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• Saliva microbiota in Finnish children

  The human intestinal microbiota may play a role in the development of overweight and obesity. However, associations between saliva microbiota and body mass index (BMI) have been sparsely studied, although the oral cavity is the major gateway for microbes into the body. The aim of this study was to identify associations between the saliva microbiota and BMI categories in Finnish children aged 9-14 years.

  Study EGAS00001003039

• Gut metagenome/FINRISK 2002 (Salosensaari et al. Nature Comms 2021)

  The main goal of the project is the study the associations between the gut metagenome and human health. The dataset contains data for n=7211 FINRISK 2002 participants who underwent fecal sampling. Demultiplexed shallow shotgun metagenomic sequences were quality filtered and adapter trimmed using Atropos (Didion et al., 2017), and human filtered using Bowtie2 (Langmead and Salzberg, 2012).

  Study EGAS00001005020

• A GWAS meta-analysis on severe acne on a European population of 26,722 individuals

  Acne vulgaris is a highly heritable common, chronic inflammatory disease of the skin. We performed a genome-wide association study of 3,823 cases and 16,144 controls followed by meta-analysis with summary statistics from a previous study, with a total sample size of 26,722. We identified 20 independent association signals at 15 risk loci, 12 of which have not been previously implicated in the disease.

  Study EGAS00001003278

• RNAseq Iron-Treated iPSC-derived Microglia

  Iron accumulation in microglia has been observed in Alzheimer’s disease and other neurodegenerative disorders and is thought to contribute to disease progression through various mechanisms including neuroinflammation. To study the interaction between iron accumulation and inflammation, we treated human induced pluripotent stem cell-derived microglia (iPSC-MG) with an increasing concentration of iron, in combination with inflammatory stimuli such as interferon gamma and amyloid β, and performed RNA sequencing.

  Study EGAS00001006112

• Somatic L1 retrotranspositions in normal human cells

  Genomic mutations accumulate in normal cells during lifetime; however, somatic mosaicism generated by mobilization of transposable elements is not well understood. Here, we explored whole-genome sequences of single-cell expanded clones to illustrate the mutational landscape produced by L1. In addition, we analyzed promoter DNA methylation status of retrotransposition-competent L1 and read-through RNA expression profiles to investigate the epigenetic regulation of L1 activity.

  Study EGAS00001006213

• Genomic abnormalities of TP53 define distinct risk groups of paediatric B-cell non-Hodgkin lymphoma

  TP53 alterations are associated with adverse outcome in many malignancies; however, whilst common in paediatric B-NHL, their utility as a risk classifier in this setting is unknown. We evaluated the clinical significance of TP53 abnormalities (mutations, deletion and/or copy number neutral loss of heterozygosity) in a large UK paediatric B-NHL cohort (n=95) and determined their impact on survival.

  Study EGAS00001005617

• Characterization of a novel MEF2D-BCL9 fusion positive acute lymphoblastic leukemia cell line WXS derived

  The lack of a well-characterized model system for many subtypes of leukemia hinders the development of targeted therapy and immunotherapy. A novel in-house-made pediatric MEF2D-BCL9 fusion positive acute lymphoblastic leukemia cell line was characterized. This cell line is highly presentative for the major clone of the original material and thus a valuable resource to examine novel therapeutic for MEF2D fused patients.

  Study EGAS00001006801

• A GWAS of Progression in Multiple Sclerosis

  This is a genome wide association study (GWAS) of disease progression in multiple sclerosis (MS). The study involved a total of 12584 cases recruited from multiple centres around the world and tested at 7.8 million common autosomal single nucleotide variants. The data from the European cases (n=4853) are included in this EGA submission in 10 country specific data sub-sets.

  Study EGAS00001007162

• Preeclampsia InterPregGen Consortium: Whole Genome Sequencing of 100 unrelated Uzbeks (DNA samples from the Institute of Immunology, Uzbek Academy of Sciences, Tashkent, Uzbekistan; Republic Specialized Scientific Practical Medical Centre of Obstetrics and Gynecology, Tashkent, Uzbekistan)

  Whole genome sequencing of 100 unrelated Uzbeks in order to impute genotypes into PE cases and controls from Uzbekistan and to provide genetic data and infrastructure for future genetic studies in Uzbekistan 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 in Tashkent, Uzbekistan at the Institute of Immunology, Uzbek Academy of Sciences and at the Republic Specialized Scientific Practical Medical Centre of Obstetrics and Gynecology.

  Dataset EGAD00001005466

• Strand-seq of hematopoietic stem and progenitor cells along human aging

  Somatic mosaicism (SM), referring to the presence of somatic mutations in sub-populations of cells within healthy individuals, is associated with an increased risk of a variety of diseases, including cancer. Blood is at particularly high risk of SM, given its rapid turnover and functionally- heterogeneous cell-type composition. While the roles of point mutations and large-scale rearrangements in blood SM have been scrutinised in recent years, the functional impact of mosaic structural variants (mSVs) remains poorly understood. Using haplotype-resolved single-cell multi-omics based on Strand-seq technology, we explored the mSV landscape of human hematopoietic stem and progenitor cells (HSPCs).

