High-confidence Coding and Noncoding Transcriptome Maps
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA381218 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The Project for High-Confidence Coding and Noncoding Transcriptome Maps
| PRJNA381216 | 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.
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 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 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:With anti-TET1 immunoprecipitated chromatin from mouse frontal cortices, we generated TET1 binding maps with 4496 high-confidence peaks and approximately 90% of TET1 peaks were within intergenic regions.
2019-07-25 | GSE124671 | GEO
Project description:Proximity labeling approaches have been widely utilized to define protein interactomes. Due to the inherent promiscuity of proximity labeling using TurboID-based approaches, identification and adoption of appropriate labeling controls is a pivotal step to mitigate background interference and enhance interactome assignment accuracy. Here, we evaluate the effectiveness of both expression controls and data normalization strategies in generating high confidence interactome maps. This dataset contains proximity labeling proteomics results, including the proximity labeling section of TurboID-HUWE1 in the 293T cell line. It serves as the raw data for Figure 5 in the article.
2025-07-14 | PXD061540 | Pride
Project description:Proximity labeling approaches have been widely utilized to define protein interactomes. Due to the inherent promiscuity of proximity labeling using TurboID-based approaches, identification and adoption of appropriate labeling controls is a pivotal step to mitigate background interference and enhance interactome assignment accuracy. Here, we evaluate the effectiveness of both expression controls and data normalization strategies in generating high confidence interactome maps. This dataset contains proximity labeling proteomics results, including the proximity labeling section of TurboID-HUWE1 in the HAP1 cell line. It serves as the raw data for Supplementary Figure 5 in the article.
2025-07-14 | PXD061564 | Pride
Project description:High-resolution view at the coding transcriptome and noncoding RNAs of major gut microbe Bacteroides thetaiotaomicron
| PRJNA603791 | ENA
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:Proximity labeling approaches have been widely utilized to define protein interactomes. Due to the inherent promiscuity of proximity labeling using TurboID-based approaches, identification and adoption of appropriate labeling controls is a pivotal step to mitigate background interference and enhance interactome assignment accuracy. Here, we evaluate the effectiveness of both expression controls and data normalization strategies in generating high confidence interactome maps. This dataset contains proximity labeling proteomics results, including the proximity labeling section of TurboID-RNF10 based on the Flp-IN system. It serves as the raw data for Figure 3 and Supplementary Figure 3 in the article.
2025-07-14 | PXD061566 | Pride