19276 results for "Buy fc 26 coins Buyfc26coins.com is EA Sports official for FC 26 coins This is the best service ever..VBsp"

in 75.84 milliseconds.

• Single Cell Genome Sequence for DLP+ library A96171A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2, patient-derived xenograft SA611 passage 3 on DLP+ library A96171A

  Dataset EGAD00001009458

• Single Cell Genome Sequence for DLP+ library A96142B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient SA1162SA on DLP+ library A96142B

  Dataset EGAD00001009455

• 10x Genomics Single Cell Gene Expression for SA610X3XB03802

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA610 passage 3

  Dataset EGAD00001009160

• 10x Genomics Single Cell Gene Expression for SA604X9XB02425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 9

  Dataset EGAD00001009159

• 10x Genomics Single Cell Gene Expression for TOV2295(R)

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma cell line TOV2295(R)

  Dataset EGAD00001009158

• 10x Genomics Single Cell Gene Expression for SA535X9XB03617

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 9

  Dataset EGAD00001009157

• 10x Genomics Single Cell Gene Expression for SA535X5XB02895

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009156

• 10x Genomics Single Cell Gene Expression for SA535X5XB02891

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009155

• 10x Genomics Single Cell Gene Expression for SA1035X6XB03216

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 6

  Dataset EGAD00001009154

• 10x Genomics Single Cell Gene Expression for SA1035X5XB03021

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 5

  Dataset EGAD00001009153

• 10x Genomics Single Cell Gene Expression for SA535X4XB02498

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 4

  Dataset EGAD00001009152

• 10x Genomics Single Cell Gene Expression for SA1035X7XB03502

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 7

  Dataset EGAD00001009151

• 10x Genomics Single Cell Gene Expression for SA1035X6XB03211

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 6

  Dataset EGAD00001009150

• 10x Genomics Single Cell Gene Expression for SA535X6XB03101

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 6

  Dataset EGAD00001009149

• 10x Genomics Single Cell Gene Expression for SA1035X8XB03425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 8

  Dataset EGAD00001009148

• 10x Genomics Single Cell Gene Expression for SA1047BX1XB01022

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1047 passage 1

  Dataset EGAD00001009146

• 10x Genomics Single Cell Gene Expression for SA1184SA

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient SA1184

  Dataset EGAD00001009145

• 10x Genomics Single Cell Gene Expression for OV2295(R2)

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma cell line OV2295(R2)

  Dataset EGAD00001009144

• 10x Genomics Single Cell Gene Expression for OV2295

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma cell line OV2295

  Dataset EGAD00001009143

• 10x Genomics Single Cell Gene Expression for SA1035X4XB02879

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 4

  Dataset EGAD00001009147

• 10x Genomics Single Cell Gene Expression for SA605X3XB01966

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA605 passage 3

  Dataset EGAD00001009142

• A95662A

  Whole genome sequencing for single cells for library A95662A 637 samples; filetype=bam

  Dataset EGAD00001008226

• A95654A

  Whole genome sequencing for single cells for library A95654A 901 samples; filetype=bam

  Dataset EGAD00001008225

• A95635D

  Whole genome sequencing for single cells for library A95635D 367 samples; filetype=bam

  Dataset EGAD00001008224

• A95635B

  Whole genome sequencing for single cells for library A95635B 593 samples; filetype=bam

  Dataset EGAD00001008223

• A95632D

  Whole genome sequencing for single cells for library A95632D 644 samples; filetype=bam

  Dataset EGAD00001008222

• A90679

  Whole genome sequencing for single cells for library A90679 1034 samples; filetype=bam

  Dataset EGAD00001008219

• A98168B

  Whole genome sequencing for single cells for library A98168B 1290 cells; filetype=bam

  Dataset EGAD00001007625

• A98168A

  Whole genome sequencing for single cells for library A98168A 1294 cells; filetype=bam

  Dataset EGAD00001007624

• A96233B

  Whole genome sequencing for single cells for library A96233B 1601 cells; filetype=bam

  Dataset EGAD00001007623

• A96226B

  Whole genome sequencing for single cells for library A96226B 1274 cells; filetype=bam

