14666 results for "PLOS+ONE+Pennsylvania+German+ACS+language+code+pdc+open+access+study"

in 19.18 milliseconds.

• Colorectal cancer functional annotation - Micro-C

  Ultra high resolution Micro-C data from multiple colorectal cancer cell lines

  Study EGAS50000000206

• Rucaparib in patients presenting a metastatic breast cancer with Homologous Recombination Deficiency, without germline BRCA1/2 mutation, pronostic value of high LOH genomic score and predictive value of HRDetect (WGS)

  Study EGAS00001004756

• Point-of-care monitoring of head and neck cancer treatment response and recurrence development using nanopore-based ctDNA consensus sequencing

  Study EGAS00001007090

• HeteroFiber (Single muscle fiber heterogeneity)

  Individual fibers of human skeletal muscle biopsies - collected at rest. m. vastus lateralis

  Study EGAS50000000418

• Pancreatic Cancer NGS WGS data

  Whole genome sequencing data for Pancreatic Cancer Samples exploring the tandem duplicator phenotype

  Study EGAS50000001078

• Whole gut virome analysis of 476 Japanese revealed a link between phage and autoimmune disease

  Elucidation of disease state by multi-layered omics analysis

  Study JGAS000415

• Reference epigenome OB56_N_PreA_smRNA-Seq data generated from KEP study

  A OB56_N_PreA_smRNA-Seq single end data for Preadipocytes(fat)

  Dataset EGAD00001003495

• Reference epigenome Fat-adipocyte04 mRNA-Seq data generated from KEP study

  Fat-adipocyte04 mRNA-Seq paired end data

  Dataset EGAD00001007158

• Reference epigenome Pancreas-Islet08 mRNA-Seq data generated from KEP study

  Pancreas-Islet08 mRNA-Seq paired end data

  Dataset EGAD00001007168

• Reference epigenome Islet-derived-iPSC01 WGBS data generated from KEP study

  Islet-derived-iPSC01 WGBS paired end data

  Dataset EGAD00001007144

• Reference epigenome Islet-derived-iPSC02 WGBS data generated from KEP study

  Islet-derived-iPSC02 WGBS paired end data

  Dataset EGAD00001007145

• Reference epigenome Fat-adipocyte03 mRNA-Seq data generated from KEP study

  Fat-adipocyte03 mRNA-Seq paired end data

  Dataset EGAD00001007157

• Reference epigenome Fat-adipocyte05 mRNA-Seq data generated from KEP study

  Fat-adipocyte05 mRNA-Seq paired end data

  Dataset EGAD00001007159

• Reference epigenome Islet-derived-MSC04 WGBS data generated from KEP study

  Islet-derived-MSC04 WGBS paired end data

  Dataset EGAD00001007143

• Reference epigenome IPS03_N_ENeuron_WGBS data generated from KEP study

  A IPS03_N_ENeuron_WGBS paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003475

• Reference epigenome IPS02_N_NPC_WGBS data generated from KEP study

  A IPS02_N_NPC_WGBS paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003474

• Reference epigenome OB57_D_PreA_mRNA-Seq data generated from KEP study

  A OB57_D_PreA_mRNA-Seq paired end data for Preadipocyte(fat)

  Dataset EGAD00001003487

• Reference epigenome CKD25_C_Podo_smRNA-Seq data generated from KEP study

  A CKD25_C_Podo_smRNA-Seq single end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003499

• Reference epigenome Islet-derived_MSC06 mRNA-Seq data generated from KEP study

  Islet-derived_MSC06 mRNA-Seq paired end data

  Dataset EGAD00001007878

• Reference epigenome Islet-derived-MSC01 WGBS data generated from KEP study

  Islet-derived-MSC01 WGBS paired end data

  Dataset EGAD00001007140

• Reference epigenome CKD25_C_Podo_mRNA-Seq data generated from KEP study

  A CKD25_C_Podo_mRNA-Seq paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003483

• RNA sequencing data to study "A biobank of patient-derived pediatric braintumor models"

  RNA sequencing data for the PDOX model EPD-613FH

  Dataset EGAD00001003573

• Pancreatic organoid - case samples

  This dataset contains 31 pancreatic organoid samples used in the 'organoid data of pancreatic cancers ' study

  Dataset EGAD00001004528

• Reference epigenome SMC05 h3k9me3 ChIP-Seq data generated from KEP study

  SMC05 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007336

• SFHS pedigrees

  Deep (>25x mean coverage) whole genome sequencing on 5-10 families drawn from the Scottish Family Health Study with four or more children.

