6365 results for "11+1双色球多少钱一注"

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• Human lung cell atlas 10x and SS2 sequencing data (1 of 3)

  Contains FASTQs for cells from 20 10x channels across 2 NovaSeq runs, 42 384-well plates across 6 NovaSeq runs, and 12 96-well plates across 4 NextSeq runs.

  Dataset EGAD00001006126

• Clonal somatic copy number altered driver events inform drug sensitivity in high-grade serous ovarian cancer.

  This dataset consists of shallow whole genome sequencing data and amplicon sequencing data for 26 ovarian cancer patients (21 high-grade serous ovarian cancer, 4 low-grade serous ovarian cancer and 1 clear cell ovarian cancer). The data are provided as single end FASTQ files for the shallow whole genome sequencing data (31 libraries) and paired end FASTQ files for the amplicon sequencing data (98 libraries).

  Dataset EGAD00001008716

• Ultra-deep sequencing of cell-free DNA derived from reference materials and a patient with asymmetric overgrowth

  This dataset contains: Ultra-deep sequencing data using the Duplex sequencing technology of: 1.) SeraSeq cfDNA reference materials with spike-in variants with allele frequencies from 0% to 5% 2.) One cfDNA sample from a CRC patient 3.) One cfDNA sample from a patient with asymmetric overgrowth Paired-end sequencing was performed with 2x151 bp reads on the NextSeq 500 system. Data is provided as mapped .bam files (aligned to GRCh38/hg38).

  Dataset EGAD00001009784

• scRNA-seq dataset of patient with immune dysregulation

  scRNA-seq dataset on a patient (P_IKZF2-het) presenting with immune dysregulation. This dataset contains raw and processed files from scRNA-seq performed on samples using the 10x Genomics Chromium Controller with the Chromium Single Cell 3′ Reagent Kit (v3 chemistry). There are three paired-end (75 bp) fastq files (I1, R1, R2) and three processed files generated with 10x Genomics Cell Ranger v3.0.2 software against GRCh38 human reference transcriptome (scrnaseq_P_IKZF2-het_barcodes.tsv.gz, scrnaseq_P_IKZF2-het_features.tsv.gz, scrnaseq_P_IKZF2-het_matrix.mtx.gz).

  Dataset EGAD00001008443

• WGS dataset of Characterization of UV DNA damage in B-cell precursor acute lymphoblastic leukemia

  Extensive characterization of mutational signature SBS7a in B-cell precursor acute lymphoblastic leukemia (BCP-ALL) samples. We aimed to describe the presentation of SBS7a in BCP-ALL, identify other pediatric cancers presenting with SBS7a, look into the link of SBS7a and UV-light in the context of BCP-ALL and cancers with provable UV exposure, and pinpoint when SBS7a was acquired.

  Dataset EGAD00001015599

• fibroblast RNAseq from a GINS3 patient and two parents

  RNA-seq was performed from 3 separate GINS3 patient fibroblast cultures and 1 replicate of fibroblasts derived from each of the two parents. RNA-seq libraries were generated with NEBNext Ultra II Directional RNA library prep for Illumina with NEBNext Poly(A) mRNA Magnetic Isolation Module (New England Biolabs) and sequenced on Illumina NextSeq500 with paired-end 150 bp read length. SIRV Set 3 (Lexogen) spike-ins were added. Two fastq files are provided for each RNAseq sample.

  Dataset EGAD00001008571

• SNF_OLINK_20

  Matrix of normalized values from Olink assay performed on baseline BM Plasma. The Olink Immuno-Oncology multiplex proteomic Panel included 92 proteins associated with human inflammatory conditions. Data is analyzed using real-time PCR analysis software via the Ct method and Normalized Protein Expression (NPX) manager. Data were normalized using internal controls in every single sample, inter-plate controls, negative controls and a correction factor and expressed as Log2 scale, which was proportional to the protein concentration. One NPX difference equals the doubling of the protein concentration.

