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

doi:10.1186/s13059-021-02296-0

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

Matthew T. Parker (Lead / Corresponding author), Katarzyna Knop, Geoffrey J. Barton, Gordon G. Simpson (Lead / Corresponding author)

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

274 Downloads (Pure)

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.

Original language	English
Article number	72
Number of pages	24
Journal	Genome Biology
Volume	22
Issue number	1
DOIs	https://doi.org/10.1186/s13059-021-02296-0
Publication status	Published - 1 Mar 2021

Keywords

splicing
long read sequencing
spliced alignment
RNA-seq
gene expression
transcriptome assembly
machine learning
nanopore
Splicing
Long-read sequencing
Gene expression
Transcriptome assembly
Nanopore sequencing
Machine learning
Spliced alignment

ASJC Scopus subject areas

Genetics
Ecology, Evolution, Behavior and Systematics
Cell Biology

Access to Document

10.1186/s13059-021-02296-0Licence: CC BY

Final Published VersionFinal published version, 2.14 MBLicence: CC BY

Cite this

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title = "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.",

keywords = "splicing, long read sequencing, spliced alignment, RNA-seq, gene expression, transcriptome assembly, machine learning, nanopore, Splicing, Long-read sequencing, Gene expression, Transcriptome assembly, Nanopore sequencing, Machine learning, Spliced alignment",

author = "Parker, {Matthew T.} and Katarzyna Knop and Barton, {Geoffrey J.} and Simpson, {Gordon G.}",

note = "This work was funded by the University of Dundee Global Challenges Research Fund, a H2020 Marie Sk{\l}odowska-Curie Actions (799300) award to Katarzyna Knop, and BBSRC awards BB/M010066/1, BB/J00247X/1 and BB/M004155/1.",

year = "2021",

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doi = "10.1186/s13059-021-02296-0",

language = "English",

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T2 - two-pass alignment using machine-learning-filtered splice junctions increases the accuracy of intron detection in long-read RNA sequencing

AU - Parker, Matthew T.

AU - Knop, Katarzyna

AU - Barton, Geoffrey J.

AU - Simpson, Gordon G.

N1 - This work was funded by the University of Dundee Global Challenges Research Fund, a H2020 Marie Skłodowska-Curie Actions (799300) award to Katarzyna Knop, and BBSRC awards BB/M010066/1, BB/J00247X/1 and BB/M004155/1.

PY - 2021/3/1

Y1 - 2021/3/1

N2 - 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.

AB - 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.

KW - splicing

KW - long read sequencing

KW - spliced alignment

KW - RNA-seq

KW - gene expression

KW - transcriptome assembly

KW - machine learning

KW - nanopore

KW - Splicing

KW - Long-read sequencing

KW - Gene expression

KW - Transcriptome assembly

KW - Nanopore sequencing

KW - Machine learning

KW - Spliced alignment

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U2 - 10.1186/s13059-021-02296-0

DO - 10.1186/s13059-021-02296-0

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C2 - 33648554

SN - 1474-7596

VL - 22

JO - Genome Biology

JF - Genome Biology

IS - 1

M1 - 72

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

A Newly Discovered Role for mRNA Methylation in Controlling Plant Gene Expression (PLANT-RNA-MET)

Diversifying Transcription Termination Function

The Arabidopsis Epitranscriptome (Joint with University of Nottingham)

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

Nanopore direct RNA sequencing of the Arabidopsis RNA exosome mutant hen2-2

Cite this

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

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Projects

A Newly Discovered Role for mRNA Methylation in Controlling Plant Gene Expression (PLANT-RNA-MET)

Diversifying Transcription Termination Function

The Arabidopsis Epitranscriptome (Joint with University of Nottingham)

Research output

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

Datasets

Nanopore direct RNA sequencing of the Arabidopsis RNA exosome mutant hen2-2

Cite this