6365 results for "11+1双色球多少钱一注"

in 24.13 milliseconds.

• Transcriptome of human CD4+ T-lymphyocyte (regulatory-T-cells) treated with Resveratrol from donor D2, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008073

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Resveratrol from donor D3, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008075

• Transcriptome of human CD4+ T-lymphyocyte (regulatory-T-cells) treated with Resveratrol from donor D3, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008077

• postQC Genotypes

  postQC genotype data from the Affymetrix AxiomTM HGCoV2 1 array in plink binary format. QC was carried out using PLINK v1.9

  Dataset EGAD00010002436

• Heart Failure Network: Functional Impact of GLP-1 for Heart Failure Treatment (HFN FIGHT-BioLINCC)

  Objectives To test whether therapy with a GLP-1 agonist improves clinical stability following hospitalization for acute heart failure. Background Heart failure was the leading cause of hospitalization in the United States with more than 4 million admissions per year from 2003–2009. Abnormal cardiac metabolism contributes to the pathophysiology of advanced heart failure with reduced left ventricular ejection fraction (LVEF). As heart failure progresses, these abnormalities become more pronounced and are observed in both patients with and without type 2 diabetes. At this time of this study, there was no heart failure therapy that targeted these metabolic abnormalities; however, in earlier clinical studies, glucagon-like peptide 1 (GLP-1) agonists showed cardioprotective effects in patients with advanced heart failure, irrespective of type 2 diabetes status. Glucagon-like peptide 1 is an endogenous incretin hormone that improves insulin sensitivity with minimal risk of hypoglycemia. This study was created to determine if use of GLP-1 agonists improved clinical stability in recently hospitalized patients with acute heart failure and reduced LVEF. Participants There were 300 participants. Design Participants were identified by hospital admission records and were enrolled during either the last 24 hours of his or her hospitalization for heart failure or the 2-week interval after the hospitalization. Baseline evaluations were conducted, which included echocardiographic measures, the 6-minute walk test, the Kansas City Cardiomyopathy Questionnaire (KCCQ), and blood tests. After the baseline evaluation, patients were randomized in a 1:1 ratio to receive either the GLP-1 agonist liraglutide or placebo as a daily subcutaneous injection. The protocol involved titration of study drug dosage as tolerated every 14 days from 0.6 mg/d to 1.2 mg/d to 1.8 mg/d during the first 30 days of the trial (as tolerated). Follow-up testing was performed at 30-, 90-, and 180-day study visits. The primary end point was a global rank score in which all participants, regardless of treatment assignment, were ranked across 3 hierarchical tiers: time to death, time to rehospitalization for heart failure, and time-averaged proportional change in N-terminal pro-B-type natriuretic peptide (NT-proBNP) level from baseline to 180 days. Conclusions The GLP-1 agonist liraglutide did not improve post-hospitalization clinical stability in patients with advanced heart failure and reduced LVEF. There was no significant between-group difference in the global rank scores and furthermore, there were no significant differences detected in any of the secondary endpoints.

  Study phs003542

• NIBIT-M4 Clinical Trial samples

  Patients (N=14) with unresectable stage III/IV melanoma received escalating doses of guadecitabine 30, 45, or 60 mg/m2/day subcutaneously on days 1 to 5 every 3 weeks, and ipilimumab 3 mg/kg intravenously on day 1 every 3 weeks, starting 1 week after guadecitabine, for four cycles. Patient-wise genome-wide DNA methylation, Whole Exome and RNA sequencing were then performed for samples obtained by surgical removal or fine-needle biopsy at baseline (W0), week 4 (W4) and week 12 (W12) after treatment. For Whole Exome Sequencing profiling, normal matched skin tissue was available only for the first eight patients.

  Study EGAS00001006736

• ICARUS-BREAST01-RNAseq

  Efficacy, safety and biomarker analysis of ICARUS-BREAST01: a phase 2 Study of Patritumab Deruxtecan in patients with HR+/HER2- advanced breast cancer. RNA-squencing is available for 44 samples comprising 22 samples collected at entry into the trial and 22 samples collected during treatment (cycle 1 day 3, cycle 1 day 19, or cycle 2 day 3) from 22 patients.

  Study EGAS50000000543

• Single cell RNA-seq, TCR-seq and bulk TCR-seq on HNSCC patients treated with ICB

  We performed single cell RNA- and TCR-sequencing (10x Genomics) on immune infiltrates (CD45+ cells) from 18 HNSCC patients enrolled in the IMCISION trial (Vos et al. 2021). Viable immune cells were isolated from pre-treatment and post-treatment primary tumor biopsies of 10 patients responding (1 partial pathological response and 9 major pathological responses) and 7 patients non-responding to anti-PD-1 and anti-CTLA4 combination immunotherapy. One patient treated with anti-PD-1 monotherapy (1 major pathological response) was included in the dataset. Bulk TCR-seq was performed on the PBMCs of responding patients, pre- and post-treatment.

