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How We Overcome Process Bias with Neomers

This is the first of several blogs we will share on our new Neomer process. The image above can be compared to a dilemma in traditional aptamer selection: are the aptamers that are enriching the product of the selection process (I would vote for the wine glass for this representation), or the product of bias of the selection process (the faces impinging on the wine glass), and how do we tell sequences apart? 

There are many possible reasons for process bias on aptamer selection, but probably the most clear is the potential for PCR bias. This is evinced in at least two ways in sequencing data, a) aptamers that show high copy number enrichment over selection rounds but little or no binding to the target, and b) aptamers that show high copy number enrichment on both positive and negative selections. The latter observation leads to non-consideration of these aptamers but still it is annoying, why are they doing this? 

It is clear that the more secondary structure in an aptamer the less well it is amplified. This often runs counter to optimum aptamer selection as strong, stable secondary structure is often a defining feature of good aptamers. PCR bias in aptamer selection is manifested as an exponential factor that is multiplied across selection rounds. Binding affinity and specificity is only a linear factor and thus can be easily overcome by PCR bias. 

In our Neomer aptamer discovery method we overcome this problem in two ways. 

One, we can perform as little as a single round of selection, thus differences in PCR efficiency are not accumulated. Two, given that we can start selection with different aliquots of the identical naïve starting library, we can run a negative control selection without target and use this to normalize for all selection effects on individual aptamer frequency, including PCR bias. 

In our next blog we will discuss the statistical basis of Neomer analysis, and how this adds strength to the removal of bias and enables identification of desirable aptamers from the selection data. With the Neomer method we can parse the wine glass and most importantly, the wine within it from artefacts that were part of the process.