ArticleGenome biology2020
Obstacles to detecting isoforms using full-length scRNA-seq data.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 56 citations in OpenAlex.
- IsoformSwitchAnalyzeR v2: analysis of functional isoform changes in long-read and single-cell sequencing data.NAR genomics and bioinformatics · 2026Article
- Amaranth: enhanced single-cell transcript assembly via discriminative modelling of UMI reads and internal reads.Bioinformatics (Oxford, England) · 2026Article
- Single cell analysis of developing Merkel cells reveals the emergence of non-coding RNA biotypes as a hallmark of terminal differentiation.Cell death and differentiation · 2026Article
- Comprehensive assessment of alternative splicing analysis methods for single-cell RNA-seq.iScience · 2026Article
- Annotating genomes at increased scale and resolution.Nature reviews. Genetics · 2026Review
- Long-read transcriptomics of purified human cortical cell types exposes glial isoform complexity and disease-relevant transcript architecture.bioRxiv : the preprint server for biology · 2026Article
- Machine learning predictions surpass individual mRNAs as a proxy of single-cell protein expression.Genome biology · 2026Article
- Analyzing cell-type-specific isoform expression using IsoDiffR and long-read single-cell RNA sequencing.Bioinformatics (Oxford, England) · 2026Article
- Single cell and spatial alternative splicing analysis with Nanopore long read sequencing.Nature communications · 2025Article
- Transcriptome assembly at single-cell resolution with Beaver.Bioinformatics (Oxford, England) · 2025Article
- Deep transcriptomics reveals cell-specific isoforms of pan-neuronal genes.Nature communications · 2025Article
- Maximal Efficacy of Alternative Splicing is Ensured by Balanced Efficiency of U1 and U2AF.bioRxiv : the preprint server for biology · 2025Article
- Detection of mRNA Transcript Variants.Genes · 2025Review
- Single-Cell Analysis Dissects the Effects of Vitamin D on Genetic Senescence Signatures Across Murine Tissues.Nutrients · 2025Article
- Single-cell and spatial transcriptomics reveal SPP1-CD44 signaling drives primary resistance to immune checkpoint inhibitors in RCC.Journal of translational medicine · 2024Article
- Characterization of alternative splicing during mammalian brain development reveals the extent of isoform diversity and potential effects on protein structural changes.Biology open · 2024Article
- Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms.Science (New York, N.Y.) · 2024Article
- Alternative splicing in shaping the molecular landscape of the cochlea.Frontiers in cell and developmental biology · 2023Review
- Analyzing alternative splicing in Alzheimer's disease postmortem brain: a cell-level perspective.Frontiers in molecular neuroscience · 2023Article
- Long read single cell RNA sequencing reveals the isoform diversity of Plasmodium vivax transcripts.PLoS neglected tropical diseases · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundEarly single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see more than one isoform being produced from a gene in a single cell, even when multiple isoforms were detected in matched bulk RNA-seq samples. However, these studies generally did not consider the impact of dropouts or isoform quantification errors, potentially confounding the results of these analyses.
resultsIn this study, we take a simulation based approach in which we explicitly account for dropouts and isoform quantification errors. We use our simulations to ask to what extent it is possible to study alternative splicing using scRNA-seq. Additionally, we ask what limitations must be overcome to make splicing analysis feasible. We find that the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative splicing. In mice and other well-established model organisms, the relatively low rate of isoform quantification errors poses a lesser obstacle to splicing analysis. We find that different models of isoform choice meaningfully change our simulation results.
conclusionsTo accurately study alternative splicing with single-cell RNA-seq, a better understanding of isoform choice and the errors associated with scRNA-seq is required. An increase in the capture efficiency of scRNA-seq would also be beneficial. Until some or all of the above are achieved, we do not recommend attempting to resolve isoforms in individual cells using scRNA-seq.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.