  Dataset EGAD00001009402

• Inherited MUTYH mutations cause elevated somatic mutation rates and distinctive mutational signatures in normal human cells

  Cellular DNA damage caused by reactive oxygen species is repaired by the base excision repair (BER) pathway which includes the DNA glycosylase MUTYH. Inherited biallelic MUTYH mutations cause predisposition to colorectal adenomas and carcinoma. However, the mechanistic progression from germline MUTYH mutations to MUTYH-Associated Polyposis (MAP) is incompletely understood. Here, we sequenced normal cell DNAs from 10 individuals with MAP and study the somatic mutation burden and mutational signatures.

  Dataset EGAD00001007997

• GEX CITE-Seq performance dataset

  To understand the impact of enzymatic treatments on gene expression and epitope preservation on major immune cell populations, skin dissociation (SkinD) and solid soft tumor dissociation (TumorD) were tested on three healthy PBMC samples in triplicate (D1, D2, D3), against an untreated control. CITE-seq performance was assessed on a solid biopsy cohort of 11 samples (5 healthy skin samples, 3 primary melanoma samples, 3 melanoma metastasis samples) as well as on a liquid biopsy PBMC cohort consisting of three healthy donors and three immunotherapy-treated melanoma patients. This dataset contains the GEX data for each sample. Data is provided in the form of pooled BAM files. Linkage between samples, BAM files and hashtags is provided in a separate linkage file.

  Dataset EGAD00001008418

• ADT/SPEX CITE-Seq performance dataset

  To understand the impact of enzymatic treatments on gene expression and epitope preservation on major immune cell populations, skin dissociation (SkinD) and solid soft tumor dissociation (TumorD) were tested on three healthy PBMC samples in triplicate (D1, D2, D3), against an untreated control. CITE-seq performance was assessed on a solid biopsy cohort of 11 samples (5 healthy skin samples, 3 primary melanoma samples, 3 melanoma metastasis samples) as well as on a liquid biopsy PBMC cohort consisting of three healthy donors and three immunotherapy-treated melanoma patients. This dataset contains the ADT/SPEX data for each sample. Data is provided in the form of pooled BAM files. Linkage between samples, BAM files and hashtags is provided in a separate linkage file.

  Dataset EGAD00001008419

• scRNA-seq and snRNA-seq of trophoblast stem cells (TSCs) differentiation into extravillous trophoblast organoids (EVTs)

  This dataset is part of a study that aims to compare in vivo human trophoblast differentiation into EVTs to different in vitro trophoblast organoids using single-cell and single-nuclei RNA sequencing. This specific dataset includes scRNA-seq and snRNA-seq data from trophoblast stem cells (TSCs). Trophoblast stem cell (TSC) lines BTS5 and BTS11 derived by Okae and colleagues were grown as described previously (Okae et al. 2018) together with EVT differentiation media. This study shows that the main regulatory programs mediating EVT invasion in vivo are preserved in in vitro models of EVT differentiation from primary trophoblast organoids and trophoblast stem cells. Data for primary trophoblast organoids is available under E-MTAB-12650.

  Dataset EGAD00001010017

• UCSF-CASCADE analysis of metastatic prostate cancer

  Targeted cancer therapy inevitably selects for tumor cells that harbor some form of therapy resistance. This phenomenon is a principle reason why advanced prostate cancer, which can be treated with agents targeting androgen signaling dependency and DNA repair failure, is a lethal condition. The influence of a patient's clinical history and disease evolution on how their disseminated tumors develop resistance has been difficulty to study, because few autopsy studies have been performed in heavily treated patients with DNA-repair deficient metastatic castration-resistant prostate cancer (mCRPC). Here, we assessed how resistance to targeted cancer therapies evolved in an autopsy cohort of 54 mCRPC tumors from six men. This dataset includes targeted sequencing, exome sequencing, and RNA-seq conducted on these biopsies.

  Dataset EGAD00001010275

• Single-cell RNA sequencing on 11,138 single T cells from 12 colon and rectal cancer (CRC) patients

  Deep single-cell RNA sequencing data for 11,138 T cells from tumour, adjacent normal tissue and peripheral blood of treatment-naive CRC patients. The DATA ACCESS AGREEMENT is provided at https://github.com/zhangyybio/single-T-cell-data-access. Applicants can request access to the data by directly downloading it or by sending an email to cancerpku@pku.edu.cn. The process that is used to approve an application includes verifying the institution, participants and research purposes of the application. In general this process will take about two weeks. In principal, any academic research institutions complying with the laws and bioethic regulation policies of China will be approved.