  Dataset EGAD00001011311

• WGS of single CTCs from 4 patients with metastatic cancer

  This dataset contains 352 paired fastq files sequenced by illumina Novoseq6000. Single-cell low-pass WGS analysis (illumina platform, PE150, 1-2x coverage) was performed on CTCs enriched using high-throughout microfluidic device from entire leukopak from patients with metastatic prostate cancer (GU-1 and GU-2) and metastatic hepatocellular carcinoma (HCC-1 and HCC-2). Patient matched white blood cells (WBC) served as internal controls.

  Dataset EGAD50000001006

• BLUEPRINT release August 2016, Bisulfite-Seq for thymocyte, on genome GRCh38

  Bisulfite-Seq data for 1 thymocyte sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002440

• RNA-Seq of novel miR (nmiR-1 and nmiR-2) overexpression and knockdown in BL

  After overexpression and knockdown of both described novel miRs nmiR-1 and nmiR-2 in BL cell lines (SU-DHL4 for nmiR-1 and Raji for nmiR-2), we performed regular RNA-Seq (including Mock controls for all cell lines) to identify their direct and indirect downstream mRNA targets.

  Dataset EGAD00001001612

• Transcriptome of (peripheral blood), from donor Sample B, replicate 1 time-course biological replicates; 10x lane replicate 3

  time-course biological replicates; 10x lane replicate 3

  Dataset EGAD00001010092

• Transcriptome of (peripheral blood), from donor Sample B, replicate 1 time-course biological replicates; 10x lane replicate 2

  time-course biological replicates; 10x lane replicate 2

  Dataset EGAD00001010090

• PD-1 antibody-bound progenitor-exhausted CD8+ T cells in lymph nodes boost PD-1-blockade anti-tumor immunity in gastrointestinal cancer

  While progenitor-exhausted T cells (Tpex) expressing TCF1 and PD-1 are crucial for the therapeutic effect of immune checkpoint inhibitors (ICIs; therapeutic anti-PD-1 antibodies), the dynamics of ICI-bound Tpex are not fully understood. In this study, we investigated ICI-bound T cells in detail using combined sequencing analysis at the single cell level. By analyzing samples from gastrointestinal cancer patients with or without ICI treatment, we found that Tpex were enriched in proximal lymph nodes (LNs) and proliferated at a high rate after ICI treatment. Importantly, ICI high-bound Tpex in LNs shared T-cell receptor clonotypes with intratumoral exhausted CD8+ T cells (Tex), suggesting their migration to tumor sites after ICI treatment. These findings indicate that ICIs initially target PD-1+CD8+ Tpex in LNs, prompting their proliferation. Subsequently, these cells migrate to tumors and differentiate into Tex, thereby exerting anti-tumor immunity.

  Study JGAS000720

• A Genome-Wide Association Study in Participants Experiencing Breast Cancer Events in High-Risk Postmenopausal Women Receiving Selective Estrogen Receptor Modulators on NSABP Trials P-1 and P-2. A Collaboration Between the NIH Pharmacogenetics Research Network and the RIKEN Yokohama Institute Center for Genomic Medicine

  The primary aim of this study is to identify genes related to the occurrence of breast events, defined as occurrence of invasive breast cancer or ductal carcinoma in situ (DCIS), in women at high risk of developing breast cancer who have received a Selective Estrogen Receptor Modulator (SERM - tamoxifen or raloxifene) on the National Surgical Adjuvant Breast and Bowel Project (NSABP) P-1 or P-2 trials. Cases and controls were selected from the tamoxifen arm in the P-1 and from the tamixifen and raloxifene arms in the P-2 trial. For the P-1 trial, cases and controls were required to be 50 years of age or older at time of entry. Cases were females who experienced an invasive breast cancer or DCIS on P-1 or P-2. Controls were females who did not experience an invasive breast cancer or DCIS. A nested matched case-control design was used, with matching on the following factors: 1) trial and treatment arm (P-1 tamoxifen, P-2 tamoxifen, P-2 raloxifene); 2) age at trial entry (when controls could not be exactly matched on age, we incremented the age of matching by +/- 1 year, in sequence until a match was obtained without exceeding +/- 5 years); 5-year predicted breast cancer risk based on the Gail model ( <2.00%, 2.01-3.00, 3.01-5.00, >5.01), 3) history of lobular carcinoma in situ (yes vs. no); 4) history of atypical hyperplasia in breast (yes vs. no); 5) time on study (controls must be on study at least as long as the time to diagnosis of the breast event for the case). Because 94.2% of the participants on P-1 and P-2 treated with tamoxifen or raloxifene were Caucasian, this study was restricted to only Caucasians. Two cases and two controls were randomly chosen as duplicates for quality control of genotype concordance. The DNA samples were plated into 96-well plates, with cases and controls randomly distributed across the plates. A Caucasian parent-child CEPH trio from the HapMap project was included to check for Mendelian transmission of alleles. Genotypes were determined by the RIKEN Center for Genomic Medicine with the Illumina Human610-Quad.

