2965 results for "愛の戦士完全破壊2-re-imagination-version-鞭首絞め・電撃・ヘソか"

in 25.93 milliseconds.

• scRNAseq of ALS patients

  The data set contains information from 9 individuals (5 ALS + 4 controls) using single cell RNA sequencing in combination with TCR V(D)J sequencing to study the immune profile of the central nervous compartment (CSF). Sequencing was done using 10x Genomics platform (5’ scRNAseq & V(D)J Reagent Kits v1.1). 5P and TCR libraries were then pooled and for sequencing on the NovaSeq sequencer. Provided files are in .fastq.gz format and per individual four files are available (Read 1&2 and Lane 1&2).

  Dataset EGAD00001009623

• BLUEPRINT release August 2016, ChIP-Seq for CD3-negative, CD4-positive, CD8-positive, double positive thymocyte, on genome GRCh38

  ChIP-Seq data for 2 CD3-negative, CD4-positive, CD8-positive, double positive thymocyte sample(s). 7 run(s), 5 experiment(s), 5 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002369

• BLUEPRINT release August 2016, ChIP-Seq for mesenchymal stem cell of the bone marrow, on genome GRCh38

  ChIP-Seq data for 2 mesenchymal stem cell of the bone marrow sample(s). 16 run(s), 14 experiment(s), 14 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002381

• BLUEPRINT release August 2016, ChIP-Seq for effector memory CD4-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 2 effector memory CD4-positive, alpha-beta T cell sample(s). 8 run(s), 7 experiment(s), 7 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002514

• Processed microarray gene expression data from NPC samples_Cohort 2

  We retrospectively collected 150 non-metastatic, pretreatment, formalin-fixed, paraffin-embedded (FFPE) nasopharyngeal carcinoma (NPC) samples as validation cohort 1. Also, we prospectively collected 32 FFPE samples from NPC patients enrolled in a trial evaluating anti-PD-1 antibody as validation cohort 2. Total RNA was extracted and hybridised to an Affymetrix HTA 2.0 microarray. In this study, we investigated the immune status of the tumour microenvironment (TME) based on gene expression profiles to classify NPC into biologically distinct immune subtypes, and clarify their associations with prognosis and immunotherapy response.

  Dataset EGAD00001006274

• BLUEPRINT release August 2015, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 2 effector memory CD8-positive, alpha-beta T cell sample(s). 10 run(s), 10 experiment(s), 10 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001472

• Single-Cell DNA Methylation Profiling via scTAMseq

  This dataset comprises single-cell DNA methylation and nuclear genotyping profiles of 7 total bone marrow aspirates (donors A.1-A.7) and 5 CD34+-enriched bone marrow samples (donors B.1-B.5) generated using the scTAMseq protocol. Sequencing was performed on the Mission Bio Tapestri platform, targeting a panel of 448 CpGs to assess epigenetic patterns and We also included 147 genomic regions commonly mutated in clonal hematopoiesis and 20 regions targeting chromosome Y. Samples B.1 & B.5, B.2 & B.4 and A.2 & A.5 were, in pairs, multiplexed into single tapestri lanes.

  Dataset EGAD00001015498

• DAC of The polarity and specificity of antiviral T lymphocyte responses determine susceptibility to SARS-CoV-2 infection in cancer patients and healthy individuals

  Dac EGAC00001002517

• EGAD00010000232

  WTCCC2 samples from Type 2 Diabetes Cohort

  Dataset EGAD00010000232

• EGAS00001001728_REL-2016-04

  HipSci - Embryonic Stem Cells - Methylation Array - April 2016

  Dataset EGAD00010000909

• EGAS00001001730_REL-2016-04

  HipSci - Embryonic Stem Cells - Expression Array - April 2016

  Dataset EGAD00010000910

• chr6_allsamples_imputated

  Chromosome 6 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002539

• chr11_allsamples_imputated

  Chromosome 11 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002538

• chr14_allsamples_imputated

  Chromosome 14 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002527

• chr7_allsamples_imputated

  Chromosome 7 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002528

• chr13_allsamples_imputated

  Chromosome 13 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002531

• chr4_allsamples_imputated

  Chromosome 4 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002529

• chr5_allsamples_imputated

  Chromosome 5 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002536

• chr10_allsamples_imputated

  Chromosome 10 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002530

• chr21_allsamples_imputated

  Chromosome 21 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002532

• chr16_allsamples_imputated

  Chromosome 16 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002534

• chr20_allsamples_imputated

  Chromosome 20 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002537

• Transcriptome of (peripheral blood), from donor Sample A, replicate 2 Freezing replicates

  Freezing replicates

  Dataset EGAD00001010085

• Transcriptome of (peripheral blood), from donor Sample A, replicate 2 Technical replicates