  Dataset EGAD00001011147

• RCC Infiltrated Macrophages and Monocytes

  Tumor infiltrated Macrophages and Monocytes were sorted on Aria II (Becton Dickinson) into TRIzol LS and flash frozen. RNA was extracted with chloroform. Isopropanol and linear acrylamide were added, and the RNA was precipitated with 75% ethanol. Total RNA (0.649–1 ng) with RNA integrity numbers 6.8 to 10 underwent amplification using the SMART-Seq v4. Ultra Low Input RNA Kit (Clontech; cat. #63488). Amplified cDNA (15 ng) was used to prepare libraries with the KAPA Hyper Prep Kit (Kapa Biosystems, KK8504) using 8 cycles of PCR.

  Dataset EGAD00001009393

• subset of DEEP IHEC release 2016 (EGAS00001001937), as used in EGAS00001001656 (bidirectional promotors paper, Fatemeh et al. 2018)

  part of the DEEP project results resulted in the publication of 'Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters', Epigenetics & Chromatin (2018), Fatemeh et al., DOI: 10.1186/s13072-018-0236-7, PMID: 30414612, PMCID: PMC6230222. This dataset contains the subset of DEEP data related to that study.

  Dataset EGAD00001005953

• TARGET-seq+ single-cell genotyping

  Single-cell genotyping data for bone marrow samples from 9 cases with clonal hematopoiesis and 1 control sample. The TARGET-seq+ protocol was used to generate plate-based 3' transcriptome data. For details on cell sorting and the TARGET-seq+ protocol see the methods section of the manuscript. One FASTQ file is provided per cell. Cells are named with their plate and well IDs and the subject ID. Empty wells (no-cell controls) are named "blank". Corresponding transcriptome files use the same naming with the "_transcriptome" suffix.

  Dataset EGAD00001011150

• Single-cell RNA-sequencing of rhabdomyosarcoma tumouroids (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumouroids data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015708

• Candidate diagnostic variants reported into DECIPHER, Wright NEJM 2023

  Candidate diagnostic variants reported into DECIPHER by 4 April 2022, annotated with clinical and automated pathogenicity assertions (see DOI: 10.1056/NEJMoa2209046). Genomic Diagnosis of Rare Pediatric Disease in the United Kingdom and Ireland, Wright et al, NEJM 2023.

  Dataset EGAD00001010137

• Dataset for Sarcoma-WGS linked from study EGAS00001004813

  Sequencing data of 77 sarcoma tumor and control runs, which were uploaded to EGAS00001004813. The sequencing was always paired.

  Dataset EGAD00001010257

• CPC-GENE primary prostate benign and tumour tissue Hi-C sequencing reads

  This dataset contains the raw sequencing data, in FASTQ format, for Hi-C assays from 17 primary prostate tissue samples. The sequencing data is paired-end, 150 bp sequencing data from an Illumina NovaSeq 6000 machine, and contains 5 benign tissue samples and 12 primary tumour samples from the Canadian Prostate Cancer Genome Network (CPC-GENE) project. Tumour samples have IDs starting with the "CPCG" prefix, and benign tissue samples have IDs starting with the "BP" suffix.

  Dataset EGAD00001008024

• Whole Genome-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015709

• Bulk RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the bulk RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015714

• Whole Genome-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015716

• Sequencing data for oesophageal and related samples - ICGC DCC release 28 (WGS)

  WGS sequencing for 409 cases (832 samples) from the ICGC ESAD-UK project Tumours 50x Normals 30x HiSeq X BAM files These samples are all available in ICGC release 28

  Dataset EGAD00001004137

• Sequencing data for oesophageal and related samples - ICGC DCC release 27 earmarked (WGS)

  WGS sequencing for 63 cases (126 samples) from the ICGC ESAD-UK project Tumours 50x Normals 30x HiSeq X BAM files These samples are earmarked for inclusion in ICGC release 27 (deferred to release 28)

