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

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.

SUBMITTER: Parker MT

PROVIDER: S-EPMC7919322 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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

Parker Matthew T MT Knop Katarzyna K Barton Geoffrey J GJ Simpson Gordon G GG

Genome biology 20210301 1

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 ...[more]

PMID: 33648554

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