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

Autor: Francisca Rojas Ringeling, Shounak Chakraborty, Caroline Vissers, Derek Reiman, Akshay M. Patel, Ki-Heon Lee, Ari Hong, Chan-Woo Park, Tim Reska, Julien Gagneur, Hyeshik Chang, Maria L. Spletter, Ki-Jun Yoon, Guo-li Ming, Hongjun Song, Stefan Canzar
Rok vydání: 2020
Předmět:
Zdroj: Nature biotechnology. 40(5)
ISSN: 1546-1696
Popis: 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
Databáze: OpenAIRE
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