ArticleNature communications2025
Clair3-RNA: a deep learning-based small variant caller for long-read RNA sequencing data.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- NanoTS: a deep learning tool for accurate SNP calling in nanopore long-read transcriptome data.Nature methods · 2026Article
- Hybrid untargeted short-read and targeted long-read RNA sequencing facilitates genotype-phenotype associations at single-cell resolution.Genome biology · 2026Article
- NanoSimFormer: an end-to-end transformer-based nanopore signal simulator with basecaller guidance.Bioinformatics (Oxford, England) · 2026Article
- Nanopore direct RNA sequencing and the epitranscriptome: Advances in mapping native RNA landscapes.iMeta · 2026Review
- An rRNA-depleted full-length transcriptome strategy using nanopore sequencing for identification of novel lncRNA isoforms.Communications biology · 2026Article
- SNP calling, haplotype phasing and allele-specific analysis with long RNA-seq reads.Nature methods · 2026Article
- Sample-specific haplotype-resolved protein isoform characterization via long-read RNA-seq-based proteogenomics.bioRxiv : the preprint server for biology · 2026Article
- A systematic assessment of machine learning for structural variant filtering.bioRxiv : the preprint server for biology · 2026Article
- Detecting foldback artifacts in long-reads.BMC genomics · 2026Article
- Improving spliced alignment by modeling splice sites with deep learning.Algorithms for molecular biology : AMB · 2026Article
- Clair3-RNA: a deep learning-based small variant caller for long-read RNA sequencing data.Nature communications · 2025Article
- Detecting Foldback Artifacts in Long-reads.bioRxiv : the preprint server for biology · 2025Article
- Article
- Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.Current issues in molecular biology · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Variant calling with long-read RNA sequencing (lrRNA-seq) helps to analyze full-length isoforms and gene expression but is complicated by high error rates, transcript diversity, RNA editing events, etc. Here, we propose Clair3-RNA, the first deep learning-based variant caller tailored for lrRNA-seq data. Building upon Clair series' pipelines, Clair3-RNA enhances lrRNA-seq variant calling using optimized techniques, such as uneven coverage normalization, refined training data, editing site discovery, and haplotype phasing to enhance performance. Clair3-RNA supports various platforms, including PacBio, ONT complementary DNA sequencing (cDNA), and ONT direct RNA sequencing (dRNA). Clair3-RNA achieved a ~ 91% SNP F1-score on the ONT platform using the latest ONT SQK-RNA004 kit (dRNA004) and a ~ 92% SNP F1-score in PacBio Iso-Seq and MAS-Seq for variants with at least 4x coverage. With least 10x coverage and disregarding zygosity, the performance reached a ~ 95% and ~96% F1-score for ONT and PacBio, respectively. After phasing, the performance reached ~97% for ONT and ~98% for PacBio. Across GIAB samples, Clair3-RNA consistently outperformed existing callers and accurately identified RNA editing sites. Clair3-RNA is open-source at ( https://github.com/HKU-BAL/Clair3-RNA ).
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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.