  Study phs000305

• WTCCC case-control study for Type 1 Diabetes - Combined Controls

  WTCCC genome-wide case-control association study for Type 1 Diabetes (T1D) using six disease collections together with the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000015

• WES and RNAseq dataset

  This dataset includes WES and RNAseq for 7 patients with metastatic melanoma (4), non-small cell lung adenocarcnioma (1), cervix adenocarcinoma (1), and Epidermoid nasophaeyngeal carcinoma (1), enrolled in phase I clinical trials (NCT03475134m NCT04643574 and NCT05195619). WES was performed on matched cancer and healthy tissues, whereas RNAseq was performed on cancer tissues, using Illumina HiSeq 4000/6000 and Illumina NextSeq 500/550 systems.

  Dataset EGAD50000000337

• single cell data from HPV-positive head and neck cancer patients receiving induction CTLA-4 and PD-1 immune checkpoint blockade

  Baseline and on-treatment tumor samples from human papillomavirus-positive oropharynx cancer patients receiving anti-CTLA-4 and anti-PD-1 immune checkpoint blockade were analyzed by single-cell RNA and TCR sequencing.

  Study EGAS50000000335

• BLUEPRINT release August 2016, Bisulfite-Seq for precursor B cell, on genome GRCh38

  Bisulfite-Seq data for 1 precursor B cell sample(s). 6 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002330

• BLUEPRINT release August 2016, Bisulfite-Seq for macrophage - T=6days B-glucan, on genome GRCh38

  Bisulfite-Seq data for 1 macrophage - T=6days B-glucan sample(s). 15 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002410

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - T=0days, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - T=0days sample(s). 15 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002405

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_T=4hrs, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_T=4hrs sample(s). 15 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002394

• BLUEPRINT release August 2016, Bisulfite-Seq for macrophage - T=6days untreated, on genome GRCh38

  Bisulfite-Seq data for 1 macrophage - T=6days untreated sample(s). 15 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002373

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_LPS_T=24hrs_RPMI_T=5days, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_LPS_T=24hrs_RPMI_T=5days sample(s). 15 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002324

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_T=1hr, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_T=1hr sample(s). 15 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002447

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_T=6days, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_T=6days sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002371

• BLUEPRINT release August 2016, Bisulfite-Seq for Acute Promyelocytic Leukemia - ATRA, on genome GRCh38

  Bisulfite-Seq data for 1 Acute Promyelocytic Leukemia - ATRA sample(s). 9 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002472

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_BG_T=4hrs, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_BG_T=4hrs sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002301

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_LPS_T=1hr, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_LPS_T=1hr sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002502

• Bulk RNASeq on CD8+CD69+CD103+ and CD8+CD69+CD103- T cells form breast cancer.

  CD8+CD69+CD103+ and CD8+CD69+CD103- T cells were flow sorted from 1 primary triple negative breast cancer, 1 primary HER2 amplified breast cancer, and 1 triple negative liver metastasis. Prior to flow sorting, fresh tumor samples were digested to produce single cell homogenates on the day of surgery. Following RNA extraction, RNASeq was performed using polyA selection.

  Dataset EGAD00001003964

• RNA-seq of multiple myeloma patient samples

  RNA from CD138+ plasma cells from patients with a plasma cell dyscrasia were sequenced using the TruSeq Stranded Total RNA Ribo-zero Gold kit (Illumina). Paired-end 75bp reads were generated on a NextSeq500 or HiSeq4000 (Illumina). The dataset consists of 1 MGUS sample, 5 SMM samples, 69 newly diagnosed myeloma samples, 1 relapsed myeloma sample, 1 previously treated myeloma samples, and 4 PCL samples. Matching whole genome sequencing data are available for these samples under study EGAS00001003164.

  Dataset EGAD00001004543

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_T=24hrs, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_T=24hrs sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002335

• BLUEPRINT September 2016, Bisulfite-Seq Acute Lymphocytic Leukemia for precursor B cell from bone marrow, on Genome GRCh38

  Bisulfite-Seq data for 1 sample(s) Acute Lymphocytic Leukemia for precursor B cell from bone marrow, on Genome GRCh38. 3 run(s), 1 experiment(s), 0 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001002969

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_BG_T=1hr, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_BG_T=1hr sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002327

• Circulating tumor DNA dynamics reveal KRAS G12C mutation heterogeneity and response to treatment with the KRAS G12C inhibitor divarasib in solid tumors