  Dataset EGAD00001007621

• A96183B

  Whole genome sequencing for single cells for library A96183B 1191 cells; filetype=bam

  Dataset EGAD00001007619

• A96162A

  Whole genome sequencing for single cells for library A96162A 1505 cells; filetype=bam

  Dataset EGAD00001007618

• A96155C

  Whole genome sequencing for single cells for library A96155C 1316 cells; filetype=bam

  Dataset EGAD00001007617

• A96124B

  Whole genome sequencing for single cells for library A96124B 1241 cells; filetype=bam

  Dataset EGAD00001007616

• A95720A

  Whole genome sequencing for single cells for library A95720A 1467 cells; filetype=bam

  Dataset EGAD00001007613

• A95703A

  Whole genome sequencing for single cells for library A95703A 740 cells; filetype=bam

  Dataset EGAD00001007612

• A95700A

  Whole genome sequencing for single cells for library A95700A 1172 cells; filetype=bam

  Dataset EGAD00001007611

• A95697B

  Whole genome sequencing for single cells for library A95697B 1233 cells; filetype=bam

  Dataset EGAD00001007610

• A95623B

  Whole genome sequencing for single cells for library A95623B 1363 cells; filetype=bam

  Dataset EGAD00001007608

• A118862B

  Whole genome sequencing for single cells for library A118862B 1152 cells; filetype=bam

  Dataset EGAD00001007606

• A118862A

  Whole genome sequencing for single cells for library A118862A 1070 cells; filetype=bam

  Dataset EGAD00001007605

• A118830A

  Whole genome sequencing for single cells for library A118830A 912 cells; filetype=bam

  Dataset EGAD00001007604

• A110673B

  Whole genome sequencing for single cells for library A110673B 1153 cells; filetype=bam

  Dataset EGAD00001007603

• A110673A

  Whole genome sequencing for single cells for library A110673A 1089 cells; filetype=bam

  Dataset EGAD00001007602

• A110621A

  Whole genome sequencing for single cells for library A110621A 1137 cells; filetype=bam

  Dataset EGAD00001007601

• A110618B

  Whole genome sequencing for single cells for library A110618B 1047 cells; filetype=bam

  Dataset EGAD00001007600

• A110618A

  Whole genome sequencing for single cells for library A110618A 1184 cells; filetype=bam

  Dataset EGAD00001007599

• A108870A

  Whole genome sequencing for single cells for library A108870A 1795 cells; filetype=bam

  Dataset EGAD00001007598

• A108863A

  Whole genome sequencing for single cells for library A108863A 1301 cells; filetype=bam

  Dataset EGAD00001007597

• A108851B

  Whole genome sequencing for single cells for library A108851B 1681 cells; filetype=bam

  Dataset EGAD00001007596

• A108842A

  Whole genome sequencing for single cells for library A108842A 1477 cells; filetype=bam

  Dataset EGAD00001007595

• A108732A

  Whole genome sequencing for single cells for library A108732A 1139 cells; filetype=bam

  Dataset EGAD00001007592

• DNA Double Strand Breaks in KMT2A-Rearranged AML patients

  DNA breakage at specific rearrangement-participating genes, which are more sensitive to a range of environmental and chemotherapeutic agents than other regions of the human genome, contributes to the generation of leukemia-causing rearrangements, and can be used as a predictor of susceptibility to cancer caused by gene rearrangements. As a direct proof-of-principle regarding the feasibility of employing fragile site breakage to identify and monitor high-risk populations to allow personalized treatments, genome-wide DNA break mapping/sequencing was used to determine whether preferential DNA breaks are generated at the major breakpoint regions of KMT2A in AML remission patients with previously diagnosed KMT2A rearrangement. We are submitting break mapping data from three patients with KMT2A rearrangements and three non-fusion patients, with and without etoposide treatments.

  Study phs002804

• Dysregulation of Naive T Cell Quiescence during Aging

  TEA-seq (novel trimodal single-cell analysis of mRNA transcripts, surface protein epitopes, and chromatin accessibility) was performed on CD3+ T cells isolated from peripheral blood of healthy pediatric (11–13 yrs, n = 8) and older adult (55–65 yrs, n = 8) female donors. Single-cell RNA sequencing (scRNA-seq) was additionally performed on peripheral blood mononuclear cells (PBMCs) from a healthy cohort of 16 pediatric, 16 young adult (25–35 yrs), and 16 older adult donors with equal sex distribution. Using these data, we dissected the compositional and molecular alterations within the T cell compartment across the spectrum of healthy age, showing broad transcriptional and epigenetic alterations within the T cell compartment of older adults compared to children, as well as a novel population of pediatric-specific CD8aa T cells.

