11925 results for "cancer rna-seq"

in 9.79 milliseconds.

• ovarian cancer sample RNA-seq

  Tissue samples were collected from 18 ovarian cancer patients. After mechanical and enzymatic dissociation, a part of these tissues underwent growth factoromics analysis. For four cases, single-cell sequencing analysis was conducted under conditions with or without estradiol. Additionally, the remains of the tissues were frozen and subsequently subjected to bulk RNA-sequencing on 17 of these samples. Sixteen cases were diagnosed as HGSOC, while two samples were clear cell or mucinous type (Extended Data Table 1). All cases were grade 3, and none had received neoadjuvant chemotherapy. - Group C1, Epithelial Ovarian Cancer(EOC) patients with estrogen responsiveness : 6 samples - Group C2, Epithelial Ovarian Cancer(EOC) patients with estrogen non-responsiveness : 10 samples - non-classified 1-sample

  Dataset EGAD50000000613

• BELOB: Ribo-minus RNA-seq data corresponding to 96x GBM samples from the BELOB trial.

  Ribo-minus RNA-seq data corresponding to 96x GBM samples from the BELOB trial.

  Study EGAS00001004570

• Bulk and single cell RNA-seq analysis of keratinocytes and human skin

  106 bulk RNA-seq samples of primary human keratinocytes 8 single cell RNA-seq samples of human epidermis

  Dataset EGAD00001005998

• Small-RNA sequencing of pheochromocytoma and paraganglioma arising from germline SDHB mutations

  Single-end 75 basepair small-RNA sequencing of 90 SDHB-deficient non-metastatic and metastatic pheochromocytoma and paraganglioma. Samples were prepared using the NEXTFLEX® Small RNA-Seq Kit v3 (Bioo Scientific ). Samples were sequenced at the molecular genomics core (Peter MacCallum Cancer Centre) using 50bp single end sequencing on the Illumina NextSeq 500 (Illumina, USA). This dataset contains raw sequencing reads in FASTQ format.

  Dataset EGAD50000000501

• RNA-Seq-Dataset

  RNA-Seq-Dataset

  Dataset EGAD00001000043

• RNA-Seq analysis

  RNA-Seq analysis

  Dataset EGAD00001000057

• Inference_of_B_cell_clonality_and_function_from_single_cell_RNA_seq_data

  cDNA libraries for RNA-seq will be prepared for sequencing. cDNA will be created following the Smart-Seq2 protocol, and a part of the cDNA from each cell will be used for preparation of RNA-seq libraries using the Nextera XT kit. The other part of the cDNA will be used as template for verification of the computationally recovered immunoglobulin sequences from the RNA-seq dataset by using a targeted PCR approach to generate a multiplexed sequencing library

  Study EGAS00001002963

• The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia

  Acute lymphoblastic leukaemia (ALL) is the most common childhood malignancy, and implementation of risk-adapted therapy has been instrumental in the dramatic improvements in clinical outcomes. A key to risk-adapted therapies includes the identification of genomic features of individual tumors, including chromosome number (for hyper- and hypodiploidy) and gene fusions, notably ETV6-RUNX1, TCF3-PBX1 and BCR-ABL1 in B-cell ALL (B-ALL). RNA-sequencing (RNA-seq) of large ALL cohorts has expanded the number of recurrent gene fusions recognized as drivers in ALL, and identification of these new entities will contribute to refining ALL risk stratification. We used RNA-seq on 126 ALL patients from our clinical service to test the utility of including RNA-seq in standard of care diagnostic pipelines to detect gene rearrangements and IKZF1 deletions. RNA-seq identified 86% of rearrangements detected by standard of care diagnostics. KMT2A-rearrangements (MLL), whilst usually identified, were the most commonly missed by RNA-seq as a result of low expression. RNA-seq identified rearrangements that were not detected by standard of care testing in nine patients. These were found in patients who were not classifiable using standard molecular assessment. We developed an approach to detect the commonest IKZF1 deletion from RNA-seq data and validated this using a RQ-PCR assay. We applied an expression classifier to identify Ph-like B-ALL patients. T-ALL proved a rich source of novel gene fusions which have clinical implications or provide insights into disease biology. Our experience shows that RNA-seq can be implemented within an individual clinical service to enhance the current molecular diagnostic risk classification of ALL.

  Study EGAS00001004212

• RNA-Seq dataset for Genomic rearrangements in Pediatric Cancer

  RNA-Seq data for systematic gene fusion detection in Pediatric Cancer

  Dataset EGAD00001008152

• RNA-seq

  RNA-seq data

  Dataset EGAD00001011376