Evidence map›Paper›PMID 33648554›Full record

ArticleGenome biology2021

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

Matthew T Parker, Katarzyna Knop, Geoffrey J Barton, Gordon G Simpson

Abstract read
In one paragraph

Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Matthew T ParkerSchool of Life Sciences, University of Dundee, Dow Street, Dundee, DD1 5EH, UK. m.t.parker@dundee.ac.uk.
Katarzyna KnopSchool of Life Sciences, University of Dundee, Dow Street, Dundee, DD1 5EH, UK.
Geoffrey J BartonSchool of Life Sciences, University of Dundee, Dow Street, Dundee, DD1 5EH, UK.
Gordon G SimpsonSchool of Life Sciences, University of Dundee, Dow Street, Dundee, DD1 5EH, UK. g.g.simpson@dundee.ac.uk.ORCID 0000-0001-6744-5889

Funding

Biotechnology and Biological Sciences Research Council BB/J00247X/1Biotechnology and Biological Sciences Research Council BB/M004155/1Biotechnology and Biological Sciences Research Council BB/M01066/1
6 · The paper itself

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.

Indexed as

AlgorithmsMachine LearningRNA-SeqRNA Splice SitesSoftwareComputational BiologyIntronsMolecular Sequence AnnotationReproducibility of ResultsRNA SplicingSequence AlignmentRNA Splice SitesGene expressionLong-read sequencingMachine learningNanopore sequencingRNA-seqSpliced alignmentSplicingTranscriptome assembly

Identifiers

PMID33648554
PMCPMC7919322

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.