  Technical replicates

  Dataset EGAD00001010083

• chr9_allsamples_imputated

  Chromosome 9 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002535

• chr12_allsamples_imputated

  Chromosome 12 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002517

• chr22_allsamples_imputated

  Chromosome 22 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002518

• chr8_allsamples_imputated

  Chromosome 8 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002519

• chr18_allsamples_imputated

  Chromosome 18 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002520

• chr3_allsamples_imputated

  Chromosome 3 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002521

• chr19_allsamples_imputated

  Chromosome 19 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002524

• chr1_allsamples_imputated

  Chromosome 1 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002525

• sc-RNA-Seq soft tissue sarcoma tumoroid biobank

  Dataset EGAD00001009002

• HipSci - Embryonic Stem Cells - Exome Sequencing - April 2016

  HipSci - Embryonic Stem Cells - Exome Sequencing - April 2016

  Dataset EGAD00001001999

• Illumina_WXS_CONT

  Whole-exome sequencing on Illumina HiSeq2000/2500 of normal colon control tissue

  Dataset EGAD00001002094

• Host Response to Respiratory Infections

  In this study, we describe a strategy to identify undiagnosed respiratory viral infections through screening nasopharyngeal (NP) swabs for the chemokine CXCL10, which is highly induced in the nasopharynx as part of the host antiviral response. Using this strategy, we conducted two screens: first, we screened NP swabs collected at Yale New Haven Hospital (YNHH) in January 2017 from patients with undiagnosed respiratory viral infections and identified patients infected with seasonal coronaviruses and the relatively uncommon influenza C virus. Second, we screened samples from the beginning of March 2020 in the weeks before YNHH tested for SARS-CoV-2 and identified four patients that had undiagnosed SARS-CoV-2 infections. Sequences uploaded as part of this study include samples with rhinovirus, human coronavirus NL63, and 8 samples as part of the January 2017 screen for undiagnosed respiratory viral infections.

  Study phs002442

• Natural History of and Genetic Modifiers in Spinocerebellar Ataxias

  Spinocerebellar ataxias (SCA) are genetic neurological diseases that cause imbalance, poor coordination, and speech difficulties. There are different kinds of SCA and this study will focus on types 1, 2, 3, and 6 (SCA 1, SCA 2, SCA 3, also known as Machado-Joseph disease and SCA 6). The diseases are rare, slowly progressive, cause increasingly severe neurological difficulties and are variable across and within genotypes. The purpose of this research study is to bring together a group of experts in the field of SCA for the purpose of learning more about the disease. The research questions are: How does the disease progress over time? What are the best ways to measure disease progression? Do some genes, other than the gene that is abnormal in the SCA disease, have any effect on the way the disease behaves?

  Study phs001332

• Genomics characterization of primary central nervous system lymphoma

  Primary central nervous system lymphoma (PCNSL) is a rare malignancy confined to the central nervous system (CNS). Despite low tendency of systemic dissemination, its prognosis is poor with median overall survival of 2?4 years. Due to its rarity, knowledge of genomic alterations underling PCNSL pathogenesis is severely limited. Here we performed whole-exome sequencing on 44 PCNSL and paired normal specimens, revealing high penetrance of nonsynonymous somatic mutations in PIM1 (95.5%), BTG2 (88.6%) and MYD88 (81.8%). Interestingly we found oncogenic mutations in GRB2, and this effect was cancelled when transformed cells are treated with inhibitors to downstream kinases MAP2K1/2. Further, when tumor cells carries MYD88 mutations, the same mutations were also found at low frequency in peripheral blood mononuclear cells (PBMNC), implying that MYD88 mutation?positive precancerous cells originate outside CNS and develop to lymphoma with additional genetic hits that fit the CNS environment.

  Study JGAS000021

• Study2_FiberBlend

  SOMAscan plasma proteome datasets generated from participants consuming the fiber blend snack prototype (study 2)

  Dataset EGAD00010002132

• Dataset that contains analyses of submission 6

  This is the description of analyses dataset of submission 6

  Dataset EGAD50000001107

• RCIDIBAPS001

  DAC created by the Research Computing platform of IDIBAPS for the submission of 2 WES (Whole exome sequencing) samples provided by Sergi Castellví-Bel

  Dac EGAC50000000466

• Illumina_WGS_CONT

  Whole-genome sequencing on Illumina HiSeq2000/2500 of normal colon control tissue

