Robust normalization of DNA chromatograms by regression for improved base-calling

The accuracy of base-calling software depends critically on the data normalization method it is using. A new regression-based method for normalizing electrophoretic DNA traces is presented. It is based on a channel-coupled exponential decay model that can track accurately the trend of dropping peak heights (the so-called signal “skyline”) even in the low SNR region of the trace. We provide the justification and formulation of the regression model as well as the analytical estimation of its parameters. The proposed skyline-based normalization scheme is not affected by the presence of artifacts, compressions, false peaks, etc. and does not depend on the dye chemistry used. We demonstrate that it can improve the interpretation of chromatograms and therefore increase base-calling accuracy when employed in the pre-processing stage of either a pattern classification type base-caller (such as the BEM presented in Pereira et al. (Discrete Appl. Math. 104(1–3) (2000) 229)) or a kernel fitting base-calling method.