Optimal structure for automatic processing of DNA sequences

The faithful recovery of the base sequence in automatic DeoxyriboNucleic Ac id (DNA) sequencing fundamentally depends on the underlying statistics of t he DNA electrophoresis time series, Current DNA sequencing algorithms are h euristic in, nature and modest in their use of statistical information, In this paper, a Formal statistical model of the DNA time series is presented and then used to construct the optimal maximum-likelihood (MZ) processor. The DNA-ML algorithm that is derived in this paper features Kalman predicti on of peak locations, peak parameter estimation, whitened waveform comparis on and multiple hypothesis processing using the M-algorithm, Properties of the algorithm are examined using both simulated and real data. Model parame ters of critical importance and their impact on different types of error me chanisms, such as insertions and deletions, are pointed out, The statistica l model of the DNA time-series and the structure of the DNA-ML algorithm pr ovides a basis for future investigation and refinement of DNA sequencing te chniques.