ArticleFrontiers in genetics2024
GCRTcall: a transformer based basecaller for nanopore RNA sequencing enhanced by gated convolution and relative position embedding via joint loss training.
Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Advanced deep learning strategies in nanopore RNA sequencing.RNA biology · 2026Review
- Review
- Nanopore direct RNA sequencing and the epitranscriptome: Advances in mapping native RNA landscapes.iMeta · 2026Review
- A dual context-aware basecaller for nanopore direct RNA sequencing.Nature communications · 2026Article
- De novo non-canonical nanopore basecalling enables private communication using heavily-modified DNA data at single-molecule level.Nature communications · 2025Article
- RMNet: An RNA m6A Cross-species Methylation Detection Method for Nanopore Sequencing.Current drug targets · 2025Article
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Authors and funding
4 authors.
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Abstract
Nanopore sequencing, renowned for its ability to sequence DNA and RNA directly with read lengths extending to several hundred kilobases or even megabases, holds significant promise in fields like transcriptomics and other omics studies. Despite its potential, the technology's limited accuracy in base identification has restricted its widespread application. Although many algorithms have been developed to improve DNA decoding, advancements in RNA sequencing remain limited. Addressing this challenge, we introduce GCRTcall, a novel approach integrating Transformer architecture with gated convolutional networks and relative positional encoding for RNA sequencing signal decoding. Our evaluation demonstrates that GCRTcall achieves state-of-the-art performance in RNA basecalling.
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