  Dataset EGAD00001002083

• HipSci - Embryonic Stem Cells - RNA Sequencing - April 2016

  HipSci - Embryonic Stem Cells - RNA Sequencing - April 2016

  Dataset EGAD00001002000

• Framingham Heart Study-Cohort (FHS-Cohort) - Imaging

  The Framingham Heart Study (FHS) is a population-based, observational cohort study initiated in 1948 to prospectively investigate the determinants of cardiovascular disease to guide public health prevention. The FHS began by recruiting an Original Cohort of 5,209 men and women between the ages of 30 and 62 from the town of Framingham, Massachusetts, who had not yet developed overt symptoms of cardiovascular disease or suffered a heart attack or stroke. The Original cohort Exam 1 took place between 1948 and 1953. Since that time the cohort has had a total of 32 biennial exams (ending in 2014) and event follow-up through 2022. In 1971, the FHS added the Offspring cohort, comprising 5124 children whose parents were enrolled in the Original cohort and the spouses of the children. This cohort on average has been examined every three to four years. However, there was an eight year gap between Exam 1 and Exam 2 and a seven year gap between Exam 7 and Exam 8. The latest exam (10) was completed in 2022. In 2002, the transgenerational FHS design was facilitated with the recruitment of 4095 children of the Offspring cohort (Third Generation), and 103 spouses of the Offspring who were not previously enrolled in the study (New Offspring Spouses, NOS). These cohorts have completed 3 exams through 2019. To reflect the changing demographic characteristics of the greater Framingham community, the FHS additionally recruited and enrolled 2 cohorts comprising racial and ethnic minority groups, termed Omni-1 and Omni-2, (n = 506 and 410, respectively) in 1995 and 2002, respectively. These cohorts included individuals of African American, Hispanic, Asian, Indian, Native American, and Pacific Islander descent. The OMNI-1 cohort have completed 5 exams through 2022. The OMNI-2 cohort have completed 3 exams through 2019. Data available for request include Echocardiogram images, available from the following exams in each cohort. Original Cohort: exams 18-32; Offspring cohort: exams 3-10; Third Generation, NOS and OMNI-2 cohorts: exams 1-3; OMNI-1 cohort: exams 1-5.CT image data at one or two timepoints were added for 4427 participants in the offspring, third generation, and OMNI1 and OMNI2 cohorts.Summary level phenotypes for the Framingham Cohort study participants can be viewed at the top-level study page phs000007 Framingham Cohort. Individual level phenotype data and molecular data for all Framingham top-level study and substudies are available by requesting Authorized Access to the Framingham Cohort study phs000007.

  Study phs003593

• RNA-seq of longitudinal human blood and nose swab samples from a controlled human infection experiment with SARS-CoV-2 virus

  Controlled human infection experiments enable longitudinal profiling of immune responses to a pathogen. 36 healthy volunteers aged 18-29 years, with no evidence of previous infection or vaccination, were inoculated with SARS-CoV-2 virus and quarantined for 14 days. Blood samples for RNA sequencing were collected into PAXgene tubes before virus challenge, 6 hours after challenge, daily thereafter for 14 days and on day 28. Mid-turbinate nose swabs for RNA sequencing were collected before virus challenge, and on days 1, 3, 5, 7, 10 and 14 after challenge, preserved in RNAprotect.

  Study EGAS50000000667

• SARS-CoV-2 host genetics and COVID-19 outcomes in admixed Brazilians with extreme phenotypes

  COVID-19 comprises clinical outcomes of SARS-CoV-2 infection and is highly heterogeneous, ranging from asymptomatic individuals to deceased young adults without comorbidities. There is growing evidence that host genetics play an important role in COVID-19 severity, including inborn errors of immunity, age-related inflammation and immunosenescence. This study proposes a strategy of subject’s ascertainment based on phenotypic extremes to take part in genomic studies and elucidate intrinsic risk factors involved in COVID-19 severe outcomes. Different cohorts were collected and submitted to Next-Generation Sequencing (Whole exome and whole genome sequencing).

  Study EGAS00001006376

• Single-cell gene expression data from CD8+ T cells from two Austrian COVID19 patients stimulated with wildtype and mutant SARS-Cov-2 peptides

  The data contains single-cell gene sequencing data (10x Genomics) from FACS-purified CD8 T lymphocytes from two Austrian patients. The cells were stimulated with one MHC class I peptides obtained from a common (wild type) variant and an emerging mutant variant of the SARS-Cov-2 virus. Then the samples were multiplexed using hashtag oligos. We provide the raw and aligned sequence data for: i. The single-cell experiments ii. The PCR-amplified samples for enrichment of the hashtag oligo multiplexing barcodes iii. The PCR-amplified samples for enrichment of the T Cell Receptor (TCR) VDJ region for immuno-profiling. The samples and libraries were processed and obtained in collaboration between St. Anna Children's Cancer Research Institute (CCRI), CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, and the Medical University of Vienna. The cell barcodes and processed data has been submitted to the GEO database with GEO accession GSE166651.

  Dataset EGAD00001006995

• Whole genome sequencing dataset from: Biased allelic expression in human primary fibroblast single cells.

  Dataset EGAD00001001083

• Batch1-2_Genotypes_PostQC

  Combined genotypes for Batch 1 and 2 after quality control. All individuals included in analyses.

