11057 results for "fc 26 best way to get coins Visit Buyfc26coins.com Gyors és hatékony szolgáltatás. Hamarosan visszatérek több FC 26 érmeért..4w1a"

in 22.28 milliseconds.

• 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

• CINECA_synthetic_cohort_EUROPE_UK1 referencing fake samples

  Please note: This synthetic data set (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY). Please ensure this preamble is included with this dataset and that the CINECA project (funding: EC H2020 grant 825775) is acknowledged. For any questions please contact isuru@ebi.ac.uk or cthomas@ebi.ac.uk This dataset (CINECA_synthetic_cohort_EUROPE_UK1) consists of 2521 samples which have genetic data based on 1000 Genomes data (https://www.nature.com/articles/nature15393), and synthetic subject attributes and phenotypic data derived from UKBiobank (https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001779). These data were initially derived using the TOFU tool (https://github.com/spiros/tofu), which generates randomly generated values based on the UKBiobank data dictionary. Categorical values were randomly generated based on the data dictionary, continuous variables generated based on the distribution of values reported by the UK Biobank showcase, and date / time values were random. Additionally we split the phenotypes and attributes into 4 main classes - general, cancer, diabetes mellitus, and cardiac. We assigned the general attributes to all the samples, and the cardiac / diabetes mellitus / cancer attributes to a proportion of the total samples. Once the initial set of phenotypes and attributes were generated, the data data was checked for consistency and where possible dependent attributes were calculated from the independent variables generated by TOFU. For example, BMI was calculated from height and weight data, and age at death generated by date of death and date of birth. These data were then loaded to the development instance of Biosamples (https://www.ebi.ac.uk/biosamples/) which accessioned each of the samples. The genetic data are derived from the 1000 Genomes Phase 3 release (https://www.internationalgenome.org/category/phase-3/). The genotype data consists of a single joint call vcf files with call genotypes for all 2504 samples, plus bed, bim, fam, and nosex files generated via plink for these samples and genotypes. The genotype data has had a variety of errors introduced to mimic real data and as a test for quality control pipelines. These include gender mismatches, ethnic background mislabelling and low call rates for a randomly chosen subset of sample data as well as deviations from Hardy Weinberg equilibrium and low call rates for a random selection of variants. Additionally 40 samples have raw genetic data available in the form of both bam and cram files, including unmapped data. The gender of the samples in the 1000 genomes data has been matched to the synthetic phenotypic data generated for these samples. The genetic data was then linked to the synthetic data in BioSamples, and submitted to EGA.

  Dataset EGAD00001006673

• Data access commitee to DAISY study

  Dac EGAC00001003031

• Virtual Growing Child 5-Dimensional Functional Models for Treating Respiratory Anomalies (dMRI-VGC)

  Objectives We are sharing a database of dynamic magnetic resonance imaging (dMRI) scans of normal children, which can serve as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The database can also be useful to advance future AI-based research on image-based object segmentation and analysis. Background In pediatric patients with respiratory abnormalities, it is important to understand the alterations in regional dynamics of the lungs and other thoracoabdominal components, which in turn requires a quantitative understanding of what is considered as normal in healthy children. Currently, such a normative database of regional respiratory structure and function in healthy children does not exist. Participants 200 normal children (ages 6-18 years) participated in our research study related to this dataset. DesignThe shared open-source normative database is from our ongoing virtual growing child (VGC) project, which includes 4D dMRI images representing one breathing cycle for each normal child and also segmentations of 10 objects at end expiration (EE) and end inspiration (EI) phases of the respiratory cycle in the 4D image. The lung volumes at EE and EI as well as the excursion volumes of chest wall and diaphragm from EE to EI, left and right sides separately, are also reported. The database has thus 4,000 3D segmentations from 200 normal children in total. The database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. All dMRI scans are acquired from normal children during free-breathing. The dMRI acquisition protocol was as follows: 3T MRI scanner (Verio, Siemens, Erlangen, Germany), true-FISP bright-blood sequence, TR=3.82 ms, TE=1.91 ms, voxel size ~1×1×6 mm3, 320×320 matrix, bandwidth 258 Hz, and flip angle 76o. With recent advances, for each sagittal location across the thorax and abdomen, we acquired 40 2D slices over several tidal breathing cycles at ~480 ms/slice. On average, 35 sagittal locations are imaged, yielding a total of ~1400 2D MRI slices, with a resulting total scan time of 11-13 minutes for any particular study participant.The collected dMRI scan data then went through the procedure of 4D image construction, image processing, object segmentation, and volumetric measurements from segmentations. 4D image construction: For the acquired dMRI scans, we utilized an automated 4D image construction approach to form one 4D image over one breathing cycle (consisting of typically 5-8 respiratory phases) from each acquired dMRI scan to represent the whole dynamic thoraco-abdominal body region. The algorithm selects 175-280 slices (35 sagittal locations × 5-8 respiratory phases) from the 1400 acquired slices in an optimal manner using an optical flux method. Image processing: Intensity standardization is performed on every time point/3D volume of the 4D image so that image values have the same tissue-specific meaning across all subjects. Object segmentation: For each subject, there are 10 objects segmented at both EE and EI time points in this database. They include the thoracoabdominal skin outer boundary, left and right lungs, liver, spleen, left and right kidneys, diaphragm, and left and right hemi-diaphragms. All dMRI scans utilize large field of view images, which include the full thorax and abdomen to the inferior aspect of the kidneys in the sagittal plane. We used a pretrained U-Net based deep learning network to first segment all objects, and then all auto-segmentation results were visually checked and manually refined as needed, under the supervision of a radiologist with over 25 years of expertise in MRI and thoracoabdominal radiology. Manual segmentations have been performed for all objects in all datasets. Volumetric measurements based on object segmentations for lung volumes (left and right separately) at EE and EI, as well as for chest wall and diaphragm excursion volumes (left and right separately) are reported. ConclusionsThe provided database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. The database has 4,000 3D segmentations from 200 normal children, which to our knowledge is the largest and only such dMRI dataset to date. All images and object segmentations are saved in DICOM. All DICOM files (176,574 in total) have been anonymized, and PHI has been removed. The database can be used as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The large amount of object segmentations can potentially benefit AI-based research on image-based object segmentation and analysis.

