A Genomic Language Model for Chimera Artifact Detection in Nanopore Direct RNA Sequencing
Li, Y.; Wang, T.-Y.; Guo, Q.; Ren, Y.; Lu, X.; Cao, Q.; Yang, R.
Show abstract
Chimera artifacts in nanopore direct RNA sequencing (dRNA-seq) can significantly distort transcriptome analyses, yet their detection and removal remain challenging due to limitations in existing basecalling models. We present Deep-Chopper, a genomic language model that precisely identifies and removes adapter sequences from base-called dRNA-seq long reads at single-base resolution, operating independently of raw signal or alignment information to effectively eliminate chimeric read artifacts. By removing these artifacts, DeepChopper substantially improves the accuracy of critical downstream analyses, such as transcript annotation and gene fusion detection, thereby enhancing the reliability and utility of nanopore dRNA-seq for transcriptomics research.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms 96%
- Uncalled4 improves nanopore DNA and RNA modification detection via fast and accurate signal alignment 96%
- Enhanced recovery of single-cell RNA-sequencing reads for missing gene expression data 95%
Similar papers in this journal
- Discovering single nucleotide variants and indels from bulk and single-cell ATAC-seq 95%
- PCLIPtools: A Robust Framework for Identifying RNA-Protein Interaction Sites from PAR-CLIP experiments. 95%
- Shiba: A versatile computational method for systematic identification of differential RNA splicing across platforms 95%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.