Comprehensive Assessment of Isoform Detection Methods for Third-Generation Sequencing Data
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA947488 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The advancement of Third-Generation Sequencing (TGS) techniques managed to increase the sequencing length to several kilobases, which leads to a bright future for completely reserving alternative splicing (AS) events and isoform expressions. In recent years, many computational methods for isoform detection from long-read sequencing data have been developed and published. However, there is no prior comparative study that systemically evaluates the performance of the software implemented with different algorithms. Here we benchmarked nine methods implemented in seven computational tools that can identify isoform structures from TGS RNA sequencing data and analyzed their performances from various aspects using both simulated datasets produced by an in-house simulator and previously published experimental data. Our results comprehensively demonstrate the relative effectiveness of the approaches and provide guidance as well as recommendations for future research on AS analysis and further improvement of the tools for isoform detection using TGS data.
2023-12-25 | GSE227911 | GEO
Project description:Alternative cleavage and polyadenylation (APA) is emerging as an important mechanism of gene regulation in eukaryotes and plays important regulatory roles in human development and diseases. Despite the widespread application of Second Generation Sequencing (SGS) technology for polyadenylation site identification, matching each identified polyadenylation site within a gene to its derived isoform remains a major challenge. To achieve the isoform-resolved APA analysis, we developed a tool termed “IDP-APA” that constructs truly expressed isoforms and identifies polyadenylation sites by integrating the respective strengths of Third Generation Sequencing (TGS) long reads and SGS short reads. Compared to existing tools, IDP-APA demonstrated superior performance in both isoform reconstruction and polyadenylation site identification. Applications to human embryonic stem cells, breast cancer cells and brain tissue from a patient with Alzheimer’s disease revealed prevalent APA events and cell-/tissue-specific APA patterns, especially in an isoform-resolved way.
2017-06-01 | GSE97688 | GEO
Project description:Strand-specific massively-parallel cDNA sequencing (RNA-Seq) is a powerful tool for novel transcript discovery, genome annotation, and expression profiling. Despite multiple published methods for strand-specific RNA-Seq, no consensus exists as to how to choose between them. Here, we developed a comprehensive computational pipeline for the comparison of library quality metrics from any RNA-Seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library construction protocols, including both published and our own novel methods. We found marked differences in complexity, strand-specificity, evenness and continuity of coverage, agreement with known annotations, and accuracy for expression profiling. Weighing each method’s performance and ease, we identify the dUTP second strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in any organism. Examination of 11 different strand-specific RNA-Seq libraries from 7 distinct methods; also 2 control non-strand-specific RNA-Seq libraries. To assess the performance of each strand-specific library in digital expression profiling, we compared them to reference expression measurements estimated from expression profiles using competitive hybridization of a mid-log RNA sample vs. genomic DNA using Agilent arrays.
2010-08-15 | E-GEOD-21739 | biostudies-arrayexpress
Project description:We generated in vitro and in silico admixtures of tumor, immune, and stromal cells and sequenced them along with the purified populations from which they were derived. We used the admixtures as ground truth in a community-wide DREAM Challenge aimed at providing an objective, unbiased assessment of published deconvolution methods and at promoting development of new approaches. We benchmarked seven published methods and found that they could predict most cell types well, though dissecting functional states of T cells remains difficult: few methods predict memory CD4+ T cells or the memory versus naïve compartments of CD8T+ cells and, those that do, show large performance gaps relative to other cell types. We spurred development of 21 novel methods, some of which were able to reliably infer these cellular functional states.
2022-06-06 | GSE199324 | GEO
Project description:Strand-specific massively-parallel cDNA sequencing (RNA-Seq) is a powerful tool for novel transcript discovery, genome annotation, and expression profiling. Despite multiple published methods for strand-specific RNA-Seq, no consensus exists as to how to choose between them. Here, we developed a comprehensive computational pipeline for the comparison of library quality metrics from any RNA-Seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library construction protocols, including both published and our own novel methods. We found marked differences in complexity, strand-specificity, evenness and continuity of coverage, agreement with known annotations, and accuracy for expression profiling. Weighing each method’s performance and ease, we identify the dUTP second strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in any organism.
2010-08-15 | GSE21739 | GEO
Project description:Shotgun proteomics is essential for protein identification and quantification in biomedical research, but protein isoform characterization is challenging due to the extensive number of peptides shared across proteins, hindering our understanding of protein isoform regulation and their roles in normal and disease biology. We systematically assess the challenge and opportunities of shotgun proteomics-based protein isoform characterization using in silico and experimental data, and then present SEPepQuant, a graph theory-based approach to maximize isoform characterization. Using published data from one induced pluripotent stem cell study and two human hepatocellular carcinoma studies, we demonstrate the ability of SEPepQuant in addressing the key limitations of existing methods, providing more comprehensive isoform-level characterization, identifying hundreds of isoform-level regulation events, and facilitating streamlined cross-study comparisons. Our analysis provides solid evidence to support a widespread role of protein isoform regulation in normal and disease processes, and SEPepQuant has broad applications to biological and translational research.
2023-10-24 | PXD044809 | Pride
Project description:Community assessment of methods to deconvolve cellular composition from bulk gene expression
| PRJNA819397 | ENA
Project description:The goal of this study was to optimize single-cell long-read RNA sequencing in pancreatic islets to enable accurate isoform-level gene expression analysis. We aimed to overcome key technical limitations—including insufficient read lengths, high error rates, and transcript abundance bias—that have hindered long-read sequencing in single cells. Specifically, we compared 5’ and 3’ library preparation strategies and evaluated transcript depletion methods to improve detection of low-abundance isoforms. Our objective was to establish a robust protocol that enhances isoform identification and reveals differential transcript usage across pancreatic islet cell types.
2026-01-26 | GSE295353 | GEO
Project description:The goal of this study was to optimize single-cell long-read RNA sequencing in pancreatic islets to enable accurate isoform-level gene expression analysis. We aimed to overcome key technical limitations—including insufficient read lengths, high error rates, and transcript abundance bias—that have hindered long-read sequencing in single cells. Specifically, we compared 5’ and 3’ library preparation strategies and evaluated transcript depletion methods to improve detection of low-abundance isoforms. Our objective was to establish a robust protocol that enhances isoform identification and reveals differential transcript usage across pancreatic islet cell types.
2026-01-26 | GSE295352 | GEO
Project description:The goal of this study was to optimize single-cell long-read RNA sequencing in pancreatic islets to enable accurate isoform-level gene expression analysis. We aimed to overcome key technical limitations—including insufficient read lengths, high error rates, and transcript abundance bias—that have hindered long-read sequencing in single cells. Specifically, we compared 5’ and 3’ library preparation strategies and evaluated transcript depletion methods to improve detection of low-abundance isoforms. Our objective was to establish a robust protocol that enhances isoform identification and reveals differential transcript usage across pancreatic islet cell types.
2026-01-26 | GSE295351 | GEO