Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data

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The quality of RNA sequencing transcriptomes is improved when mRNAs are separated by length. The accuracy of methods for assembling transcripts from short-read RNA sequencing data is limited by the lack of long-range information. Here we introduce Ladder-seq, an approach that separates transcripts according to their lengths before sequencing and uses the additional information to improve the quantification and assembly of transcripts. Using simulated data, we show that a kallisto algorithm extended to process Ladder-seq data quantifies transcripts of complex genes with substantially higher accuracy than conventional kallisto. For reference-based assembly, a tailored scheme based on the StringTie2 algorithm reconstructs a single transcript with 30.8% higher precision than its conventional counterpart and is more than 30% more sensitive for complex genes. For de novo assembly, a similar scheme based on the Trinity algorithm correctly assembles 78% more transcripts than conventional Trinity while improving precision by 78%. In experimental data, Ladder-seq reveals 40% more genes harboring isoform switches compared to conventional RNA sequencing and unveils widespread changes in isoform usage upon m(6)A depletion by Mettl14 knockout.
Publisher
NATURE PORTFOLIO
Issue Date
2022-05
Language
English
Article Type
Article
Citation

NATURE BIOTECHNOLOGY, v.40, no.5, pp.741 - 750

ISSN
1087-0156
DOI
10.1038/s41587-021-01136-7
URI
http://hdl.handle.net/10203/296749
Appears in Collection
BS-Journal Papers(저널논문)
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