Long-read transcriptomic analysis of ddm1
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB49338 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Purpose: To generate a reference long-read transcriptomic data set for use in developing new analysis pipelines and comparing their performance with existing methods. Synthetic “sequin” RNA standards (Hardwick et al. 2016) were sequenced using the Oxford Nanopore Technologies (ONT) GridION platform.
2020-06-18 | GSE151984 | GEO
Project description:Osteosarcoma is the most common primary bone cancer in children, adolescents and young adults. It is a rare cancer type. To comprehensively reveal the transcriptomic characteristics of osteosarcoma, we performed Oxford Nanopore Technologies (ONT) long-read RNA-Seq of tumor and adjacent normal tissues from 23 patients with osteosarcoma.
2023-07-19 | GSE218035 | GEO
Project description:The ONT long-read data of NCIH2170 focusing on chr17 and chr8
2025-05-12 | GSE293274 | GEO
Project description:Pioneering studies (PXD014844) have identified many interesting molecules in tick saliva by LC-MS/MS proteomics, but the protein databases used to assign mass spectra were based on short Illumina reads of the Amblyomma americanum transcriptome and may not have captured the diversity and complexity of longer transcripts. Here we apply long-read Pacific Bioscience technologies to complement the previously reported short-read Illumina transcriptome-based proteome in an effort to increase spectrum assignments. Our dataset reveals a small increase in assignable spectra to supplement the previously released short-read transcriptome-based proteome.
2022-05-31 | PXD033870 | Pride
Project description:Alternative splicing is widely acknowledged to be a crucial regulator of gene expression and is a key contributor to both normal developmental processes and disease states. While cost-effective and accurate for quantification, short-read RNA-seq lacks the ability to resolve full-length transcript isoforms despite increasingly sophisticated computational methods. Long-read sequencing platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore (ONT) bypass the transcript reconstruction challenges of short-reads. Here we describe TALON, the ENCODE4 pipeline for analyzing PacBio cDNA and ONT direct-RNA transcriptomes. We apply TALON to three human ENCODE Tier 1 cell lines and show that while both technologies perform well at full-transcript discovery and quantification, each one displayed distinct artifacts. We further apply TALON to mouse cortical and hippocampal transcriptomes and find that a substantial proportion of neuronal genes have more reads associated with novel isoforms than with annotated ones. These data show that TALON is a technology-agnostic long-read transcriptome discovery and quantification pipeline capable of tracking both known and novel transcript models, as well as their expression levels, across datasets for both simple studies and in larger projects. These properties will enable TALON users to move beyond the limitations of short-read data to perform isoform discovery and quantification in a uniform manner on existing and future long-read platforms.
2020-03-18 | GSE147118 | GEO
Project description:Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer. To date, long-read RNA sequencing has not been applied to kidney cancer. Here, we used ONT long-read Direct RNA sequencing to profile the transcriptomes of ccRCC cell line RCC4, with and without exposure to pro-inflammatory cytokines. Our results revealed differentially expressed genes induced by the pro-inflammatory cytokines. Moreover, results here revealed potential tumour origin of novel isoforms and genes that were discovered in the archival tumour samples by long-read sequencing.
2023-09-04 | GSE242084 | GEO
Project description:Alternative splicing is widely acknowledged to be a crucial regulator of gene expression and is a key contributor to both normal developmental processes and disease states. While cost-effective and accurate for quantification, short-read RNA-seq lacks the ability to resolve full-length transcript isoforms despite increasingly sophisticated computational methods. Long-read sequencing platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore (ONT) bypass the transcript reconstruction challenges of short-reads. Here we describe TALON, the ENCODE4 pipeline for analyzing PacBio cDNA and ONT direct-RNA transcriptomes. We apply TALON to three human ENCODE Tier 1 cell lines and show that while both technologies perform well at full-transcript discovery and quantification, each technology has its distinct artifacts. We further apply TALON to mouse cortical and hippocampal transcriptomes and find that a substantial proportion of neuronal genes have more reads associated with novel isoforms than annotated ones. The TALON pipeline for technology-agnostic, long-read transcriptome discovery and quantification tracks both known and novel transcript models as well as expression levels across datasets for both simple studies and larger projects such as ENCODE that seek to decode transcriptional regulation in the human and mouse genomes to predict more accurate expression levels of genes and transcripts than possible with short-reads alone.
2019-06-15 | GSE132766 | GEO
Project description:This project aims to leverage Oxford Nanopore Technologies (ONT) long-read RNA sequencing to achieve a comprehensive analysis of the human pancreatic cancer transcriptome. Traditional short-read sequencing methods often struggle with accurately reconstructing full-length transcripts and discerning complex splicing events due to their limited read lengths. In contrast, ONT's long-read sequencing can generate reads that span entire RNA molecules, facilitating precise identification of transcript isoforms, alternative splicing patterns, and poly(A) tail length. By applying this technology, we seek to enhance the annotation of the pancreatic cancer transcriptome, uncover novel transcripts, and gain deeper insights into gene expression dynamics. The findings from this study have the potential to advance our understanding of gene regulation and contribute to the development of novel therapeutic strategies.
2025-07-23 | GSE293661 | GEO
Project description:LocusMasterTE: long-read assisted short-read RNA-seq TE quantification [long]
| PRJNA935368 | ENA
Project description:Long-read RNA sequencing technologies offer unparalleled in- sights into transcriptomes by enabling full-length sequencing of RNA molecules, uncovering novel isoforms and alternative splicing events. While long-read sequencing platforms, such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), have historically been associated with higher error rates, recent advancements in both platforms have significantly en- hanced read accuracy, broadening their applicability for tran- scriptomic studies. With the rapid evolution of sequencing protocols and bioin- formatics tools, the trade-offs between sequencing throughput, read length, accuracy, and cost present significant challenges in selecting the optimal approach. Systematic benchmarking studies that compare these options are crucial to inform fu- ture research directions. However, many existing benchmark- ing datasets with matched data across multiple platforms have limitations, including: 1) a lack of realistic biological replicates, which may restrict the generalisability of differential analysis results to real-world scenarios, and 2) the use of earlier sequenc- ing kits, which may not reflect the latest advancements in se- quencing technology, limiting their relevance for future studies that typically use newer sequencing protocols. Here we present LongBench, a comprehensive benchmarking dataset designed to fill these critical gaps. Derived from eight lung cancer cell lines with synthetic RNA spike-ins, LongBench includes bulk, single-cell, and single-nucleus RNA-seq data from three state-of-the-art long-read sequencing platforms — ONT PCR-cDNA, ONT direct RNA, PacBio Kinnex — alongside Il- lumina short-read data for robust cross-platform comparisons. The LongBench dataset is a valuable resource for benchmarking and improving sequencing protocols and bioinformatics tools. With the LongBench dataset we present a systematic evaluation of transcript capture, quantification, and differential expression analyses, examining the strengths and limitations of each se- quencing platform in various biological contexts, enabling re- searchers to make more informed decisions on platform and method selection.
2025-09-01 | GSE303762 | GEO