175 matched controls already genotyped on the 610Quad platform. In this phase we will only include the study sites that have collected blood specimens (MDACC, Mayo Clinic, Karmanos Cancer Institute, The University of Liverpool Cancer research Centre, Institute of Cancer Research in Sutton, and Lunenfeld Research Institute in Toronto, Canada). All the samples will be sent to the independent lab for genotyping, to reduce site-specific technical artifacts. The final sample will consist of 1540 cases and 1540 controls matched by study site. Aim 2: To perform the second phase (validation) analysis of significant SNPs identified in aim 1 using an independent set of cases and controls. SNPs associated with risk at the significance level of 0.01 or below in the discovery set will be included in the replication phase. The proposed threshold guarantees an adequate power to retain SNPs with the typical effect size of 1.3. We plan to carry 6000-7000 SNPs for validation. The independent replication set will include 800 cases and 800 controls, mostly from sites that collected tissue (Mayo Clinic, Karmanos Cancer Institute, UT Southwestern) or buccal specimens (UCLA), but also blood samples (Imperial College London, University of Pennsylvana, German Cancer Research Center, Heidelberg, National Research Center for Environment and Health, Neuherberg, Carmel Medical Center, Haifa). We will then perform a joint analysis to test the significance of the SNPs identified in the first stage using a stringent critical p-value of 10-7. There will be 2340 cases and 2340 controls in the joint set. Based on our experience with GWAS in smokers and assuming that genetic component in lung cancer risk in never smokers can be higher than genetic component in smokers, we expect to identify about 5-10 candidate regions associated with lung cancer risk in never smokers. Aim 3: To identify and explore pathways associated with the risk of lung cancer in never smokers. Results of the number of studies on the molecular mechanisms and drug response suggest that lung cancer in never smokers is a different disease and different pathways will be associated with lung cancer risk in non-smokers and smokers. To identify pathways and molecular functions associated with lung cancer risk in never smokers we will apply Ingenuity and DAVID bioinformatics tools. We will use at least 300 top candidate genes identified in joint and discovery analysis. The reason why we select rather large number of candidate genes for functional annotation is two-fold: 1. Both algorithms are looking for enrichment of pathways and function by most significant genes and they produce statistically robust results only when number of genes is relatively high. 2. Despite the fact that this study will be largest possible for never smokers we still are underpowered to detect SNPs with relatively small effect size. But though those SNPs will not reach genome wide level of significance they will tend to be on the top of the list. In other words genes from the gray zone (significant on individual level and non-significant for genome wide level) are expected to be enriched by true discoveries. True discoveries are likely to be associated with limited number of pathways / functions while false positives are expected to be uniformly distributed across functions and pathways. Therefore significant clustering of the gene to a given function will suggest that that those genes are true discoveries. This is the first GWAS aiming at identifying the genetic control of susceptibility to lung cancer in Caucasian never smokers. We will combine the available resources from the multiple sites to achieve the sample size sufficient for this study. The study will identify genetic architecture of the predisposition to the lung cancer in never smokers.

