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

Genome Biol. 2021 Mar 1;22(1):72. doi: 10.1186/s13059-021-02296-0.

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 types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Computational Biology / methods*
  • Introns
  • Machine Learning*
  • Molecular Sequence Annotation
  • RNA Splice Sites*
  • RNA Splicing
  • RNA-Seq* / methods
  • Reproducibility of Results
  • Sequence Alignment / methods*
  • Software*

Substances

  • RNA Splice Sites