The Project for High-Confidence Coding and Noncoding Transcriptome Maps
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA381216 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR3974378_1.fastq.gz | Fastqsanger.gz | |||
| SRR3974378_2.fastq.gz | Fastqsanger.gz | |||
| SRR3974379_1.fastq.gz | Fastqsanger.gz | |||
| SRR3974379_2.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 4 of 4 |
Similar Datasets
Project description:High-confidence Coding and Noncoding Transcriptome Maps
| PRJNA381218 | ENA
Project description:The advent of high-throughput RNA sequencing (RNA-seq) has led to the discovery of unprecedentedly immense transcriptomes encoded by eukaryotic genomes. However, the transcriptome maps are still incomplete partly because they were mostly reconstructed based on RNA-seq reads that lack their orientations (known as unstranded reads) and certain boundary information. Methods to expand the usability of unstranded RNA-seq data by predetermining the orientation of the reads and precisely determining the boundaries of assembled transcripts could significantly benefit the quality of the resulting transcriptome maps. Here, we present a high-performing transcriptome assembly pipeline, called CAFE, that significantly improves the original assemblies, respectively assembled with stranded and/or unstranded RNA-seq data, by orienting unstranded reads using the maximum likelihood estimation and by integrating information about transcription start sites and cleavage and polyadenylation sites. Applying large-scale transcriptomic data comprising 230 billion RNA-seq reads from the ENCODE, Human BodyMap Projects, The Cancer Genome Atlas, and GTEx, CAFE enabled us to predict the directions of about 220 billion unstranded reads, which led to the construction of more accurate transcriptome maps, comparable to the manually curated map, and a comprehensive lncRNA catalogue that includes thousands of novel lncRNAs. Our pipeline should not only help to build comprehensive, precise transcriptome maps from complex genomes but also to expand the universe of non-coding genomes. This SuperSeries is composed of the SubSeries listed below.
2017-04-01 | GSE97212 | GEO
Project description:The advent of high-throughput RNA sequencing (RNA-seq) has led to the discovery of unprecedentedly immense transcriptomes encoded by eukaryotic genomes. However, the transcriptome maps are still incomplete partly because they were mostly reconstructed based on RNA-seq reads that lack their orientations (known as unstranded reads) and certain boundary information. Methods to expand the usability of unstranded RNA-seq data by predetermining the orientation of the reads and precisely determining the boundaries of assembled transcripts could significantly benefit the quality of the resulting transcriptome maps. Here, we present a high-performing transcriptome assembly pipeline, called CAFE, that significantly improves the original assemblies, respectively assembled with stranded and/or unstranded RNA-seq data, by orienting unstranded reads using the maximum likelihood estimation and by integrating information about transcription start sites and cleavage and polyadenylation sites. Applying large-scale transcriptomic data comprising 230 billion RNA-seq reads from the ENCODE, Human BodyMap Projects, The Cancer Genome Atlas, and GTEx, CAFE enabled us to predict the directions of about 220 billion unstranded reads, which led to the construction of more accurate transcriptome maps, comparable to the manually curated map, and a comprehensive lncRNA catalogue that includes thousands of novel lncRNAs. Our pipeline should not only help to build comprehensive, precise transcriptome maps from complex genomes but also to expand the universe of non-coding genomes.
2017-04-01 | GSE97211 | GEO
Project description:The advent of high-throughput RNA sequencing (RNA-seq) has led to the discovery of unprecedentedly immense transcriptomes encoded by eukaryotic genomes. However, the transcriptome maps are still incomplete partly because they were mostly reconstructed based on RNA-seq reads that lack their orientations (known as unstranded reads) and certain boundary information. Methods to expand the usability of unstranded RNA-seq data by predetermining the orientation of the reads and precisely determining the boundaries of assembled transcripts could significantly benefit the quality of the resulting transcriptome maps. Here, we present a high-performing transcriptome assembly pipeline, called CAFE, that significantly improves the original assemblies, respectively assembled with stranded and/or unstranded RNA-seq data, by orienting unstranded reads using the maximum likelihood estimation and by integrating information about transcription start sites and cleavage and polyadenylation sites. Applying large-scale transcriptomic data comprising 230 billion RNA-seq reads from the ENCODE, Human BodyMap 2.0, The Cancer Genome Atlas, and GTEx projects, CAFE enabled us to predict the directions of about 220 billion unstranded reads, which led to the construction of more accurate transcriptome maps, comparable to the manually curated map, and a comprehensive lncRNA catalog that includes thousands of novel lncRNAs. Our pipeline should not only help to build comprehensive, precise transcriptome maps from complex genomes but also to expand the universe of noncoding genomes.
| S-EPMC5453319 | biostudies-literature
Project description:1. Evaluate the diagnostic value of long noncoding RNA (CCAT1) expression by RT-PCR in peripheral blood in colorectal cancer patients versus normal healthy control personal.
2. Evaluate the clinical utility of detecting long noncoding RNA (CCAT1) expression in diagnosis of colorectal cancer patients & its relation to tumor staging.
