Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Long-TUC-seq is a robust method for quantification of metabolically labeled full-length isoforms

ABSTRACT: Long-TUC-seq is a robust method for quantification of metabolically labeled full-length isoforms

PROVIDER: PRJNA629096 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR11638300.fastq.gz	Fastqsanger.gz
	SRR11638301.fastq.gz	Fastqsanger.gz
	SRR11638302.fastq.gz	Fastqsanger.gz
	SRR11638303.fastq.gz	Fastqsanger.gz
	SRR11638297_subreads.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 7

Similar Datasets

Long-TUC-seq is a robust method for quantification of metabolically labeled full-length isoforms

Project description:The steady state expression of each gene is the result of transcriptional rate, dynamic processing, and degradation of RNA. While regular RNA-seq methods only measure steady state expression levels, RNA-seq of metabolically labeled RNA identifies transcripts that were transcribed during the window of metabolic labeling. Whereas short-read RNA sequencing can identify metabolically labeled RNA at the gene level, long-read sequencing results in dramatically improved resolution of isoform-level transcription. Here we combine thiouridine-to-cytosine conversion (TUC) with PacBio long-read sequencing to study the dynamics of mRNA transcription in the GM12878 cell line. We show that using long-TUC-seq, we can detect metabolically labeled mRNA of distinct isoforms more reliably than using short reads. Long-TUC-seq holds the promise of capturing isoform dynamics robustly and without the need for enrichment.

2020-04-30 | GSE149551 | GEO

Effect of TUC-seq protocol versus SLAM-seq on quantification bias in nucleotide conversion RNA-seq data

Project description:To investigate the effect of TUC-seq versus SLAM-seq on quantification bias in nucleotide conversion RNA-seq, we performed SLAM-seq and TUC-seq on NIH cells. We then measured 4sU dropout in 4sU samples compared to 4sU naive samples.

2024-02-06 | GSE253169 | GEO

Sequencing of metabolically labeled transcripts in single cells from RPE1-FUCCI cells and murine intestinal organoids.

Project description:Measurements of metabolically labeled transcripts in single cells for estimation of mRNA synthesis and degradation rates throughout the cell cycle and intestinal organoid differentiation.

2020-03-05 | GSE128365 | GEO

ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data

Project description:Long-read RNA sequencing (RNA-seq) holds great potential for characterizing transcriptome variation and full-length transcript isoforms, but the relatively high error rate of current long-read sequencing platforms poses a major challenge. We present ESPRESSO, a computational tool for robust discovery and quantification of transcript isoforms from error-prone long reads. ESPRESSO jointly considers alignments of all long reads aligned to a gene and uses error profiles of individual reads to improve the identification of splice junctions and the discovery of their corresponding transcript isoforms. On both a synthetic spike-in RNA sample and human RNA samples, ESPRESSO outperforms multiple contemporary tools in not only transcript isoform discovery but also transcript isoform quantification. In total, we generated and analyzed ~1.1 billion nanopore RNA-seq reads covering 30 human tissue samples and three human cell lines. ESPRESSO and its companion dataset provide a useful resource for studying the RNA repertoire of eukaryotic transcriptomes.

2022-10-14 | GSE192955 | GEO

Quantification of transcript isoforms at the single-cell level using SCALPEL

Project description:Quantification of transcript isoforms at the single-cell level using SCALPEL

| PRJNA1115197 | ENA

Effect of TUC-seq protocol versus SLAM-seq on quantification bias in nucleotide conversion RNA-seq data

Project description:Effect of TUC-seq protocol versus SLAM-seq on quantification bias in nucleotide conversion RNA-seq data

| PRJNA1064143 | ENA

Sequencing of metabolically labeled transcripts in single cells from RPE1-FUCCI cells and murine intestinal organoids.

Project description:Sequencing of metabolically labeled transcripts in single cells from RPE1-FUCCI cells and murine intestinal organoids.

| PRJNA527268 | ENA

Identification and quantification of gene isoforms during the differentiation of human embryonic stem cells into pharyngeal endoderm and primordial germ cell-like cells

Project description:Long-read RNA-seq is now widely applied for gene isoform identification and efforts to long-read RNA-seq for gene isoform quantification are emerging. We develop a software named miniQuant to quantify gene isoforms using long reads alone or in combination with short reads. We demonstrate the utility of miniQuant by its application in uncovering the expression dynamics of gene isoforms during the differentiation of human embryonic stem cells (ESC) into pharyngeal endoderm (PE) and primordial germ cell-like cells (PGC).

2025-02-17 | GSE265988 | GEO

Quantification of transcript isoforms at the single-cell level using SCALPEL

Project description:3' based single-cell RNA sequencing (scRNA-seq) has enabled the study of gene expression at the individual cell level and the development of few methods to quantify transcriptomic variability at the single-cell level. Yet, most of these methods have low sensitivity and specificity. Here, we present SCALPEL, a new tool to quantify and characterize transcript isoforms at the single-cell level using standard 3' based single-cell transcriptomics data. SCALPEL predictions have higher sensitivity than that of other tools and can be validated experimentally. We have used SCALPEL to study the changes in isoform usage during mouse spermatogenesis and in the differentiation of induced pluripotent stem cells (iPSCs) to neural progenitors (NPCs). These analyses confirmed known changes in the 3' UTR length during cell differentiation and allowed the identification of new cell populations and cell type specific microRNA signatures controlling isoform expression in individual cells. Together, our results highlight the importance of isoform quantification to gain insight on the gene regulatory mechanisms at the single-cell level.

2025-06-06 | GSE268222 | GEO

Unbiased characterization and systematic profiling of metabolically labeled RNA-binding sites

Project description:Here we introduce pRBS-ID, an amenable platform that enables robust profiling of PAR-crosslinked RBSs. Using tandem mass spectrometry (MS/MS) approaches, we unbiasedly characterize the PAR adducts on peptides, to develop an optimal MS/MS search pipeline that enables robust identification and quantification. Using pRBS-ID, we systematically profile RBSs crosslinked to U or G in PAR-labeled mRNA, which can serve as a rich resource for studying RNA-protein interactions. Especially, pRBS-ID is widely applicable to studies on time-course RNP remodelling, host-virus interactions, and RNP structure prediction.

2021-11-02 | PXD023401 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data