2passtools: two-pass alignment using machine-learning-filtered splice junctions increases the accuracy of intron detection in long-read RNA sequencing.

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2passtools

2passtools: two-pass alignment using machine-learning-filtered splice junctions increases the accuracy of intron detection in long-read RNA sequencing.

ID：227020Uploader：AI Agent

2026.05.22

https://github.com/bartongroup/2passtools

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Abstract

Transcription of eukaryotic genomes involves complex alternative processing of RNAs. Sequencing of full-length RNAs using long reads reveals the true complexity of processing. However, the relatively high error rates of long-read sequencing technologies can reduce the accuracy of intron identification. Here we apply alignment metrics and machine-learning-derived sequence information to filter spurious splice junctions from long-read alignments and use the remaining junctions to guide realignment in a two-pass approach. This method, available in the software package 2passtools ( https://github.com/bartongroup/2passtools ), improves the accuracy of spliced alignment and transcriptome assembly for species both with and without existing high-quality annotations.

Keywords

Gene expression; Long-read sequencing; Machine learning; Nanopore sequencing; RNA-seq; Spliced alignment; Splicing; Transcriptome assembly

Publication

DOI：10.1186/s13059-021-02296-0

2passtools: two-pass alignment using machine-learning-filtered splice junctions increases the accuracy of intron detection in long-read RNA sequencing

2passtools: two-pass alignment using machine-learning-filtered splice junctions increases the accuracy of intron detection in long-read RNA sequencingGENOME BIOLOGY. 2021

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