Abstract
Pattern recognition-informed (PRI) feedback using channel current cheminformatics (CCC) software is shown to be possible in “real-time” experimental efforts. The accuracy of the PRI classification is shown to inherit the high accuracy of our offline classifier: 99.9% accuracy in distinguishing between terminal base pairs of two DNA hairpins. The pattern recognition software consists of hidden Markov model (HMM) feature extraction software, and support vector machine (SVM) classification/ clustering software that is optimized for data acquired on a nanopore channel detection system. For general nanopore detection, the distributed HMM and SVM processing used here provides a processing speedup that allows pattern recognition to complete within the time frame of the signal acquisition – where the sampling is halted if the blockade signal is identified as not in the desired subset of events (or once recognized as nondiagnostic in general). We demonstrate that Nanopore Detection with PRI offers significant advantage when applied to data acquisition on antibody-antigen system, or other complex biomolecular mixtures, due to the reduction in wasted observation time on eventually rejected “junk” (nondiagnostic) signals.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vercoutere W, Winters-Hilt S, Akeson M et al (2001) Rapid discrimination among individual DNA hairpin molecules at single-nucleotide resolution using an ion channel. Nat. Biotechnol. 19, 3, 248–252.
Winters-Hilt S, Vercoutere W, DeGuzman VS, Deamer DW, Akeson M, Haussler D (2003) Highly accurate classification of Watson–Crick basepairs on termini of single DNA molecules. Biophys. J. 84, 967–976.
Winters-Hilt S (2007) The alpha-hemolysin nanopore transduction detector: single-molecule binding studies and immunological screening of antibodies and aptamers. BMC Bioinformatics 8, Suppl 7, S9.
Durbin R (1998) Biological sequence analysis: probalistic models of proteins and nucleic acids. Cambridge, UK/New York: Cambridge University Press, xi, 356 p.
Winters-Hilt S (2006) Hidden Markov model variants and their application. BMC Bioinformatics 7, Suppl 2, S14.
Winters-Hilt S, Baribault C (2007) A novel, fast, HMM-with-duration implementation – for application with a new, pattern recognition informed, nanopore detector. BMC Bioinformatics 8, Suppl 7, S19.
Vapik V (1995) The nature of statistical learning theory. New York: Springer-Verlag.
Platt JC (1998) Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, Microsoft Research, Technical Report MSR-TR-98-14.
Winters-Hilt S, Yelundur A, McChesney C, Landry M(2006) SVM clustering. BMC Bioinformatics 7, S2–S4.
Winters-Hilt S and S Merat (2007) SVM clustering. BMC Bioinformatics 8, Suppl 7, S18.
Churbanov A, Winters-Hilt S (2008) Implementing EM and Viterbi algorithms for hidden Markov model in linear memory. BMC Bioinformatics 9, 228.
Winters-Hilt, S. Nanopore transduction analysis of biotin-streptavidin binding. Submitted to BMC Biotechnology.
Vercoutere W, Winters-Hilt D, Akeson M et al (2003) Discrimination among individual Watson–Crick base-pairs at the termini of single DNA hairpin molecules. Nucleic Acids Res. 31, 1311–1318.
Winters-Hilt S (2006) Nanopore detector based analysis of single-molecule conformational kinetics and binding interactions. BMC Bioinformatics 7, Suppl 2, S21.
Acknowledgments
Federal funding was provided by NIH K-22 (SWH PI, 5K22LM008794). State funding was provided from a LaBOR Enhancement (SWH PI).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this paper
Cite this paper
Eren, A.M., Amin, I., Alba, A., Morales, E., Stoyanov, A., Winters-Hilt, S. (2010). Pattern Recognition-Informed Feedback for Nanopore Detector Cheminformatics. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_12
Download citation
DOI: https://doi.org/10.1007/978-1-4419-5913-3_12
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-5912-6
Online ISBN: 978-1-4419-5913-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)