13342 results for ""60+mice"+"6+weeks"+diet+study+2014+2017+DOI"

in 25.76 milliseconds.

• Capture Hi-C on Hodgkin lymphoma

  In situ promoter capture Hi-C on Hodgkin lymphoma cell line L-428 in experimental triplicates. Hi-C libraries were prepared as previously described (Orlando et al., 2018, https://currentprotocols.onlinelibrary.wiley.com/doi/pdf/10.1002/cphg.63). Promoter capture was based on 32,313 biotinylated 120-mer RNA baits (Agilent). Hi-C libraries were sequenced using Illumina HiSeq 2000 technology. The files are in FASTQ format.

  Dataset EGAD00001004321

• Genetic screening of GPI-anchor protein synthesis

  This study will analyse the guide sequence which were used for making mutations in the Cas9-expressing cells. We used GeCKO v2 library which were released by Feng Zhang, 2014. 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 EGAD00001001928

• Gabriella Miller Kids First Pediatric Research Program in Whole Genome Sequencing of African and Asian Orofacial Clefts Case-Parent Triads

  The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). All of the WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. The focus of this study is to identify novel risk variants for OFC in Africa and Asian OFC case-parent triads through analysis of Whole Genome Sequencing data.

  Study phs001997

• Epilepsy Genetics Initiative

  The Epilepsy Genetics Initiative (EGI) was created in 2014 with the goal of amassing a database of clinically-generated exome sequence data for patients with epilepsy who have had diagnostic whole exome sequencing. Most of the subjects enrolled in the study did not receive a secure genetic diagnosis from their initial sequencing. Following participant/family consent, EGI transfers raw data from the respective clinical lab to the Institute for Genomic Medicine at Columbia University Medical Center. The strategy of the initiative has been to carry out systematic reanalysis of data to identify new diagnoses that were not possible or missed at the time of initial sequencing and to aid in novel gene discovery in epilepsy.

  Study phs001551

• Malnutrition and Enteric Disease Network (Mal-ED) Birth Cohort in Brazil

  The Malnutrition and Enteric Disease Network (Mal-ED) Birth Cohort in Brazil enrolled 242 children between 2010 and 2014; an additional 101 infants recruited under the ICIDR (International Center for Infectious Disease Research) program and evaluated by the same procedures as Mal-ED were included in the cohort. The aim of the study was to evaluate the role of enteropathogens in causing intestinal inflammation and a failure of growth and development.This submission includes genetic and corresponding anthropometric data on a subset of 279 participants. More information on this project and its related projects can be found here: Studies of diarrheal susceptibility, growth, and development, in pediatric populations in Northeastern Brazil

  Study phs003172

• Tourette International Collaborative Genetics (TIC Genetics) Study - NJCTS and NIMH

  The Tourette International Collaborative Genetics (TIC Genetics) Study is an international collaboration of scientists and clinicians specialized in Tourette Disorder (TD) from more than 20 sites across the United States, Europe, and South Korea. The study was established to further our understanding of the genetic architecture of tic disorders by developing a large sample of genotypically and phenotypically well-characterized affected probands and their relatives. We employ state-of-the-art genetic technologies to identify major genetic variants contributing to TD and the most commonly comorbid disorders, such as Obsessive-Compulsive Disorder (OCD) and Attention-Deficit/Hyperactivity Disorder (ADHD). TIC Genetics is a direct result of work of the New Jersey Center for Tourette Syndrome (NJCTS) Sharing Repository (Heiman et. al., 2008; PMID: 19036136), funded by a grant from NJCTS Center of Excellence. Established in 2011 (Dietrich et. al., 2015; PMID: 24771252), the TIC Genetic study focuses on both on familial genetic variants with large effects within multiplex affected pedigrees and on de novo mutations ascertained through the analysis of apparently simplex parent-child trios with non-familial tics. In May 2017, we published a whole-exome sequencing study on apparently 311 parent-child trios (Willsey et. al., 2017; PMID: 28472652). These data, both phenotypes and sequencing data, are available through dbGaP. There were 120 subject samples included in the publication that did not have consent for sharing. These are excluded from dbGaP.In November 2021, we published a whole-exome sequencing study on 13 multiplex TD families (Cao et. al., 2021). These data, both phenotypes and sequencing data, are available through dbGaP.

