Nanopore Event-Transduction Signal Stabilization for Wide pH Range under Extreme Chaotrope Conditions.

Operation of an α-hemolysin nanopore transduction detector is found to be surprisingly robust over a critical range of pH (6-9), including physiological pH = 7.4 and polymerase chain reaction (PCR) pH = 8.4, and extreme chaotrope concentration, including 5 M urea.

The NTD Nanoscope: potential applications and implementations.

Background: Nanopore transduction detection (NTD) offers prospects for a number of highly sensitive and discriminative applications, including: (i) single nucleotide polymorphism (SNP) detection; (ii) targeted DNA re-sequencing; (iii) protein isoform assaying; and (iv) biosensing via antibody or aptamer coupled molecules. Nanopore event transduction involves single-molecule biophysics, engineered information flows, and nanopore cheminformatics. The NTD Nanoscope has seen limited use in the scientific community, however, due to lack of information about potential applications, and lack of availability for the device itself.

Pattern recognition-informed feedback for nanopore detector cheminformatics.

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.

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 alpha-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.

Hybrid MM/SVM structural sensors for stochastic sequential data.

In this paper we present preliminary results stemming from a novel application of Markov Models and Support Vector Machines to splice site classification of Intron-Exon and Exon-Intron (5' and 3') splice sites. We present the use of Markov based statistical methods, in a log likelihood discriminator framework, to create a non-summed, fixed-length, feature vector for SVM-based classification. We also explore the use of Shannon-entropy based analysis for automated identification of minimal-size models (where smaller models have known information loss according to the specified Shannon entropy representation).

Accumulation of GC donor splice signals in mammals.

Unlabelled: The GT dinucleotide in the first two intron positions is the most conserved element of the U2 donor splice signals. However, in a small fraction of donor sites, GT is replaced by GC. A substantial enrichment of GC in donor sites of alternatively spliced genes has been observed previously in human, nematode and Arabidopsis, suggesting that GC signals are important for regulation of alternative splicing.

Implementing EM and Viterbi algorithms for Hidden Markov Model in linear memory.

Background: The Baum-Welch learning procedure for Hidden Markov Models (HMMs) provides a powerful tool for tailoring HMM topologies to data for use in knowledge discovery and clustering. A linear memory procedure recently proposed by Miklós, I. and Meyer, I.

The alpha-hemolysin nanopore transduction detector - single-molecule binding studies and immunological screening of antibodies and aptamers.

Background: Nanopore detection is based on observations of the ionic current threading a single, highly stable, nanometer-scale channel. The dimensions are such that small biomolecules and biopolymers (like DNA and peptides) can translocate or be captured in the channel. The identities of translocating or captured molecules can often be discerned, one from another, based on their channel blockade "signatures".

Nanopore-based kinetics analysis of individual antibody-channel and antibody-antigen interactions.

Background: The UNO/RIC Nanopore Detector provides a new way to study the binding and conformational changes of individual antibodies. Many critical questions regarding antibody function are still unresolved, questions that can be approached in a new way with the nanopore detector.

Results: We present evidence that different forms of channel blockade can be associated with the same antibody, we associate these different blockades with different orientations of "capture" of an antibody in the detector's nanometer-scale channel.

A novel, fast, HMM-with-Duration implementation - for application with a new, pattern recognition informed, nanopore detector.

Background: Hidden Markov Models (HMMs) provide an excellent means for structure identification and feature extraction on stochastic sequential data. An HMM-with-Duration (HMMwD) is an HMM that can also exactly model the hidden-label length (recurrence) distributions - while the regular HMM will impose a best-fit geometric distribution in its modeling/representation.

Results: A Novel, Fast, HMM-with-Duration (HMMwD) Implementation is presented, and experimental results are shown that demonstrate its performance on two-state synthetic data designed to model Nanopore Detector Data.

SVM clustering.