  Dataset EGAD00001003910

• Whole exome sequencing data of tumor/normal pairs for the study "TGF-β attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells"

  Tumor DNA was extracted from formalin-fixed and paraffin embedded tumors of a large cohort of bladder cancer patients before treatment with anti-PD-L1. Normal DNA was extracted from matched PBMCs. Whole exome sequencing was performed. This is a subset of patients for which RNA sequencing is also provided (with more detailed phenotypic information).

  Dataset EGAD00001004218

• Whole-exome variant calling of individuals from the study of sepsis and acute distress respiratory syndrome in Spain

  Research study of genetic susceptibility in sepsis and acute distress respiratory syndrome (ARDS). ARDS is a lethal condition, often derived from sepsis, lacking effective targeted treatments. Growing evidence indicates that the genetic component significantly influences susceptibility and severity of ARDS. We used whole-exome sequencing data in case-control studies to characterize genetic factors associated with an increase of ARDS risk.

  Study EGAS50000001119

• Whole-Genome Sequencing in Multiplex Epilepsy Families

  Epilepsy is one of the most common neurologic disorders, affecting approximately 4% of individuals at some time in their lives. More than 30% of people with epilepsy continue to have seizures despite treatment, and improved approaches to treatment and prevention are sorely needed. In the search for new strategies to reduce the burden of disease, the discovery of specific genes that influence risk offers a novel opportunity to clarify pathogenic mechanisms, identify susceptible individuals prior to seizure onset, and treat and prevent seizures in people at risk. Despite clear evidence of the importance of genetics in susceptibility to epilepsy, only limited progress has been made in identifying the specific genes that influence risk. One of the greatest challenges for genetic research on this disorder is its extreme clinical and genetic heterogeneity. Although epilepsy is broadly defined by recurrent unprovoked seizures, it is so variable in its clinical manifestations, natural history, and treatment response that most epileptologists view it as a collection of different syndromes ("epilepsies") with distinct etiologies. The genetic effects on susceptibility are also likely to be extremely variable, ranging from rare variants with high penetrance (some of which produce Mendelian patterns of inheritance) to common variants with low penetrance. Recent findings strongly suggest that rare gene variants play a major role in the genetic architecture of the epilepsies. The purpose of this study was to discover new genetic risk factors for epilepsy. The primary approach was to use whole-genome sequencing to interrogate classes of genetic variants, including very rare variants, in multiplex families. Our main hypothesis was that, in at least some proportion of these families, a single variant would explain all instances of epilepsy. Variants identified within the families could then be tested for cosegregation within the family and also validated by seeing enrichment in sporadic epilepsy cases. Furthermore, understanding the impact of rare variation in epilepsy also has the potential to provide insight into the genetic architecture of other complex human diseases. Our analysis focused on families containing multiple individuals with non-acquired (idiopathic or unknown cause) epilepsy under the hypothesis that these would be enriched for genetic control. The families studied had been previously collected and phenotyped in detail, and contain an average of 3.8 individuals per family with a range of different types of epilepsy. We selected one or two affected individuals from each family for sequencing. This work has generated NGS data on 60 samples from 29 multiplex epilepsy families. We have established that, under a monogenic model of inheritance, sequencing pairs of distantly related relatives is an effective method for reducing the number of candidate variants. Critically, we have found that no single variant can explain a large proportion of these epilepsy families. This work emphasizes that identifying causal variants among the many genetic candidates found in this work will require very large sample sizes and gene-based analyses.

  Study phs000690

• INCLUDE Data Hub: NDA GUIDs for Down Syndrome Research

  The INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) Project is an NIH-wide collaboration that seeks to improve health and quality of life for people with Down syndrome. The INCLUDE Project Data Coordinating Center (DCC) and partners created the INCLUDE Data Hub, a centralized data resource that allows access to large-scale clinical and multi-omics datasets specific to Down syndrome and supports collaborative, cloud-based analysis to accelerate scientific discoveries related to Down syndrome and its co-occurring conditions. The INCLUDE Data Hub includes an innovative researcher data repository, a portal, and a cloud-based platform for data analysis. The portal provides access to de-identified demographics and clinical metadata, as well as multi-omics datasets, including whole genome sequences, transcriptomes, proteomes, and metabolomes. The INCLUDE Data Hub uses a 'registered access' model, whereby users can access the portal and associated data after agreeing to terms of use. From the Data Hub, users can also reach 'controlled access' data (e.g., whole genome sequences) only for those studies for which they have obtained approval by an NIH Data Access Committee through the corresponding dbGaP study. A thoughtful linkage strategy to identify duplicate participants across studies enables researchers to merge multiple datasets and data types to create richer datasets and reduce costly redundancies in large-scale data generation (e.g., -omics) while mitigating potential risks introduced by linkage.Based on recommendations from the NICHD Office of Data Science and Sharing, which completed a comprehensive assessment of existing Privacy Protecting Record Linkage (PPRL) and associated data governance solutions, the INCLUDE DCC and the associated NIH Steering Committee have implemented NIMH Data Archive Global Unique Identifiers (NDA GUIDs) in the INCLUDE Data Hub. The NDA GUID Tool is a government-owned and operated PPRL technology that enables researchers to link data from multiple sources to the same individual without revealing personally identifiable information (PII). With the NDA GUID Tool, the same participant information will return the same GUID whenever or wherever it is entered. The GUID itself is not personally identifiable information or protected health information. Sharing data with NDA GUIDs allows approved researchers to link together data on a single participant, even if the data were collected at different locations or through different studies or are distributed from different data repositories. This dbGaP study enables researchers to access an INCLUDE GUID Mapping File that includes all available NDA GUIDs and their associations to study-specific Participant IDs across studies in the INCLUDE Data Hub. This dbGaP study only distributes NDA GUIDs associated with the INCLUDE Data Hub datasets. To access the associated controlled-access datasets, add the corresponding dbGaP studies to your Data Access Request. A list of these studies is available on the INCLUDE Data Hub Portal: https://portal.includedcc.org/studies.