  Study phs001423

• Genetic screening of GPI-anchor protein synthesis (2018-08-13)

  This study will analyse the guide sequence which were used for making mutations in the Cas9-expressing cells. We used GeCKO v2 library which were released by Feng Zhang, 2014. 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 2018-08-13.

  Dataset EGAD00001004294

• Description and determinants of the faecal resistome and microbiome of farmers and slaughterhouse workers: a metagenome-wide cross-sectional study.

  Within this study, we extracted DNA of 194 stool samples of persons living or working on pig and broiler farms (54 pig farmers, 24 broiler farmers), 70 pig slaughterhouse workers and 46 control subjects (Lifelines population living in the Northern parts of the Netherlands, and without recent antimicrobial use and occupational livestock contact). Total DNA was sequenced and metagenomic analysis was performed. Subsequently, the faecal resistomes and microbiomes were described and analysed (doi: 10.1016/j.envint.2020.105939). Highlights study: • Food chain workers are exposed to antimicrobial resistant (AMR) bacteria in livestock. • We describe faecal resistomes and microbiomes of farmers and slaughterhouse workers. • Faecal AMR gene loads in pork exposed workers exceed those in broiler farmers/controls. • Resistome and microbiome differences are shown between and within occupational groups. • On-farm working hours and working/living on a pig farm determine the faecal resistome. • Contact with livestock may be a determinant for human AMR gene carriage. This study is part of the EFFORT project: http://www.effort-against-amr.eu/.

  Study EGAS00001003944

• WGS data for ependymomas (5 tumor-control pairs)

  Whole genome sequencing data for ependymomas (5 tumor-control pairs). See Mack, Witt et al. Nature 506(7489):445-50, 2014 (PMID: 24553142).

  Dataset EGAD00001000950

• WES/WXS data for ependymomas (42 tumor-control pairs)

  Whole exome sequencing data for ependymomas (42 tumor-control pairs). See Mack, Witt et al. Nature 506(7489):445-50, 2014 (PMID: 24553142).

  Dataset EGAD00001000951

• A reference map of potential determinants for the human serum metabolome

  The collection of metabolites circulating in the human blood, termed the serum metabolome, contains a plethora of biomarkers and causative agents. Although the origin of specific compounds is known, we have a poor understanding of the key determinants of most metabolites. Here, we measured the levels of 1251 circulating metabolites in serum samples from a healthy human cohort of 491 individuals, and devised machine learning algorithms to predict their levels in held-out subjects based on a comprehensive profile consisting of host genetics, gut microbiome, clinical parameters, diet, lifestyle, anthropometric measurements and medication data. Notably, we obtained statistically significant predictions for over 76% of the profiled metabolites. Despite using the strict out-of-sample prediction metric, which is a lower bound for the explained variance, diet and microbiome each explained hundreds of metabolites, with over 50% of the variance explained in some metabolites. We further validated the robustness of the microbiome related associations by showing a high replication rate in two geographically independent cohorts that were not available to us when developing the algorithms. We also demonstrate that some of these interactions are causal, as some metabolites we predicted to be positively associated with bread increased in level following a randomized clinical trial of bread intervention. Microbiome-explained metabolites were enriched with unnamed metabolites, and we devised an algorithm that accurately predicts their biological pathway, finding that they mainly include food components, aromatic amino acids and secondary bile acid derivatives. Overall, our results unravel potential determinants of over 800 metabolites, paving the way towards mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating circulating metabolite levels.

