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

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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)741-750
Number of pages10
JournalNature Biotechnology
Issue number5
StatePublished - May 2022

All Science Journal Classification (ASJC) codes

  • Applied Microbiology and Biotechnology
  • Bioengineering
  • Molecular Medicine
  • Biotechnology
  • Biomedical Engineering


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