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

Ringeling, Francisca Rojas; Chakraborty, Shounak; Vissers, Caroline; Reiman, Derek; Patel, Akshay M.; Lee, Ki-Heon; Hong, Ari; Park, Chan-Woo; Reska, Tim; Gagneur, Julien; Chang, Hyeshik; Spletter, Maria L.; Yoon, Ki-Jun; Ming, Guo-li; Song, Hongjun und Canzar, Stefan (2022): Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data. In: Nature Biotechnology, Bd. 40, Nr. 5: S. 741-750

Abstract

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.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Biologie
• Themengebiete: 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften; Biologie
• ISSN: 1087-0156
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 452881907