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Biophysical Journal 84:967-976 (2003)
© 2003 The Biophysical Society

Highly Accurate Classification of Watson-Crick Basepairs on Termini of Single DNA Molecules

Stephen Winters-Hilt*,{dagger},§, Wenonah Vercoutere{ddagger}, Veronica S. DeGuzman*,{ddagger}, David Deamer{ddagger}, Mark Akeson*,§ and David Haussler*,{dagger},§

* Center for Biomolecular Science and Engineering, University of California, Santa Cruz, California 95064; {dagger} Computer Science Department, University of California, Santa Cruz, California 95064; {ddagger} Department of Chemistry and Biochemistry, University of California, Santa Cruz, California 95064; and § Howard Hughes Medical Institute, University of California, Santa Cruz, California 95064

Correspondence: Address reprint requests to Stephen Winters-Hilt, 209 Koshland Way, Santa Cruz, CA 95064. Tel.: 831-420-1395; E-mail: winters{at}cse.ucsc.edu or haussler{at}cse.ucsc.edu.

We introduce a computational method for classification of individual DNA molecules measured by an {alpha}-hemolysin channel detector. We show classification with better than 99% accuracy for DNA hairpin molecules that differ only in their terminal Watson-Crick basepairs. Signal classification was done in silico to establish performance metrics (i.e., where train and test data were of known type, via single-species data files). It was then performed in solution to assay real mixtures of DNA hairpins. Hidden Markov Models (HMMs) were used with Expectation/Maximization for denoising and for associating a feature vector with the ionic current blockade of the DNA molecule. Support Vector Machines (SVMs) were used as discriminators, and were the focus of off-line training. A multiclass SVM architecture was designed to place less discriminatory load on weaker discriminators, and novel SVM kernels were used to boost discrimination strength. The tuning on HMMs and SVMs enabled biophysical analysis of the captured molecule states and state transitions; structure revealed in the biophysical analysis was used for better feature selection.




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