  Study phs000634

• Prader-Willi Syndrome and Early-onset Morbid Obesity Natural History Protocol

  This observational study will involve the comprehensive assessment of the medical, behavioral and nutritional history and the clinical features of individuals with Prader-Willi syndrome and individuals with features of PWS-like/EMO. A blood sample will be obtained from the participants in order to create a DNA and RNA repository. In those PWS participants with a known deletion a blood sample will be collected for DNA in order to perform array comparative genome hybridization (aCGH) microarray or Methylation-Specific Multiplex Ligation-dependent Probe Amplification (MS-MLPA) studies to characterize the extent of the deletion. DNA samples will be sent to the Consortium Center at Baylor College of Medicine for the performance of the aCGH studies and to either Dr. Driscoll or Dr. Butler lab for MS-MLPA analysis. No drugs or treatments will be administered through this protocol. Our goal is to enroll as many participants as possible over a 8 year period. Currently we have enrolled 297 total subjects of which 265 have PWS and 32 EMO. Our original goal was to have enrolled over the first 5 years a clinical database of 200 individuals with PWS and 100 with EMO. We have exceeded expectations in PWS enrollment, but have fallen short in the enrollment of individuals with EMO. Therefore, we would like to reset our goals. We now propose to have an enrollment goal of 400 total subjects of which 350 will have PWS and 50 will have EMO. Eligible participants will be initially enrolled in the natural history study from the 4 clinic sites currently involved with PWS (University of Florida, Kansas University Medical Center, Vanderbilt University and University of California at Irvine). Participants assessed will serve to provide a background of information and a willing cohort for future interventional studies. All participants will have had standard genetic testing at a commercial laboratory prior to entry into the study. The PWS participants will be classified into one of the 3 main molecular classes (deletion, UPD and ID). The EMO participants will have had laboratory testing ruling out PWS and chromosomal aneuploidy (e.g., trisomy 21, Klinefelter syndrome, etc.). Going forward we feel it is important that all newly recruited EMO subjects have an aCGH as part of their evaluation so we can identify those with discrete deletions, duplications or copy number variants (CNV) and compare them to those without aCGH abnormalities. The participants will be assessed annually (0-3 years of age) or biennially (&#62; 3 years) in the Clinical Research Center (or equivalent facility) using standardized protocol including photographs for documentation purposes to compare the evolution of the clinical phenotype.

  Study phs000575

• Genome-Wide Association Studies of Prematurity and Its Complications (African American)

  Preterm delivery resulting in the birth of a premature infant is a complex problem with a devastating impact on individuals, families and society. The prevalence of preterm birth has increased steadily in developed countries over the last 20 years and more than three million children die of preterm birth worldwide each year. Despite the importance of the problem and its disproportionate occurrence in poor and minority populations, the underlying causes have been difficult to identify. Spontaneous preterm labor has as its suspected triggers infection, stress, poor nutrition and inherited factors. The single best predictor for preterm delivery is a previous preterm birth. Studies of twins and of recurrences within families provide evidence that genetic factors underlie a substantive component of the risk for prematurity. One major challenge in studying genetic factors in prematurity is that the risk case is not truly established. The genetic risk could reside either in the mother and her uterus or in the infant/placenta. Identification of genetic factors in the mother and/or infant could provide insights into identifying relevant environmental covariates that may be more amenable to rapid interventions but difficult to find using standard epidemiology alone. A comprehensive genome-wide association study (GWAS) is the ideal way to identify those genes that would not be suspected based on our current understanding of the biology of parturition. We are using 2200 African American samples with term or preterm labor. A subset of these (~800) are infant samples recruited by the Neonatal Research Network as part of a study on cytokines and infection in extremely low birth weight infants (Schelonka RL, et al., 2011. PMID: 21145756). Therefore, this group consists of infants &lt;1,000 grams with clinical outcome data for the infant allowing study of the genetic contributors for not only preterm birth but also the complications that often accompany preterm birth. The result will enable a better understanding of the biology of parturition and suggest environmental modifications that can prolong gestations to improve neonatal and adult outcomes. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to preterm birth through large-scale genome-wide association studies of African-American cases and controls from multiple sites in the United States. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000353