  Study EGAS00001004512

• Methylation of Ewing sarcoma tumors - RRBS (ICGC)

  RRBS data of 86 Ewing patients (French). Illumina HiSeq 2000/2500 (Fastq files available). Sheffield et al. Nat Med. 2017 Jan 30

  Dataset EGAD00001003133

• Breast Cancer Histology Images

  Fresh frozen breast cancer H&E tissue images collected and annotated by the International Cancer Genome Consortium (ICGC), that included the BASIS collaboration. Associated with whole genome sequence data as originally described by Nik-Zainal et al, Nature, 2016 (DOI: 10.1038/nature17676) and deposited with ID EGAS00001001178

  Dataset EGAD00010001911

• Molecular Profiling Reveals Unique Immune and Metabolic Features of Melanoma Brain Metastases. Cancer Discovery doi: 10.1158/2159-8290. PMID: 30787016 PMCID: PMC6497554

  The BAM files for WES and RNA seq used in the article "Molecular Profiling Reveals Unique Immune and Metabolic Features of Melanoma Brain Metastases." on cancer Discovery 2019. PMID: 30787016 PMCID: PMC6497554. Authors : Grant M Fischer, ..., Michael A Davies.

  Dataset EGAD00001005046

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - mobile_elements

  Samples from Edwards et al 2015 - doi:10.1186/s12864-015-1685-z

  Dataset EGAD00001001048

• Rapid Acceleration of Diagnostics - Digital Health Technologies (RADx-DHT): NIH Digital Health Solutions for COVID-19: IBM Covid19 Contact Tracing and Data Exchange Tools

  The goal of this project is to develop both contact tracing and secure data exchange tools. The contact tracing solution securely combines data from a variety of sources (including manual self-report data and mobile device surveys) to enable tracing of contacts with individuals that have tested positive for COVID-19 or have been exposed to COVID-19. The data exchange solution is a secure mechanism that empowers users to control the data they share in the course of their return to work, including the ability to provide a verifiable health status claim. These tools are being used by an employer to evaluate the risk of allowing individuals to return to normal activities, and also the ability to trace user contact with individuals diagnosed with or suspected of having contracted COVID-19. DOI: https://rapids.ll.mit.edu/10.57895/h0an-m559

  Study phs002516

• Organoid BulkRNAseq

  Here, we aim to understand early determinants of brain organoid quality and generated a set of brain organoids from 12 different hPSC lines using an adaptation of the unguided Lancaster protocol (Lancaster & Knoblich, 2014). All cell lines were obtained from healthy donors. 12 donor hPSC lines were differentiated into brain organoids and a set of 72 individual organoids (6 per line) were randomly selected morphologically diverse organoids and subjected to bulk RNA sequencing for gene expression analysis. RNA was extracted using the RNeasy kit (Qiagen, Germany) according to the manufacturer’s instructions. A total of 1000 ng per sample was sent for RNA sequencing to Azenta Life Sciences (Genewiz Leipzig, Germany) for sequencing. Library preparation was performed using the NEBNext Ultra II RNA Library Prep Kit for Illumina following manufacturer’s instructions (NEB, Ipswich, MA, USA). mRNAs were first enriched with Oligo(dT) beads. The samples were sequenced using a 2x150 Pair-End (PE) configuration v1.5 at a depth of >20 million reads in each sample. Raw sequence data (.bcl files) generated from Illumina NovaSeq6000 was converted into fastq files and de-multiplexed using Illumina bcl2fastq program version 2.20. The raw sequence reads were aligned to the human reference genome GRCh38 using the STAR aligner (Dobin et al., 2013). Gene expression levels were quantified using the FeatureCounts tool (Liao et al., 2014).