  Study phs003678

• Kids First: Whole Genome Sequencing in Structural Defects of the Neural Tube

  Myelomeningocele (aka meningomyelocele, MM) is the most severe form of spina bifida, a neural tube defect (NTD) in humans and the most common CNS birth defect. MM is considered a genetically complex disease, and occurs in 3.72/10,000 live US birth, and is partly preventable with prenatal folate, but the genetic basis and the mechanisms by which folate work to reduce disease incidence remain obscure. MM is associated nearly uniformly with prenatal hydrocephalus and the Arnold-Chiari malformation, as well as paraplegia and lifelong neuromotor disability. The genes for several rare syndromic forms of NTDs are known, but the causes for the majority with sporadic MM remain unknown. Despite the importance of MM, most previous research has been limited to targeted sequencing and association studies of folate metabolism genes, or very small-scale exome sequencing. We hypothesize that de novo mutations (DNMs) produce likely gene disrupting (LGD) events that contribution to MM risk. Using conservative estimates of between 50-100 recurrently mutated discoverable genes contributing to risk, and our preliminary data demonstrating an excess of LGD DNMs in MM compared with control individuals, we estimate that with a cohort size of 1000 trios, we should uncover between 5-20 new recurrently mutated genes underlying MM, with minimal false-discovery. With this in mind, we formed the Spina Bifida Sequencing Consortium, and established a platform for data and sample sharing. Preliminary analysis of our first batch of 100 trios analyzed by WGS from GMKF suggests a wealth of important gene mutations. We have embarked on a new recruitment effort of an additional cohort of 400 new simplex MM trios, in collaboration with the US Spina Bifida Association, consented trios to allow for data sharing, and have performed detailed sample quality control. We also have preliminary data that use of dried bloodspot DNA from trios performs comparably to whole blood DNA, so will be happy to swap saliva for bloodspot recruitment at NIH's preference. This cohort is now half-way assembled, with the remaining cohort to be ascertained in the next 6 months. We have established a workflow for de novo SNP/INDEL/SV detection from WGS and have ample computer storage and nodes to see the project to completion. We also plan to continue recruitment into the future with the goal of 2000 trios in the next 5 years. We propose a detailed bioinformatics workflow to identify gene mutations within a statistical framework, considering detailed scRNA expression profiling from developing mammalian neural tube, and have developed a robust functional validation workflow using Xenopus and mouse gene targeting. Our project has the potential to uncover a host of causes for this most common of the CNS birth defects, paving the way for future breakthroughs in detection, treatment, and prevention.

  Study phs002591

• Genetic Testing to Understand and Address Renal Disease Disparities

  People of African ancestry (Blacks) have increased risk of kidney failure due to numerous socioeconomic, environmental, and clinical factors. Two variants in the APOL1 gene (GeneID: 8542) are now thought to account for much of the racial disparity associated with hypertensive kidney failure in Blacks. One in 7 AAs carry this risk allele, but it is nearly absent in other populations; the variants protect against infection with African trypanosomiasis which is endemic to Africa. However, this knowledge has not been translated into clinical care to help improve patient outcomes and address disparities. It is unknown whether patient or clinician knowledge of genetic risk impacts patient care (i.e., renal surveillance, BP medication use/intensification) or outcomes such as BP control and CKD. GUARDD is a randomized trial to evaluate the effects and challenges of incorporating genetic risk information into primary care. Hypertensive, nondiabetic, adults with self-reported African ancestry, without kidney dysfunction, are recruited from diverse clinical settings and randomized to undergo APOL1 genetic testing at baseline (intervention) or at one year (waitlist control). Prior to enrolling their patients, providers are educated about genomics and APOL1. Guided by a genetic counselor, trained staff return APOL1 results to patients and provide low-literacy educational materials. Real-time clinical decision support tools alert clinicians of their patients' APOL1 results and associated risk status at the point of care. Our academic - community clinical partnership designed a study to generate information about the impact of genetic risk information on our primary outcome - patient care (renal surveillance by clinicians, of serum creatinine and/or urine albumin tests. with a sub-aim of reduction of systolic blood pressure). Secondary outcomes include impact on primary outcomes at 12 months, psycho-behavioral differences of patients between groups and over time, clinician knowledge, attitudes and beliefs at baseline and 12 months, and differences in outcomes between those tested and not tested. GUARDD will help establish the effective implementation of APOL1 risk informed management of hypertensive patients at high risk of CKD, and will provide a robust framework for future endeavors to implement genomic medicine in diverse clinical practices. It will also add to the important dialog about factors that contribute to and may help eliminate racial disparities in kidney disease.

