7190 results for "({!join from=dataset_studies to=id v=$q1}) OR ("transcriptome" OR "RNA-seq" OR "rna sequencing" OR "rna-sequencing")"

in 37.79 milliseconds.

• SF10931

  RNA-exome

  Dataset EGAD00001006765

• SE6136

  RNA-exome

  Dataset EGAD00001006833

• SE9581

  RNA-exome

  Dataset EGAD00001006837

• SF7196

  RNA-exome

  Dataset EGAD00001006762

• SF9135

  RNA-exome

  Dataset EGAD00001006747

• SF12165

  RNA-exome

  Dataset EGAD00001006745

• SF8963

  RNA-exome

  Dataset EGAD00001006764

• SF6136

  RNA-exome

  Dataset EGAD00001006754

• SF10770

  RNA-exome

  Dataset EGAD00001006761

• SF10750

  RNA-exome

  Dataset EGAD00001006748

• SF11094

  RNA-exome

  Dataset EGAD00001006755

• SF11780

  RNA-exome

  Dataset EGAD00001006757

• SF12243

  RNA-exome

  Dataset EGAD00001006758

• SF9301

  RNA-exome

  Dataset EGAD00001006759

• SF6996

  RNA-exome

  Dataset EGAD00001006760

• SF4324

  RNA-exome

  Dataset EGAD00001006766

• SE10497

  RNA-exome

  Dataset EGAD00001006834

• SE4987

  RNA-exome

  Dataset EGAD00001006835

• SF8642

  RNA-exome

  Dataset EGAD00001006749

• SF9490

  RNA-exome

  Dataset EGAD00001006753

• SF11215

  RNA-exome

  Dataset EGAD00001006763

• SF6839

  RNA-exome

  Dataset EGAD00001006769

• SF11587

  RNA-exome

  Dataset EGAD00001006770

• SF9640

  RNA-exome

  Dataset EGAD00001006771

• SF6587

  RNA-exome

  Dataset EGAD00001006772

• SF10228

  RNA-exome

  Dataset EGAD00001006756

• SE4831

  RNA-exome

  Dataset EGAD00001006830

• SE9037

  RNA-exome

  Dataset EGAD00001006832

• SF10127

  RNA-exome

  Dataset EGAD00001006767

• SF10064

  RNA exome

  Dataset EGAD00001006744

• SF4209

  RNA-exome

  Dataset EGAD00001006768

• SE8570

  RNA-exome

  Dataset EGAD00001006831

• SF11082

  RNA-exome

  Dataset EGAD00001006773

• SE4567

  RNA-exome

  Dataset EGAD00001006836

• ATRT RNASeq

  ATRT RNASeq

  Dataset EGAD00001004837

• SF6953

  RNA-exome

  Dataset EGAD00001006750

• SF9259

  RNA-exome

  Dataset EGAD00001006751

• SF11248

  RNA-exome

  Dataset EGAD00001006752

• Identification and characterization of molecular markers in aging and neuronal disorders

  Supercentenarians, people who have reached 110 years of age, are a great model of healthy aging. Their characteristics of delayed onset of age-related diseases and compression of morbidity imply that their immune system remains functional. We performed single-cell transcriptome analysis of peripheral blood mononuclear cells (PBMCs), derived from supercentenarians and younger controls. We identified a marked increase of cytotoxic CD4 T-cells (CD4 CTLs) as a novel signature of supercentenarians. Furthermore, single-cell T-cell receptor sequencing of two supercentenarians revealed that CD4 CTLs had accumulated through massive clonal expansion. The CD4 CTLs exhibited substantial heterogeneity in their degree of cytotoxicity as well as a nearly identical transcriptome to that of CD8 CTLs. This indicates that CD4 CTLs utilize the transcriptional program of the CD8 lineage while retaining CD4 expression. Our study reveals that supercentenarians have unique characteristics in their circulating lymphocytes, which may represent an essential adaptation to achieve exceptional longevity by sustaining immune responses to infections and diseases.

  Study JGAS000230

• Blood transcriptome profiling links immunity to disease severity in myotonic dystrophy type 1 (DM1).

  The blood transcriptome was examined in relation to disease severity in type I myotonic dystrophy (DM1) patients who participated in the Observational Prolonged Trial In DM1 to Improve QoL- Standards (OPTIMISTIC) study. This sought to a) ascertain if transcriptome changes were associated with increasing disease severity, as measured by the muscle impairment rating scale (MIRS) and b) es-tablish if these changes in mRNA expression and associated biological pathways were also observed in the Dystrophia Myotonica Biomarker Discovery Initiative (DMBDI) microarray dataset in blood (with equivalent MIRS/DMPK repeat length). The changes in gene expression were compared using a number of complementary pathways, gene ontology and upstream regulator analyses, which suggested that symptom severity in DM1 was linked to transcriptomic alterations in innate and adaptive im-munity associated with muscle wasting. Future studies should explore the role of immunity in DM1 in more detail to assess its relevance to DM1.

  Study EGAS00001006926

• DAC: Whole-genome and transcriptome sequencing of primary cutaneous CD8+ aggressive epidermotropic cytotoxic T-cell lymphoma

  Dac EGAC00001001533

• The FF bladder signature conservation set

  Dataset EGAD00001000833

• The paired FF/FFPE bladder set

  Dataset EGAD00001000832

• The paired FF/FFPE prostate set

  Dataset EGAD00001000830

• Transcriptome Analysis Offers a Comprehensive Illustration of the Genetic Background of Pediatric Acute Myeloid Leukemia

  Study EGAS00001003701

• Hepatocellular carcinoma xenografts established from needle biopsies preserve the characteristics of the originating tumors

  Study EGAS00001003396

• Swarm Learning to identify COVID-19, tuberculosis and leukemia patients based on blood transcriptomes

  Study EGAS00001004502

• Transcriptomic profiling of prostate cancer metastasis xenograft models reveals conservation of bone microenvironment signatures

  Study EGAS00001004770

• Altered neutrophil and granulopoiesis biology underlie a poor outcome sepsis endotype

  Study EGAS00001006283

• Small RNA sequencing of endometrial cancer patients with samples collected prior to diagnosis and controls

  This dataset contains unpaired FASTQ files of 316 endometrial cancer cases and 316 matched controls representing circulating RNAs. RNA count files are provided as the output of sncRNA pipeline and represent 12 RNA types (isomiR, lncRNA, precursor miRNA, miRNA, miscRNA, mRNA, piRNA, scaRNA, snoRNA, snRNA, tRF, and tRNA). We also provide metadata of the samples.

  Dataset EGAD50000000391