ArticleNature communications2026
Genomic language model mitigates chimera artifacts in nanopore direct RNA sequencing.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
The trial behind it
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.
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Who cites it
2 citing papers in PubMed.
- Tumor-specific lncRNA IGF1R-AS1 trans-regulates chromatin interactions associated with oncogenic MYC signaling.Nature communications · 2026Article
- Detecting foldback artifacts in long-reads.BMC genomics · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
Chimera artifacts in nanopore direct RNA sequencing (dRNA-seq) introduce substantial inaccuracies, complicating downstream applications such as transcript annotation and gene fusion detection. Current basecalling models are unable to detect or mitigate these artifacts, limiting the reliability and utility of dRNA-seq for transcriptomics research. To address this challenge, we present DeepChopper, a genomic language model specifically designed to identify and remove adapter sequences from base-called dRNA-seq long reads with single-base precision. Operating independently of raw signal or alignment information, DeepChopper effectively eliminates adapter-bridged artifacts. Here, we show that DeepChopper enhances the accuracy of downstream analyses and unlocks the full potential of nanopore dRNA-seq, establishing it as a more robust tool for diverse transcriptomics applications.
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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.