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• Sequencing data for oesophageal and related samples - Nowicki-Osuch, Zhuang et al (WGS)

  Data supporting: “Deep molecular phenotyping reveals the identity of Barrett’s esophagus and its malignant transition.” Nowicki-Osuch, Zhuang et al. WGS (BAM files) 5 Barrett's samples 5 normal oesophageal samples 5 normal gastric cardia samples 5 normal duodenal samples

  Dataset EGAD00001006874

• Sequencing data for oesophageal and related samples - Nowicki-Osuch, Zhuang et al (bulk RNA)

  Data supporting: “Deep molecular phenotyping reveals the identity of Barrett’s esophagus and its malignant transition.” Nowicki-Osuch, Zhuang et al. RNAseq (BAM files) 12 Barrett's samples 12 normal oesophageal samples 11 normal gastric cardia samples

  Dataset EGAD00001006882

• Identification of therapeutic target molecules for prostate cancer by using next generation sequencer

  Comprehensive transcriptional characterization of cancer tissues can reveal important molecular signatures associated with the disease progression. Long non-coding RNA (LncRNA) has a diversity of functions such as transcription, epigenetic and gene regulation in cancer cells including prostate cancer cells. Here, we performed directional RNA-sequence (RNA-seq) on clinical samples obtained from prostate cancer (PC) and lethal castration-resistant PC (CRPC) patients to identify novel molecular signatures including protein coding genes and non-coding RNAs induced in CRPC tissues.

  Study JGAS000198

• A single-cell multi-omic atlas of nodal B-cell non-Hodgkin lymphomas

  CITE-Seq (cellular indexing of transcriptomes and epitopes) of 51 lymph node (LN) samples from including mantle cell lymphoma (MCL, n = 8), follicular lymphoma (FL, n = 12), germinal center (GCB, n = 5) or activated B-cell (non-GCB/ABC, n = 7) diffuse large B-cell lymphoma (DLBCL), and marginal zone lymphomas (MZL, n = 11), in addition to non-malignant reactive lymph nodes (rLN, n = 8). Of the malignant LN samples, 20 were collected at the time of initial diagnosis and 23 were from patients who had previously undergone one or more lines of systemic treatment. Relapse samples were collected at least 3 months after cessation of systemic treatment. This dataset also includes 5 prime RNA sequencing and immune receptor sequencing (10X Genomics) for 11 of these samples (2 rLN, 2 MCL, 3 FL, 2 DLBCL (GCB) and 2 MZL).

  Dataset EGAD50000000497

• Spatial multi-omic map of human myocardial infarction

  We applied an integrative single-cell genomics strategy with single nucleus RNA sequencing (snRNA-seq) and single nucleus Assay for Transposase-Accessible Chromatin sequencing (snATAC-seq) together with spatial transcriptomics from the same tissue mapping human cardiac cells in homeostasis and after myocardial infarction (MI) at unprecedented spatial and molecular resolution. We profiled in total 31 samples from 23 patients including four non-transplanted donor hearts as controls and samples from tissues with necrotic tissue areas (ischemic zone, IZ), border zone (BZ), and the non-affected left ventricular myocardium (remote zone, RZ) of patients with acute MI.

  Dataset EGAD00001008952

• Single-cell RNA sequencing of SarBC-01 treated with Dexamethasone vs DMSO, with or without Matrigel.

  This dataset contains raw FASTQ files from single cell RNA sequencing of SarBC-01 cells treated with Dexamethasone vs DMSO, with or without Matrigel, processed with MULTI-seq. Cells from 4 different culture conditions (+Mat/Dex, +Mat/DMSO, -Mat/Dex, -Mat/DMSO) were harvested, processed for multiplexing using the MULTI-seq protocol and loaded in a Chromium Single Cell 3ʹ GEM Library and Gel Bead Kit v3 (10x Genomics). Gene expression (cDNA) and MULTI-seq libraries were prepared according to the manufacturers’ protocol of Chromium Next GEM Single Cell 3’ reagents Kits v3.1 (Dual Index). Finally, cDNA and MULTI-seq libraries were analyzed using an Agilent Bioanalyzer (DNA High Sensitivity kit) and sequenced on a NovaSeq6000 (S2 flow cell) platform. MULTISEQ BARCODES: TTAGCCAG => Matrigel/DMSO CCACAATG => Matrigel/Dexamethasone GCACACGC => noMatrigel/DMSO AGAGAGAG => noMatrigel/Dexamethasone

  Dataset EGAD00001011155

• NIHR BioResource Rare Diseases WGS project - Steroid Resistant Nephrotic Syndrome (SRNS) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Steroid Resistant Nephrotic Syndrome (SRNS) Rare Disease domain

  Dataset EGAD00001004518

• NIHR BioResource Rare Diseases WGS project - Cerebral Small Vessel Disease (CSVD) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Cerebral Small Vessel Disease (CSVD) Rare Disease domain

  Dataset EGAD00001004513

• NIHR BioResource Rare Diseases WGS project - Hypertrophic Cardiomyopathy (HCM) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Hypertrophic Cardiomyopathy (HCM) Rare Disease domain

  Dataset EGAD00001004514

• NIHR BioResource Rare Diseases WGS project - Intrahepatic Cholestasis of Pregnancy (ICP) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project.Participants from the Intrahepatic Cholestasis of Pregnancy (ICP) Rare Disease domain