  Purpose: To inform prognosis, treatment response, disease biology, and KRAS G12C mutation heterogeneity, we conducted exploratory circulating tumor DNA (ctDNA) profiling on 134 patients with solid tumors harboring a KRAS G12C mutation treated with single-agent divarasib (GDC-6036) in a phase 1 study. Experimental design: Plasma samples were collected for serial ctDNA profiling at baseline (Cycle 1 Day 1 prior to treatment) and multiple on-treatment time points (Cycle 1 Day 15 and Cycle 3 Day 1). Results: KRAS G12C ctDNA was detectable from plasma samples in 72.9% (43/59) and 92.6% (50/54) of patients with non-small cell lung cancer and colorectal cancer, respectively, the majority of whom were eligible for study participation based on a local test detecting the KRAS G12C mutation in tumor tissue. Baseline ctDNA tumor fraction was associated with tumor type, disease burden, and metastatic sites. A decline in ctDNA level was observed as early as Cycle 1 Day 15. Serial assessment showed a decline in ctDNA tumor fraction associated with response and progression-free survival. Except for a few cases of KRAS G12C sub-clonality, on-treatment changes in KRAS G12C variant allele frequency mirrored changes in the overall ctDNA tumor fraction. Conclusion: Across tumor types, the KRAS G12C mutation likely represents a truncal mutation in the majority of patients. Rapid and deep decline in ctDNA tumor fraction was observed in patients responding to divarasib treatment. Early on-treatment dynamics of ctDNA were associated with patient outcomes and tumor response to divarasib treatment.

  Study EGAS50000000315

• Singel-cell RNA sequencing and CUT&RUN sequencing of human RUNX2-deficient osteoblasts

  Runx2 is a master regulator of bone formation, and its dysfunction causes cleidocranial dysplasia (CCD) in humans. When iPS cells were generated from patients with CCD and Runx2-deficient iPS cells were generated using gene-editing techniques, abnormal laminopathy-like nuclei were observed. Runx2-deficient cells showed reduced Lamin A/C expression, but not protein levels. However, in Runx2-deficient cells, both the gene expression and protein levels of Nesprin-1 were reduced, perinuclear actin fibers were sparser, and nuclear stiffness was reduced. Forced expression of Lamin A/C increased nuclear stiffness but did not improve nuclear morphology. In contrast, the induction of Nesprin-1 expression alone normalized nuclear stiffness and restored nuclear morphology and perinuclear actin distribution. In Runx2-null cells, mechanical stress-induced phosphorylation of emerin was not observed. In contrast, forced expression of nesprin-1 in Runx2-null cells resulted in phosphorylation of emerin, indicating the restoration of intracellular tension. These observations were confirmed by atomic force microscopy. Therefore, the intracellular tension was inferred to pull the nuclear membrane into its normal shape. CUT&RUN assay and single RNA-seq analysis showed that an aberrant nuclear membrane caused loss of nuclear lamina gene regulation machinery, making the progression of normal osteogenic differentiation impossible; however, supplementation with Nesprin-1 restored gene regulation mechanisms and promoted pre-osteoblast formation with normal nuclear morphology. Nesprin-1 expression induced by Runx2 is essential for epigenetic regulation of the nuclear lamina. We propose CCD as a new type of laminopathy involving defective expression of Nesprin-1 regulated by Runx2.

  Study JGAS000663

• NICOLA Epigenetic Data Wave 1 - Beta values

  Beta values generated from Illumina EPIC methylation array data for a subset of NICOLA Wave 1 samples.

  Dataset EGAD50000001965

• TENX064

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX064 1 samples; filetype=fastq

  Dataset EGAD00001006479

• SCRNA10X_SA_CHIP0163_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0163_002 1 samples; filetype=fastq

  Dataset EGAD00001006474

• SCRNA10X_SA_CHIP0142_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0142_001 1 samples; filetype=fastq

  Dataset EGAD00001006464

• SCRNA10X_SA_CHIP0149_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0149_001 1 samples; filetype=fastq

  Dataset EGAD00001006467

• SCRNA10X_SA_CHIP0080_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0080_002 1 samples; filetype=fastq

  Dataset EGAD00001006462

• TENX066

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX066 1 samples; filetype=fastq

  Dataset EGAD00001006481

• SCRNA10X_SA_CHIP0163_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0163_001 1 samples; filetype=fastq

  Dataset EGAD00001006473

• SCRNA10X_SA_CHIP0063_000

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0063_000 1 samples; filetype=fastq

  Dataset EGAD00001006478

• SCRNA10X_SA_CHIP0152_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0152_002 1 samples; filetype=fastq

  Dataset EGAD00001006472

• SCRNA10X_SA_CHIP0172_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0172_001 1 samples; filetype=fastq

  Dataset EGAD00001006475

• TENX062

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX062 1 samples; filetype=fastq

  Dataset EGAD00001006477

• TENX065

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX065 1 samples; filetype=fastq

