Long-TUC-seq is a robust method for quantification of metabolically labeled full-length isoforms
Rahmanian, S.; Balderrama-Gutierrez, G.; Wyman, D.; McGill, C. J.; Nguyen, K.; Spitale, R.; Mortazavi, A.
Show abstract
The steady state expression of each gene is the result of a dynamic transcription and degradation of that gene. While regular RNA-seq methods only measure steady state expression levels, RNA-seq of metabolically labeled RNA identifies transcripts that were transcribed during the window of metabolic labeling. Whereas short-read RNA sequencing can identify metabolically labeled RNA at the gene level, long-read sequencing provides much better resolution of isoform-level transcription. Here we combine thiouridine-to-cytosine conversion (TUC) with PacBio long-read sequencing to study the dynamics of mRNA transcription in the GM12878 cell line. We show that using long-TUC-seq, we can detect metabolically labeled mRNA of distinct isoforms more reliably than using short reads. Long-TUC-seq holds the promise of capturing isoform dynamics robustly and without the need for enrichment.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Illumina But With Nanopore: Sequencing Illumina libraries at high accuracy on the ONT MinION using R2C2 95%
- Simple and efficient measurement of transcription initiation and transcript levels with STRIPE-seq 95%
- DNA-m6A calling and integrated long-read epigenetic and genetic analysis with fibertools 95%
Similar papers in this journal
- Cas13d-mediated isoform-specific RNA knockdown with a unified computational and experimental toolbox 96%
- Semi-quantitative detection of pseudouridine modifications and type I/II hypermodifications in human mRNAs using direct and long-read sequencing 95%
- High throughput, error corrected Nanopore single cell transcriptome sequencing 95%
Similar papers in this journal
- RATTLE: Reference-free reconstruction and quantification of transcriptomes from Nanopore sequencing 95%
- MAAPER: model-based analysis of alternative polyadenylation using 3' end-linked reads 95%
- Two-pass alignment using machine-learning-filtered splice junctions increases the accuracy of intron detection in long-read RNA sequencing 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.