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• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0837_SA501X2

  40 samples; filetype=bam

  Dataset EGAD00001004812

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0443_SA501X7A

  48 samples; filetype=bam

  Dataset EGAD00001004813

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0472_SA501X7A

  50 samples; filetype=bam

  Dataset EGAD00001004814

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0582_SA501X10A

  45 samples; filetype=bam

  Dataset EGAD00001004815

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0680_SA501X10A

  61 samples; filetype=bam

  Dataset EGAD00001004816

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0544_SA532X6

  68 samples; filetype=bam

  Dataset EGAD00001004817

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0650_SA532X6

  71 samples; filetype=bam

  Dataset EGAD00001004818

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0738_SA609X6

  48 samples; filetype=bam

  Dataset EGAD00001004819

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0739_SA609X6

  52 samples; filetype=bam

  Dataset EGAD00001004820

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0740_SA609X6

  60 samples; filetype=bam

  Dataset EGAD00001004821

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0741_SA609X6

  55 samples; filetype=bam

  Dataset EGAD00001004822

• Haplotype Reference Consortium Release 1.1

  Haplotype Reference Consortium Release 1.1 - subset for release via the EGA

  Dataset EGAD00001002729

• A96146A

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96146A 1195 samples; filetype=bam

  Dataset EGAD00001005338

• A96172B

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96172B 1694 samples; filetype=bam

  Dataset EGAD00001005340

• A96226B

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96226B 1274 samples; filetype=bam

  Dataset EGAD00001005345

• A96193B

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96193B 2410 samples; filetype=bam

  Dataset EGAD00001005347

• A96199A

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96199A 843 samples; filetype=bam

  Dataset EGAD00001005348

• A96199B

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96199B 1170 samples; filetype=bam

  Dataset EGAD00001005353

• A96211C

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96211C 1397 samples; filetype=bam

  Dataset EGAD00001005354

• A96225C

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96225C 1034 samples; filetype=bam

  Dataset EGAD00001005355

• NIHR BioResource Rare Diseases WGS project - Leber Hereditary Optic Neuropathy (LHON) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Leber Hereditary Optic Neuropathy (LHON) Rare Disease domain

  Dataset EGAD00001005122

• Childhood arthritis DNA (2020-01-15)

  The aim of this study is to describe the transcriptome of single arthritic cells. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005785

• HuBMAP: A 3-D Tissue Map of the Human Lymphatic System

  The major goal of the 3-D Tissue Map of the Human Lymphatic System is to use microscopic and biomolecular procedures to facilitate co-registration pipelines and common 3D reconstruction algorithms. Tissue collected from human spleen, thymus and lymph node will be spatially resolved at the single cell level both within and across individuals. The approach employed involves sequencing of transcriptomes of dissociated cells and mapping to histological sections using CO-Detection by indEXing (CODEX) and /or Imaging mass spectroscopy, two highly multiplexed methods employing antibody-tagged target epitopes. Additionally, light sheet fluorescent microscopy is used to provide a higher level context for structural localization on a larger volume. The molecular data provided by this project is obtained through single-cell RNA-seq.

  Study phs002268

• Single cell RNAseq data of human neurons, Bouwen et al Nat Comm 2025

  Brain tumor patients often suffer from epilepsy, but the underlying neuronal changes leading to this neurological symptom are not well understood. This study examined the transcriptomic profiles of human cortical neurons collected from brain tumor patients with or without seizures. The goals of the study were to identify the molecular identities of the recorded neurons and confirm that they are layer 2/3 pyramidal neurons rather than neuron-like tumor cells; and to compare the differential gene expression profiles between patients with and without epilepsy. For each patient, cortical neurons were rapidly isolated from surgically resected tissue and prepared using the Smart-seq2 protocol. Samples from 12 patients were sequenced on an Illumina HiSeq 2500 platform to generate their transcriptomic profiles.

  Study EGAS50000001369

• Tracing tumor evolution and heterogeneity of pleomorphic carcinoma of the lung

  Pulmonary pleomorphic carcinoma (PPC) is an aggressive and highly heterogeneous non-small-cell lung carcinoma whose underlying biology is still poorly understood. Forty-two tumor areas including 39 primary tumors and 3 metastases from 20 PPC patients were microdissected and the histologically distinct components were subjected to whole exome sequencing (WES) separately. Twenty non-tumor areas from matched PPC patients were also subjected to WES. Twist Human Core Exome + RefSeq + Mito-Panel kit (Twist Bioscience) was used for the whole exome capturing according to manufacturer’s guidelines. Paired-end 100-bp reads were generated on the Illumina NovaSeq 6000. After the sequencing, reads were aligned against the reference human genome GRCh38 using Burrows-Wheeler Aligner (BWA, v0.7.12)

  Study EGAS50000000314

• A108879A

  Whole genome sequencing for single cells for library A108879A 1567 cells; filetype=bam

  Dataset EGAD00001007093

• Whole genome sequencing of 50 trios, where the child is affected with ID, and the parents are unaffected

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. Each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targetted gene and whole exome screening.