3. Evaluate the clinical utility of detecting long noncoding RNA (CCAT1) expression in precancerous colorectal diseases.
4. Compare long noncoding RNA (CCAT1) expression with traditional marker; carcinoembryonic antigen (CEA) and Carbohydrate antigen 19-9 (CA19-9) in diagnosis of colorectal cancer.
| 2330122 | ecrin-mdr-crc
Project description:To understand the role of long non-coding RNAs and interaction with coding RNAs in bladder urothelial cell carcinoma (BUCC), we performed genome-wide screening long non-coding RNAs and coding RNAs expression on primary BUCC tissues and normal tissues using long non-coding RNA array (Agilent plateform (GPL13825). By comparing these two groups, significantly differentially expressed lncRNAs and coding RNAs were identified. We further identifed a subset of long noncoding RNAs and their correlation with neighboring coding genes using bioinformatic tools. This analysis provides foundamental understaning of transcriptomic landscape changing during bladder carcinogenesis. 12 BUCC primary tumors and 3 normal tissues were used for long noncoding RNA array experiments which including long non-coding RNAs and coding RNAs. The differential expression of subset of long noncoding RNAs and their interaction with coding RNAs in BUCC compared with normal tissue will be identified with comtational analysis.
2013-12-31 | E-GEOD-51493 | biostudies-arrayexpress
Project description:Age and poverty status alter the coding and noncoding transcriptome
| PRJNA508945 | ENA
Project description:Methyl-7-guanosine (m7G) “capping” of coding and some noncoding RNAs is critical for their maturation and subsequent activity. Here, we discovered that eukaryotic translation initiation factor 4E(eIF4E), itself a cap-binding protein, drives the expression of the capping machinery and the increased capping efficiency of ∼100 coding and noncoding RNAs. This dataset collects transcriptomic data for quantitative cap immunoprecipitation (CapIP) assay in eIF4E-Flag or vector stable U2Os cells.
2020-09-30 | GSE158728 | GEO
Project description:Long non-coding RNAs (lncRNAs) and miRNAs have emerged as crucial regulators of gene expression and cell fate decisions. Here we present an integrated analysis of the ncRNA-landscape of purified human hematopoietic stem cells (HSCs) and their differentiated progenies, including granulocytes, monocytes, T-cells, NK-cells, B-cells, megakaryocytes and erythroid precursors. For each blood cell population, RNA from 5 healthy donors was hybridized onto three microarray platforms (Arraystar lncRNA V2.0, NCode™-miRNA/-ncRNA), yielding a coverage of more than 40,000 lncRNAs, 25,000 mRNAs and 900 miRNAs on 146 arrays. T-distributed stochastic neighbor embedding (t-SNE) on noncoding genes structured the dataset into groups of samples that matched the input populations, demonstrating their unique lncRNA expression profiles. Self-organizing maps (SOMs) revealed clusters of lncRNAs and mRNAs that were coordinately expressed in HSCs and during lineage commitment. Using a “guilt-by-association” approach we assigned putative functions to lncRNAs regulated during differentiation, which predicted LINC00173 as a novel non-coding regulator of granulopoiesis. We knocked down LINC00173 using two independent shRNA constructs, which resulted in diminished granulocytic in vitro differentiation, myeloid colony-formation and function. Next, we uncovered a strong and highly coordinated upregulation of miRNAs, small nucleolar RNAs (snoRNAs) and lncRNAs within the DLK1-DIO3 locus on chromosome 14 (hsa14) during megakaryocytic maturation. shRNA-mediated knock-down of noncoding members of the locus reduced erythroid colony-formation and megakaryocytic cell proliferation in vitro implicating the functional importance of this ncRNA locus in megakaryopoiesis.
2017-05-31 | GSE98791 | GEO
Project description:Long non-coding RNAs (lncRNAs) and miRNAs have emerged as crucial regulators of gene expression and cell fate decisions. Here we present an integrated analysis of the ncRNA-landscape of purified human hematopoietic stem cells (HSCs) and their differentiated progenies, including granulocytes, monocytes, T-cells, NK-cells, B-cells, megakaryocytes and erythroid precursors. For each blood cell population, RNA from 5 healthy donors was hybridized onto three microarray platforms (Arraystar lncRNA V2.0, NCode™-miRNA/-ncRNA), yielding a coverage of more than 40,000 lncRNAs, 25,000 mRNAs and 900 miRNAs on 146 arrays. T-distributed stochastic neighbor embedding (t-SNE) on noncoding genes structured the dataset into groups of samples that matched the input populations, demonstrating their unique lncRNA expression profiles. Self-organizing maps (SOMs) revealed clusters of lncRNAs and mRNAs that were coordinately expressed in HSCs and during lineage commitment. Using a “guilt-by-association” approach we assigned putative functions to lncRNAs regulated during differentiation, which predicted LINC00173 as a novel non-coding regulator of granulopoiesis. We knocked down LINC00173 using two independent shRNA constructs, which resulted in diminished granulocytic in vitro differentiation, myeloid colony-formation and function. Next, we uncovered a strong and highly coordinated upregulation of miRNAs, small nucleolar RNAs (snoRNAs) and lncRNAs within the DLK1-DIO3 locus on chromosome 14 (hsa14) during megakaryocytic maturation. shRNA-mediated knock-down of noncoding members of the locus reduced erythroid colony-formation and megakaryocytic cell proliferation in vitro implicating the functional importance of this ncRNA locus in megakaryopoiesis.
2017-05-31 | GSE98633 | GEO