  Study EGAS50000000659

• JMML targeted sequencing (2013)

  Juvenile myelomonocytic leukemia (JMML) is a rare myelodysplastic myeloproliferative neoplasm of early childhood initiated by germline or somatic RAS-activating mutations (Chang, Dvorak et al. 2014). Clinical evolution is heterogeneous, with acute myeloid leukemia (AML) transformation in 1/3 of cases and frequent relapses after hematopoietic-stem-cell transplantation (HSCT), although some patients experience spontaneous remission (Locatelli, Nollke et al. 2005; Matsuda, Shimada et al. 2007; Takagi, Piao et al. 2013; Locatelli and Niemeyer 2015). The initiating lesion incompletely predicts outcome, suggesting the involvement of additional mutations. Genetic profiling of a large JMML cohort (n=118), including whole-exome sequencing (WES) in 30 cases, uncovered additional genetic abnormalities in 56 cases (47%). Somatic events were rare (0.38/case/Mb) and restricted to sporadic (47/78; 60%) or NF1-associated (8/8; 100%) JMML. Multiple concomitant genetic hits were identified targeting the RAS pathway in 13/78 (17%), RAC2D63V, which activates the PI3K/PDK1/AKT and the mTORC2 pathways, and a novel pathway for JMML, the polycomb repressive complex 2 (PRC2), in 26/78 (33%) of the sporadic JMML cases. Interestingly, PRC2 imbalance was recently shown to synergize with RAS activation (De Raedt, Beert et al. 2014). JMML outcome was associated to the mutational profile, suggesting a dose-dependent effect for RAS-pathway activation, distinguishing very aggressive JMML rapidly progressing to AML.

  Study EGAS00001001324

• KASP1-22

  234 samples genotyped at 15 loci LGC Genomics, Hoddesden, UK using the PCR-based KASP assay (Semagn,e tal (2014). Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement. Mol Breeding 33, 1–14.)

  Dataset EGAD00010001905

• scRNA-seq of HSPC treated with gemcitabine and carboplatin

  4 samples of 2000000 HSPCs in 2 ml media each were then prepared and treated for 24 hours: carboplatin-high: 150 µg/ml carboplatin carboplatin-low: 18.75 µg/ml carboplatin gemcitabine: 25 ng/ml gemcitabine control: no drug Single-cells were subsequently extracted using the ddSEQ™ Single-Cell Isolator (Bio-Rad) and later sequenced using the SureCell™ Whole Transcriptome Analysis 3' Library Prep Kit (Illumina) on the NextSeq 500 System (Illumina) using the NextSeq 500/550 High Output Kit v2.5 150 Cycles (Illumina) all following the manufacturer's instructions. The raw FASTQ-files are available as read 1 and read 2 files from 4 lanes for each sample giving a total of 16 FASTQ-files. The FASTQ-files were also processed following the ddSeeker (Romagnoli, D., et al., BMC Genomics 2018, doi:10.1186/s12864-018-5249-x) and Drop-seq (Macosko, E.Z., et al., Cell 2015, doi:10.1016/j.cell.2015.05.002) protocols for processing scRNA-seq data to yield the final digital gene expression (dge) for each cell of each sample. This has resulted in one dge text file and one dge summary text file per sample. These 8 text-files with dge data are found in the zip-compressed analysis data-file.

  Dataset EGAD00001006080

• The Ultrasound Study of Tamoxifen

  The Ultrasound Study of Tamoxifen is a 12-month longitudinal study to describe volumetric breast density change over time among women with and without tamoxifen exposure. Women were enrolled at the Karmanos Cancer Institute and Henry Ford Health Systems in Detroit, Michigan between 2011-2014. Enrollment included women prescribed tamoxifen for clinical indication (n=82) and a comparison group of women frequency matched on age, race, and menopausal status (n=165). Whole breast ultrasound tomography (UST) scans were used to measure volume averaged sound speed (m/s) as a surrogate of volumetric breast density. Ultrasound tomography (UST) scans were completed at baseline prior to tamoxifen initiation and were repeated at 1-3 months, 4-6 months, and 12 months after tamoxifen initiation. For the comparison group, UST scans were completed at baseline and 12 months. Questionnaires were completed at each scan.

