Paper doi abstract bibtex

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.

@Article{andrade03signal, author = {Andrade, Lucio and Manolakos, Elias S.}, title = {Signal Background Estimation and Baseline Correction Algorithms for Accurate DNA Sequencing}, journal = {Journal of VLSI signal processing systems for signal, image and video technology}, year = {2003}, volume = {35}, number = {3}, pages = {229--243}, issn = {0922-5773}, 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.}, doi = {10.1023/B:VLSI.0000003022.86639.1f}, owner = {Purva}, timestamp = {2016-09-15}, url = {http://dx.doi.org/10.1023/B:VLSI.0000003022.86639.1f}, }

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