  Study phs001620

• Region-specific Transcriptome Analysis of the Human Retina and Retinal Pigment Epithelium (RPE)/Choroid

  Proper spatial differentiation of retinal cell types is necessary for normal human vision. Many retinal diseases, such as Best disease and male germ cell associated kinase (MAK)-associated retinitis pigmentosa, preferentially affect distinct topographic regions of the retina. While much is known about the distribution of cell types in the retina, the distribution of molecular components across the posterior pole of the eye has not been well-studied. To investigate regional difference in molecular composition of ocular tissues, we assessed differential gene expression across the temporal, macular, and nasal retina and retinal pigment epithelium (RPE)/choroid of human eyes using RNA-Seq. RNA from temporal, macular, and nasal retina and RPE/choroid from four human donor eyes was extracted, poly-A selected, fragmented, and sequenced as 100 bp read pairs. Digital read files were mapped to the human genome and analyzed for differential expression using the Tuxedo software suite. Retina and RPE/choroid samples were clearly distinguishable at the transcriptome level. Numerous transcription factors were differentially expressed between regions of the retina and RPE/choroid. Photoreceptor-specific genes were enriched in the peripheral samples, while ganglion cell and amacrine cell genes were enriched in the macula. Within the RPE/choroid, RPE-specific genes were upregulated at the periphery while endothelium associated genes were upregulated in the macula. Consistent with previous studies, BEST1 expression was lower in macular than extramacular regions. The MAK gene was expressed at lower levels in macula than in extramacular regions, but did not exhibit a significant difference between nasal and temporal retina. The regional molecular distinction is greatest between macula and periphery and decreases between different peripheral regions within a tissue. Datasets such as these can be used to prioritize candidate genes for possible involvement in retinal diseases with regional phenotypes. Reprinted from Whitmore, S.S., Wagner, A.H., DeLuca, A.P., Drack, A.V., Stone, E.M., Tucker, B.A., Zeng, S., Braun, T.A., Mullins, R.F., Scheetz, T.E., 2014. Transcriptomic analysis across nasal, temporal, and macular regions of human neural retina and RPE/choroid by RNA-Seq. Experimental Eye Research. Used under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license. Additional RNA sequencing was performed on temporal, macular, nasal, inferior, and superior retina from a fifth subject and is included in this dataset. See original publication for details.

  Study phs001151

• Discovery of Colorectal Cancer Susceptibility Genes in High-Risk Families

  Colorectal cancer remains one of the most common cancers in the US with 146,970 new diagnoses and 49,920 deaths estimated for 2009. Colon cancer is also one of the most familial of cancers. Individuals with a first-degree relative with colon cancer have a 2- to 3-fold increased risk, and those with more than one first-degree relative with colon cancer, or a single first-degree relative affected at age 50 years, have a 3- to 6-fold greater risk than those with no family history. The most prominent high-risk colorectal cancer susceptibility genes, APC, MLH1, MSH2, MSH6, and PTEN, were all discovered more than a decade ago. Currently, mutation screening of these genes, plus a short list of additional genes that are responsible for a very small fraction of colorectal cancer, plays an important role in the clinical management of individuals with a strong family history of the disease or syndromic evidence for the presence of a gene mutation. At the other end of the risk spectrum, genome-wide association studies have identified a number of common alleles with very modest effects on colorectal cancer risk; their clinical utility has yet to be established. However, taken together, the known spectrum of genetic effects only explain about one quarter of the overall familial excess of colorectal cancer. It should be emphasized that, at present, the vast majority of individuals seen at familial cancer clinics are counseled on the basis of their family history alone because they do not have mutations in the known susceptibility genes. Accordingly, the long-term objective of this project is to identify the majority of genes responsible for the unexplained component of inherited colorectal cancer risk. Nextgen DNA sequencing is well suited for application to research questions in genetic susceptibility for which linkage analysis is confounded by extensive genetic heterogeneity. Taking advantage of unparalleled familial cancer genetics resources available through the Utah Population Database, candidate genes have been identified by sequencing all of the gene exons in the human genome from a series of colorectal cancer cases with a very strong family history of colorectal cancer not explained by one of the currently known high-risk susceptibility genes.

  Study phs001787

• Sequencing data (BAM files) from - A mutation in VPS15 (PIK3R4) causes a ciliopathy and affects IFT20 release from the cis-Golgi. The Data Access Committee (DAC) is responsible for sequencing data release to external requestors based on consent and/or National Research Ethics terms.

