Duration learning for analysis of nanopore ionic current blockades.
BACKGROUND
Ionic current blockade signal processing, for use in nanopore detection, offers a promising new way to analyze single molecule properties, with potential implications for DNA sequencing. The alpha-Hemolysin transmembrane channel interacts with a translocating molecule in a nontrivial way, frequently evidenced by a complex ionic flow blockade pattern. Typically, recorded current blockade signals have several levels of blockade, with various durations, all obeying a fixed statistical profile for a given molecule. Hidden Markov Model (HMM) based duration learning experiments on artificial two-level Gaussian blockade signals helped us to identify proper modeling framework. We then apply our framework to the real multi-level DNA hairpin blockade signal.


RESULTS
The identified upper level blockade state is observed with durations that are geometrically distributed (consistent with an a physical decay process for remaining in any given state). We show that mixture of convolution chains of geometrically distributed states is better for presenting multimodal long-tailed duration phenomena. Based on learned HMM profiles we are able to classify 9 base-pair DNA hairpins with accuracy up to 99.5% on signals from same-day experiments.


CONCLUSIONS
We have demonstrated several implementations for de novo estimation of duration distribution probability density function with HMM framework and applied our model topology to the real data. The proposed design could be handy in molecular analysis based on nanopore current blockade signal.
Churbanov
Alexander
A
The Research Institute for Children, 200 Henry Clay Ave, New Orleans, LA 70118, USA. atchourb@cs.uno.edu
Baribault
Carl
C
Winters-Hilt
Stephen
S
D000465:Algorithms; D052157:Aptamers, Nucleotide / Q000032:analysis* / Q000276:immunology; D001185:Artificial Intelligence; D001427:Bacterial Toxins / Q000737:chemistry*; D015374:Biosensing Techniques / Q000295:instrumentation*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006460:Hemolysin Proteins / Q000737:chemistry*; D015640:Ion Channel Gating; D007700:Kinetics; D008564:Membrane Potentials; D036103:Nanotechnology / Q000295:instrumentation* / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D016062:Porosity; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014159:Transducers
D052157:Aptamers, Nucleotide; D001427:Bacterial Toxins; D006460:Hemolysin Proteins; C021091:staphylococcal alpha-toxin