Abstract
Accurate identification of a DNA sequence depends on the ability to precisely track the time varying signal baseline in all parts of the electrophoretic trace. We propose a statistical learning formulation of the signal background estimation problem that can be solved using an Expectation-Maximization type algorithm. We also present an alternative method for estimating the background level of a signal in small size windows based on a recursive histogram computation. Both background estimation algorithms introduced here can be combined with regression methods in order to track slow and fast baseline changes occurring in different regions of a DNA chromatogram. Accurate baseline tracking improves cluster separation and thus contributes to the reduction in classification errors when the Bayesian EM (BEM) base-calling system, developed in our group (Pereira et al., Discrete Applied Mathematics, 2000), is employed to decide how many bases are “hidden” in every base-call event pattern extracted from the chromatogram.
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Andrade, L., Manolakos, E.S. Signal Background Estimation and Baseline Correction Algorithms for Accurate DNA Sequencing. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 35, 229–243 (2003). https://doi.org/10.1023/B:VLSI.0000003022.86639.1f
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DOI: https://doi.org/10.1023/B:VLSI.0000003022.86639.1f