  Dac EGAC00001000552

• The T cell receptor repertoire of tumor infiltrating T cells is predictive and prognostic for cancer survival

  Transcriptomic sequencing on pre-immunotherapy melanoma patients.

  Dataset EGAD00001007710

• Usher patients and USH2A-associated RP patients

  This is a Next Generation Sequencing approach based on whole Usher Syndrome genes sequencing with the aim of diagnosing USH patients and USH2A-associated RP patients

  Dataset EGAD50000000687

• 10X single-nuclei RNA sequencing

  Single-nuclei sequencing data from four neuroblastoma patients. Each patient was run on two lanes, resulting in two runs per patient. Data is provided in paired-end fastq files.

  Dataset EGAD50000000727

• NeoPAL study - RNAseq and Targeted DNA sequencing data

  This dataset is composed of 159 RNAseq data (fastq, bam and count tables) and 35 targeted DNAseq data (fastq).

  Dataset EGAD50000001490

• ICR Centre for Paediatric Experimental Medicine

  This is the DAC for granting access to consented patient data generated in association with the Centre for Paediatric Experimental Medicine at ICR. Data access can be granted by bioinformatician Claire Lynn: claire.lynn@icr.ac.uk

  Dac EGAC50000000362

• RNA-seq cohort of non-tumorous breast tissue from BRCA1/2 carriers

  Non-tumorous breast tissues from BRCA1 or BRCA2 carriers were subject to RNA sequencing. The total number of samples is 130.

  Dataset EGAD00001006746

• Single-cell RNA sequencing of CML patients

  This dataset contains 15 single-cell RNA sequencing samples from 6 CML patients before and after TKI-cessation. The raw data is available as fastq files.

  Dataset EGAD00001012842

• MGA-NUTM1 fusion in high grade spindle cell sarcoma Data

  There is a total of 4 sample data (2 WGS and 2 RNAseq) belong to 2 patient deposited in this study.

  Dataset EGAD00001004479

• GenomeDenmark Phase 2 - HLA validation sequencing data

  Validation of HLA variation of 8 individuals from the GenomeDenmark Phase 2 study. Validation is performed Sanger sequencing of selected amplicons (5-10 amplicons per sample).

  Dataset EGAD00001003454

• Splenic Marginal Zone Lymphoma with villous lymphocytes exome sequencing

  Splenic Marginal Zone Lymphoma with villous lymphocytes exome sequencing

  Dataset EGAD00001000081

• APP-G-1

  Array data from 4778 individuals from general population of rural Uganda

  Dataset EGAD00010000965

• 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

• Average_5hmC_DIFF_Roadmap

  Average hydroxymethylation difference 12 months vs 0 months at Roadmap Epigenomics chromatin state annotations from different cell types. Data from 8 individuals.

  Dataset EGAD00010002415

• Sequencing data for oesophageal and related samples - OACs release 1 (RNA)

  Dataset EGAD00001002259

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

  Dataset EGAD00001008058

• WXS_TALL_t_14_16_translocation

  Whole exome-sequencing for patients with t(14;16) translocated T-ALL.

  Dataset EGAD50000001783

• CSC DDR dataset

  CSC DDR dataset contains 4 bam files of two pairs of colorectal CSCs sensitive and resistant to ATR or CHK1 inhibitor

  Dataset EGAD00001006774

• Targeted capture ctDNA Library CRCQV42Run017-8

  Targeted capture ctDNA Library CRCQV42Run017-8 from patient PBC0001224, plasma 15 month sample

  Dataset EGAD00001010415

• Targeted capture ctDNA Library CRCQV42Run035-24

  Targeted capture ctDNA Library CRCQV42Run035-24 from patient PBC0004565, plasma 9 month sample

  Dataset EGAD00001010588

• Targeted capture ctDNA Library CRCQV42Run045-12

  Targeted capture ctDNA Library CRCQV42Run045-12 from patient PBC0001068, plasma 3 month sample

  Dataset EGAD00001010683

• Reference epigenome SMC07 mRNA-Seq data generated from KEP study

  SMC07 mRNA-Seq paired end data

  Dataset EGAD00001007174

• Targeted capture ctDNA Library CRCQV42Run017-11

  Targeted capture ctDNA Library CRCQV42Run017-11 from patient PBC0001845, plasma 6 month sample

  Dataset EGAD00001010404

• Targeted capture ctDNA Library CRCQV42Run024-9

  Targeted capture ctDNA Library CRCQV42Run024-9 from patient PBC0003641, plasma baseline sample

  Dataset EGAD00001010475

• Targeted capture ctDNA Library CRCQV42Run032-8

  Targeted capture ctDNA Library CRCQV42Run032-8 from patient PBC0001589, buffy coat sample

