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

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.

Automated summary

This study introduces a method to improve the accuracy of long-read RNA sequencing using alignment metrics and machine-learning-derived sequence information, effectively filtering out spurious splice junctions and guiding realignment in a two-pass approach. This method enhances the accuracy of spliced alignment and transcriptome assembly for species with and without high-quality annotations.