  Dataset EGAD00010002127

• RNAseq for 8 PDX

  RNAseq for #111, #1177, #206, #201, #29, #931, WO-19, WO-2

  Dataset EGAD50000000116

• Single cell RNAseq of PBMC in chimeric twins

  scRNAseq data generated with 10x genomics

  Dataset EGAD50000000132

• RCRF release 1

  Rare Cancer Research Foundation: leiomyosarcoma, large cell neuroendorcrine carcinoma, clear cell carcinoma

  Dataset EGAD00001010199

• McGill EMC Release 4 in tissue "kidney"

  McGill EMC Release 4 in tissue "kidney"

  Dataset EGAD00001001287

• ChIPseq data

  ChIPseq data for H3K4me1 and H3K9me3 in HT29; H3K4me1, H3K27me3, H3K9me3, H3K36me3 in LoVo.

  Dataset EGAD00001003258

• EGAS00001001730_REL-2016-04

  HipSci - Embryonic Stem Cells - Genotyping Array - April 2016

  Dataset EGAD00010000911

• MB_450k_methylation

  450k methylation arrays of primary and relapse tumor of a single case of sonic hedgehog medulloblastoma with Li-Fraumeni syndrome

  Dataset EGAD00010002370

• Short read sequencing dataset of pathogen-stimulated PBMCs

  QuantSeq 3'-mRNAseq. 5 donors, 4 stimuli, 2 time points

  Dataset EGAD50000000012

• HipSci - Retinitis Pigmentosa - RNA Sequencing - July 2017

  HipSci - Retinitis Pigmentosa - RNA Sequencing - July 2017

  Dataset EGAD00001003542

• RNA-seq from normal human tissues

  RNA-seq from normal human tissues (2 x 75 bp)

  Dataset EGAD00001001057

• HipSci - Retinitis Pigmentosa - Exome Sequencing - July 2017

  HipSci - Retinitis Pigmentosa - Exome Sequencing - July 2017

  Dataset EGAD00001003527

• RNA-seq from normal human tissues

  RNA-seq from normal human tissues (2 x 250 bp)

  Dataset EGAD00001001922

• UK10K_RARE_SIR UK10K_EXOME_EXTRAS

  UK10K_RARE_SIR UK10K_EXOME_EXTRAS

  Dataset EGAD00001000757

• GWAS membranous nephropathy Stanescu et al., 2011 UK cohort, chr2 region of interest, imputed

  334 biopsy-confirmed membranous nephropathy (MN) cases and 349 ethnically matched healthy controls: the British cohort of the Stanescu et al., 2011 GWAS. Genotyped and imputed region of interest on chromosome 2 (hg19 chr2:160500000-161500000), as used to determine Neanderthal introgression near the PLA2R1 MN risk locus (lead SNP: rs4664308).

  Study EGAS00001007700

• Drop-BS: High-Throughput Single-Cell Bisulfite Sequencing on a Microfluidic Droplet Platform

  We present a high-throughput droplet-based bisulfite sequencing library preparation platform. Our protocol allows the production of a BS library of 2,000-10,000 single cells within 2 days. In addition to validation using mixed cell lines, we also used the technology to profile various cell types in mouse and human brain samples.

  Study phs002123

• DCLRE1B/Apollo germline mutations associated with renal cell carcinoma

  Highlights of Bories et al. article submitted to BBA - Molecular Basis of Disease: We identified 2 germline mutations in the DCLRE1B gene encoding the Apollo protein by Whole Exome Sequencing (WES) in two families with inherited clear-cell Renal Cell Carcinoma. Our study suggests a putative link between DNA repair, telomere protection and renal oncogenesis.

  Study EGAS50000000216

• PDX gene expression

  Investigation of the anti-tumor activity of the potent, highly selective, orally bioavailable CHK1 inhibitor (CHK1i), SRA737, in both acquired PARPi-resistant BRCA1/2 mutant and CCNE1amp high-grade serous ovarian cancer (HGSOC) models. We analyzed gene expression in five patient derived xenografts (PDX) that responded to CHK1i and three PDX that had a poor response to CHK1i.

  Study EGAS50000000084

• Bulk RNA-sequencing datasets from Caco-2 cells under normal or inflamed conditions treated with 4HTBZ or vehicle.

  This dataset contains 4 bulk RNA sequencing files (.fastq.gz) and a metadata file (.csv) from Caco-2/TC7 intestinal epithelial cells under normal or inflamed conditions, followed by treatment with 4HTBZ or vehicle. The cDNA libraries were prepared with the QuantSeq 3′ mRNA-Seq Kit (Lexogen, Inc.) and sequenced on an Illumina HiSeq4000 system. The metadata file provides sample information (experimental condition and code identifier). Raw sequencing reads were quality-checked with FastQC, trimmed with Trimmomatic, and aligned to the human reference genome (GRCh38) using HISAT2.

  Dataset EGAD50000001759

• Single-cell RNA-seq profiling of patient derived organoids

  The aim of this study was to profile developement of resistance to cancer treatments using organoids. Two patient derived colorectal cancer organoids were profiled using single cell RNAseq after treatment with a combination of different anticancer drugs. The organoids were profiled both after 4 weeks of treatment and after a drug holiday of 2 weeks.

