3659 results for "RNA AND sequencing OR transcriptome"

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• Oxford Nanopore RNA sequencing for HLA typing

  A targeted RNA-based method for typing of 12 classical HLA genes using Oxford Nanopore sequencing. In the method, were enriched HLA genes from cDNA of 50 individuals using gene-specific reverse primers. The library molecules were then barcoded, pooled into 2 separate gene pools, and sequenced on MinION R9.4 SpotON flow cells.

  Study EGAS00001004918

• Total RNA sequencing from the TNT trial (NCT00532727)

  Primary tumours from 186 TNT participants (13 matched recurrences) were profiled using total RNA-sequencing. Four transcriptional DDR-related and 25 immune-related signatures were evaluated. We assessed their association with objective-response-rate (ORR) and progression-free-survival (PFS) and performed conditional inference forest clustering to integrate multi-modal data.

  Study EGAS00001007398

• Single-cell RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015710

• Single-cell RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015717

• Genetic Effects on miRNA Expression During Mid-Gestation Neocortical Development

  In this study we aimed to find common genetic variants that influence miRNA expression during mid-gestation neocortical development. Small RNA-sequencing from 212 fetal cortical tissue samples between 14 and 21 weeks gestation were used to profile miRNA expression. Combined with donor genotypes, we were able to find miRNA-eQTLs during neocortical development. The dataset provided here includes 212 Small RNA-sequencing files in FASTQ format from cortical wall samples. Also provided here are 212 donor genotypes in PLINK format (hg19).

  Study phs003106

• TIRE-seq: an Integrated Sample Extraction and Transcriptomics Workflow for High Throughput Perturbation Studies

  TIRE-seq is a novel RNA sequencing method that integrates mRNA purification directly into library preparation, eliminating separate RNA extraction. The technique demonstrated utility across three biological applications, including capturing transcriptional changes in human T cells, identifying genes driving murine dendritic cell differentiation, and analyzing the dose-response effects of temozolomide on patient-derived neurospheres. With its simplified approach and high sequencing efficiency, TIRE-seq offers a cost-effective solution for large-scale gene expression studies.

  Study EGAS50000000867

• Analysis of genomic alterations in dedifferentiated liposarcoma

  Multiomics analysis of rare cancers to identify genetic alterations related with pathogenesis and prognosis. To identification of genetics alterations of Dedifferentiated liposarcoma, DNAs and RNAs were extracted from dedifferentiated liposarcoma and paired non-cancer (normal) tissues. NGS libaraies were prepared by using Agilent SureSelect XT Human All Exon V5 + IncRNA kit for whole exome sequencing (WES) and TruSeq Stranded mRNA Prep kit or TruSeq RNA Access Library Prep kit (RNA seq). Sequencing was perfomred by Illumina HiSeq 2500.

  Study JGAS000182

• Idiosyncratic and generic single nuclei and spatial transcriptional patterns in papillary and anaplastic thyroid cancers

  Thyroid cancer is the most common endocrine malignancy. This dataset encompasses two types of thyroid cancer : anaplastic which is the most de-differentiated and aggressive one, and papillary which is the most common one. We profiled 14 patients, including 10 papillary and 4 anaplastic thyroid carcinomas, using both single nuclei RNA sequencing and spatial transcriptomics to link single cell resolution RNA sequencing with tissue morphology and better understand inter and intratumoral thyroid cancer heterogeneity.

  Study EGAS00001007574

• Pediatric HGG WES and RNA-Seq

  Paediatric high-grade glioma (pHGG) is the leading cause of paediatric brain tumour death. Here we performed RNA and whole exome sequencing on pHGG samples to analyse transcriptomic and genetic alterations associated with this devastating disease, and to understand the relationship between them.

  Study EGAS00001005687

• 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