  Dataset EGAD00001004515

• NIHR BioResource Rare Diseases WGS project - Neuropathic Pain Disorders (NPD) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Neuropathic Pain Disorders (NPD) Rare Disease domain

  Dataset EGAD00001004516

• NIHR BioResource Rare Diseases WGS project - Primary Membranoproliferative Glomerulonephritis (PMG) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Primary Membranoproliferative Glomerulonephritis (PMG) Rare Disease domain

  Dataset EGAD00001004517

• NIHR BioResource Rare Diseases WGS project - Bleeding, Thrombotic and Platelet Disorders (BPD) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Bleeding, Thrombotic and Platelet Disorders (BPD) Rare Disease domain

  Dataset EGAD00001004519

• NIHR BioResource Rare Diseases WGS project - Inherited Retinal Disorders (IRD) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Inherited Retinal Disorders (IRD) Rare Disease domain

  Dataset EGAD00001004520

• NIHR BioResource Rare Diseases WGS project - Multiple Primary Malignant Tumours (MPMT) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Multiple Primary Malignant Tumours (MPMT) Rare Disease domain

  Dataset EGAD00001004521

• NIHR BioResource Rare Diseases WGS project - Neurological and Developmental Disorders (NDD) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Neurological and Developmental Disorders (NDD) Rare Disease domain

  Dataset EGAD00001004522

• NIHR BioResource Rare Diseases WGS project - Primary Immune Disorders (PID) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project.Participants from the Primary Immune Disorders (PID) Rare Disease domain

  Dataset EGAD00001004523

• Hi-C dataset for testicular germ cell tumour GWAS risk loci, as described in the Oncoarray Litchfield et al. 2016 paper.

  Genome-wide association studies (GWAS) have transformed our understanding of testicular germ cell tumour (TGCT) susceptibility but much of the heritability remains unexplained. Here we report a new GWAS, a meta-analysis with previous GWAS and a replication series, totalling 7,319 TGCT cases and 23,082 controls. We identify 19 new TGCT risk loci, approximately doubling the number of known TGCT risk loci to 44. By performing in-situ Hi-C in TGCT cells, we establish a network of physical interactions between all 44 TGCT risk SNPs and candidate causal genes. Our findings reveal widespread disruption of developmental transcriptional regulators as a basis of disease susceptibility, consistent with failed primordial germ cell differentiation as an initiating step in TGCT oncogenesis1. Defective microtubule assembly and dysregulation of KIT-MAPK signalling also feature as recurrently disrupted pathways. Our findings support a polygenic model of disease risk and provide insight into the biological basis of TGCT.

  Study EGAS00001001930

• WGS files for paper titled "Preclinical Pediatric Molecular Analysis for Therapy Choice (MATCH)"

  WGS files for paper titled "Preclinical Pediatric Molecular Analysis for Therapy Choice (MATCH)"

  Dataset EGAD00001015697

• Single cell and plasma RNA sequencing

  Single cell and plasma RNA sequencing for RNA liquid biopsy for hepatocellular carcinoma

  Study EGAS00001005194

• Dataset for Manuscript: Cancer genome standards for long-read sequencing using cancer cell line mixtures

  Dataset for Manuscript: Cancer genome standards for long-read sequencing using cancer cell line mixtures

  Dataset EGAD00001015628

• Nanobody-Tethered Transposition Allows for Multifactorial Chromatin Profiling at Single-Cell Resolution

  Chromatin states are functionally defined by a complex combination of histone modifications, transcription factor binding, DNA accessibility, and other factors. However, most current single-cell-resolution methods are unable to measure more than one aspect of chromatin state in a single experiment, limiting our ability to accurately measure chromatin states. Here we introduce nanobody-tethered transposition followed by sequencing (NTT-seq), a new assay capable of measuring the genome-wide presence of multiple histone modifications and protein-DNA binding sites at single-cell resolution. NTT-seq utilizes recombinant Tn5 transposase fused to a set of secondary nanobodies (nb). Each nb-Tn5 fusion protein specifically binds to different immunoglobulin-G antibodies, enabling a mixture of primary antibodies binding different epitopes to be used in a single experiment. We apply bulk- and single-cell NTT-seq to generate high-resolution multimodal maps of chromatin states in cell culture and cells of the human immune system, demonstrating the high accuracy and sensitivity of the method. We further extend NTT-seq to enable simultaneous profiling of cell-surface protein expression alongside multimodal chromatin states to study cells of the immune system.

  Study phs003068

• Hypothesis Testing for Predictive Variables

  Hypothesis Testing for Predictive Variables

  Study EGAS00001007763

• RNA-seq of cells after injection into immunodeficient mice

  This dataset includes 73 samples profiled by high-throughput Illumina sequencing, in bam format, aligned to GRCh37. Normal human CD34+ cord blood (CB), bone marrow (BM), or post-natal thymus (PNT) cells were transduced with various combinations of T-ALL oncogenes, cultured in vitro on OP9-DL1 feeders for up to 25 days, and then transplanted into immunodeficient NSG or NRG mice. The samples were collected after development of frank leukemia in recipient mice.

  Dataset EGAD00001009751

• Organoid Derivation Project - GRCh38 - RNAseq (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011092