  Dataset EGAD00001001214

• Reference epigenome Islet-derived-MSC02 WGBS data generated from KEP study

  Islet-derived-MSC02 WGBS paired end data

  Dataset EGAD00001007141

• Reference epigenome Pancreas-Islet07 mRNA-Seq data generated from KEP study

  Pancreas-Islet07 mRNA-Seq paired end data

  Dataset EGAD00001007167

• Reference epigenome CKD24_C_Podo_mRNA-Seq data generated from KEP study

  A CKD24_C_Podo_mRNA-Seq paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003482

• STREP GENE Study Genome-wide Genotyping by Illumina Array

  Genotyping of 43 cases of invasive GAS infection by Illumina HumanCore-24 array and Illumina Global Screening Array.

  Dataset EGAD00001004558

• Reference epigenome OB57_D_PreA_smRNA-Seq data generated from KEP study

  A OB57_D_PreA_smRNA-Seq single end data for Preadipocyte(fat)

  Dataset EGAD00001003496

• Reference epigenome Pancreas-Islet06 mRNA-Seq data generated from KEP study

  Pancreas-Islet06 mRNA-Seq paired end data

  Dataset EGAD00001007166

• Reference epigenome SMC01 h3k4me3 ChIP-Seq data generated from KEP study

  SMC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007311

• PRDM13 exome sequencing set

  Exome sequencing study on 4 individuals from a pedigree with CHH and cerebellar hypoplasia.

  Dataset EGAD00001008420

• Reference epigenome CKD24_C_Podo_smRNA-Seq data generated from KEP study

  A CKD24_C_Podo_smRNA-Seq single end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003498

• Reference epigenome Islet-derived-MSC03 WGBS data generated from KEP study

  Islet-derived-MSC03 WGBS paired end data

  Dataset EGAD00001007142

• Reference epigenome ADMSC05 h3k27me3 ChIP-Seq data generated from KEP study

  ADMSC05 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007375

• SFHS pedigrees_X10

  Deep (>25x mean coverage) whole genome sequencing on 5-10 families drawn from the Scottish Family Health Study with four or more children.

  Dataset EGAD00001003211

• Multiple_myeloma_precursor_genomics

  Whole genome sequencing of up to 45 multiple myeloma precursor samples and matched normals pairs.

  Study EGAS00001006312

• Juvenile Idiopathic Arthritis exome sequencing in a consanguineous family

  Identify the causative gene defect in three affected patients with JIA.

  Study EGAS00001003510

• BLUEPRINT Bisulfite-seq and Whole Genome Sequencing of mantle cell lymphoma

  Bisulfite-seq and Whole Genome Sequencing of mantle cell lymphoma

  Study EGAS00001001638

• Colorectal_organoids_and_tumour_tissue___Whole_Genome_X10

  Whole genome sequencing of single cell derived organoids from normal colon tissue and colorectal cancer.

  Study EGAS00001001100

• V4_Colorectal_panel_test

  Targeted gene screen of cell line tumours for testing the new V4 Colorectal gene panel.

  Study EGAS00001001807

• Mutational_signatures_and_clonal_dynamics_in_normal_human_tissues

  Somatic mutations (burdens and signatures) and clonal dynamics in normal human tissues

  Study EGAS00001002471

• OAC RNASeq

  Please see our related publication, 'Molecular profiling to predict immunochemotherapy outcomes in esophageal adenocarcinoma'

  Study EGAS00001006468

• Bolleboom-Gao peri-tumoral snRNA-seq glioblastoma dataset 2022/A

  Bolleboom-Gao peri-tumoral snRNA-seq glioblastoma dataset 2022/A

  Study EGAS00001006935

• Whole Genome Sequence and RNASeq Samples for Lung Cancer

  Short Read Whole Genome Sequence and RNASeq Samples for Lung Cancer

  Study EGAS00001007832

• Colon cancer targeted sequencing study contaning WBCs, primary tumor tissue and plasma samples