  Study EGAS50000001025

• Diseased_heart_analysis__RNA_Adult

  Samples for this project are taken from adult human hearts which have been preserved using 2 different methods (cold static storage on ice, and hypothermic perfusion). Samples have been acquired at various time points during post-preservation normothermic perfusion. The aim of the project is to assess the transcriptional changes caused by these differing methods of preservation.

  Study EGAS00001008302

• Whole exome sequencing of 49 tumor-blood pairs and transcriptome sequencing of 44 tumors for adrenocortical tumors

  Adrenal Cushing syndrome, which leads to type 2 diabetes, obesity and hypertension, is caused by ACTH-independent production of glucocorticoid by adrenocortical tumors. To investigate the genetic origins of cortisol-producing adrenocortical tumors, we performed whole exome sequencing of 49 tumor-blood pairs and transcriptome sequencing of 44 tumors

  Study EGAS00001000712

• ATAC-seq of uncultured CD112high and CD112low long-term(LT) human hematopoietic stem cells (HSC) from umbilical cord blood

  ATAC-sequencing was performed to establish chromatin accessibility signatures that underlie the differential in vitro priming and regeneration phenotypes of CD112high and CD112low LT-HSC. CD112high and CD112low LT-HSC from 2-3 independent human umbilical cord blood pools were prospectively isolated for DNA extraction, library preparation and sequencing.

  Study EGAS00001005121

• Single-cell RNA sequencing dissects gene-environment interactions on gene expression and regulation in immune cells.

  This study aims to dissect gene-environment interactions on gene expression and regulation in immune cells using scRNA-seq data from 120 donors being stimulated with 3 pathogens, for 2 different timepoints. More specifically, we identified context- and cell-type-specific eQTLs and co-expression QTLs in PBMCs.

  Study EGAS00001005376

• Loss of RREB1 in pancreatic beta cells reduces cellular insulin content and affects endocrine cell gene expression

  Genome-wide studies have uncovered multiple independent signals at the RREB1 locus associated with altered type 2 diabetes risk and related glycemic traits. However, little is known about the function of the zinc finger transcription factor RREB1 in glucose homeostasis or how changes in its expression and/or function influences diabetes risk.

  Study EGAS00001006314

• The gut microbiota in prediabetes and diabetes: a population-based cross-sectional study

  The link between the gut microbiota and type 2 diabetes (T2D) warrants further investigation because of known confounding effects from antidiabetic treatment. Here we profiled the gut microbiome in a discovery (n=1011) and validation (n=484) cohort comprising Swedish subjects naive for diabetes treatment and grouped by glycemic status.

  Study EGAS00001004480

• MutWP5: CRUK Mutographs of Cancer: Breast: Cancer Mastectomy (Exome) (2020-01-29)

  Sequencing of LCM-derived microbiopsies from 30 women who underwent mastectomies due to a breast cancer diagnosis. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequently be sent for whole-genome sequencing. Results from this portion of the study will be compared to women who had cosmetic breast reduction surgeries and those with germline BRCA 1/2 mutations. . This dataset contains all the data available for this study on 2020-01-29.

  Dataset EGAD00001005924

• Copy-choice recombination during mitochondrial L-strand synthesis causes DNA deletions

  The dataset comprises of seven samples described below 1. Muscle samples from three patients with late-onset PEO caused by compound heterozygous POLG variants M0305 POLG W748S/R1096C M1105 POLG A467T/T251I+P587L M1804 POLG A467T/X1240G+35aa 2. Muscle sample from a patient with adPEO with heterozygous TWNK variants M0230 TWNK p.Arg357Pro 3. Blood control samples from two patients with late-onset PEO caused by compound heterozygous POLG variants DNA2012-1630_S1 POLG W748S/R1096C DNA2018-0168_S2 POLG A467T/T251I+P587L 4. Muscle samples from healthy control individuals DNA2018-0172_S4 Healthy control 2 DNA2018-0173_S5 Healthy control 1

  Dataset EGAD00001004271

• Solve-RD_RND_cohort-1_DF1+2_V1

  Solve-RD data submitted to the ERN-RND cohort for re-analysis (Data freeze 1+2) v1

  Dataset EGAD00001009769

• DAC for Combined MEK and BCL-2/XL inhibition is effective in high-grade serous ovarian cancer patient-derived xenograft models and BIM levels are predictive of responsiveness

  Dac EGAC00001001250

• EGAD00010000124

  Psoriasis cases as part of WTCCC2 phase 2

  Dataset EGAD00010000124

• PDAC organoid genomic heterogeneity

  Two primary tumor-derived PDAC organoids were subjected to SNP array, RNA-seq, and single-cell WGS