  Dataset EGAD00001004028

• Sequencing data for oesophageal and related samples - Alex Frankell et al (WGS)

  Data supporting: "The landscape of selection in 551 Esophageal Adenocarcinomas defines genomic biomarkers for the clinic." Frankell et al. WGS (BAM files) 379 matched tumour-normal pairs

  Dataset EGAD00001004417

• Comparison of 3 protocols for deriving pancreatic progenitors from hPSCs (RNA-seq)

  This dataset contains 26 mapped bam files. The samples were generated with 3 different protocols for deriving pancreatic progenitors from hPSC. Three parallel differentiations were performed, all done in a hPSC NKX6.1-GFP reporter line. For each protocol there are three cellular populations: total (presort), GFP+ and GFP- . In summary: 3 differentiations x 3 protocols x 3 cellular populations. We prepared Smart-Seq2 RNA-seq libraries for the 27 samples, 1 sample failed library preparation and is not therefore included in this dataset.

  Dataset EGAD00001004823

• Sequencing data for oesophageal and related samples - Noorani et al (WGS)

  Data supporting: "Genomic evidence supports a clonal diaspora model for metastases of esophageal adenocarcinoma." Noorani et al. WGS (BAM files) 134 samples for 18 cases Includes primary, lymph-node, distant metastatic, Barrett's and normal samples.

  Dataset EGAD00001005434

• RNA-seq dataset of Characterization of UV DNA damage in B-cell precursor acute lymphoblastic leukemia

  Extensive characterization of mutational signature SBS7a in B-cell precursor acute lymphoblastic leukemia (BCP-ALL) samples. We aimed to describe the presentation of SBS7a in BCP-ALL, identify other pediatric cancers presenting with SBS7a, look into the link of SBS7a and UV-light in the context of BCP-ALL and cancers with provable UV exposure, and pinpoint when SBS7a was acquired.

  Dataset EGAD00001015598

• The UCSD / O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension

  Study Design: The UCSD / O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension contains N=797 participants. At the time of recruitment individuals ranged from 15-84 years of age and had varying ethnic backgrounds (Whites, African-Americans and Hispanics). The cohort was developed to study the genetic basis of autonomic dysfunction in human essential hypertension (reviewed in Zhang et al. Curr Hypertens Rep. 2011 13: 36-45. PMID: 21104344) and contains cross-sectionally collected phenotypes related to blood pressure regulation.  Phenotypes: Phenotype data specifically focuses on the sympathetic branch of the autonomic system, which is a key regulator of blood pressure. Data includes circulating concentrations of the secretory "quantum" (catecholamines, neuropeptides, chromogranins) which influence vascular responses to sympatho-adrenal activation resulting in blood pressure. Other measures on the twins and a subset of family members include a standard urine panel with absolute concentrations of potassium, sodium, uric acid, phosphorous; DynaPulse acquired heart rate and blood pressure measures including systolic and diastolic blood pressure, mean arterial pressure, heart rate, Left Ventricular (LV) ejection time, LV contractility, cardiac output, stroke volume data including; and absolute concentrations of plasma aldosterone, endothelin, leptin, and insulin. Phenotype data is included for the 697 individuals sequenced here and 100 family members.  Whole Genome Sequence Data Generation: Whole genome sequencing (20X coverage) was conducted on plasma-derived DNA. The WGS was generated using the Illumina HiSeq X Ten sequencing platform with variants called by Human Longevity, Inc (HLI; Ref 1). Preparation of sample libraries was conducted by mechanical shearing of gDNA (100-800 bp), isolation of DNA fragments 300-500bp in size, followed by library PCR amplification employing high-fidelity, low-bias PCR to amplify library fragments consisting of appropriate adapter sequences on both ends. BAM files were created by alignment of sequence reads to the reference genome (GRCh38) using iSAAC-01.14.02.06 (Python2.7). Note: The user may want to realign the sequence data and recall variants. In total, the WGS dataset contains 30 monozygotic twin pairs represented by one member of the twin pair, 133 monozygotic twin pairs with both individuals sequenced, 7 dizygotic twin pairs represented by one member of the twin pair, 67 dizygotic twin pairs with both individuals sequenced, 8 twin pairs with undetermined zygosity (both individuals sequenced) and 1 triplet with mother (all individuals sequenced). The dataset also contains sequences of 260 family members of twins including their mother, father, non-twin sibling, and offspring. Table 1 describes the composition of families ("sibling" refers to a non-twin sibling in the cohort). Note: We suggest users check gender and family relatedness. Table 1: Description of the families with sequence data                                                                                                               Units with:Twin PairsTwin (no pairs)No twinSibshipsMother and Father511Mother only1222Father only113One or more siblings22133Mother and siblings132Father and siblings(s)211Offspring of twins11Non twin siblings2Sib with unknown relative1Only one member of8The UCSD/O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension is now being managed at the UCSD School of Medicine's Institute for Genomic Medicine (IGM). The TSP Resource has collected a wide range of information (phenotypic, genotypic, demographic, metabolomic) and reagents (blood, plasma, urine, DNA, human induced pluripotent stem cells). For additional information about the resource, including other data types available as well as references and links to data from studies with previous collaborators, please go to the resource website at IGM [http://igm.ucsd.edu/research/twins-resource.shtml] or contact Dr. Brinda Rana at UCSD [bkrana@ucsd.edu]. 