  Dataset EGAD00001006480

• SCRNA10X_SA_CHIP0172_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0172_002 1 samples; filetype=fastq

  Dataset EGAD00001006476

• SCRNA10X_SA_CHIP0152_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0152_001 1 samples; filetype=fastq

  Dataset EGAD00001006471

• SCRNA10X_SA_CHIP0006_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0006_001 1 samples; filetype=fastq

  Dataset EGAD00001006460

• SCRNA10X_SA_CHIP0080_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0080_001 1 samples; filetype=fastq

  Dataset EGAD00001006461

• SCRNA10X_SA_CHIP0149_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0149_002 1 samples; filetype=fastq

  Dataset EGAD00001006468

• SCRNA10X_SA_CHIP0150_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0150_001 1 samples; filetype=fastq

  Dataset EGAD00001006469

• SCRNA10X_SA_CHIP0150_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0150_002 1 samples; filetype=fastq

  Dataset EGAD00001006470

• SCRNA10X_SA_CHIP0142_003

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0142_003 1 samples; filetype=fastq

  Dataset EGAD00001006465

• SCRNA10X_SA_CHIP0146_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0146_002 1 samples; filetype=fastq

  Dataset EGAD00001006466

• Metagenome Sequencing Data Cachexia Non-Cachexia

  120 samples of cachectic and non cachectic. 240 Fastq files from Illumina metagenomic shothun paired End Sequencing

  Dataset EGAD00001010279

• TENX069

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX069 1 samples; filetype=fastq

  Dataset EGAD00001006483

• SCRNA10X_SA_CHIP0141_004

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0141_004 1 samples; filetype=fastq

  Dataset EGAD00001006463

• TENX068

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX068 1 samples; filetype=fastq

  Dataset EGAD00001006482

• Human Cancer-Targeted Immunity via Transgenic Hematopoietic Stem Cell Progeny

  Analysis of a patient biopsy from the clinical trial (NCT03240861), a first-in-human study of transgenic NY-ESO-1 TCR- T cells and NY-ESO-1 TCR+ sr39tk autologous hematopoietic stem cells tandem therapy for solid tumors after myeloablative conditioning chemotherapy with busulfan and fludarabine. Single-cell RNAseq and single-cell ATACseq analysis from peripheral blood mononuclear cells obtained from subject NYSCT-03.

  Study phs003898

• CUT&RUN of AML1-ETO binding occupancy following miR-130a KD in Kasumi-1 cells

  We performed CUT&RUN assay with control Ig , anti-Flag and anti-ETO antibodies in Flag-tag AML1-ETO Kasumi-1 cells to determine binding occupancy of AML1-ETO following miR-130a KD. Kasumi-1 cells were transduced in 3 independent replicates for control and miR-130a KD samples with lentiviruses containing GFP reporter gene. GFP+ cells were sorted 3 days post-transduction and the cells were used in the CUT&RUN assay. Three antibodies were used for each biological sample.

  Study EGAS00001005867

• snRNA-sequencing data from 3 FSHD and 1 control multinucleated myotube cell cultures

  snRNA-sequencing data from 3 FSHD and 1 control multinucleated myotube cell cultures

  Dataset EGAD50000000207

• ERDERA WES reanalysis - DPF1 Batch 1

  Processed data for 100 WES from the Data Processing Freeze 1

  Dataset EGAD50000002187

• Res1_H23_exp1_MC_04.03.22

  1 sample is pure plasmid DNA and 8 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Dataset EGAD00001012229

• Res1_HT29_exp1_MC_02.03.22

  1 sample is pure plasmid DNA and 10 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Dataset EGAD00001012230

• Optimizing single-cell transcriptomic discrimination of atopic dermatitis versus psoriasis vulgaris

  AD Samples 1 and 2. AD1 has 2 runs of additional sequencing, AD2 has 1 run of additional sequencing.

  Dataset EGAD00001011286

• H3Africa ACEGID H3Africa Array Genotype

  SNP Genotyping for Lassa Fever cases and population controls from Nigeria and Sierra Leone using Illumina H3Africa array version 1

  Dataset EGAD00010002510

• RNA Sequencing of Control and Myotonic Dystrophy Type 1 Cells During Myogenic Differentiation

  RNAseq data of 30 muscle cell samples (proliferating myoblasts to differentiating myotubes) of immortalized control cells, and myotonic dystrophy type 1 (DM1) patient-derived cells and their edited versions.