  Study phs004002

• To reveal the spectrum of gene mutations in grade II/III gliomas, whole exome sequencing of 52 samples including 4 multi-regional and 10 multi-time points sampling cases and 293 SNP-array were performed.

  To reveal the spectrum of gene mutations in grade II/III gliomas, whole exome sequencing of 52 samples including 4 multi-regional and 10 multi-time points sampling cases and 293 SNP-array were performed. Our analysis revealed high degrees of temporal/spatial heterogeneity generated during tumor expansion and relapse.

  Study EGAS00001001045

• 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.

  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.

  Study EGAS00001002718

• Luminal progenitor cell line iHBEC(CD117)

  Basal-like breast cancer originates in luminal progenitors, frequently with an altered PI3K pathway, and focally in close association with genetically altered myoepithelial cells at the site of tumor initiation. The exact trajectory behind this bi-lineage phenomenon remains poorly understood. Here we used a breast cancer relevant transduction protocol including hTERT, shp16, shp53, and PIK3CA(H1047R) to immortalize FACS isolated luminal cells, and we identified a candidate multipotent progenitor. We found that the apparent luminal phenotype of these oncogene transduced progenitors was metastable giving rise to basal-like cells dependent on culture conditions. After culturing the cells for more than 60 passages, cells were subjected to scRNA-seq as well as bulk RNA sequencing of two subpopulations (CD271+ and CD271-).

  Dataset EGAD50000000723

• Whole genome sequencing to detect spontaneous acquired mutations in mismatch repair-deficient human colon organoids

  Individual outgrowing organoids surviving the medium-based selection were manually picked and propagated as clonal lines for a couple of passages to avoid unexpected mutation accumulation before being harvested for WGS/WES. 1 µg of DNA was used for WGS/WES by Agilent SureSelect V7 kit, with 8Gb per sample (50X average mapped) data throughput, sequencing on NovaSeq 150PE

  Study EGAS50000000114

• RoCK and ROI single-cell transcriptome of one acute lymphoblastic leukemia patient

  Whole-transcriptome single-cell RNA sequencing (scRNA-seq) was performed following the manufacturer’s protocol (BD Rhapsody, BD Biosciences). Targeted enrichment of BCR::ABL1 transcripts was conducted using the RoCK and ROI-seq approach, based on the barcoded bead technology of the BD Rhapsody platform. In the RoCK and ROI-seq workflow, the template-switch oligonucleotides (TSOs) attached to BD Rhapsody beads were modified with one or multiple capture sequences specific to transcripts of interest, enabling targeted transcript capture during reverse transcription. ROI primers that are added during library preparation to direct amplification and sequencing toward these defined regions of interest (ROIs). Resulting libraries were sequenced on an Illumina sequencer.

  Dataset EGAD50000001976

• Summarized somatic variant calls from CMMRD-associated high-grade gliomas

  This dataset contains summarized somatic variant call data derived from paired tumor–blood whole-exome sequencing of human samples. Access to the dataset is controlled and granted only upon submission and approval of a formal data access request. Requests are reviewed by the Data Access Committee led by the Principal Investigator to ensure compliance with applicable legal, ethical, and data protection requirements. Users must not attempt to re-identify study participants and must comply with all relevant confidentiality and data protection regulations. In accordance with local legal and ethical requirements, individual-level raw sequencing data are not shared.

