2163 results for "2025年12月10日300883股价走势分析"

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• MiSeq-Low Coverage

  Fourteen files of patients 1, 2, 4, 6, 7, 8, 9, 12, 14, 16, 17, 18, 19 and 27 with WGS done on Illumina MiSeq with low coverage. For research purpose and authorised user only.

  Dataset EGAD00001003437

• EBV-AID project

  Whole genome sequencing of EBV-transformed B cells in order to determine whether EBV induction of activation-induced cytidine deaminase (AID) produces genome-wide mutations and/or chromosomal rearrangements.

  Dataset EGAD00001001458

• Genomic Landscape of Multiple Myeloma and of its Precursor Conditions, and its Clinical Implications

  Reliable strategies to capture patients at risk of progression from precursor stages of multiple myeloma (MM) to overt disease are still missing. In this study, we generated whole-genome sequencing (WGS) data from matched tumor and normal samples from 98 individuals from the PCROWD study (Precursor Crowd, NCT02269592). These individuals have either a precursor condition for MM, which can be a monoclonal gammopathy of undetermined significance (MGUS) or smoldering multiple myeloma (SMM), or an active multiple myeloma (MM). To do so, we enriched tumor plasma cells from the bone marrow by magnetic bead selection (CD138+), sequenced the DNA of these samples to a target depth of 60X (tumor), and analyzed them against normal white blood cells (peripheral blood mononuclear cells) from the same individuals sequenced to a target depth of 30X (normal). In our first publication [Alberge, Dutta, Poletti, et al., 2025], we integrated and homogeneously analyzed these data together with data from mostly publicly available datasets (phs003084 and phs000748) to reach 1,030 total samples. We used these data to identify recurrent coding and non-coding candidate drivers, as well as significant hotspots of structural variation, and used those drivers to define and validate a simple “MM-like” score which we could use to place patients' tumors on a gradual axis of progression toward active disease. Our MM precursor genomic map provides new insights on the time of initiation and cell of origin of the disease, on the order of acquisition of genomic alterations, and on mutational processes found across stages of the transformation. Taken together, we highlight here the potential of genome sequencing to better inform risk assessment and monitoring of MM precursor conditions.

  Study phs003846

• Label-free single-cell RNA Multiplexing leveraging Genetic Variability

  SNP-based demultiplexing of single-cell RNA-seq data

  Dataset EGAD50000000928

• PacBio medulloblastoma datasets

  PacBio data from 5 medulloblastoma tumor-normal pairs. The data consists of BAM files aligned using NGMLR

  Dataset EGAD00001010852

• Whole genome sequencing of familial isolated oesophagus atresia.

  Then individuals from three families segregating esophagus atresia.

  Dataset EGAD00001006092

• Transcriptomic Profiles of Neoantigen-Reactive T Cells in Human Gastrointestinal Cancers

  This study explores the transcriptomic profiles of neoantigen-reactive tumor-infiltrating lymphocytes (TILs) from human bile duct and pancreatic cancer. The submitted data are bulk tumor RNA-Seq, tumor and germline whole-exome sequencing from 10 patients, and single cell RNA-Seq data from TIL of 5 of these patients.

  Study phs002765

• B Cell Receptor Study From Early Breast Cancer Tumour Samples

  B and T cells are key components of the adaptive immune system and play an important role in anti-cancer immunity. In this study, we performed an integrative analysis of the B cell and T cell repertoires across 25 serial breast tumour biopsies from 10 patients during neoadjuvant therapy.

  Study EGAS50000000241

• 2017_prospective_v2 Whole Exome Sequencing

  2017 AML prospective study contains 10 paired WES samples which were collected from SNUH.The study was designed to examine the molecular abnormalities from leukemic patients at initial diagnosis in comparison with corresponding germ line control (saliva samples). The results of WXS were analyzed by Mutect for ranking cancer variants and creating mutational matrix.

  Study EGAS00001002628

• BLUEPRINT release August 2016, ChIP-Seq for CD14-positive, CD16-negative classical monocyte, on genome GRCh38

  ChIP-Seq data for 10 CD14-positive, CD16-negative classical monocyte sample(s). 80 run(s), 76 experiment(s), 76 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002484