437 results for "《云南省人民政府办公厅关于印发云南省省级信息化项目管理办法的通知》（云政办发〔2024〕21号）"

in 5.50 milliseconds.

• RNA-seq FASTQ files from newborn screening dried blood spot samples

  Paired-end RNA-seq FASTQ files from 21 newborn screening dried blood spot (DBS) samples. These DBS samples were obtained from extremely low gestional age newborns, where 10 of them were affected by a fetal inflammatory response (FIR) before birth, and 11 were unaffected. Total RNA was sequenced using an Illumina NextSeq-500 instrument. The sample preparation protocol included the depletion of rRNA and globin mRNA using the Globin Zero Gold rRNA Removal Kit from Illumina. Libraries were prepared using the NebNext Ultra TM II Directionl RNA LIbrary Prep Kit (New England Biolabs). Each sample was sequenced in 4 lanes, leading to 8 FASTQ files per sample and a total of 21x8=168 FASTQ files. There is an additional number of 8 FASTQ files corresponding to sample BS13, which was downsampled to 1/4 of its original depth (see BS13_README file for details).

  Dataset EGAD00001004991

• RNA_TPO3_2023

  Human engineered CRC organoids (APC KO; KRAS G12D; TP53 KO) were grown in either glucose or lactate, in the presence and absence of DCA or BRD4 inhibition (JQ1). Samples were collected and processed for bulk RNA-seq.

  Dataset EGAD50000000089

• Human tumour and matched-normal single-cell RNAseq

  We profiled 16 high-grade gliomas patient tumour samples by single-cell and single-nuclei RNA-seq and 3 normal-matched single-cell RNA-seq using 10X Chromium 3'. The fastq files are provided.

  Dataset EGAD00001006097

• Genesis of Two Most Prevalent Variants Causing Combined Pituitary Hormone Deficiency in 21 Populations

  Two variants (c.[301_302delAG];[301_302delAG] and c.[150delA];[150delA]) in the PROP1 gene are the most common genetic causes of recessively inherited combined pituitary hormones deficiency (CPHD). Our objective was to analyze in detail the origin of the two most prevalent variants. In the multicentric study were included 237 patients with CPHD and their 15 relatives carrying c.[301_302delAG];[301_302delAG] or c.[150delA];[150delA] or c.[301_302delAG];[ 150delA]. They originated from 21 different countries worldwide. We genotyped 21 single nucleotide variant markers flanking the 9.6Mb region around the PROP1 gene that are not in mutual linkage disequilibrium in the general populations - a finding of a common haplotype would be indicative of ancestral origin of the variant. Haplotypes were reconstructed by Phase and Haploview software, and the variant age was estimated using an allelic association method. We demonstrated ancestral origin of both variants: c.[301_302delAG] was carried on 0.2Mb-long haplotype in a majority of European patients arising approximately 101 generations ago (confidence interval 90.1-116.4). Patients from the Iberian Peninsula displayed a different haplotype which was estimated to have emerged 23.3 (20.1-29.1) generations ago. Subsequently, the data indicated that both haplotypes were transmitted to Latin American patients approximately 13.8 (12.2-17.0) and 16.4 (14.4-20.1) generations ago, respectively. The c.[150delA] variant that was carried on a haplotype spanning about 0.3Mb was estimated to appear 43.7 (38.4-52.7) generations ago. We present strong evidence that the most frequent variants in the PROP1 gene are not a consequence of variant hot-spots as previously assumed, but are founder variants.

  Study EGAS00001001165

• Exome - Uveal Melanomas

  Whole exome sequencing on 11 uveal melanoma samples and corresponding germline samples. Libraries were prepared using the SureSelectXT2 Clinical Research Exome V2 kit (Agilent, 5190-9500 and G9621B). Paired-end libraries (2 x 100 bp) were sequenced on HiSeq 2000/2500 or NovaSeq 6000 instruments (Illumina).

  Dataset EGAD50000000767

• Transcriptome sequencing of replication repair deficient brain cancers

  Hypermutant tumors which harbor many somatic mutations may obscure the interpretation of targetable genomic events. This dataset contains transcriptome sequencing from 21 replication repair deficient brain tumor samples as well as healthy controls.

  Dataset EGAD00001006921

• Whole genome sequencing of paired samples from primary and relapsed IDH-wt glioblastomas with matched blood controls

  This dataset contains whole genome sequencing data from 21 primary and relapsed IDH-wt glioblastomas and matched blood controls. Tumors were sequenced at a target coverage of 150x, blood controls at 80x.