  Dataset EGAD50000002180

• 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.

  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.

  Study EGAS00001002719

• Application of Hi-C sequencing to detect structural variants in B-cell acute lymphoblastic leukemia

  Large structural variants are easily detected by Hi-C sequencing as strong, distant signals on a Hi-C contact map. Translocations are frequently evident in B-ALL and accurate identification of these translocations can guide clinical treatment options. Here were apply shallow Hi-C sequencing as an unbiased, cost-effective approach to identify translocations among B-ALL patients.

  Study EGAS00001005605

• Understand Paratyphoid Disease - host responses to human challenge with S. Paratyphi A (2019-08-28)

  This project aims to evaluate the transcriptional response to disease measured in whole blood of participants who developed enteric fever after challenge and, importantly, those who were challenged but stayed well throughout the challenge period. This data will provide unique coverage of the transcriptome and will yield invaluable insight after integration with a wealth of clinical data collected during this trial. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-08-28.

  Dataset EGAD00001005298

• Reconstruction of complex rearrangement patterns causing the initiation of clear cell renal cell carcinoma.

  The genomic hallmark of clear cell renal cell carcinoma is the loss of the short arm of chromosome three. This appears to be the earliest genomic event in the formation of these cancers. Often chromosome 3 is lost at the same time as part of chromosome 5 is duplicated via an unbalanced translocation, often with features consistent with focal chromothripsis. In this study, we sought to reconstruct the chromothriptic event that underlies the initiation of kidney cancer. We used long read sequencing (promethION, Oxford Nanopore Technologies) of patient tumour-derived DNA to elucidate how a single cell division error can generate cancer genome complexity.

  Dataset EGAD00001005495

• HV31 - Read identifier list for local CCS, CLR, ONT and MGI reads

  This file contains read identifiers for local CCS, CLR, ONT and MGI reads for each of the eight selected genomic regions (HLA, KIR, IGH, IGK, IGL, TRA, TRD, andTRG). We extracted these reads by aligning whole-genome sequencing data to a draft whole-genome de novo assembly, and selecting reads that map to contigs representing each region. These reads were involved in the polishing and validation of the HV31 assembly. Please refer to the relevant manuscript (https://doi.org/10.1101/2021.02.03.429586) for additional details. Read identifiers are stored in JSON format. Along with the full FASTQ files, this file enables convenient re-analysis of the HV31 sequencing data in the eight selected regions.

  Dataset EGAD00001007761

• Genomic characterisation of SDH deficient renal cell carcinoma - RNA

  SDH deficient renal cell carcinomas are a rare and recently defined subtype of kidney cancer, often associated with an inherited mutation in one of the SDH gene subunits. This dataset sought to understand the genomic events that underpin tumour formation, from putative cell of origin, characterisation of the tumour microenvironment, to the genomic evolution of these rare tumours. We performed whole genome and RNA sequencing of 4 patients with SDH deficient renal cell carcinomas, including one patient who had an additional paraganglioma. An addition patient in this cohort had the initial diagnosis revised to a clear cell renal cell carcinoma.

  Dataset EGAD00001008470

• Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild type primary glioblastoma

  We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy.

  Dataset EGAD00001010156

• MutWP5: CRUK Mutographs of Cancer: Breast: Cancer Mastectomy (WG) (2020-01-29)

  Sequencing of LCM-derived microbiopsies from 40 women who underwent mastectomies due to breast cancer. LCM and sequencing will be conducted on both normal, unaffected breast, and, where possible, tumour tissue. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue, and compare findings between the normal and associated cancer tissues. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this portion of the study will be compared to women who had cosmetic breast reduction surgeries and those who are BRCA carriers. . This dataset contains all the data available for this study on 2020-01-29.

  Dataset EGAD00001005922

• ChIP-seq of primary AML patients with t(3;3)/inv(3)

  ChIP-seq was conducted in blasts from patients with t(3;3) AML to assess differences of the GATA2 super-enhancer between the translocated allele and the non-translocated allele. The dataset includes 2x H3K27ac ChIP-seq and 1x MYB ChIP-seq. ChIP samples were processed according to the Illumina TruSeq ChIP Sample Preparation Protocol (Illumina) or Diagenode Library V3 preparation protocol (Diagenode) and either sequenced single-end (1x 50 bp) on the HiSeq 2500 platform (Illumina) or paired-end (2x100 bp) on the Novaseq 6000 platform (Illumina). Briefly, reads were aligned to the human reference genome build hg19 with bowtie for single-end runs and bowtie2 for paired-end runs.