Background: Support Vector Machines (SVMs) provide a powerful method for classification (supervised learning). Use of SVMs for clustering (unsupervised learning) is now being considered in a number of different ways.

Results: An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes.

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.

Analysis of nanopore detector measurements using Machine-Learning methods, with application to single-molecule kinetic analysis.

Background: A nanopore detector has a nanometer-scale trans-membrane channel across which a potential difference is established, resulting in an ionic current through the channel in the pA-nA range. A distinctive channel current blockade signal is created as individually "captured" DNA molecules interact with the channel and modulate the channel's ionic current. The nanopore detector is sensitive enough that nearly identical DNA molecules can be classified with very high accuracy using machine learning techniques such as Hidden Markov Models (HMMs) and Support Vector Machines (SVMs).

Preliminary nanopore cheminformatics analysis of aptamer-target binding strength.

Background: Aptamers are nucleic acids selected for their ability to bind to molecules of interest and may provide the basis for a whole new class of medicines. If the aptamer is simply a dsDNA molecule with a ssDNA overhang (a "sticky" end) then the segment of ssDNA that complements that overhang provides a known binding target with binding strength adjustable according to length of overhang.

Results: Two bifunctional aptamers are examined using a nanopore detector.

Nanopore current transduction analysis of protein binding to non-terminal and terminal DNA regions: analysis of transcription factor binding, retroviral DNA terminus dynamics, and retroviral integrase-DNA binding.

Background: Synthetic transcription factors (STFs) promise to offer a powerful new therapeutic against Cancer, AIDS, and genetic disease. Currently, 10% of drugs are of this type, including salicylate and tamoxifen. STFs that can appropriately target (and release) their transcription factor binding sites on native genomic DNA provide a means to directly influence cellular mRNA production.

Support vector machine implementations for classification & clustering.

Background: We describe Support Vector Machine (SVM) applications to classification and clustering of channel current data. SVMs are variational-calculus based methods that are constrained to have structural risk minimization (SRM), i.e.

Cheminformatics methods for novel nanopore analysis of HIV DNA termini.

Background: Channel current feature extraction methods, using Hidden Markov Models (HMMs) have been designed for tracking individual-molecule conformational changes. This information is derived from observation of changes in ionic channel current blockade "signal" upon that molecule's interaction with (and occlusion of) a single nanometer-scale channel in a "nanopore detector". In effect, a nanopore detector transduces single molecule events into channel current blockades.

Nanopore Detector based analysis of single-molecule conformational kinetics and binding interactions.

Background: A Nanopore Detector provides a means to transduce single molecule events into observable channel current changes. Nanopore-based detection can report directly, or indirectly, on single molecule kinetics. The nanopore-based detector can directly measure molecular characteristics in terms of the blockade properties of individual molecules--this is possible due to the kinetic information that is embedded in the blockade measurements, where the adsorption-desorption history of the molecule to the surrounding channel, and the configurational changes in the molecule itself, imprint on the ionic flow through the channel.

DNA molecule classification using feature primitives.

Background: We present a novel strategy for classification of DNA molecules using measurements from an alpha-Hemolysin channel detector. The proposed approach provides excellent classification performance for five different DNA hairpins that differ in only one base-pair. For multi-class DNA classification problems, practitioners usually adopt approaches that use decision trees consisting of binary classifiers.

Hidden Markov model variants and their application.

Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way. This approach is based on mutual information, probabilistic measures, hidden Markov models, and other purely statistical inputs. This approach also provides a uniquely common ground for comparative prokaryotic genomics.

Nanopore cheminformatics.

A cheminformatics method is described for classification, and biophysical examination, of individual molecules. A novel molecular detector is used--one based on current blockade measurements through a nanometer-scale ion channel (alpha-hemolysin). Classification results are described for blockades caused by DNA molecules in the alpha-hemolysin nanopore detector, with signal analysis and pattern recognition performed using a combination of methods from bioinformatics and machine learning.