Signal Background Estimation and Baseline Correction Algorithms for Accurate DNA Sequencing. Andrade, L. & Manolakos, E. S. Journal of VLSI signal processing systems for signal, image and video technology, 35(3):229–243, 2003.
Signal Background Estimation and Baseline Correction Algorithms for Accurate DNA Sequencing [link]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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