  Study EGAS00001000769

• A96228B

  Whole genome sequencing for single cells for library A96228B 1125 samples; filetype=bam

  Dataset EGAD00001008218

• A95707A

  Whole genome sequencing for single cells for library A95707A 1359 samples; filetype=bam

  Dataset EGAD00001008228

• A95732B

  Whole genome sequencing for single cells for library A95732B 655 samples; filetype=bam

  Dataset EGAD00001008231

• A96145A

  Whole genome sequencing for single cells for library A96145A 642 samples; filetype=bam

  Dataset EGAD00001008232

• A96149A

  Whole genome sequencing for single cells for library A96149A 751 samples; filetype=bam

  Dataset EGAD00001008234

• A96155B

  Whole genome sequencing for single cells for library A96155B 1028 samples; filetype=bam

  Dataset EGAD00001008236

• A96157C

  Whole genome sequencing for single cells for library A96157C 938 samples; filetype=bam

  Dataset EGAD00001008237

• A96165A

  Whole genome sequencing for single cells for library A96165A 779 samples; filetype=bam

  Dataset EGAD00001008239

• A96174B

  Whole genome sequencing for single cells for library A96174B 1447 samples; filetype=bam

  Dataset EGAD00001008242

• A96177B

  Whole genome sequencing for single cells for library A96177B 683 samples; filetype=bam

  Dataset EGAD00001008244

• WGS and WTS data of patient diagnosed with HSTCL

  WGS and WTS data of a single patient diagnosed with HSTCL. Whole-genome sequencing (WGS) was performed for the tumor-normal sample. Genomic DNA from tumor tissue was extracted with QIAamp DNA Mini Kit. The DNA for the matching normal was obtained from blood or buccal swabs and purified by Blood and Cell Culture DNA Mini kit or E.Z.N.A. Tissue DNA Kit (Omega Bio-tek) according to manufacturer’s instructions. The quantity and quality were assessed by Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen) and agarose gel electrophoresis. All sequencing libraries were prepared using TruSeq Nano DNA Library Prep Kit (Illumina). Paired-end sequencing was performed on Illumina HiSeq 4000 2x151 bp read length. Whole-transcriptome sequencing (WTS): Total RNA from snap frozen EITL tumor samples was extracted using TRIzol (Invitrogen) and purified with RNeasy Mini Kit (Qiagen) according to manufacturer’s instructions. The integrity of RNA was determined by electrophoresis using 2100 Bioanalyzer (Agilent Technologies). 500 ng of total RNA was reverse transcribed with iScript cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA). Quantification was performed using SsoFast EvaGreen Supermix and CFX96 Real-Time PCR System (both Bio-Rad). Sequencing libraries were prepared using the TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero (Illumina) and WTS was performed on Illumina HiSeq 2500 with 2x101 bp read length.

  Dataset EGAD00001005229

• ImmunAID

  The ImmunAID study is a multi-center research program aimed at improving the diagnosis and understanding of systemic autoinflammatory diseases. The study integrates immunological, and molecular data from patients across several European cohorts. Its objective is to identify biomarkers and biological pathways that characterize disease mechanisms and help differentiate between inflammatory conditions. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779295. The project has generated genomic datasets, which are available through the European Genome-phenome Archive (EGA). In addition, it has produced several non-genomic datasets, listed below. All datasets have been fully anonymized in accordance with applicable ethical and regulatory frameworks. These non-genomic data are available upon request. To request access to these datasets, please contact: immunaid2024@gmail.com Your request will be studied by the Data Access Committee, and data will be made available upon approval. Non-genomic datasets available: Flow cytometry (general immune phenotype) — Dataset ID: ICKD2021569, size: 23 GB, 342 patients Somascan proteomics data — Dataset ID: ICKD2021570, size: 59 MB, 444 patients ELISA panel (Ferritin, CRP, HO1, IL-1B, IL-6, IL-8, IL-10, IL-12, IL-18, IFN-γ, TNF-α) — Dataset ID: ICKD2021571, size: 295 KB, 439 patients Mass spectrometry (MS/MS) — Dataset ID: ICKD2021572, size: 1.8 TB, 447 patients ELISA (CRP/ SAA) — Dataset ID: ICKD2021573, size: 321 KB, 443 patients Flow cytometry (inflammasome activity) — Dataset ID: ICKD2021574, size: 899 KB, 307 patients Plasma lipidomics — Dataset ID: ICKD2021575, size: 1.1 MB, 427 patients Urine lipidomics — Dataset ID: ICKD2021576, size: 833 KB, 368 patients ELISA (alarmins) — Dataset ID: ICKD2021577, size: 129 KB, 108 patients ELISA (IL-18 / IL-1) — Dataset ID: ICKD2021578, size: 271 KB, 330 patients Flow cytometry (NK cell alterations) — Dataset ID: ICKD2021579, size: 14 GB, 216 patients Chemokine measurements — Dataset ID: ICKD2021580, size: 489 KB, 404 patients Luminex (multiple analyte measurements) — Dataset ID: ICKD2021581, size: 759 KB, 369 patients

  Study EGAS50000001393

• NHLBI TOPMed: Early-Onset Atrial Fibrillation in the Estonian Biobank

  The Estonian Biobank is the population-based biobank of the Estonian Genome Centre of University of Tartu. The biobank is conducted according to the Estonian Gene Research Act and all participants have signed broad informed consent. The cohort size is currently 51,535 people from 18 years of age and up.