  Dataset EGAD00001010547

• Targeted capture ctDNA Library CRCQV42Run039-21

  Targeted capture ctDNA Library CRCQV42Run039-21 from patient PBC0003014, plasma 3 month sample

  Dataset EGAD00001010616

• Targeted capture ctDNA Library CRCQV42Run044-18

  Targeted capture ctDNA Library CRCQV42Run044-18 from patient PBC0003376, plasma 12 month sample

  Dataset EGAD00001010668

• Targeted capture ctDNA Library CRCQV42Run046-20

  Targeted capture ctDNA Library CRCQV42Run046-20 from patient PBC0001176, plasma 3 month sample

  Dataset EGAD00001010711

• Targeted capture ctDNA Library CRCQV42Run041-24

  Targeted capture ctDNA Library CRCQV42Run041-24 from patient PBC0005116, plasma 3 month sample

  Dataset EGAD00001010633

• Reference epigenome Pancreas-Islet07 WGBS data generated from KEP study

  Pancreas-Islet07 WGBS paired end data

  Dataset EGAD00001007146

• Reference epigenome Fat-adipocyte03 WGBS data generated from KEP study

  Fat-adipocyte03 WGBS paired end data

  Dataset EGAD00001007147

• Targeted capture ctDNA Library CRCQV42Run035-19

  Targeted capture ctDNA Library CRCQV42Run035-19 from patient PBC0002853, plasma 9 month sample

  Dataset EGAD00001010584

• Reference epigenome Fat-adipocyte04 WGBS data generated from KEP study

  Fat-adipocyte04 WGBS paired end data

  Dataset EGAD00001007148

• Reference epigenome SMC05 mRNA-Seq data generated from KEP study

  SMC05 mRNA-Seq paired end data

  Dataset EGAD00001007173

• Reference epigenome SMC08 mRNA-Seq data generated from KEP study

  SMC08 mRNA-Seq paired end data

  Dataset EGAD00001007175

• Reference epigenome SMC09 mRNA-Seq data generated from KEP study

  SMC09 mRNA-Seq paired end data

  Dataset EGAD00001007176

• Reference epigenome Fat-adipocyte05 WGBS data generated from KEP study

  Fat-adipocyte05 WGBS paired end data

  Dataset EGAD00001007149

• WGS files for Zhang GenomePaint paper

  WGS files for GenomePaint paper titled "Exploration of coding and non-coding variants in cancer using GenomePaint."

  Dataset EGAD00001006679

• Targeted capture ctDNA Library CRCQV42Run031-21

  Targeted capture ctDNA Library CRCQV42Run031-21 from patient PBC0001413, buffy coat sample

  Dataset EGAD00001010532

• Targeted capture ctDNA Library CRCQV42Run031-9

  Targeted capture ctDNA Library CRCQV42Run031-9 from patient PBC0001304, buffy coat sample

  Dataset EGAD00001010537

• Targeted capture ctDNA Library CRCQV42Run032-22

  Targeted capture ctDNA Library CRCQV42Run032-22 from patient PBC0001653, buffy coat sample

  Dataset EGAD00001010545

• Targeted capture ctDNA Library CRCQV42Run032-21

  Targeted capture ctDNA Library CRCQV42Run032-21 from patient PBC0001627, buffy coat sample

  Dataset EGAD00001010544

• Targeted capture ctDNA Library CRCQV42Run036-23

  Targeted capture ctDNA Library CRCQV42Run036-23 from patient PBC0002406, plasma 21 month sample

  Dataset EGAD00001010601

• Reference epigenome Islet-derived-iPSC04 WGBS data generated from KEP study

  Islet-derived_iPSC04 WGBS paired end data

  Dataset EGAD00001007877

• Targeted capture ctDNA Library CRCQV42Run041-17

  Targeted capture ctDNA Library CRCQV42Run041-17 from patient PBC0002406, plasma 9 month sample

  Dataset EGAD00001010628

• Targeted capture ctDNA Library CRCQV42Run041-12

  Targeted capture ctDNA Library CRCQV42Run041-12 from patient PBC0001413, plasma 3 month sample

  Dataset EGAD00001010626

• Targeted capture ctDNA Library CRCQV42Run044-20

  Targeted capture ctDNA Library CRCQV42Run044-20 from patient PBC0005209, plasma 6 month sample

  Dataset EGAD00001010670

• Targeted capture ctDNA Library CRCQV42Run044-22

  Targeted capture ctDNA Library CRCQV42Run044-22 from patient PBC0006040, plasma 6 month sample

  Dataset EGAD00001010672

• Targeted capture ctDNA Library CRCQV42Run046-10

  Targeted capture ctDNA Library CRCQV42Run046-10 from patient PBC0001552, plasma 12 month sample

  Dataset EGAD00001010701

• Targeted capture ctDNA Library CRCQV42Run046-11

  Targeted capture ctDNA Library CRCQV42Run046-11 from patient PBC0001284, plasma 6 month sample