  Dac EGAC50000000592

• WES for Patient 1 to 8 of NIBIT-M4 clinical trial

  WES for Patient 1 to 8 of NIBIT-M4 clinical trial

  Dataset EGAD00001009701

• RNAseq_of_ribosomal_footprints

  Cancer genes can affect ribosomal RNA processing and this can underlie their essentiality to cells, making them cell-essential in the same way as ribosomal genes themselves. We want to confirm this, in order to understand the results of our CRISPR drop-out screens.NOTE FROM BESPOKE TEAM: Run a single read 1 (forward read) of 30 bases, then an index 1 read as normal. This would fit a 50cycle kit

  Study EGAS00001001591

• 5 scRNA-seq with TCR Enrichment of Tumour-Involved Lymph Nodes, Malignant Seromas and Patient-Derived Xenografts from 18 T-Cell Lymphoma Patients

  This raw data contains 5' scRNA-seq with TCR enrichment data from 18 individual patients analysing cells taken from tumour-involved lymph nodes (LN), malignant seromas and patient-derived xenografts (PDX) including systemic/nodal ALK+ ALCL (n=3 PDX), BIA-ALCL (n=2 seromas), PTCL-NOS (n=5 nodal tumours), nodal T follicular helper lymphoma - angioimmunoblastic type (nTFHL-AI) (n=1), nTFHL, not otherwise specified (nTFHL, NOS, n=1), TLBL (n=5) and 1 case of Sezary Syndrome (SS, cutaneous tumour). Droplet-based 5’ scRNA-seq with TCR enrichment (10x Chromium platform) was performed for all live cells, or the viable CD45+CD3- and CD45+CD3+ fractions following cell sorting by flow cytometry.

  Dataset EGAD00001015703

• H3Africa AWI-Gen Phase 1 Pilot Microbiome Phenotype

  This is human phenotype data for participants in a gut microbiome study. This data was collected at the same time as the stool samples used for the microbiome component. Participants were also part of the AWI-Gen Phase 1 main study. https://www.ebi.ac.uk/ena/data/view/PRJEB40733

  Dataset EGAD00001006581

• Dataset of Study assessing the efficacy and safety of durvalumab plus olaparib plus fulvestrant in selected metastatic or locally advanced ER-positive, HER2-negative breast cancer patients.

  This dataset is made of 166 samples extracted from 1) FFPE blocks of the most recent biopsy (metastasis when applicable or primary tumor) of the breast tumor 2) Plasma samples and blood samples at baseline, at Day 1 Cycle2, Day 1 Cycle 3 and at progression or treatment discontinuation. Target sequencing has been perfomed using GREAT_A_v3 panel

  Dataset EGAD50000001665

• Dynamics of checkpoint receptors in γδ T cell subsets are associated with clinical responses during anti-PD-1 immunotherapies

  scRNA-seq data from circulating gamma delta T cells derived from healthy donors and patients with stage IV melanoma that either responded or did not respond to anti-PD-1 monotherapy or anti-PD-1 and anti-CTLA-4 combination therapy. This dataset includes paired samples from these patients before and 3 months after the start of immunotherapy.

  Dataset EGAD50000001812

• Diabetes Control and Complications Trial (DCCT) and Epidemiology of Diabetes Interventions and Complications Study (EDIC)

  The Diabetes Control and Complications Trial (DCCT, 1982-93) and the Epidemiology of Diabetes Interventions and Complications follow-up study (EDIC, 1994-2016) have been ongoing for more than twenty years. After a mean follow-up of approximately 16 years, the DCCT-EDIC cohort of 1,441 Type 1 diabetics had remained remarkably complete with more than 90% of the original cohort being actively followed. Taken together, the DCCT clinical trial and subsequent EDIC longitudinal follow-up provide a uniquely rich source of information on the impact of intensive therapy on glycemia and the its long-term complications for persons with Type 1 diabetes. The DCCT was a multicenter, randomized clinical trial (1, 2) designed to compare intensive with conventional diabetes therapy with regard to their effects on the development and progression of the early complications of Type 1 diabetes. The DCCT trial found that "intensive therapy effectively delays the onset and slows the progression of diabetic retinopathy, nephropathy, and neuropathy" in patients with Type 1 diabetes (1). The goal of the EDIC follow-up was to examine the longer term effects of the original DCCT interventions, especially as they apply to late-occurring complications, such as cardiovascular disease and more advanced stages of retinal and renal disease (3). The EDIC study has been remarkably fruitful in discovering the long term "imprinting" effects (metabolic memory) of the previous intensive therapy, and in delineating the interactions among risk factors with regard to microvascular complications (4-6). In addition, EDIC established, for the first time, the role of intensive therapy and chronic glycemia on atherosclerosis (7-9). Note: This study description has been prepared using materials authored by the DCCT-EDIC Data Coordinating Center. DETAILED DESCRIPTION OF STUDY

  Study phs000086

• APL nanopore sequencing data

  APL nanopore sequencing data are deposited into 2 data formats: 1. CRAM files 2. h5 files

  Dataset EGAD00001008146

• Analysis of the Patient derived cells that model the intratumoral heterogeneity of hypermutated IDH1 mutant glioma

  Dataset contains paired-end Whole Exome sequencing data from 2 glioma patients (1 oligodendroglioma and 1 astrocytoma) , derived cultured cells, and derived murine xenografts.