  Dataset EGAD00001004563

• First datasets available in the Federated EGA Network

  In February 2024, a great milestone was reached for the human omics data community: the first datasets are now available in the Federated European Genome-phenome Archive (FEGA). These datasets - hosted by national nodes in Poland, Norway, and Sweden - are discoverable in the EGA portal and accessible according to national laws. These datasets include: From FEGA Poland - The Genomic Map Of Poland From FEGA Norway - CRCbiome: Gut metagenome of Norwegian screening participants using FIT sampling Also from FEGA Norway - Single cell transcriptomics in expanded Tregs of APS-1 patients From FEGA Sweden - A Combined Omics and Tissue Biobank for Paediatric Cancers The establishment of these datasets represents a significant step towards achieving the broader objectives of the FEGA; to facilitate the sharing of high-quality human genomic and phenotypic data across borders. “The value for users is twofold: it becomes easier to make data available securely compared to storing data locally, and data becomes easier to find for other researchers. The possibility of reuse improves the chances of both verifying scientific discoveries and reaching groundbreaking conclusions, of benefit to the research community and later also patients,” said Anna Hagwall, project manager and head of human data at FEGA Sweden, NBIS. About Federated EGA FEGA was launched in 2022 as a network to store and lawfully share sensitive human omics data safely. It relies on the technology originally developed by the EGA and support from ongoing collaborations with ELIXIR - a European life sciences infrastructure. FEGA has contributed to and benefitted from the implementation of community standards for sensitive data management including those developed by the Global Alliance for Genomics and Health (GA4GH). “The FEGA concept and the development of the Nordic FEGA nodes have been made possible thanks to several projects coordinated by NeIC,” said Kjell Petersen, FEGA Norway technical lead. Managing human omics data is challenging due to its sheer size and privacy concerns, often resulting in restrictions on how and where sensitive data can be shared. The FEGA network follows a federated data access model whereby sensitive data is stored securely within a local or national jurisdiction, while discovery across the network for data of interest is enabled via a central portal. As of February 2024, the FEGA network comprises seven European national nodes with tens of additional national partners working towards establishing a FEGA node. As the FEGA network continues to onboard nodes from around the world, populated with more and more data, we move closer to a vision of a truly global resource for discovery of and access to sensitive human omics data to accelerate disease research and improve human health. "The Federated EGA network is entering a key phase where the first research datasets are now available across the network. In coming years, we look forward to expanding these datasets to cover key areas such as cancer and rare diseases for the benefit of patients" said Thomas Keane, Group Leader at EMBL-EBI.

  Blog first-datasets-available-in-Federated-EGA

• Precursor_lesions__clonal_architecture_and_relapse_in_Wilms_nephroblastoma

  Targeted resequencing at high depth (21 genes, 9 chromosomal regions): at least 4 FFPE samples per case and matched germline DNA: •100 cases with detailed outcome data, including 15 cases with tumour relapse (515 samples)•40 cases with matched pre-chemotherapy biopsies (240 samples)•50 nephrogenic rests matched to above cases (50 samples)We expect a proportion (possibly 10%) of cases to be mutationally silent on the above studies, and propose to subsequently carry out integrated whole-genome, methylome and transcriptome studies on matched frozen tissue from these cases

  Study EGAS00001001422

• Wilm's tumor sequencing data

  No. of samples: 80 (28 ULP-WGS, 26 WES, 26 RNA-SEQ) File types: FASTQ (28 ULP-WGS, 19 WES, 18 RNA) and BAM (7 WES, 8 RNA) Technology used: Sequencing - Illumina Novoseq 6000; Map/Align - Illumina DRAGEN v3.7.5; Genome assembly - GrCh38p13 Filename nomenclature: - SampleName_Passage_SampleType_TissueType_SequencingType - Passage of: PX = unknown; PZ = from patient; P0 = first passage from patient on plastic; P1 = first passage from plastic/PDX/organoid - SampleType: STN = normal; STT = tumor - SampleType STN: 00 = tissue unknown; 01 = adjacent normal; 02 = fibroblast; 03 = germline blood; 21 = cell line from patient tissue; 22 = cell line from PDX; 23 = cell line from patient fibroblast - SampleType STT: 00 = tissue unknown; 01 = primary tumor; 21 = cell line from patient; 22 = cell line from PDX - TissueType: WT = Wilm's tumor; 00 = kidney unknown; 01 = kidney left; 02 = kidney right - SequencingType: 00 = unknown; 02 = ultra-low pass whole-genome sequencing; 20 = whole-exome; 61 = bulk RNA-sequencing

  Dataset EGAD00001011111