Accurate estimation of the signal baseline in DNA chromatograms

Estimating accurately the varying baseline level in different parts of a DNA chromatogram is a challenging and important problem for accurate base-calling. We are formulating the problem in a statistical learning framework and propose an Expectation-Maximization algorithm for its solution. In addition we also present a faster, iterative histogram based method for estimating the background of the signal in small size windows. The two methods can be combined with regression techniques to correct the baseline in all regions of the chromatogram and are shown to work well even in areas of low SNR. By improving the separation of clusters, baseline correction actions reduce the classification errors when using the BEM base-caller developed in our group.