  Study phs003400

• Investigating Genetics in Suspected Congenital Syndromes

  We employed forward and reverse genetics to identify individuals with heterozygous JAK1 variants associated with autoimmune and inflammatory diseases. We first recruited six unrelated probands and seven affected family members presenting with a range of autoimmune and inflammatory conditions. Whole exome sequencing revealed 4 different variants spanning all domains of the JAK1 protein: FERM domain (chr1: 64873438-G>A; c.415C>T; p.E139K), SH2 domain (chr1: 64855641-G>A; c.1516C>T; p.R506C), pseudokinase domain (chr1: 64845529-C>T; c.2099G>A; p.S700N, and kinase domain (chr1: 64838479-C>T; c.2953G>A; p.V985I). We then performed a systematic review of electronic health records of the BioME BioBank database and identified 43 additional individuals with at least one of these JAK1 variants. Comparing variant-positive and variant-negative individuals, our study revealed increased occurrences of autoimmunity, atopy, colitis, and dermatitis in patients harboring these JAK1 gain-of-function variants. We have deposited in dbGaP the whole exome sequencing files of the 43 patients identified in the BioME biobank, as well as their phenotype information.

  Study phs003453

• Spatial transcriptomics reveal topological immune landscapes of Asian head and neck angiosarcoma

  Angiosarcomas are rare malignant tumors of the endothelium, arising commonly from the head and neck region (AS-HN) and recently associated with ultraviolet (UV) exposure and human herpesvirus-7 (HHV-7) infection. We examined 81 cases of angiosarcomas, including 47 cases of AS-HN, integrating information from whole genome sequencing, gene expression profiling and spatial transcriptomics (10X Visium). In the AS-HN cohort, we observed recurrent somatic mutations in CSMD3 (18%), LRP1B (18%), MUC16 (18%), POT1 (16%) and TP53 (16%). UV-positive AS-HN harbored significantly higher tumor mutation burden (TMB) than UV-negative cases (p=0.0294). NanoString profiling identified three clusters with distinct tumor inflammation signature (TIS) scores (p<0.001). Spatial transcriptomics revealed topological profiles of the tumor microenvironment, identifying dominant but tumor-excluded inflammatory signals in “immune-hot” cases and immune foci even in otherwise “cold” cases. In conclusion, spatial transcriptomics reveal the tumor immune landscape of angiosarcoma, and in combination with multi-omic information, may improve implementation of treatment strategies.

  Study EGAS00001007083

• Sequence analysis of colorectal serrated lesions

  Recent studies have shown that colorectal serrated lesions, which include sessile serrated adenomas (SSAs) and traditional serrated adenomas (TSAs), are precursors of colorectal cancer. However, the molecular mechanisms underlying the carcinogenesis, particularly in TSAs, remain largely uncharacterized. To clarify their molecular and clinicopathological characteristics, we performed mutation and methylation analyses of cancer-associated genes in 78 serrated lesions, including TSAs, SSAs and microvesicular hyperplastic polyps. Target exon sequence analysis was performed with 39 genes, including genes known to be frequently mutated in colorectal cancers and/or serrated lesions. We also used bisulfite pyrosequencing to assess the methylation status of various cancer-associated genes and marker genes of the CpG island methylator phenotype (CIMP). The prevalence of mutations in genes associated with Wnt signaling was significantly higher in TSAs than SSAs (65% vs. 28%, p < 0.01). Among those, RNF43 mutations were observed in 38% of TSAs and 17% of SSAs. In immunohistochemical studies of 39 serrated lesions, the prevalence of abnormal nuclear ?-catenin accumulation was significantly higher in TSAs (57%) than SSAs (8%) (P = 0.01). SMOC1 methylation was detected in 54% of TSAs but in no SSAs (p < 0.01). Additionally, SMOC1 methylation was more prevalent among TSAs with KRAS mutation (82%) than with BRAF mutation (38%, p = 0.03). Lesions with CIMP-high or RNF43 mutations were detected only in TSAs with BRAF mutation, suggesting two distinct carcinogenic pathways in TSAs. Mutations in genes associated with Wnt signaling play a greater role in the carcinogenesis of TSAs than SSAs.

