Ribocutter: Cas9-mediated rRNA depletion from multiplexed riboseq libraries
Wilkins, O. G.; Ule, J.
Show abstract
RNA sequencing libraries produced from fragmented RNA, especially Ribo-seq libraries, contain high proportions of reads from abundant non-coding RNAs. Here, we describe a streamlined Cas9-based protocol for removing abundant rRNA/ncRNA contaminants from Ribo-seq (or other small RNA-seq) libraries and an easy-to-use software tool, ribocutter, for designing ready-to-order sgRNA templates. Following sgRNA template design, the pool of templates is in vitro transcribed using a 1-step commercial kit, which produces enough sgRNAs for multiple treatments. A single multiplexed sequencing library is then treated with Cas9/sgRNAs, followed by a short PCR program, which can increase the fraction of useful reads by more than 3-fold. Comparison of samples before and after depletion demonstrates that Cas9 produces minimal off-target effects and preserves key features (eg. footprint length, periodicity) of Ribo-seq libraries. The method is thus highly effective, costs <{pound}0.50 per sample, and minimises non-specific depletion and technical variation between samples.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Nuclease-mediated depletion biases in ribosome footprint profiling libraries 97%
- Synthesis of modified nucleotide polymers by the poly(U) polymerase Cid1: Application to direct RNA sequencing on nanopores. 96%
- Improved precision, sensitivity, and adaptability of Ordered Two-Template Relay cDNA library preparation for RNA sequencing 96%
Similar papers in this journal
Similar papers in this journal
- Cas10 residues lining the target RNA binding channel regulate interference by distinguishing cognate target RNA from mismatched targets 93%
- Identification of RNA 3' ends and termination sites in Haloferax volcanii 93%
- rG4-seeker enables high-confidence identification of novel and non-canonical rG4 motifs from rG4-seq experiments 92%
"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.