• Inflammatory Bowel Disease Exome Sequencing Study

  The Broad Institute and Massachusetts General Hospital (MGH) are launching a new initiative to perform large-scale exome sequencing in inflammatory bowel disease (Crohn&#39;s disease and ulcerative colitis). Given the considerable unmet therapeutic need in inflammatory bowel disease (IBD), the recent emergence of rapid and efficient genome sequencing technologies and the compelling evidence for the role of genetics in these disorders have motivated the founding of a collaborative sequencing effort geared toward the discovery of high-impact genetic variants influencing IBD risk that can serve as guides to future therapeutic development and diagnostic tools. The initiative will be directed by experienced IBD researchers from MGH and the Broad Institute but aims to develop a collaborative exome sequencing network that will partner with researchers worldwide. Two large pilot exome sequencing projects are being launched immediately as part of this initiative based on established genetic study designs that enrich for the discovery of rare, high-impact risk and protective variants. 1) Early-onset pediatric IBD: a major focus of the work will surround full-exome sequencing of the earliest-onset childhood IBD cases. It has long been recognized in many diseases that penetrant genetic risk factors are much more likely to be found in cases with unusually early onset. The Broad Institute has completed more than 50,000 exomes in the past two years; and their technical expertise in generating and processing such sequencing data, along with population genetic variation patterns from these experiments, will provide a foundation for obtaining the best outcome in the interpretation of these cases. Samples with IBD onset before 6 years of age - and in some cases going up to age 10 - collected by IBD researchers around the world are welcomed for inclusion in this study. 2) Adult-onset IBD case-control study: Genetic mapping has provided dramatic insights into IBD pathogenesis in recent years, but a substantial amount of heritability - in particular the role of strong-acting rare mutations - is yet to be elucidated. To deliver those insights, IBD cases with an unusually low burden of known genetic risk factors (particularly emphasizing those with positive family history and/or from isolated or enriched populations) will be contrasted with population controls that have an extremely high burden of known IBD risk factors, enhancing discovery of critical protective variants that may provide the best clues for therapeutic development. This project will also be initiated as an international collaboration, particularly among researchers with sample sets with Immunochip genotyping that will enable the immediate genetic identification of these enriched target populations. This project will be supported by the NHGRI Large-Scale Sequencing Program, with all generated data made publicly available to the research community through standard NIH databases.

  Study phs001076

• Birth Defects: Moebius Syndrome and Related Congenital Facial Weakness Disorders

  It is estimated that about 1 in every 33 infants in the United States is born with a birth defect. Among these, the subset of birth defect syndromes associated with facial weakness and lack of facial expression can have profound implications for social interactions and psychosocial development. Moebius syndrome is defined by congenital and non-progressive facial weakness and limited eye abduction, and most cases are believed to result from dysfunction of cranial nuclei/nerves VI and VII. Although rare, this syndrome causes significant impairment because of facial weakness and associated intellectual disabilities, autism, hearing loss, difficulty swallowing and breathing, peripheral neuropathy, muscle hypotonia, heart defects, chest wall abnormalities, and limb malformations. The phenotypic spectrum and the associated genetic and environmental factors underlying Moebius syndrome are poorly understood. The goal of this research is to identify causative gene mutations for Moebius syndrome and related conditions, such as Moebius-Poland or -Robin sequence, hereditary congenital facial paresis (HCFP), and oculoauriculovertebral dysplasia (Goldenhar syndrome). This proposal builds on an ongoing collaboration among researchers at Mount Sinai Medical Center, Boston Children's Hospital, NHGRI intramural program, and the Moebius Syndrome Foundation, but aims to greatly extend that work by collaboration with investigators in the NIH Clinical Center (CC). Our groups have already defined a new autosomal dominant syndrome with Moebius syndrome, Kallmann syndrome, cyclic vomiting resulting from a TUBB3 E410K substitution. We have also identified a new autosomal recessive Moebius-related syndrome with bilateral facial palsy, hearing loss, and strabismus resulting from a HOXB1 R207C substitution. Through collaborative efforts of these extramural teams with intramural investigators, whole exome sequencing (WES) has been conducted for four families with Moebius-like features, and data analyses are ongoing. The overall goal of this new grant application will be to conduct extensive phenotype analysis on approximately 24 families per year with Moebius and other undefined syndromes with facial weakness. Studies to be conducted at the CC include neurology, psychiatry, neurocognitive, rehabilitation medicine (muscle strength, speech/language pathology), ophthalmology, audiology, and genetics evaluations; autism screening; electromyography, nerve conduction, and blink reflex studies; videoscopy of quantitative eye movement recordings; 3D-CT craniofacial imaging; MRI of the brain, orbit, internal auditory canals, posterior fossa including brain diffusion tensor imaging for tractography; and genetic counseling. Jointly with the extramural teams, WES analysis and variant confirmation will be performed. A more comprehensive definition of the phenotypic and genotypic spectrum of these birth defects will have a significant impact on our understanding of the molecular pathways underlying dysmorphologies, cranial nerve development, and more common childhood disorders such as autism. Thus, this project will lead to strategies for prevention and treatment of birth defects.

