Understanding Data Noise and Uncertainty through Analysis of Replicate Samples in DNA-Encoded Library Selection

By analyzing data sets of replicate
DNA-Encoded Library (DEL) selections,
an approach for estimating the noise level of the experiment has been
developed. Using a logarithm transformation of the number of counts
associated with each compound and a subset of compounds with the highest
number of counts, it is possible to assess the quality of the data
through normalizing the replicates and use this same data to estimate
the noise in the experiment. The noise level is seen to be dependent
on sequencing depth as well as specific selection conditions. The
noise estimation is independent of any cutoff used to remove low frequency
compounds from the data analysis. The removal of compounds with only
1–5 read counts greatly reduces some of the challenges encountered
in DEL data analysis as it can reduce the data set by greater than
100-fold without impacting the interpretation of the results.