  Dataset EGAD00010002408

• Transcriptome of (peripheral blood), from donor Sample A, replicate 2 FACS processing technical replicates

  FACS processing technical replicates

  Dataset EGAD00001010087

• Pulldown DNA methylation study v2

  Pilot study to set up sequencing protocols for targeted pulldown methylation profiling

  Dataset EGAD00001001242

• Whole genome sequencing of adult t-cell leukemia/lymphoma

  Whole genome sequencing of single adult t-cell leukemia/lymphoma case

  Dataset EGAD00001001326

• The UCSF Low Grade Glioma Genome Project #2"

  Dataset contains WES data from 3 astrocytoma patients: blood as control, primary tumor and recurrent tumor

  Dataset EGAD00001001305

• Multimodal Immune Profiling to Determine Mechanisms of COVID-19 Clinical Trajectory in Uganda

  We conducted a prospective cohort study of adults (RESERVE-U-C19) admitted to Entebbe Regional Referral Hospital (ERRH), a national COVID-19 referral center, across three viral variant-driven phases of the pandemic in Uganda. In this cohort, which reflects the entire spectrum of SARS-CoV-2 infection, we integrated clinical data with serum proteomics (N=306) and whole-blood transcriptomics (N=100) to determine a range of host responses associated with COVID-19 severity, SARS-CoV-2/HIV co-infection, and circulating viral variants. Beyond the prognostic importance of myeloid cell-driven immune activation and lymphopenia, we show that multifaceted impairment of host protein synthesis and extensive redox imbalance define core metabolic signatures of severe COVID-19, with central roles for IL-7, IL-15, and lymphotoxin-α in COVID-19 respiratory failure. While prognostic signatures were generally consistent in SARS-CoV-2/HIV-coinfection, type I interferon responses uniquely scaled with COVID-19 severity in persons living with HIV. Throughout the pandemic, COVID-19 severity peaked during phases dominated by A.23/A.23.1 and Delta B.1.617.2/AY variants, with Delta phase COVID-19 distinguished by exaggerated pro-inflammatory myeloid cell and inflammasome activation, NK and CD8+ T-cell depletion, impaired host protein synthesis, and upregulation of viral cell entry and trafficking pathways. Data from this study available through dbGaP include raw RNA-Seq data from whole-blood of adult participants (N=100).

  Study phs003246

• Field studies of Cryptosporidiosis and Enteropathogens in Bangladesh

  Cryptosporidiosis causes severe diarrhea in infants in the developing world. There is no vaccine to prevent it, and little in the way of treatment. This study on Bangladeshi urban slum children aims to support the design of a vaccine, both by determining how the immune system protects from infection and by identifying the genotypes of the parasite that should be included in a vaccine, as well as aid in development of therapies by identifying human genes that control infection. The primary objective of the study was to determine the incidence and contribution to disease of the different species and genotypes of cryptosporidia. Secondary objectives were designed to determine acquired immune response to cryptosporidiosis and identify human genes that influence susceptibility to cryptosporidiosis. This is an observational study. Children were recruited from Mirpur Dhaka slum (Cohort 1) and rural Mirzapur (Cohort 2) and followed for cryptosporidium infection longitudinally from birth through age 4 years in Cohort 1 and birth through age 2 years in Cohort 2. Biweekly household visits for diarrheal surveillance were made in addition to anthropometric measurements of mother and child, blood samples collected two times each year from the child, a work-up of diarrheal stools and non-diarrheal surveillance stools for cryptosporidium and other enteropathogens, and blood and breast milk samples from the mother. A detailed description of the study design and procedures can be obtained from publication: Kevin L Steiner, et al., 2018, PMID: 29897482.

  Study phs001665

• Whole-genome sequencing of a thyroid carcinoma harboring a ETV6-NTRK3 fusion and a NTRK3 G623R mutation (HIPO-021)

  Dataset EGAD00001006243

• HipSci - Bardet-Biedl Syndrome - RNA Sequencing - April 2015

  HipSci - Bardet-Biedl Syndrome - RNA Sequencing - April 2015

  Dataset EGAD00001001952

• PacBio long-read scRNA-seq

  Full-length single-cell cDNA PacBio Kinnex whole-transcriptome sequencing data for adrenal sampleA and sampleB.