  In the setting of localized colon cancer (CC), circulating tumor DNA (ctDNA) monitoring in plasma has shown potential for detecting minimal residual disease (MRD) and predicting higher risk of recurrence. With the tumor-only sequencing approach, however, germline variants may be misidentified as somatic variations, precluding the possibility of tracking in up to 11% of patients due to a lack of known somatic mutations. In this study comprising 148 prospectively recruited localized CC patients, a custom 29-gene panel was utilized to sequence both tumor tissue and matched white blood cells (WBCs) to enhance the accuracy of sequencing results. Performing targeted sequencing of paired tumor tissue and WBCs samples detected additional somatic mutations and increased the number of patients eligible for MRD tracking in plasma, although MRD detection sensitivity was not increased. Furthermore, the germline testing approach revealed the presence of pathogenic germline variants, thereby helping identify patients at elevated risk of hereditary cancer syndromes.

  Study EGAS50000000059

• Genome-Wide Association Study of Endometrial Cancer in the Epidemiology of Endometrial Cancer Consortium (E2C2)

  Endometrial cancer, the most common gynecological malignancy in the United States, has both an environmental and genetic component. To this end, we conducted a genome-wide association study to identify genes involved in endometrial cancer using studies from the NCI-supported Epidemiology of Endometrial Cancer Consortium (E2C2). For the discovery stage we included samples from 6 cohort and 7 case control studies through 2007. The total number of cases genotyped were 2,307, white women of European descent, and 2,307 matched controls using the Illumina HumanOmniExpress platform. We conducted the replication using the Infinium HumanExome BeadChip, which successfully genotyped 177,139 variants in 1055 cases and 1778 controls from four ethnically diverse studies that are part of the Epidemiology of Endometrial Cancer Consortium (E2C2). The overall goal is to determine whether certain genotypes are predictive of future endometrial cancer risk, and whether the genotypes interact with established endometrial risk factors.

  Study phs000893

• The Finland-United States Investigation of NIDDM Genetics (FUSION) Study

  The Finland-United States Investigation of NIDDM Genetics (FUSION) study is a long-term effort to identify genetic variants that predispose to type 2 diabetes (T2D) or that impact the variability of T2D-related quantitative traits. The initial effort involved linkage analysis of affected-sibling-pair (ASP) families based on over 5,000 individuals living in Finland, and association fine mapping based on these family members and additional T2D cases and controls. We completed a genome-wide association scan on 1161 T2D cases and 1174 normal glucose tolerant (NGT) controls. Individual-level data is available for the 919 T2D cases and 787 NGT controls who reconsented to the use of their data or are deceased (phs000100). In addition, we selected these 919 T2D cases and a matched set of 919 NGT controls (774 overlapping with GWAS) for targeted sequencing of 78 genes associated with glucose, insulin, and/or lipids. 400 of these T2D cases were also chosen for whole-exome sequencing (phs000702).

  Study phs000867

• International Multiple Sclerosis Genomics Consortium (IMSGC) Genome Wide Association Study of Multiple Sclerosis

  The goal of this study is to perform a comprehensive allelic and genotypic association analysis of the entire human genome in multiple sclerosis. The recent definitive linkage genome screen demonstrated that there is no other MS risk gene with an effect size anywhere near that of the MHC. However, linkage analysis is significantly hampered by reduced power in the face of heterogeneity and requires multiplex families, which also hampers acquiring an appropriate sample size. In contrast, genotyping 500K SNPs allows us to survey a significant amount of the genome (we estimate >70%) directly for allelic or genotypic association. This uses the improved power of association analysis and can also take advantage of the linkage disequilibrium relationships among SNPs to further increase power (e.g. haplotype analysis). Quality control and data analysis are significant challenges. We will initially perform substantial QC checks and analyze the data using both TDT and AFBAC approaches. Multigenic interactions will also be tested using MDR.

  Study phs000139