A reference guide to rRNA depletion for RNA-seq: what it does, how it compares to poly-A selection, and how riboLASSO®'s mechanism differs from probe- and enzyme-based kits.
Ribosomal RNA (rRNA) makes up roughly 80–90% of total RNA in a typical cell, even though it carries almost none of the information most RNA-seq experiments are actually looking for. Ribosomal RNA depletion is the process of selectively removing that rRNA from a total RNA sample before library preparation, so sequencing reads go toward the transcripts you actually want to study, mRNA, long non-coding RNA, and other regulatory RNA, instead of ribosomal sequence you'll never analyze.
Because rRNA dominates total RNA by such a wide margin, skipping depletion has a direct, quantifiable cost in usable sequencing depth. The table below illustrates the relationship for a typical eukaryotic sample, where rRNA makes up roughly 90% of total RNA:
| Depletion efficiency | Approx. share of reads that are informative (non-rRNA) |
|---|---|
| None (unprocessed total RNA) | ~10% |
| 85% | ~40% |
| 95% | ~70% |
| 99% | ~90% |
Illustrative figures based on a typical ~90% rRNA share of total RNA; actual results depend on your sample, organism, and RNA quality.
In practice, that means a library built from undepleted total RNA spends the large majority of its sequencing budget re-reading ribosomal sequence you'll discard anyway, before a single informative read is generated. Even a modest improvement in depletion efficiency translates into a large gain in usable data per sequencing run.
Poly-A selection enriches for mRNA by capturing its poly-A tail. It's fast and specific for well-preserved eukaryotic mRNA, but it discards anything without an intact poly-A tail, including many long non-coding RNAs, most bacterial RNA, and degraded or fragmented mRNA that has lost its 3' end. rRNA depletion works directly on the unwanted rRNA itself, regardless of polyadenylation status, so it captures the full transcriptome, including non-poly-A RNA, and tends to hold up better on degraded or low-input samples where poly-A selection struggles.
Most established rRNA depletion kits use one of two mechanisms:
Both work well on intact, well-annotated samples, but performance drops on degraded RNA, low-input samples, or organisms whose rRNA sequences aren't fully characterized, since probe panels and enzymatic efficiency both depend on well-behaved, intact hybridization targets.
riboLASSO® uses a different mechanism: sequence-specific DNA crosslinkers drive a synthetic polymer network to selectively engulf rRNA targets through a phase-change reaction, forming a separable pellet recovered by centrifugation. It doesn't rely on magnetic beads or on RNase H or DNase digestion, so performance is more consistent across degraded or low-input samples than depletion methods that depend on enzymatic efficiency.
See the full riboLASSO® kit details →
riboLASSO® is currently configured for human total RNA. If you're working with another organism, or need to capture or deplete something other than rRNA entirely, DNA, RNA, or protein, LASSOflex lets you swap in a custom catcher strand for your own target sequence on the same LASSO platform. Tell us about your sample and target through the pilot program, and we'll help you scope the right approach.
No, they're alternative strategies for the same underlying problem, not complementary steps. Poly-A selection enriches for polyadenylated mRNA; rRNA depletion removes rRNA directly, regardless of poly-A status, so it also captures non-polyadenylated RNA that poly-A selection would discard. Combining both is uncommon and rarely necessary.
The current riboLASSO kit is pre-configured for human total RNA. For other organisms, LASSOflex lets you design a custom catcher strand for a different rRNA sequence; use the pilot program to scope your sample and target.
No. riboLASSO is bead-free and enzyme-free. It uses DNA-crosslinked polymer capture that forms a separable pellet, recovered by centrifugation, rather than probe hybridization with magnetic bead pulldown or RNase H digestion.
It depends on starting rRNA content and depletion efficiency, but the effect is large: since rRNA typically makes up the large majority of total RNA, even modest improvements in depletion efficiency meaningfully increase the share of sequencing reads that are actually informative.