  Study phs003183

• sn-RNAseq profiling of the impact of a cytokine storm model in human cardiac organoids

  We developed conditions to model conditions of diastolic dysfunction caused by inflammatory factors in human cardiac organoids. Our organoid model is based on conditions promoting maturation (Mills et al., PNAS, 2017 and Voges et al., In Revision) and incorporating human pluripotent stem cell derived cardiomyocytes, epicardial cells, fibroblasts, endothelial cells and pericytes. Following 15 days of differentiation (Voges et al., Development, 2017) we formed human cardiac organoids incorporating both our multicellular differentiation (Mills et al., PNAS, 2017) and additional endothelial cells (Voges et al., In Revision). After another 12 days of maturation in 5 days maturation medium and 5 days weaning medium (Voges et al., In Revision) human cardiac organoids were either treated with weaning medium or “cardiac cytokine storm” comprised of 100 ng/ml IFNg, 10 ng/ml IL-1 β and 10 µg/ml poly(I:C). After 48 hours human cardiac organoids were harvested and snap frozen in -80C before nuclei isolation and single nuclei RNA sequencing.Pooled hCO (~40) were homogenized in 4 mL lysis buffer (300 mM sucrose, 10 mM Tris-HCl (pH = 8), 5 mM CaCl2, 5 mM magnesium acetate, 2 mM EDTA, 0.5 mM EGTA, 1 mM DTT)(all Sigma-Aldrich) with 30 strokes of a dounce tissue grinder (Wheaton). Large pieces of hCO were allowed to settle and homogenate was passed through pre-wetted 40 µm cell strainers (Becton Dickinson). Remaining hCO material in the douncer was resuspended in 4 mL and the douncing and filtering steps were repeated twice. All steps of the homogenization were performed on ice. The filtered homogenate was centrifuged at 1500 x g for 5 min at 4oC. Nuclei pellets were then re-suspended in PBS. A fraction of resuspended nuclei were then stained with hoechst nuclear stain (1:500 dilution) and counted on a haemocytometer under fluorescent microscope.The nuclei were then re-centrifuged (1500 x g for 5 min at 4°C) and resuspended at a density to load ~5,000 nuclei per sample. Cell were loaded into the Chromium Controller (10X Genomics) for gel bead emulsion (GEM) formation. Library preparation was conducted according to the manufacturer’s recommended protocol using the Chromium Next GEM Single Cell 3’ GEM, Library & Gel Bead Kit v3.1. Libraries were sequenced on the NextSeq 500/550 v2 (Illumina) with 150 bp reads.In the snRNA-seq we note the lower than expected percentage of non-myocytes and the loss of endothelial cells (Mills et al., PNAS, 2017 and Voges et al., In Revision), indicating the protocol requires further optimization for hCO samples.

  Study EGAS00001005174

• SUDC Registry and Research Collaborative

  The Sudden Unexplained Death in Childhood Registry and Research Collaborative (SUDCRRC), created at NYU Langone Health in 2014, connects academic investigators, forensic pathologists, and medical examiner and coroner partner offices to study sudden unexpected pediatric deaths, aged 1 month to 18 years, whose death is unexplained or unclear at the end of the public mandated investigation, including autopsy. Each case includes collection and analysis of all records (medical history from prenatal records throughout life, death scene investigation, autopsy/toxicology/ancillary death investigation reports including photographs and histology slides), biospecimens, and in-depth family interviews. Whole exome sequencing is performed on each deceased child and both biological parents. Additional studies are pursued when indicated by pathology review. Each case receives a multidisciplinary masked review process and parental written consent is obtained. The NYU institutional review board approved this study.

  Study phs003383

• WGBS data for ependymomas and normal controls (fetal and adult)

  Whole genome bisulfite sequencing data for 6 ependymomas plus 3 fetal controls (f1, f2, f4) and 3 adult controls (a2, a3, a4). See Mack, Witt et al. Nature 506(7489):445-50, 2014 (PMID: 24553142).

  Dataset EGAD00001000966

• 20210303_EGA_Melanoma Garg et al Nat Commun,  2021

  RNAseq of 55 melanoma tumors that were used as a validation dataset in Garg et al Nat Commun,  2021 Feb 18;12(1):1137. doi: 10.1038/s41467-021-21207-2.

  Dataset EGAD00001007055