Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE29041
PROVIDER: PRJNA140335 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR196216.fastq.gz | Fastqsanger.gz | |||
| SRR196217.fastq.gz | Fastqsanger.gz | |||
| SRR196218.fastq.gz | Fastqsanger.gz | |||
| SRR196219.fastq.gz | Fastqsanger.gz | |||
| SRR196220.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 20 |
Similar Datasets
Project description:RNA Capture-Seq resolves the deep complexity of the human transcriptome (454)
| PRJNA142905 | ENA
Project description:RNA Capture-Seq resolves the deep complexity of the human transcriptome (Illumina)
| PRJNA142903 | ENA
Project description:Transcriptomic analyses have revealed an unexpected complexity of the human transcriptome, whose breadth and depth exceeds current RNA sequencing capacity 1-3. Here we combine tiling arrays and RNA sequencing technologies, in an approach we term RNA Capture-Seq, to enable the assembly and characterisation of transcripts whose expression level is below the detection limits of conventional sequencing approaches. By this technique we identify novel exons and splicing patterns to even well-studied genes, such as the p53 and Hox loci, and expose widespread, regulated and remarkably complex noncoding transcription in intergenic regions. We show that unassigned tags observed in conventional RNA sequencing datasets are not noise but can derive from rare transcripts fully assembled by RNA Capture-Seq. We expect RNA Capture-Seq to prove an invaluable technique for future research into gene expression and its relationship to phenotypic variation. Application of RNA Capture sequencing from human fibroblasts for identifying rare novel transcripts. Hybridization enhancing oligos are designed to cover the full sequencing adaptor and MID sequences during hybridisation. Enhancing Oligo A 5 CCATCTCATCCCTGCGTGTCTCCGACTCAG/3ddc/ Enhancing Oligo B 5' CCTATCCCCTGTGTGCCTTGGCAGTCTCAG/3ddc/
2011-09-12 | E-GEOD-29040 | biostudies-arrayexpress
Project description:Transcriptomic analyses have revealed an unexpected complexity of the human transcriptome, whose breadth and depth exceeds current RNA sequencing capacity 1-3. Here we combine tiling arrays and RNA sequencing technologies, in an approach we term RNA Capture-Seq, to enable the assembly and characterisation of transcripts whose expression level is below the detection limits of conventional sequencing approaches. By this technique we identify novel exons and splicing patterns to even well-studied genes, such as the p53 and Hox loci, and expose widespread, regulated and remarkably complex noncoding transcription in intergenic gene deserts. We show that unassigned tags observed in conventional RNA sequencing datasets are not noise but can derive from rare transcripts fully assembled by RNA Capture-Seq. We expect RNA Capture-Seq to prove an invaluable technique for future research into gene expression and its relationship to phenotypic variation. Application of RNA Capture sequencing from human fibroblasts for identifying rare novel transcripts. Hybridization enhancing oligos are designed to cover the full sequencing adaptor and MID sequences during hybridisation. Enhancing Oligo A 5 CCATCTCATCCCTGCGTGTCTCCGACTCAG/3ddc/ Enhancing Oligo B 5' CCTATCCCCTGTGTGCCTTGGCAGTCTCAG/3ddc/
2011-09-12 | E-GEOD-29038 | biostudies-arrayexpress
Project description:Integrative analyses reveal complexity of human brain transcriptome by deep RNA-sequencing
| PRJNA244070 | ENA
Project description:Transcriptomic analyses have revealed an unexpected complexity of the human transcriptome, whose breadth and depth exceeds current RNA sequencing capacity 1-3. Here we combine tiling arrays and RNA sequencing technologies, in an approach we term RNA Capture-Seq, to enable the assembly and characterisation of transcripts whose expression level is below the detection limits of conventional sequencing approaches. By this technique we identify novel exons and splicing patterns to even well-studied genes, such as the p53 and Hox loci, and expose widespread, regulated and remarkably complex noncoding transcription in intergenic regions. We show that unassigned tags observed in conventional RNA sequencing datasets are not noise but can derive from rare transcripts fully assembled by RNA Capture-Seq. We expect RNA Capture-Seq to prove an invaluable technique for future research into gene expression and its relationship to phenotypic variation.
2011-09-12 | GSE29040 | GEO
Project description:Transcriptomic analyses have revealed an unexpected complexity of the human transcriptome, whose breadth and depth exceeds current RNA sequencing capacity 1-3. Here we combine tiling arrays and RNA sequencing technologies, in an approach we term RNA Capture-Seq, to enable the assembly and characterisation of transcripts whose expression level is below the detection limits of conventional sequencing approaches. By this technique we identify novel exons and splicing patterns to even well-studied genes, such as the p53 and Hox loci, and expose widespread, regulated and remarkably complex noncoding transcription in intergenic gene deserts. We show that unassigned tags observed in conventional RNA sequencing datasets are not noise but can derive from rare transcripts fully assembled by RNA Capture-Seq. We expect RNA Capture-Seq to prove an invaluable technique for future research into gene expression and its relationship to phenotypic variation.
2011-09-12 | GSE29038 | GEO
Project description:Comprehensive transcriptome analysis of four distinct human limbal compartments, including basal limbal crypts (BLCs), superficial limbal crypts (SLCs), cornea, and the supporting stroma, with the aid of laser capture microdissection and deep RNA sequencing.
2013-07-01 | E-MTAB-1498 | biostudies-arrayexpress
Project description:Recent studies have revealed the importance of long noncoding RNAs (lncRNAs) as tissue-specific regulators of gene expression. There is ample evidence that distinct types of vasculature undergo tight transcriptional control to preserve their structure, identity, and functions. We determined, for the first time, the global lineage-specific lncRNAome of human dermal blood and lymphatic endothelial cells (BECs and LECs), combining RNA-Seq and CAGE-Seq. A subsequent genome-wide antisense oligonucleotide-knockdown profiling of two BEC- and two LEC-specific lncRNAs identified LETR1 as a critical gatekeeper of the global LEC transcriptome. Deep RNA-DNA and RNA-protein interaction studies, and phenotype rescue analyses revealed that LETR1 is a nuclear trans-acting lncRNA modulating, via key epigenetic factors, the expression of essential target genes governing the growth and migratory ability of LECs. Together, our study provides new evidence supporting the intriguing concept that every cell type expresses precise lncRNA signatures to control lineage-specific regulatory programs.
2020-12-21 | PXD018578 | Pride
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
2023-09-25 | GSE241835 | GEO