  Study JGAS000217

• The Sys4MS cohort clinical data

  The Sys4MS cohort clinical data - 419 patients

  Dataset EGAD00001010867

• GWAS genotype data of the Japanese population

  GWAS genotype data of the Japanese population (N=2,380).

  Dataset EGAD00001009070

• Institute of Neuropathology - University of Freiburg Medical Center

  The Data Access Committee of the Institute of Neuropathology Freiburg.

  Dac EGAC50000000033

• Whole_Genome_Sequencing_of_INTERVAL

  Whole genome sequencing of participants from the INTERVAL study.

  Study EGAS00001001355

• GWAS data of the AlpeDPD study

  GWAS data of the AlpeDPD trail cohort

  Dataset EGAD00010002684

• Elucidation of the association between viruses and autoimmune diseases and COVID-19

  We determined the presence of endogenous human herpesvirus 6 (eHHV-6) and calculated anellovirus load from whole-genome sequencing data of 1,164 patients with COVID-19.

  Study JGAS000739

• The elucidation of molecular mechanisms of hematopoietic stem cells focusing on paroxysmal nocturnal hemoglobinuria (PNH)-type cells

  To elucidate the pathogenicity of aplastic anemia, we conducted exome seqence analysis in 5 patients with aplastic anemia with uniparental disomy in 6p.

  Study JGAS000094

• cytogenetically visibile inversions

  Pacbio Hifi whole genome sequencing data from six individuals carrying cytogenetically visible inversions. The HiFi data was produced on the PacBio Revio machine, using 1 flowcell per sample. The resulting data was aligned to hg19 using minimap2, and converted into bam using samtools.

  Dataset EGAD50000000635

• Genome-wide array data from Eivissan and Menorcan Individuals

  VCF file with autosomal genotypes from 22 and 20 Eivissan and Menorcan samples. Affymetrix Human Origins array was used to genotype the samples and the variants were lifted over to the hg38 human genome reference.

  Dataset EGAD50000000614

• Genotype-Multiple-Myeloma-case

  Genome-Wide Human SNP Array 6.0 or the CytoScan HD array, according to the manufacturer’s instructions (Affymetrix, Santa Clara, CA, USA) now part of Thermo Fisher Scientific (Thermo Fisher Scientific, Inc.)

  Dataset EGAD00010002102

• Understanding the multicellular dynamics of clear cell renal cell carcinoma - spatial transcriptomics

  The dataset comprises of transcriptomes of tissue sections derived from either the tumour normal interface or tumour core from clear cell renal cell carcinomas. 16 sections are sampled in total using 10x Genomics' Visium technology.

  Dataset EGAD00001008781

• Single nucleus and in situ RNA sequencing reveals cell topographies in the human pancreas

  To investigate the cellular composition of the human pancreas, we performed single-nucleus sequencing from snap frozen biopsies of pancreata from adult, neonatal and diseased (chronic pancreatitis) human donors.

  Dataset EGAD00001006396

• Paired-end RNA-seq analysis of the 3D evolution of glioma cell populations. Part 1.

  Dataset includes 160 double-stranded RNAseq libraries collected from 16 patients with adult diffuse glioma. The majority of these samples were spatially-mapped during sample collection, enabling the genomic information derived from them to be mapped in 3D space.

  Dataset EGAD00001005222

• Exome sequencing of synchronous colorectal cancers

  The data consists of 47 exome-sequenced synchronous colorectal cancers from 23 patients. The exomes of corresponding normal samples were used to remove germline variants. All patients are Finnish (white Caucasian). All except one patient (sync_11 who belongs to a LS family) were assumed sporadic. The sequence data was produced with Illumina HiSeq 4000.