  Dataset EGAD00001010702

• Targeted capture ctDNA Library CRCQV42Run045-5

  Targeted capture ctDNA Library CRCQV42Run045-5 from patient PBC0002851, plasma 6 month sample

  Dataset EGAD00001010697

• Targeted capture ctDNA Library CRCQV42Run046-12

  Targeted capture ctDNA Library CRCQV42Run046-12 from patient PBC0001135, plasma 6 month sample

  Dataset EGAD00001010703

• Targeted capture ctDNA Library CRCQV42Run046-19

  Targeted capture ctDNA Library CRCQV42Run046-19 from patient PBC0001527, plasma 9 month sample

  Dataset EGAD00001010710

• Targeted capture ctDNA Library CRCQV42Run046-16

  Targeted capture ctDNA Library CRCQV42Run046-16 from patient PBC0001042, plasma 3 month sample

  Dataset EGAD00001010707

• Targeted capture ctDNA Library CRCQV42Run42-6

  Targeted capture ctDNA Library CRCQV42Run42-6 from patient PBC0001224, plasma 9 month sample

  Dataset EGAD00001010830

• Reference epigenome Islet-derived-MSC08 WGBS data generated from KEP study

  Islet-derived_MSC08 WGBS paired end data

  Dataset EGAD00001007876

• Targeted capture ctDNA Library CRCQV42Run028-11

  Targeted capture ctDNA Library CRCQV42Run028-11 from patient PBC0003643, plasma baseline sample

  Dataset EGAD00001010501

• Targeted capture ctDNA Library CRCQV42Run030-14

  Targeted capture ctDNA Library CRCQV42Run030-14 from patient PBC0001315, buffy coat sample

  Dataset EGAD00001010520

• Targeted capture ctDNA Library CRCQV42Run032-19

  Targeted capture ctDNA Library CRCQV42Run032-19 from patient PBC0001432, buffy coat sample

  Dataset EGAD00001010542

• Targeted capture ctDNA Library CRCQV42Run044-7

  Targeted capture ctDNA Library CRCQV42Run044-7 from patient PBC0001314, plasma 3 month sample

  Dataset EGAD00001010678

• Targeted capture ctDNA Library CRCQV42Run036-11

  Targeted capture ctDNA Library CRCQV42Run036-11 from patient PBC0002826, plasma 15 month sample

  Dataset EGAD00001010593

• Targeted capture ctDNA Library CRCQV42Run044-6

  Targeted capture ctDNA Library CRCQV42Run044-6 from patient PBC0001279, plasma 18 month sample

  Dataset EGAD00001010677

• Targeted capture ctDNA Library CRCQV42Run041-20

  Targeted capture ctDNA Library CRCQV42Run041-20 from patient PBC0002744, plasma baseline sample

  Dataset EGAD00001010629

• Targeted capture ctDNA Library CRCQV42Run043-22

  Targeted capture ctDNA Library CRCQV42Run043-22 from patient PBC0003448, plasma 6 month sample

  Dataset EGAD00001010651

• Targeted capture ctDNA Library CRCQV42Run044-9

  Targeted capture ctDNA Library CRCQV42Run044-9 from patient PBC0001445, plasma 6 month sample

  Dataset EGAD00001010680

• Reference epigenome ADMSC07 mRNA-Seq data generated from KEP study

  ADMSC07 mRNA-Seq paired end data

  Dataset EGAD00001007155

• Reference epigenome ADMSC08 mRNA-Seq data generated from KEP study

  ADMSC08 mRNA-Seq paired end data

  Dataset EGAD00001007156

• Reference epigenome ADMSC05 mRNA-Seq data generated from KEP study

  ADMSC05 mRNA-Seq paired end data

  Dataset EGAD00001007153

• Reference epigenome ADMSC06 mRNA-Seq data generated from KEP study

  ADMSC06 mRNA-Seq paired end data

  Dataset EGAD00001007154

• OLINK metadata EGA v2

  Sample metadata for the olink dataset. This dataset includes subject-level data and longitudinal visit day information for the corresponding samples.

  Dataset EGAD00001011165

• Covacta OLINK NPX

  Olink Explore 1536 dataset from serum of patients enrolled in the COVACTA trial. This dataset includes the NPX values provided by Olink.

  Dataset EGAD00001011169

• Targeted capture ctDNA Library CRCQV42Run041-4

  Targeted capture ctDNA Library CRCQV42Run041-4 from patient PBC0001375, plasma 3 month sample

  Dataset EGAD00001010634

• Targeted capture ctDNA Library CRCQV42Run017-20

  Targeted capture ctDNA Library CRCQV42Run017-20 from patient PBC0001051, plasma 18 month sample

  Dataset EGAD00001010412

• Reference epigenome SMC01 mRNA-Seq data generated from KEP study

  SMC01 mRNA-Seq paired end data

  Dataset EGAD00001007169

• Reference epigenome SMC02 mRNA-Seq data generated from KEP study

  SMC02 mRNA-Seq paired end data

  Dataset EGAD00001007170