  Dataset EGAD50000002211

• Genetic Factors associated with Conversion from Active Surveillance to Treatment for Prostate Cancer

  This is a genome-wide association study (GWAS) of conversion from active surveillance (AS) for prostate cancer (PC) to treatment. AS is the monitoring with screening tests and/or biopsies over time of men usually with low-risk, early-stage PC to avoid harmful side effects of PC treatment. "Conversion" refers to patients who withdraw from AS (switch to active treatment) due to changes in their cancer development, anxiety about delayed treatment, or other reasons. Patients who converted because of anxiety alone were censored in the GWAS analysis. The overall goal of this study is to develop an easily measurable, germline-based panel of genetic variants to aid in selecting PC patients appropriate for AS and/or the intensity of monitoring. In total, 6,324 PC patients from 28 collaborating institutions are included in this study. Patients are those with PC who are either on AS or who have previously converted from AS to treatment. In this study, 70% of all subjects were diagnosed with very low-risk, low-risk, or low-volume intermediate-risk PC (Kryvenko and Epstein, 2016; PMID: 26709152) and have been enrolled in an AS program. DNA was extracted from blood, saliva, or formalin-fixed, paraffin-embedded (FFPE) tissue collected from the study subjects at the participating sites. Genotyping, quality control processes, and imputation were performed at the Center for Inherited Disease Research (CIDR) at Johns Hopkins University. CIDR used the Illumina Infinium Multi-Ethnic Global Array (MEGA) version 1, which included genome-wide tag SNPs and 10,000 SNPs selected with custom content. Quality control and assurance (QC/QA) checks on genotyped data involved four domains: sample-level checks, batch effects, SNP-level checks, and preliminary association tests. Samples and SNPs passing the QC/QA checks were imputed to appropriate reference panels (HRC r1.1 for patients of European ancestry) using the Michigan Imputation Server (PMID: 27571263). Unmeasured genetic variants were imputed using the Trans-Omics for Precision Medicine (TOPMed) Imputation Server, with 97,256 reference samples and 308,107,085 SNPs. Variants with imputation quality (INFO) score < 0.3 were excluded, leaving a total of 22,691,641 SNPs successfully imputed. After QC steps, a total of 5,936 samples genotyped at CIDR remained for inclusion in the GWAS.

  Study phs002056

• The Effect of the Menstrual Cycle on DNA Expression in the Normal Human Breast Epithelium

  The Susan G. Komen for the Cure® Tissue Bank at the IU Simon Cancer Center [www.komentissuebank.iu.edu] (KTB) was established expressly for the prospective collection of normal, healthy breast tissue from volunteer donors. Blood is also obtained from donors at the time of donation and is processed for serum, plasma and peripheral blood leukocyte DNA. Specimens are annotated and data includes age, race, ethnicity, personal health history, family cancer history, medication usage at the time of donation, and breast cancer risk factors. Premenopausal donors to the KTB were identified by a query of the Bank's database. Hematoxylin and eosin stained sections of the formalin-fixed paraffin-embedded tissue of the identified donors were reviewed and tissue was graded on the basis of the abundance of epithelium within the section. Only tissue containing abundant epithelium was considered for this study. Based on dates, the specimens of nine women in the follicular phase of the menstrual cycle and five in the luteal phase were chosen. Six donors using hormonal contraception at the time of donation were also included. Whole blood obtained from 19 of the 20 donors at the time of tissue donation was available and it was processed for serum. Estradiol, estriol, luteinizing hormone and progesterone concentrations were determined by the Indiana University Health Pathology Laboratory using a Beckman Unicel DxI 800 Immunoassay System. The phase of the menstrual cycle was verified by serum progesterone concentration. The epithelium of these 20 specimens was microdissected from multiple 8 micron frozen tissue sections. Total RNA extracted from the tissue was subsequently depleted of rRNA via locked nucleic acid probes. This enabled profiling of both poly-A and non-poly-A RNA species. Barcoded cDNA libraries from the 20 normal breast epithelia were prepared and sequenced on an Applied Biosystems (AB) SOLiD3 or SOLiD 4 platform. RNA-seq reads for each sample were then mapped to the human genome (hg19) using the LifeScope software version 2.5.1 (Life Technologies, Foster City, CA) and BAM (Binary Alignment/Map) files generated. Read counts for each gene were derived from the output BAM files using the RefSeq database (UCSC Genome Brower) as the gene model.

  Study phs000644

• Kids First: The Genomic Basis of Structural Birth Defects Associated with Chromosome 18 Copy Number Changes

  Chromosome abnormalities are a common cause of structural birth defects. Both childhood cancers and structural birth defects are critical and costly conditions associated with substantial morbidity and mortality. Elucidating the underlying genetic etiology of these diseases has the potential to profoundly improve preventative measures, diagnostics, and therapeutic interventions.However, because these genomic copy number changes involve multiple genes and because most of these conditions are individually rare, defining the specific causative genes behind specific phenotypes has been a challenge. We met that challenge by spending the last 28 years enrolling and evaluating anyone with a chromosome 18 abnormality. The cohort now includes over 700 individuals with a wide variety of chromosome 18 copy number changes (CNV), as well as their parents. Because the vast majority of these participants have individually unique CNVs, we have been able to perform extensive genotype-phenotype correlations resulting in 58 publications. Of the 263 genes on chromosome 18, 28 are linked to specific hemizygous phenotypes. However, most have low penetrance. Genomic sequence data could identify variants in the extant allele that are hypermorphic and compensate for hemizygosity, or are hypomorphic and exacerbate hemizygosity. Additionally, there are phenotypes within this cohort that are rare, or an extreme version of one of the more common phenotypes. Genomic sequence data could help discover new biallelic conditions by revealing functional sequence variants of the extant allele. In both of these cases, there could also be variants in other genes on other chromosomes that may be associated with the phenotype that confer susceptibility or resilience. Inclusion of this unique cohort in the Gabriella Miller Kids First Pediatric Research Program can advance the goals of the program in several ways. First, by adding known susceptibility loci to the existing Kids First cohorts for the structural birth defects that are also found in our cohort. Second, for those structural birth defects known to be polygenetic, this cohort has a single defined risk factor which can simplify the search for secondary factors. Third, these studies can also bring clarity to people with chromosome 18 conditions and help to make the genotype more accurately predict phenotype. This approach could be a model for understanding the many other rare chromosome abnormalities which collectively are a common cause of structural birth defects.

