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Nat Biotechnol ; 40(5): 741-750, 2022 05.
Article in English | MEDLINE | ID: mdl-35013600

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 m6A depletion by Mettl14 knockout.


Subject(s)
RNA , Transcriptome , Gene Expression Profiling/methods , High-Throughput Nucleotide Sequencing/methods , Protein Isoforms , RNA-Seq , Sequence Analysis, RNA/methods , Transcriptome/genetics
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