  Dataset EGAD00001004884

• Comprehensive genetic analysis of Epstein-Barr virus-associated hematological malignancy

  Comprehensive genetic analyses including whole-exome sequencing, targeted sequencing, and whole-genome sequencing of the human genome and the Epstein-Barr virus (EBV) genome were performed to reveal the molecular pathogenesis of EBV-associated hematological malignancy.

  Dataset EGAD00001004286

• Delineate genomic and epigenomic changes in esophageal squamous cell carcinoma following radiotherapy

  We performed whole-exome sequencing and whole epigenome sequencing (RRBS) of samples collected from different time points during radiotherapy from thirty-four ESCC patients. We compared the genetic and epigenetic features of the different time biopsy samples to reveal the changes in ESCC received radiotherapy.

  Dataset EGAD00001003799

• Single cell mRNA sequencing of primary GBM - SF 10281

  We collected fresh tissue from an untreated GBM (SF10281) directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine, resulting in sequencing libraries from 96 individual cells.

  Dataset EGAD00001003114

• Single cell mRNA sequencing of primary GBM - SF 10679

  We collected fresh tissue from an untreated GBM (SF10679) directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine, resulting in sequencing libraries from 96 individual cells.

  Dataset EGAD00001003113

• Single cell mRNA sequencing of primary GBM - SF 10592

  We collected fresh tissue from an untreated GBM (SF10592) directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine, resulting in sequencing libraries from 96 individual cells.

  Dataset EGAD00001003112

• The Transcriptome of PLX4032 resistance

  We have collected material from a patient who had BrafV600E mutant melanoma that was treated with PLX4032. We have germline DNA from the patient and DNA and RNA from distinct lesions before and after treatment with PLX4032. We have transcriptome sequenced these samples to obtain a snap shot of the mechanisms of resistance that are operative.

  Dataset EGAD00001000599

• ChIP-seq of plasma cell-free nucleosomes identifies gene expression programs of the cells-of-origin

  Cell-free DNA in human plasma provides access to molecular information about the pathological processes in the organs or tumors from which it originates. These DNA fragments are derived from fragmented chromatin in dying cells, and retain some of the cell of origin histone modifications. Here, we apply chromatin immunoprecipitation of cell-free nucleosomes carrying active chromatin modifications followed by sequencing (cfChIP-seq) to 268 human samples. In healthy donors, we identified bone marrow megakaryocytes, but not erythroblasts, as major contributors to the cfDNA pool. In patients with a range of liver diseases, we show that we can identify pathology-related changes in hepatocyte transcriptional programs. In metastatic colorectal carcinoma patients, we detected clinically relevant, and patient-specific information, including transcriptionally active HER2 amplifications. Altogether, cfChIP-seq using low sequencing depth, provides systemic and genome-wide information, can inform diagnosis, and facilitate interrogation of physiological and pathological processes using blood samples.

  Study EGAS00001004913

• A cycling, progenitor-like cell population at the root of atypical teratoid rhabdoid tumor subtype differentiation trajectories.

  Background: Atypical teratoid rhabdoid tumors (ATRTs), characterized by the loss of SMARCB1, are among the most lethal pediatric central nervous system (CNS) tumors. Three molecular subtypes have been identified, each defined by distinct molecular and clinical features. No subtype-specific treatments are available, highlighting the necessity to better understand inter- and intra-subtype heterogeneity. Methods: We generated a single-nucleus transcriptome atlas of ATRTs, validated by single-cell ATAC-seq and spatial transcriptomics, to study subtype-specific differentiation trajectories. Results: We identified brain progenitor-like expression profiles within unique subtype-specific differentiation lineages. A shared cycling, progenitor-like cell population, interspersed throughout tumors, was observed across all ATRT samples. Moreover, we demonstrate that subtype-specific differentiation trajectories can be induced pharmacologically in ATRT tumoroids directing tumor cells towards a non-proliferative, mature phenotype. Conclusions: Our study reveals that ATRT subtypes exhibit signaling trajectories mirroring normal fetal brain development, enabling the development of maturation therapies tailored towards ATRTs.

  Study EGAS00001008108

• CeDNN Data Access Committee UMCG

  Celiac Disease Northern Netherlands cohort, set up by the Genetics department of the University Medical Center Groningen (UMCG).