  Study phs002627

• All you need to know about the new Submitter Portal

  In September we launched new services for all EGA users, including a new version of the former Submitter Portal. Our main objective with this Portal is to transform the metadata submission in a simplified and user-friendly method. Although it is possible to find documentation about the Submitter Portal on our website, we wanted to highlight the new features in this article. Enjoy! Credentials: who needs to create a new user to access the Submitter Portal and who doesn't? Users with an old submission account (ega-box) can already access the new Submitter Portal. If it is the first time that you want to access, it is time to request a submitter role linked to the EGA User. In the form, it is mandatory to explain which type of data you want to deposit, the size of your submission, add any comment you’d like us to read, and manage the EGA Data Processing Agreement (DPA) document. This document should be checked by the legal department of your institution and signed by a person with authority to sign contracts on behalf of the institution. Note that we provide a DPA template for new Users; it can be found and downloaded in the Submitter Request section. With the DPA signed and the Submitter Request sent, it is time to wait for our Helpdesk team to validate the request. Metadata submission now can be collaborative It is possible to add collaborator(s) to your submissions. Only registered EGA Users will appear on the search bar. When adding a collaborator, it is mandatory to define the permission granted: Read only: collaborators with this permission can check if the information of the submission is correct but cannot modify anything. Read & write: collaborators with this label can register new objects and modify the registered ones. We wanted to save time for submitters that usually rely on the same collaborators. For that, we have implemented the possibility of adding the same collaborators from previous submissions; all you need to do is to search the submission, and the exact set of EGA users will have access to your submission. You can check the collaborators and their permissions at any point of the submission by going to the collaborators section. Even adding more people to complete the submission is allowed by following the same steps: looking for the EGA User and defining the permission granted (please, note that during the submission it is not possible to add collaborators from previous submissions). Use external links to complement the study information When filling the information about the study we recommend users to enrich the submission with complementary fields such as PubMeds IDs, custom tags and external links. External links are used to connect a study to an external repository. We have implemented an external link to Euro-Bioimaging for a European Project (EuCanImage). Now, we are looking forward to keeping increasing the list of external repositories! Choose the expected release date for your metadata to be searchable on our website Once all the metadata objects are registered it is time to finalise the submission so that the Helpdesk team can approve it. In the new version of the Submitter Portal users can choose the expected release date for the metadata to be searchable on the EGA website. Please, note that the selected date will have a minimum of one week margin from the day of finalisation. This time is necessary for your files to be archived and for the submission to be validated by the Helpdesk Team. Are you looking for the Array submission within the Submitter Portal? Our Submitter Portal does not support array submissions. The submission of Array-based metadata must be done using the EGA programmatic submission. You will find our Array-based format template and all the necessary information on our website, and you can find all the steps in our submission quickguide! More information about the submission process at the EGA Our Team has developed an extended documentation related to metadata submission. A good first step is to take a closer look at the Submission FAQ. To have a general overview of all the metadata submission process we recommend taking a glimpse of our take-the-tour or watching the tutorial available on our YouTube Channel. Don't miss our section on EGA Schemas, to learn more about how the EGA is built. It can be useful to fully ensure that your submissions comply with the EGA's standards and contribute to a valuable and accessible genomic resource.

  Blog new-submitter-portal

• methylation_risk_reducing_surgery_breast

  Raw methylation data from breast tissue collected during risk-reducing surgery in BRCA1/2 mutation carriers.

  Dataset EGAD00010002075

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008038

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008048

• Sequencing data for oesophageal and related samples - OACs release 2 (RNA)

  Dataset EGAD00001003839

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008058

• Spatial transcriptomic analysis of renal cell carcinoma

  Spatial transcriptomic analysis of two Japanese ELOC-mutated renal cell carcinoma

  Dataset EGAD50000001400

• HGS-3 - nanopore

  WGS Nanopore nanopore sequencing of organoid line HGS-3.1 and matching blood reference HGS-3

  Dataset EGAD00001005476