转录组
计算生物学
生物
核糖核酸
RNA序列
基因
基因表达谱
基因表达
小RNA
基因组
作者
Francisca Rojas Ringeling,Shounak Chakraborty,Caroline Vissers,Derek Reiman,Akshay Patel,Ki-Heon Lee,Ari Hong,Chanwoo Park,Tim Reska,Julien Gagneur,Hyeshik Chang,Maria L. Spletter,Ki‐Jun Yoon,Guo Li Ming,Hongjun Song,Stefan Canzar
标识
DOI:10.1038/s41587-021-01136-7
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI