RealSeq®-AC is a novel proprietary method for preparing small-RNA sequencing libraries that nearly eliminates incorporation bias in Next-Generation-Sequencing (NSG). Commonly used sequencing library preparations lead to underestimation of the abundance of most miRNAs, some by as much as 10,000-fold.

This under-representation can obscure the presence of some RNAs, including ones that could be useful biomarkers if they were detected accurately. Accurate quantification of microRNAs (miRNA) and other small RNAs is important for understanding their biology and for developing new biomarkers and therapeutic targets.

Most bias stems from sequence-dependent variability in the enzymatic ligation reactions that attach the two adapters to the 3’ and 5’ ends of the miRNAs /small RNAs during preparation of sequencing libraries. By using a novel single adapter and circularization, RealSeq®-AC greatly reduces library preparation bias. Shown below are bias results comparing RealSeq®-AC with a commonly used 2-adapter protocol.

RealSeq®-AC reduces sequencing bias in known problematic miRNAs

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Figure 1: Spectrum of bias assessed using synthetic miRNAs. A commonly used 2-adapter protocol is compared with RealSeq®-AC’s one-adapter protocol. Typically under- or overrepresented miRNAs are assayed and deviation from the optimal "0" value are shown. The RealSeq®-AC method significantly reduces the detection bias.


RealSeq®-AC reduces incorporation bias compared to other small RNA library preparation kits

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Figure 2: Bias Measurements for various small-RNA library preparation kits. All sequencing libraries were prepared in triplicate using 1 pmole of miRXPlore Universal Reference pool (Miltenyi Biotec), which consists of equimolar concentrations of 963 synthetic mature miRNAs. Purified libraries were sequenced on the Illumina MiSeq platform. Trimmed sequencing reads were aligned to a custom miRNA reference (Bowtie2, Langmead et al. 2012). Reads mapping to miRNAs were counted and fold-deviations from the equimolar input were calculated and plotted as log2 values. Measurements of miRNA levels falling within a factor of two of the expected values (between vertical lines) are considered unbiased according to Fuchs et al (2015). The method of adapter attachment to the miRNAs is noted under the graphs.


RealSeq®- workflow is efficient and gel-free

work times

Figure 3: The RealSeq®-AC workflow to sequence-ready libraries


RealSeq® - detects more miRNAs in total RNA than other miRNA library preparation kits

 Kit IKit NKit BKit TRealSeq®-AC
miRNAs with >5 reads 404 452 412 324 500
miRNAs with >10 reads 328 365 352 239 385

Figure 4: RealSeq®-AC detects more miRNAs with over 5 or 10 reads per million, respectively, from total RNA samples compared to other kits for miRNA sequencing library preparation. RealSeq®-AC is optimized for inputs between 1 μg to 100 ng of total RNA. 


The RealSeq® platform is being expanded to include:


low input samples
targeted RNA- seq
highly fragmented, tumor-derived cell-free DNA (cf-DNA).


RealSeq®-AC Technology Schematic:

real seq fig 05

Figure 5: RealSeq®-AC schematic

Order RealSeq®-AC Kit

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