  Dataset EGAD00001006340

• The Extracellular RNA Quality Control (exRNAQC) study (phase 1)

  mRNA capture sequencing and small RNA sequencing data (FASTQ files) of the exRNAQC study phase 1.

  Dataset EGAD00001007697

• EGAD00000000008

  WTCCC1 project Type 1 Diabetes (T1D) samples

  Dataset EGAD00000000008

• FFPE CRC WGS Data

  The data consists of 3 BAM files. Two of three BAMs are tumour FFPE samples (1 repaired-FFPE; 1 unrepaired-FFPE); The other BAM file is sequenced from normal colon tissue

  Dataset EGAD00001007716

• Single cell analysis reveals new evolutionary complexity in uveal melanoma

  Uveal melanoma (UM) is a rare eye cancer that is highly aggressive and leads to metastatic death in up to half of patients. Here, we conduct droplet-based single-cell RNA sequencing of 59,915 neoplastic and non-neoplastic cells from 8 primary and 3 metastatic UMs. Single-cell VDJ sequencing of RNA is conducted to determine clonally expanded B/T- cell populations. Single-cell DNA sequencing of 1 primary and 1 metastasis is utilized.

  Study phs001861

• scRNAseq of ARID1B+/+ (control) and ARID1B+/- human telencephalic organoids at D120

  This dataset contains scRNAseq data of human telencephalic organoids, at day 120, from 4 different sequencing runs (libraries), as follows (please see cell line clone nomenclature in original publication): 1. Library 177136: Pat.2 ARID1B+/+ clone 2c (2 organoids, E11rep_1, E11rep_2), Pat.2 ARID1B+/- clone 2a (2 organoids: B002orig_1, B002orig_2) 2. Library 178119: Pat.2 ARID1B+/+ clone 2c (3 organoids: E11rep_1, E11rep_2, E11rep_3), Pat.2 ARID1B+/- clone 2a (3 organoids: B002orig_1, B002orig_2, B002orig_3) 3. Library 178120: Pat.2 ARID1B+/+ clone 2d (3 organoids: A3rep_1, A3rep_2, A3rep_3), Pat.2 ARID1B+/- clone 2b (3 organoids: B002_F7_1, B002_F7_2, B002_F7_3) 4. Library 184337: Pat.1 ARID1B+/- clone 1a (3 organoids: B001orig_1, B001orig_2, B001orig_3), HD.1 ARID1B+/+ clone 3a (3 organoids: 176_1, 176_2, 176_3), HD.1 ARID1B+/- clone 3b (3 organoids: F10_1, F10_2, F10_3)

  Dataset EGAD50000000498

• Clonal diversity analysis and inter/intra-organ heterogeneity in urothelial carcinoma under immunotherapy

  We studied a unique autopsy case of metastatic urothelial carcinoma with both sensitivity and resistance to anti-PD-1 therapy. We performed multiregional analyses of 58 paired whole exome sequencing and RNA sequencing samples of 8 different bulk tumor masses, 8 matched whole genome sequencing samples to identify structural abnormalities of each mass. Furthermore, we performed 7 whole exome sequencing from metastatic urothelial carcinoma patients who received anti-PD-1 therapy.

  Study JGAS000725

• Interval whole-genome sequence (WGS) data

  Whole-genome sequencing (WGS) genotype data generated as part of the Interval project. The data are reported, separately per chromosome, in variant call format (VCF). The genotypes are denoted in diploid format (for chrY the genotype 1 denoted as 1/1 and 0 denoted as 0/0). Note that multi-allelic variants are present in the data, but encoded to appear on separate, consecutive lines. The data are reported in following versions - unphased, phased, phased with imputation, sites only. Note: the unphased version has additional genotype information, while the phased versions only contain the genotypes.

  Dataset EGAD00001008661

• Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell RCC -- CA209-009

  Immune checkpoint inhibitors targeting the PD-1 pathway have transformed the management of many advanced malignancies, including clear cell renal cell carcinoma (ccRCC), but the drivers and resistors of PD-1 response remain incompletely elucidated. Here, we analyzed 592 tumors collected from advanced ccRCC patients enrolled in prospective clinical trials of treatment with PD-1 blockade (or mTOR inhibition as control arm) by whole-exome and RNA-sequencing, integrated with immunofluorescence analysis, to define the somatic alteration landscape of late-stage ccRCC and to uncover the immunogenomic determinants of therapeutic response. While conventional genomic markers (tumor mutation burden, neoantigen load) and degree of CD8+ T cell infiltration were not associated with clinical response, we discovered numerous chromosomal alterations in advanced ccRCC associated with response or resistance to PD-1 blockade. These advanced tumors were highly CD8+ T cell infiltrated, with only 22% and 5% with an immune desert and immune excluded phenotype, respectively. Our analysis revealed that CD8+ infiltrated tumors are depleted of favorable PBRM1 mutations and are enriched for unfavorable chromosomal losses of 9p21.3 when compared to non-infiltrated tumors. These data demonstrate how the interplay of somatic alterations and immunophenotypes impacts therapeutic efficacy.