  Dac EGAC50000000584

• Transcriptome of insulinomas

  Characterization of the transcriptome of insulinomas by RNA-seq.

  Study EGAS50000000320

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from the Netherlands (posthuma)

  Study EGAS00001005857

• Giant congenital nevi exome sequencing

  Characterization of the genomic landscape of giant congenital nevi.

  Study EGAS00001004541

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from the Netherlands (Ophoff)

  Study EGAS00001005853

• Genomic and epigenomic insights into the origin, pathogenesis and clinical behavior of mantle cell lymphoma subtypes

  Mantle Cell Lymphoma can be subdivided into conventional (cMCL) and leukemic non-nodal MCL (nnMCL), which differ in the cell of origin, gene expression, biological characteristics and clinical evolution. Using genomic, epigenomic and transcriptomic data we identified common and particular alterations in the two subtypes, either point mutations, copy number alterations or structural variants some of them with prognostic impact.

  Study EGAS00001004165

• APCDR AGV Project: The African Genome Variation Project(dense array genotyping data)

  The African Genome Variation Project (AGVP) aims to provide a global resource to understand population diversity and help design, implement and interpret genomic studies in sub-Saharan Africa (SSA), and worldwide. The AGVP represents dense genotyping data on 1,481 individuals from 18 ethno-linguistic groups and low coverage whole genome sequencing data on 320 individuals from 7 ethno-linguistic groups in SSA.

  Study EGAS00001000959

• Data distribution Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue Here, we show the amount of data distributed by the EGA. Data Distributed / Live EGA Data Distribution / Live SFTP Aspera HTSGET lineChartLive("svg#ega-dist-live", document.querySelector("#ega-figure-dist-live figcaption")); Data Distributed The below figure represents the cumulative EGA data distribution via the download clients(v2 and v3.x api versions) using HTTPS, FTP, and download boxes using ASPERA. You can see cumulative values by clicking "Cumulative". EGA Data Distribution //lineChart('#ega-data-distribution svg.stats', 'https://stats.ega-archive.org/distribution', // ['Amount of Distributed Data', ['Aspera', 'Download Clients', 'Total']]) curveChart('#ega-data-distribution', [ { domId: 'stats-aspera', label: 'Aspera', url: 'https://stats.ega-archive.org/distribution/aspera' }, { domId: 'stats-sftp' , label: 'SFTP', url: 'https://stats.ega-archive.org/distribution/sftp' }, //{ domId: 'stats-htsget', label: 'HTSGET', url: 'https://stats.ega-archive.org/distribution/htsget' }, { domId: 'stats-streamer' , label: 'Download Client', url: 'https://stats.ega-archive.org/distribution/streamer' }, { domId: 'stats-total' , label: 'Total', url: 'https://stats.ega-archive.org/distribution/total' } ] );

  Documentation about/statistics/distribution

• scRNAseq of distal colon biopsies from patients with ulcerative colitis and healthy controls

  scRNAseq dataset of colonic epithelium from distal colon biopsies from 4 patients with ulcerative colitis and 4 healthy individuals. Includes 11 samples split into three conditions: healthy, healthy margin and ulcerated. Dataset includes raw Fastq files and processed csv count matrices. Fastq files are divided into 4 lanes and into index (I1) and read (R1, R2) files. Count matrices contain comma-separated values with cell barcodes as column names and gene names as row names. Cell Ranger (v3.0.1) software from 10x Genomics was used to process the output and align the reads. The refdata-cellranger-GRCh38-3.0.0 reference was downloaded from the 10x Genomics website. First, cellranger mkfastq function was used to demultiplex raw base call files into FASTQ files. Then, the FASTQ files were aligned and filtered with cellranger count function.

  Dataset EGAD00001010167

• The Transcriptional Landscape of SHH Medulloblastoma

  To further understand the biology of Sonic hedgehog medulloblastoma and its molecular subtypes, we studied 250 human Shh-MB using strand-specific RNA sequencing. We identified novel alterations within the cAMP dependent pathway and found that 18% of tumors have genetic events that directly target the abundance and/or stability of MYCN. We also discovered an extensive network of fusions in focally amplified regions, and several loss-of-function fusions in tumor suppressor genes PTCH, SUFU and NCOR1. Molecular convergence on a core of specific genes by nucleotide variants, copy number aberrations, and gene fusions highlights key roles of specific pathways in the pathogenesis of Sonic hedgehog medulloblastoma.

