Clustering ionic flow blockade toggles with a Mixture of HMMs

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 -Hemolysin transmembrane channel interacts with a translocating molecule in a nontrivial way, frequently evidenced by a complex ionic flow blockade pattern with readily distinguishable modes of toggling. Effective processing of such signals requires developing machine learning methods capable of learning the various blockade modes for classification and knowledge discovery purposes. Here we propose a method aimed to improve our stochastic analysis capabilities to better understand the discriminatory capabilities of the observed the nanopore channel interactions with analyte. Results: We tailored our memory-sparse distributed implementation of a Mixture of Hidden Markov Models (MHMMs) to the problem of channel current blockade clustering and associated analyte classification. By using pr...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Alexander G. Churbanov, Stephen Winters-Hilt
• Keywords: Blockade, BMCBI 2008, Channel Blockade, Current Blockade