  Study phs001383

• Human Microbiome Project Demonstration Study of Cutaneous Microbiome in Psoriasis

  Psoriasis, a highly prevalent disease of humans of unknown cause, is a chronic inflammatory disorder primarily involving skin, with distinctive clinical characteristics. With the newly developed tools that facilitate microbiome research, it now is possible to assess whether the cutaneous microbiome plays a role in the pathogenesis of this disorder. Preliminary data from our studies suggest that the cutaneous microbiome in psoriasis is complex and possibly different from normal. To deal with this complexity, we propose to examine the cutaneous microbiome in relation to psoriasis with explorations at several taxonomic and informatic levels. Our overall objective is to examine how changes in the normal cutaneous microbiome contribute to the pathogenesis of psoriasis. Since causality is complex and often difficult to prove, and beyond the scope of this RFP, our overall hypothesis is that there are alterations in the cutaneous microbiome in areas of skin affected by psoriasis in comparison with the range observed in clinically unaffected areas, or in healthy persons. We also hypothesize that the characteristics of the microbiome may affect clinical responses to the immunomodulatory agents used to treat psoriasis. An alternative hypothesis is that effective treatment of psoriasis with systemic immunomodulatory agents will not substantially affect the disordered microbial ecosystem. Such observations would provide evidence for the roles of the microbiota in this disorder. Since an important consideration in microbiome research is the optimal level (e.g. phylum, genus, species, strain, gene) at which to examine a scientific question, and we are not yet certain what are the optimal levels for psoriasis, this also will be examined. Our studies of psoriasis should allow development of both approaches and tools that will have general utility for Microbiome research. To test our hypothesis, we propose the following specific aims: 1) To understand the cutaneous microbiome species composition overlaying psoriatic lesions; 2)To investigate differences in metagenome content for psoriatic lesions compared to normal skin; 3) To identify differences in the transcriptional profiles of the microbiome and the host between normal skin and psoriatic lesions using high-throughput sequencing; and 4) To estimate the effects of systemic immunomodulatory therapy for psoriasis on microbiome composition. In total, these studies should help us understand the role of the microbiome in psoriasis pathogenesis. We sought to characterize and compare the cutaneous microbiota of psoriatic lesions (lesion), unaffected contralateral skin from psoriatic patients (normal), and similar skin loci in matched healthy controls (control) in order to discern patterns that govern skin colonization and their relationship to clinical diagnosis. Using high-throughput 16S rRNA sequencing, we assayed the cutaneous bacterial communities of 51 matched triplets and characterized these samples using community data analysis techniques.