  Dataset EGAD00001006305

• endogene.bio

  The Endogene Bio Data Access Committee oversees and manages access to controlled genomic and clinical datasets deposited by the Endogene Bio research team.

  Dac EGAC50000000786

• Cancer Risk Estimates Related to Susceptibility Genes (CARRIERS)

  The CARRIERS study utilized custom amplicon-based variant screening of known cancer predisposition genes in germline DNA from breast cancer cases and age matched unaffected controls taken from predominantly cohort-based breast cancer case-control studies to establish the risks of breast cancer associated with pathogenic variants in these genes.

  Study phs002820

• Neoantigen Landscape in a Hypermutated Glioblastoma Arising in a Patient with Germline POLE Deficiency Treated with Checkpoint Blockade Immunotherapy

  Whole genome and exome-based DNA sequencing of germline and tumor specimens from serial samples of patients with glioblastoma. The data set includes several metastases and recurrences from a patient with germline POLE-deficiency. The data reveal extensive tumor heterogeneity, clonal evolution, and genomic responses to immunotherapy.

  Study phs001663

• Characterization of Immune-Related Gene Expression in Lung Cancer

  We performed a comprehensive analysis of inter- and intratumor heterogeneity of immune gene expression in 160 lung cancer patients from the Environment And Genetics in Lung cancer Etiology (EAGLE) study. The transcriptome was profiled with RNA sequencing in 269 tumor samples and 110 normal tissue samples from 160 subjects.

  Study phs002346

• Understanding_population_genetics_and_patterns_of_genome_wide_heterozygosity_in_a_sample_of_the_Croatian_isolated_populations__ESGIDalmatians_

  Using high-throughput sequencing technologies and analytical tools, we conduct an exome sequencing study that will help understand the populationgenetics of a Croatian island isolate, in a sample of 200 subjects from the Adriatic island of Vis who wereselected to reflect islanders with at least four known ancestors in grandparental line who are original islanders.

  Study EGAS00001000336

• Analysis_of_resistance_to_PLX4032

  We have collected material from a patient who had BrafV600E mutant melanoma that wastreated with PLX4032. We have germline DNA from the patient and DNA and RNA fromdistinct lesions before and after treatment with PLX4032. We would like to exome sequencethese samples to gain a snap shot of the mechanisms of resistance that are operative.

  Study EGAS00001000415

• Patients with metastatic urothelial carcinoma

  Samples from three patients with urothelial carcinoma and paired lymph node metastases and distant metastases. Patient 1: Muscle invasive tumor and lymph node metastasis. Patient 2: Muscle invasive tumor, lymph node metastasis, and distant metastasis in the small intestine. Patient 3: T1b tumor, lymph node metastasis, and distant metastasis in the lung

  Study EGAS00001002114

• The_Transcriptome_of_PLX4032_resistance

  We have collected material from a patient who had BrafV600E mutant melanoma that wastreated with PLX4032. We have germline DNA from the patient and DNA and RNA fromdistinct lesions before and after treatment with PLX4032. We have transcriptome sequenced these samples to obtain a snap shot of the mechanisms of resistance that are operative.

  Study EGAS00001000413

• Whole-exome sequencing of acute erythroid leukemia

  To clarify the genomic characteristics of acute erythroid leukemia (AEL), 24 AEL cases with matched controls were analyzed using whole exome sequencing. Based on the patterns of genetic alterations, AEL were divided into 4 subgroups. EPOR/JAK/STAT pathway are frequently affected in AEL and could be a therapeutic target.

  Study EGAS00001003696

• Dense_fine_mapping_study_identifies_new_susceptibility_loci_for_primary_biliary_cirrhosis

  2,981 primary biliary cirrhosis (PBC) cases were collected by the UK PBC Consortium and genotyped on the Illumina Immunochip where 2,861 passed quality control. These cases were then compared to a UK population control to identify novel PBC risk loci and fine-map association signals. (Liu &amp; Almarri et al., Nat Genet, 2012)

  Study EGAS00001001837