Since the early days of RNA-seq there has been a heavy focus on the development of effective methods for the removal of ribosomal RNA (rRNA), which account for 95-98% of the transcriptome, from total RNA samples. Because the area of interest for RNA-seq encompasses the sequencing of mRNAs, small RNAs and lincRNAs, reads spent sequencing rRNA are inefficient and end up driving up the cost of the experiment.
To combat this inefficiency, RNA-seq library construction will involve enrichment away from the rRNA. There are plethora of kit based options available from polyA enrichment to probe based ribosomal RNA removal and RNase H based methods. However, the jury is still out on the best method for ribosomal removal. Several publications have shown extensive side-by-side tests of one method versus another and have declared different winners.
In reality the method of choice primarily depends on the RNA snapshot you are looking for. Experiments looking at non-coding RNA or transcripts in prokaryotes will by default rely on probe based methods for rRNA removal since these RNAs do not contain a polyA tail for oligo-dT based purification. Small RNA-seq normally does not require prior rRNA removal since the library method relies on the specific ligation of adapters to small RNA molecules coupled with a size selection of the small RNA away from the rest of the RNA in the pool.
For eukaryotic samples, polyA selection is one of the simplest ways to enrich for mRNA and gives very low carryover of rRNA. However, this method requires that RNA be of very high quality with intact polyA tails. polyA enrichment also has the unintended effect of bringing along mitochondrial RNAs, many of which are polyadenylated. Some rRNA probe based kits also include probes that target mitochondrial RNA for removal. In situations where RNA amount is very limiting, the sample normally has to go through an RNA amplification procedure that selectively excludes ribosomal RNA.
Another important factor to consider is the sample type and amount. Since probe based rRNA removal methods are sequence specific, the availability of specific probes (or ones close enough in sequence) is a determining factor.
Regardless of the rRNA removal strategy, there will always be some rRNA carryover and a certain percentage of the RNA-seq data will be lost to ribosomal or mitochondrial RNA reads. Depending on the technique used this may range from 3% at the low end all the way up to 35% or higher. Therefore, RNA-seq experiments must always plan for this loss of sequence reads (i.e., you may need more reads than you think). On the analysis side, ribosomal and mitochondrial DNA content will normally have to be filtered out so that differences in rRNA reads across samples do not affect alignment rates and skew subsequent normalization of the data.
At Cofactor, our extensive experience with RNA-seq means that we can provide you with recommendations on experimental aspects like rRNA removal that factor in results from our previous projects. It’s all a part of the Build, Measure, Learn philosophy that drives our R&D, which helps save you money and provides you with higher quality results. If you’re interested in learning more about how Cofactor can advance your research, get in touch.
