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

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Title
Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data
Author(s)
Ringeling, Francisca Rojas; Chakraborty, Shounak; Vissers, Caroline; Reiman, Derek; Patel, Akshay M.; Lee, Ki-Heon; Ari Hong; Park, Chan-Woo; Reska, Tim; Gagneur, Julien; Hyeshik Chang; Spletter, Maria L.; Yoon, Ki-Jun; Ming, Guo-li; Song, Hongjun; Canzar, Stefan
Publication Date
2022-05
Journal
Nature Biotechnology, v.40, no.5, pp.741 - 750
Publisher
Nature Research
Abstract
© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.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 m6A depletion by Mettl14 knockout.
URI
https://pr.ibs.re.kr/handle/8788114/11540
DOI
10.1038/s41587-021-01136-7
ISSN
1087-0156
Appears in Collections:
Center for RNA Research(RNA 연구단) > 1. Journal Papers (저널논문)
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