  Study phs001606

• Transcriptomic Profiles of Neoantigen-Reactive T Cells in Human Gastrointestinal Cancers

  This study explores the transcriptomic profiles of neoantigen-reactive tumor-infiltrating lymphocytes (TILs) from human bile duct and pancreatic cancer. The submitted data are bulk tumor RNA-Seq, tumor and germline whole-exome sequencing from 10 patients, and single cell RNA-Seq data from TIL of 5 of these patients.

  Study phs002765

• Integrated Somatic and Germline Molecular Properties Dictating Biological and Clinical Phenotypes in Cancer

  We performed single-nuclei matched RNA- and ATAC-sequencing of frozen biopsies from normal adjacent tissue, primary and metastatic esophageal adenocarcinoma specimens. The data was then further stratified into a tumor microenvironment and malignant compartment and was computationally analyzed to reveal cancer cell subtypes and programs driving malignancy.

  Study phs003438

• Dietary convergence induces individual responses in faecal microbiome composition

  This dietary intervention employed an ABA design where participants maintained their habitual diet during baseline and washout periods, while restricting intake to only oats, whole milk, and water during the intervention phase. The study deliberately imposed this severe dietary shift to maximize observable effects on gut microbiome composition and function.

  Study EGAS50000000948

• Batches 1-3 prostatectomy analysis

  Whole Genome Sequencing Illumina HiSeq data from 95 men with prostate cancer. Samples were taken from primary tissue obtained at prostatectomy (target sequencing depth 50X) with matched blood control (target sequencing depth 30X). This data is from batches 1 to 3 and is the bulk of the data used in Wedge et al, Nature Genetics 2018 (PMID: 29662167). As of September 2020, some of the studies using these data include: Wedge et al, Nature Genetics 2018 (PMID: 29662167) Pan-Cancer Analysis of Whole Genomes, Nature 2020 (PMID: 32025007)

  Dataset EGAD00001001116

• CXCR4 induces memory formation over exhaustion in CAR-T cells to achieve durable leukemia targeting

  This project focuses on acute myeloid leukemia and the development of chimeric antigen receptor (CAR) T cell therapy. We investigated how enforced expression of the chemokine receptor CXCR4 promotes memory acquisition and long-term persistence of CAR-T cells, thereby enhancing their therapeutic efficacy and durability against leukemia.

  Study JGAS000848

• Building a Database of Genomic Structural Variants in Japanese population

  Epstein-Barr virus immortalized B-lymphoblast cell lines were obtained through the Japanese Collection of Research Bioresources (Japanese B cell DNA bank), National Institutes of Biomedical Innovation, Health and Nutrition, and cultured at UNITECH Co., Ltd. We extracted genomic DNAs, sequenced them using Nanopore PromethION, and analyzed genomic structural variations.

  Study JGAS000580

• RNA-sequence analysis in patients with inclusion body myositis

  This study aimed to elucidate the molecular features of inclusion body myositis (IBM). We performed RNA sequencing analysis of muscle biopsy samples from myositis patients with the pathological finding of CD8-positive T cells invading non-necrotic muscle fibers expressing major histocompatibility complex class I (IBM, polymyositis, and unclassifiable myositis), and controls.

  Study JGAS000068

• GTestimate: Improving relative gene expression estimation in scRNA-seq using the Good-Turing estimator

  This study introduces a novel scRNA-seq normalization method, which takes unobserved genes into account. To validate GTestimate we designed a cell targeted amplification strategy, enabling us to sequence a small set of cells twice, once at a "typical" sequencing depth and once at a "ultra-deep" sequencing depth.

  Study EGAS50000000915

• A distinct monocyte cellular state links systemic immune dysregulation to pulmonary impairment in long COVID

  This is an independent cohort of Long COVID patients recruited from a rehabilitation centre. These patients had Long COVID from 8 months to more than 2 years since their acute COVID-19 disease. PBMC from these patients were collected and single cell transcriptomics dataset was generated.

  Study EGAS50000001216

• Identifying mutations in patient-derived melanoma DCC lines

  To identify mutations that cause resistance, we analyzed two patient-derived cell lines: Mel-DCC-11 (the parental line, sensitive to the BRAF inhibitor) and Mel-DCC-11-R (resistant to the BRAF inhibitor). We employed targeted-panel sequencing to examine the coding sequences of 410 cancer-associated target genes.

  Study EGAS50000001225