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

Parker, M. T.; Barton, G. J.; Simpson, G. G.

doi:10.1101/2020.05.27.118679

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bioRxiv · 10.1101/2020.05.27.118679

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

Parker, M. T.·Barton, G. J.·Simpson, G. G.

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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.

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Parker, M. T., Barton, G. J., Simpson, G. G.. 2020-05-30. Two-pass alignment using machine-learning-filtered splice junctions increases the accuracy of intron detection in long-read RNA sequencing. https://doi.org/10.1101/2020.05.27.118679

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