  Dataset EGAD00001006821

• Whole Genome, RNA, and ChIP Sequencing of matched brain tumor-normal pairs (ICGC)

  Whole Genome Seq: Illumina HiSeq sequence data (with >30x coverage) were aligned to the hg19 human reference genome assembly using BWA (Li and Durbin, 2009);duplicate reads were removed from the final BAM file. No realignment or recalibration was performed. Paired-end RNA sequencing reads were mapped to the hg19 assembly of the human reference genome using BWA.Each ChIP-seq library was sequenced with two complete lanes on the Illumina HiSeq 2500 in the 101-bases paired-end rapid mode and aligned to hg19 using bwa.This resulted in the following coverage values (genome-wide, after deduplication, including all uniquely mapping reads):GBM103 macroH2A1: 17x H3K36me3: 20xMB59 macroH2A1: 11x H3K36me3: 11x

  Dataset EGAD00001000664

• European Prospective Investigation into Cancer and Nutrition BAMs

  516 DNA samples were collected from individuals upon enrollment into the European Prospective Investigation into Cancer and Nutrition study between 1993 and 1998 across 17 different centers. 126bp pair-end reads sequencing data from the Illumina platform were converted to fastq format, the 2bp molecular barcode information at each read of the pair was trimmed and was written in the reads name. The Thymine nucleotide required for ligation was removed from the sequences. Burroughs-Wheeler Aligner (BWA-mem) was used for alignment of the processed fastq files to the reference hg19 genome, following indel-re-alignment using GATK. An in-house algorithm was written to collapse read families that share the same molecular barcode sequence

  Dataset EGAD00001003583

• Whole_Exome_Sequencing_for_Characterization_of_Disease_Causing_Mutations_in_two_Pakistani_Families_Suffering_from_Autosomal_Recessive_Ocular_Disorders_

  We propose to use whole exome Agilent solutionprobes and paired end Illumina sequencing to sequence 4 individualsfrom two families suffering from novel autosomal recessive disease[microphthalmia, MOP (OMIM %251600) and non-syndromic persistenthyperplastic primary vitreous, PHPV (OMIM %611311)]. Selectedcandidate variants will subsequently be genotyped in the remainingfamily members in Pakistan with the aim of identifying the rarehomozygous recessive mutations responsible for the disease phenotype. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001000026

• Test Study for EGA using data from 1000 Genomes Project - Big CRAM, BAM, VCF and BCF files used for testing

  This is a test study for EGA using data from 1000 Genomes Project, specifically created to add more diversity to the existing dataset EGAD00001003338. This Test Study should NOT be confused with the real study of 1000 Genomes Project - Phase 3. Specifically, this study encompasses 4 CRAM, 4 BAM, 2 VCF and 2 BCF files, of different sizes (all above 1 GB), used for testing downloads and monitoring services. It also contains their respective indexes: crai for crams, bai for bams, tbi for vcfs and csi for bcfs.

  Study EGAS00001006718

• Identification of the underlying causal variant in a multi-generational family with autosomal dominant common variable immunodeficiency

  Common variable immunodeficiency (CVID) is the most common form of primary immunodeficiency with an estimated incidence of 1:10,000. It has been apparent for many years that CVID has a genetic component, occurs frequently in families and can have both a recessive or dominant mode of inheritance. In recent years, 4 genes underlying CVID have been identified; however, mutations within in them are estimated to account for no more than 10% of all cases of CVID. We have identified a multi-generational family with autosomal dominant CVID. Genome-wide linkage analysis has mapped the locus underlying CVID in this family to an approximately 9.2 Mb interval on chromosome 3q27.3-q29, between the markers D3S3570 and D3S1265. This locus is distinct from any of the previously mapped susceptibility loci suggesting a novel genetic variant is responsible for disease in this family. The aim of this study is to use exome sequencing of affected (n = 4) and unaffected (n = 4) individuals, in tandem with the available genetic mapping data, to identify the causal variant underlying CVID in this family.

  Dataset EGAD00001000363

• Genomic gain of EBV's LMP-1 in NKTCL

  The study found a high recurrence of genomic gain of LMP1 locus within EBV-associated NKTCL. The pilot data set and extended dataset of 77 WGS NKTCLs found 18/77 tumoral genomes to harbour genomic gain of LMP-1 locus within the EBV viral genomes within NKTCL tumoral sequencing data. The study also found 1/10 NKTCL cell lines to be positive for LMP-1 locus gain within EBV genomes too. This dataset includes solely the confirmatory WGS data of LMP-1 gain from NKYS cell lines using Oxford Nanopore long-read sequencing technology.

  Study EGAS50000000260