  Study phs000251

• Analyzing Somatic Mutagenesis in Systemic Sclerosis

  The study was undertaken with the aim of estimating differences in the mutational burden in lung tissues from healthy vs systemic sclerosis (SSc) patients. SSc is an auto-immune disease, with patients often developing cancer. However, the etiology of this phenomenon is poorly understood. As such we sought to uncover potential molecular mechanisms prevalent within SSc tissues that might be potentiating cancer. Samples were randomly collected from individuals from two broad ethnic groups, European (EA) and African American (AA). In total, samples were obtained from 7 individuals belonging to the EA group and 4 individuals from the AA group. For this study, ethnicity was not included as a confounding variable, given that the data set has roughly comparable number of individuals from both ethnicities for the healthy and SSc cohorts. Study participants ranged in age from 37-63 years old. Median age (52 years old) of healthy individuals was comparable to the SSc cohort (~48 years old). Participants were self-described as current/former smokers or non-smokers. Lung tissues were obtained under a protocol approved by the University of Pittsburgh Institutional Review Board. Written informed consent statements were provided to the University of Pittsburgh IRB by the study participants/patients. Sample sizes were determined by the ability of bulk lung fibroblast tissues to stably propagate in culture until single cell clones could be obtained. Samples for which sufficient cell density could not be achieved for downstream high-depth sequencing analysis were excluded from the study and are not reported. Five or greater than 5 samples are collected from each cohort including healthy and SSc patients. Subjects were allocated to two basic cohorts based on diagnosis at the time of explant. Participants with no reported lung anomalies were allocated to the "healthy" cohort, whereas individuals with reported lung abnormalities, primarily SSc with pulmonary fibrosis and/or hypertension were allocated to the "SSc" cohort. All tissue samples were cultured, propagated and processed in identical fashion without requiring individual-specific protocols. Additionally, equivalent amounts of DNA were used for preparing sequencing libraries from both cohorts, and all samples were sequenced using Illumina whole genome sequencing (WGS) and analyzed using identical computational pipelines. Our study primarily discovered that there is a significantly higher burden of somatic mutations in SSc samples, compared to healthy samples. We observed an overall increase in a range of mutations in genomes of SSc patients, including single base substitutions, indels, structural variants and copy number alterations. Additionally, we uncovered a novel mutation signature associated with SSc samples, hinting at the activity of POLH in these samples, and highlighting the potential nexus between inflammation, DNA damage, and cancer.

  Study phs003700

• Prevention and Early Treatment of Acute Lung Injury (PETAL) Acetaminophen in Sepsis: Targeted Therapy to Enhance Recovery (ASTER) (PETAL ASTER-BioLINCC)

  Data Access NOTE: Please refer to the &#8220;Authorized Access&#8221; section below for information about how access to the data from this accession differs from many other dbGaP accessions. Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from PETAL ASTER include plasma, urine and whole blood. Please note that use of biospecimens in genetic research is subject to a tiered consent. Objectives: To determine whether acetaminophen increases days alive and free of organ dysfunction in sepsis participants compared with placebo.Background: Acetaminophen (paracetamol) has many effects that can be beneficial in sepsis treatment, including analgesia, antipyresis, cyclooxygenase-2 inhibition, as well as a potent and specific hemoprotein reduction that can block hemoglobin-induced oxidation of lipids and other substrates. The majority of sepsis participants experience elevated circulating cell-free hemoglobin levels, which is associated with development of organ dysfunction including acute respiratory distress syndrome (ARDS) and death.Acetaminophen has been found in observational studies to be associated with improved survival in critically ill sepsis participants with elevated plasma cell-free hemoglobin, and small clinical trials have had positive sepsis participant outcomes such as reduced plasma biomarkers of lipid peroxidation and improved kidney function. However, a large, randomized trial of acetaminophen administration for treatment of fever in participants with suspected infection did not show a mortality benefit. The NHLBI PETAL Network initiated ASTER as a larger phase trial to examine the utility of plasma cell-free hemoglobin level as a biomarker for future sepsis trials and whether acetaminophen would increase the number of days alive and free of organ support for participants with sepsis and respiratory or circulatory organ dysfunction. Participants: A total of 447 participants were enrolled and randomized, 227 to the acetaminophen arm and 220 to the placebo arm. Data from 40 participants who were randomized to the Vitamin C arm, which was stopped early (see Design section below), is also included.Design: ASTER was a phase 2b multicenter, randomized, double-blind trial. The study originally had a 3-arm platform trial in which participants were randomized 1:1:1 to treatment with intravenous acetaminophen, vitamin C, or a common placebo. The vitamin C arm of the trial was stopped after enrolling 79 participants due to external clinical trial data for vitamin C.Participants randomized to the acetaminophen arm received acetaminophen at the dose of 1 g in 100 mL diluent (or 15 mg/kg if actual body weight was Conclusions: Intravenous acetaminophen was considered to be safe but did not significantly improve days alive and free of organ support in critically ill sepsis participants. There was no significant interaction between cell-free hemoglobin levels and acetaminophen.

