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

Abstract

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.

A Machine-learning-based Data Analysis Method for Cell-based Selection of DNA-encoded libraries (DELs)

“…72,84 A "blank" selection was conducted with the beads without CA-2, and it was used as the control to calculate the enrichment level of the compounds. 55,64 Previously, Zhu et al proposed that DEL data noise level was dependent on the sequencing depth and the specific selection conditions. 55 We have conducted three biological replicates for each selection and employed sufficient sequencing depth to minimize the impacts of these factors and variables.…”

“…55,64 Previously, Zhu et al proposed that DEL data noise level was dependent on the sequencing depth and the specific selection conditions. 55 We have conducted three biological replicates for each selection and employed sufficient sequencing depth to minimize the impacts of these factors and variables. The sequencing data under different experimental conditions are summarized in Table 1.…”

“…In contrast, the A and OA datasets showed much lower maximal sequence counts (143~336 for three replicates; Table 1), which are only 1-3 folds greater than the blank control selection (Table 1). Moreover, the ratio of the random sequencing noise (the boundaries of background noise were defined as 85% agreement between the two replicates; 55 Figure 4A) and the maximal counts of the A and OA data sets are much higher than the P dataset. Furthermore, the OA dataset showed higher maximal sequence count than the A dataset, indicating that target overexpression could enhance the signal of the enriched compounds and improve the signal-to-noise ratio.…”

“…In fact, selection data analysis for reliable hit picking is one of the key issues in DEL research, especially for large DELs where the library quality is compromised by the truncated and/or side products during library synthesis. [51][52][53][54][55][56] In the past, many methods have been developed to process noisy DEL selection data. [51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] A commonly used technique is aggregation, which is used to reduce the variability from the relatively small number of sequencing counts.…”

“…[51][52][53][54][55][56] In the past, many methods have been developed to process noisy DEL selection data. [51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] A commonly used technique is aggregation, which is used to reduce the variability from the relatively small number of sequencing counts. 56 Kuai and co-workers proposed a framework for data normalization and enrichment calculation based on the estimation of Poisson confidence interval.…”