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Ringeling, F.R.* ; Chakraborty, S.* ; Vissers, C.* ; Reiman, D.* ; Patel, A.M.* ; Lee, K.H.* ; Hong, A.* ; Park, C.W.* ; Reska, T.* ; Gagneur, J. ; Chang, H.* ; Spletter, M.L.* ; Yoon, K.J.* ; Ming, G.l.* ; Song, H.* ; Canzar, S.*

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

Nat. Biotechnol. 40, 741–750 (2022)

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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.

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