ArticleNature biotechnology2022
Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data.
Article in Nature biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- PacBio full-length transcriptome and WGCNA reveal varietal co-expression networks and regulatory dynamics during tea plant (Frontiers in plant science · 2026Article
- Mapping medically relevant RNA isoform diversity in the aged human frontal cortex with deep long-read RNA-seq.Nature biotechnology · 2025Article
- Environmental community transcriptomics: strategies and struggles.Briefings in functional genomics · 2025Review
- Using deep long-read RNAseq in Alzheimer's disease brain to assess medical relevance of RNA isoform diversity.bioRxiv : the preprint server for biology · 2023Article
- Nanopore sequencing unveils the complexity of the cold-activated murine brown adipose tissue transcriptome.iScience · 2023Article
- Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq.Advanced genetics (Hoboken, N.J.) · 2023Review
- Epitranscriptomic regulation of cortical neurogenesis via Mettl8-dependent mitochondrial tRNA mCell stem cell · 2023Article
- ScanExitronLR: characterization and quantification of exitron splicing events in long-read RNA-seq data.Bioinformatics (Oxford, England) · 2022Article
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16 authors.
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Abstract
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
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