  Study phs003900

• Exploratory novel biomarker and resistance mechanism of milademetan, an MDM2 inhibitor, in MDM2 amplified intimal sarcoma from an open-label phase 1b/2 trial ���NCCH1806/MK004���

  Amplified Murine double minute 2 (MDM2) is found in > 70% of intimal sarcoma, known as one of the ultra-rare sarcomas. Milademetan (DS3032, RAIN-32) is a novel, specific, small-molecule MDM2 inhibitor that disrupts MDM2 and the tumor suppressor protein p53 interactions in tumor cells. We conducted a phase 1b/2 trial (Trial registration No: JMA-IIA00402) in patients with amplified MDM2 wild-type TP53 intimal sarcoma as a sub-study under the nationwide large registry for rare cancers in Japan (MASTERKEY Project). Eleven patients were enrolled, and ten were included in the efficacy analysis. Two (20%) patients had durable responses for > 15 months. Milademetan provided clinical benefits in patients with amplified MDM2 intimal sarcoma. Predictive biomarkers other than amplified MDM2 and acquired resistance mechanisms for milademetan are unknown. Whole-exome and RNA sequencing analyses of pre-treatment tissue samples were conducted to identify determinants of response. Genomic alterations were analyzed for 10 patients, and gene expression was analyzed for 9 patients using their pre-treatment tissue samples. Targeted sequencing of cell-free DNA (cfDNA) samples (liquid biopsy) was also conducted sequentially at three points [before treatment with milademetan (baseline), at Cycle 2 Day1, and at the time of disease progression] to identify determinants of response and resistance. From whole-exome and RNA sequencing analyses of pre-treatment tissue samples, we could not find any molecular pathways associated with the anti-tumor activity of milademetan. Focusing on 8 genes (CDK4, CDKN2A, CDKN2B, EGFR, ERBB3, MDM2, PDGFRA, TP53) known to be frequently affected in intimal sarcoma and 10 genes (AKT1, ATM, BBC3, CDKN1A, CDKN1C, CHEK2, MDM4, PMAIP1, PPM1D, TWIST1) reported to be associated with MDM2 inhibitor responses; we found that anti-tumor activity correlated with amplified TWIST1 (p-value = 0.028) and negatively with CDKN2A loss (p-value = 0.071). Eight of the 10 patients had their cfDNA collected sequentially at baseline, at Cycle 2 Day1 and disease progression; however, one did not consent to the exploratory analysis study using cfDNA, and one had cfDNA collected at baseline but not at disease progression due to ongoing treatment. Of the eight patients, TP53 mutations in cfDNA were detected in one and five patients at baseline and disease progression, respectively. The cfDNA allele frequency of TP53 mutations increased with disease progression. CDKN2A loss and amplified TWIST1 could be associated with the anti-tumor activity of milademetan in patients with MDM2 amplified intimal sarcoma. Acquired TP53 mutations were detected in sequential liquid biopsies as loss-of-function mutations, and these TP53 mutations might compromise the anti-tumor activity.

  Study JGAS000619

• A small cell lung cancer genome reports complex tobacco exposure signatures

  Cancer is driven by mutation. Worldwide, tobacco smoking is the major lifestyle exposure that causes cancer, exerting carcinogenicity through 60 chemicals that bind and mutate DNA. Using massively parallel sequencing technology, we sequenced a small cell lung cancer cell line, NCI-H209, to explore the mutational burden associated with tobacco smoking. 22,910 somatic substitutions were identified, including 132 in coding exons. Multiple mutation signatures testify to the cocktail of carcinogens in tobacco smoke and their proclivities for particular bases and surrounding sequence context. Effects of transcription-coupled repair and a second, more general expression-linked repair pathway were evident. We identified a tandem duplication that duplicates exons 3-8 of CHD7 in-frame, and another two lines carrying PVT1-CHD7 fusion genes, suggesting that CHD7 may be recurrently rearranged in this disease. These findings illustrate the potential for next-generation sequencing to provide unprecedented insights into mutational processes, cellular repair pathways and gene networks associated with cancer.

  Study EGAS00000000051