  Study EGAS00001004290

• Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell RCC -- CA209-010

  Immune checkpoint inhibitors targeting the PD-1 pathway have transformed the management of many advanced malignancies, including clear cell renal cell carcinoma (ccRCC), but the drivers and resistors of PD-1 response remain incompletely elucidated. Here, we analyzed 592 tumors collected from advanced ccRCC patients enrolled in prospective clinical trials of treatment with PD-1 blockade (or mTOR inhibition as control arm) by whole-exome and RNA-sequencing, integrated with immunofluorescence analysis, to define the somatic alteration landscape of late-stage ccRCC and to uncover the immunogenomic determinants of therapeutic response. While conventional genomic markers (tumor mutation burden, neoantigen load) and degree of CD8+ T cell infiltration were not associated with clinical response, we discovered numerous chromosomal alterations in advanced ccRCC associated with response or resistance to PD-1 blockade. These advanced tumors were highly CD8+ T cell infiltrated, with only 22% and 5% with an immune desert and immune excluded phenotype, respectively. Our analysis revealed that CD8+ infiltrated tumors are depleted of favorable PBRM1 mutations and are enriched for unfavorable chromosomal losses of 9p21.3 when compared to non-infiltrated tumors. These data demonstrate how the interplay of somatic alterations and immunophenotypes impacts therapeutic efficacy.

  Study EGAS00001004291

• Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell RCC-- CA209-025

  Immune checkpoint inhibitors targeting the PD-1 pathway have transformed the management of many advanced malignancies, including clear cell renal cell carcinoma (ccRCC), but the drivers and resistors of PD-1 response remain incompletely elucidated. Here, we analyzed 592 tumors collected from advanced ccRCC patients enrolled in prospective clinical trials of treatment with PD-1 blockade (or mTOR inhibition as control arm) by whole-exome and RNA-sequencing, integrated with immunofluorescence analysis, to define the somatic alteration landscape of late-stage ccRCC and to uncover the immunogenomic determinants of therapeutic response. While conventional genomic markers (tumor mutation burden, neoantigen load) and degree of CD8+ T cell infiltration were not associated with clinical response, we discovered numerous chromosomal alterations in advanced ccRCC associated with response or resistance to PD-1 blockade. These advanced tumors were highly CD8+ T cell infiltrated, with only 22% and 5% with an immune desert and immune excluded phenotype, respectively. Our analysis revealed that CD8+ infiltrated tumors are depleted of favorable PBRM1 mutations and are enriched for unfavorable chromosomal losses of 9p21.3 when compared to non-infiltrated tumors. These data demonstrate how the interplay of somatic alterations and immunophenotypes impacts therapeutic efficacy.

  Study EGAS00001004292

• arcOGEN_HumanCoreExome-12v1-1_subset_2

  Subset 2 of osteoarthritis cases from the arcOGEN Consortium (http://www.arcogen.org.uk/) genotyped on HumanCoreExome-12v1-1 with consent for osteoarthritis studies only.

  Dataset EGAD00010000924

• CIRCLE-seq of PDO

  CIRCLE-seq data of PDAC 1 patient-derived organoid (PDO) - Unmapped reads, libraries were sequenced using the Illumina NextSeq500 with the NextSeq 500/550 Mid Output Kit v2.5 (300 Cycles), generating around 10M paired end 150bp reads per sample

  Dataset EGAD50000000281

• Colorectal_organoids_and_tumoroids___pulldown

  Targeted capture of cancer gene panel bait set in single cell derived organoids from colon tissue and colorectal cancer from 1 patient.

  Study EGAS00001000869

• 10x Genomics Single Cell Gene Expression for SA1051AX1XB01452

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1051 passage 1

  Dataset EGAD00001009124

• 10x Genomics Single Cell Gene Expression for SA1052DX1XB01519

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1052 passage 1

  Dataset EGAD00001009122

• 10x Genomics Single Cell Gene Expression for SA1050FX1XB01446

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1050 passage 1

  Dataset EGAD00001009125

• 10x Genomics Single Cell Gene Expression for SA1093CX1XB01917

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1093 passage 1

  Dataset EGAD00001009129

• 10x Genomics Single Cell Gene Expression for SA1049AX1XB01417

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1049 passage 1

  Dataset EGAD00001009121

• 10x Genomics Single Cell Gene Expression for SA1091AX1XB01790

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1091 passage 1

  Dataset EGAD00001009130

• 10x Genomics Single Cell Gene Expression for SA1052BX1XB01516

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1052 passage 1

  Dataset EGAD00001009127