ArticleNucleic acids research2024
Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 33 citations in OpenAlex.
- Clinical response to azacitidine in MDS is associated with distinct DNA methylation changes in HSPCs.Nature communications · 2025Trial
- A Real-Data-Driven Framework for Evaluating Differential Transcript Usage Methods Across Long-Read Bulk, Single-Cell, and Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The roles of the acetyltransferase domains of the chromatin regulators KAT6A and KAT6B in vivo.Development (Cambridge, England) · 2026Article
- Shape-constrained, changepoint additive models for time series omics data with cpam.Nucleic acids research · 2026Article
- Long-read assembly reveals vast transcriptional complexity in the placenta associated with metabolic and endocrine function.Nature communications · 2026Article
- Improving isoform-level eQTL and integrative genetic analyses of breast cancer risk with long-read RNA transcript assemblies.bioRxiv : the preprint server for biology · 2026Article
- Natural variation in transplacental transfer efficiency exposes distinct transcriptional network architectures of PFAS effects on birth weight and gestational age.bioRxiv : the preprint server for biology · 2026Article
- Improving gene isoform quantification with miniQuant.Nature biotechnology · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- A systematic benchmark of high-accuracy PacBio long-read RNA sequencing for transcript-level quantification.Genome biology · 2026Article
- Composite SMG5-SMG6 PIN domain formation is essential for NMD.Nature communications · 2026Article
- A comprehensive evaluation of long-read de novo transcriptome assembly.Genome biology · 2026Article
- LDHA as a Potential Therapeutic Target for Lactylation Regulation in Spinal Cord Injury: Integrated Bioinformatics Analysis, Experimental Validation, and Drug Prediction.Journal of molecular neuroscience : MN · 2026Article
- Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases.Computational and structural biotechnology journal · 2026Review
- Dividing out quantification uncertainty enables assessment of differential transcript usage with limma and edgeR.Nucleic acids research · 2025Article
- Tree-based differential testing using inferential uncertainty for RNA-seq.Genome research · 2025Article
- BLVRA promotes glioblastoma progression by regulating fatty acid metabolism.Scientific reports · 2025Article
- Design specifications for biomedical virtual twins in engineered adoptive cellular immunotherapies.NPJ digital medicine · 2025Review
- Phenotypic, transcriptomic and metabolomic changes in Klebsiella pneumoniae after long term exposure to simulated microgravity.NPJ microgravity · 2025Article
- Oarfish: enhanced probabilistic modeling leads to improved accuracy in long read transcriptome quantification.Bioinformatics (Oxford, England) · 2025Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Differential expression analysis of RNA-seq is one of the most commonly performed bioinformatics analyses. Transcript-level quantifications are inherently more uncertain than gene-level read counts because of ambiguous assignment of sequence reads to transcripts. While sequence reads can usually be assigned unambiguously to a gene, reads are very often compatible with multiple transcripts for that gene, particularly for genes with many isoforms. Software tools designed for gene-level differential expression do not perform optimally on transcript counts because the read-to-transcript ambiguity (RTA) disrupts the mean-variance relationship normally observed for gene level RNA-seq data and interferes with the efficiency of the empirical Bayes dispersion estimation procedures. The pseudoaligners kallisto and Salmon provide bootstrap samples from which quantification uncertainty can be assessed. We show that the overdispersion arising from RTA can be elegantly estimated by fitting a quasi-Poisson model to the bootstrap counts for each transcript. The technical overdispersion arising from RTA can then be divided out of the transcript counts, leading to scaled counts that can be input for analysis by established gene-level software tools with full statistical efficiency. Comprehensive simulations and test data show that an edgeR analysis of the scaled counts is more powerful and efficient than previous differential transcript expression pipelines while providing correct control of the false discovery rate. Simulations explore a wide range of scenarios including the effects of paired vs single-end reads, different read lengths and different numbers of replicates.
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Registered trials
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.