  Study phs002230

• Type 1 Diabetes Genetics Consortium (T1DGC): Multi-Ethnic ImmunoChip Study

  Type 1 diabetes (T1D) is characterized by an autoimmune attack that destroys the insulin-producing pancreatic beta cells and is driven by diverse genetic and environmental factors. Between 8-100 chromosome regions are known to contain variants that alter T1D risk, substantially fewer than many other common diseases. Less is known about the contribution of T1D risk alleles in non-European populations, despite recent increases in T1D diagnoses in multiple non-European ancestry groups. To address these gaps in T1D genetics, we doubled the sample size from the previous largest T1D association study, genotyped ancestrally diverse T1D cases, controls, and affected families with the Illumina ImmunoChip array, and imputed additional variants using a large haplotype reference panel. After quality filtering, a total of 61,427 participants and 140,333 genotyped ImmunoChip variants were included in analysis, providing dense coverage in 188 autosomal regions ("ImmunoChip regions") and sparse genotyping in other regions of the genome. Each participant was assigned to one of five ancestry groups using k-means clustering of ImmunoChip genotype principal components: European (EUR, N = 47,319), African Admixed (AFR, N = 4,290), Finnish (FIN, N = 6,991), East Asian (EAS, N = 588) and Other Admixed (AMR, N = 2,239). In total, 16,159 T1D cases, 25,386 controls (unrelated) and 6,143 trio families (i.e., an affected child and both parents) were genotyped and included in association analyses. Imputation with the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) multi-ethnic reference panel was used to improve discovery power and fine-mapping resolution. After imputation, the number of variants in ImmunoChip regions with imputation R-squared > 0.8 and MAF > 0.005 in each ancestry group was 322,084 (AFR), 166,274 (EUR), 163,612 (FIN), 137,730 (EAS) and 188,550 (AMR). Imputed genotypes were compared in the same participants who have whole genome sequencing data, resulting in high concordance. Unrelated cases and controls (N = 41,545) were analyzed initially, assuming an additive inheritance model. T1D trio families (N = 6,143 trios, some trio families were multiplex and analyzed as multiple trios) were analyzed separately, with meta-analysis of case-control and trio results used for discovery of regions associated with T1D (p < 5×10-8). Summary-level data at each SNP (genotype and imputed) for type 1 diabetes (outcome) are provided in dbGaP.

  Study phs002468