bioRxiv · 10.1101/2020.06.24.169102
Optimized design of antisense oligomers for targeted rRNA depletion
Abstract
RNA sequencing (RNA-seq) has become a standard method for quantifying gene expression transcriptome-wide. Although RNA-seq is often paired with polyadenylate (poly(A)) selection to enrich for messenger RNA (mRNA), many applications require alternate approaches to counteract the high proportion of ribosomal RNA (rRNA) in total RNA. Recently, selective rRNA digestion, using RNaseH and antisense DNA oligomers that tile the entire length of target RNAs, has emerged as an alternative to commercial rRNA depletion kits. Here, we present a streamlined, more economical RNaseH-mediated rRNA depletion method with substantially lower up-front costs, using shorter antisense oligos only sparsely tiled along the target RNA, in a digestion reaction of only 5 minutes. We introduce a novel Web tool, Oligo-ASST, that simplifies oligo design to target regions with optimal thermodynamic properties, and additionally can generate compact, common oligo pools that simultaneously target divergent RNAs, e.g. across different species. We demonstrate the efficacy of these strategies by designing oligo sets to deplete rRNA in Xenopus laevis and in zebrafish, which expresses two distinct versions of rRNAs during embryogenesis. The resulting RNA-seq libraries reduce rRNA to <5% of aligned reads, on par with poly(A) selection, and also reveal expression of many non-adenylated RNA species. Oligo-ASST is freely available at https://mtleelab.pitt.edu/oligo to design antisense oligos for any taxon or to target any abundant RNA for depletion. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/169102v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@c05b57org.highwire.dtl.DTLVardef@ace8a3org.highwire.dtl.DTLVardef@1dd25adorg.highwire.dtl.DTLVardef@1d4adeb_HPS_FORMAT_FIGEXP M_FIG C_FIG
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Phelps, W. A., Carlson, A. E., Lee, M. T.. 2020-06-25. Optimized design of antisense oligomers for targeted rRNA depletion. https://doi.org/10.1101/2020.06.24.169102
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