ArticleGenome biology2026
IFDlong: a model-based isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data.
Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Review
- IFDlong: a model-based isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data.Genome biology · 2026Article
- Investigating RNA dynamics from single molecule transcriptomes.Trends in genetics : TIG · 2025Review
Corrections and comments
- Update of
Authors and funding
13 authors.
Funding
Abstract
Long-read transcriptome sequencing (long-RNA-seq) revolutionizes transcriptome research by enabling full-length transcript analysis for comprehensive exploration of isoform diversity. We developed IFDlong, a probabilistic framework and software suite for detecting isoform and fusion transcripts from bulk or single-cell long-RNA-seq data. IFDlong annotates each long read, identifies novel isoforms, quantifies expression via an expectation-maximization algorithm, and profiles fusion transcripts. In large-scale simulation and real data analyses, IFDlong outperforms existing tools and demonstrated high accuracy and robustness across multiple in-house and public datasets, including healthy tissues, cell lines, and different diseases.
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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.