HMM Segmentation for Single Molecule Sequencing Signals
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Solution Overview
Problem
Current DNA and protein sequencing methods, such as nanopore DNA sequencing, face challenges in effectively segmenting and labeling signals that lack noticeably different signal levels, particularly when dealing with complex noise profiles and spiky noise, as they are inadequate for sequencing molecules beyond short fragments and are limited in processing longer sequences.
Innovation Solution
The use of a Hidden Markov Model (HMM) is employed to segment and label signals generated by molecular detection, by fitting the model based on signal characteristics and applying it to unknown signals to identify and label events, thereby overcoming the limitations of traditional methods in handling complex noise and varying signal levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional segmentation methods are used for signals with similar signal levels, then the method is simple to implement, but the segmentation accuracy deteriorates due to inability to distinguish events with comparable signal levels
Solution Approach 1:
The patent transforms the segmentation problem from direct signal level comparison to a probabilistic state transition problem by changing the parameter space. Instead of comparing raw signal levels, the HMM models signal characteristics through probability distributions of states, allowing accurate distinction of events with similar signal levels by analyzing temporal patterns and state transitions rather than absolute signal magnitudes
Solution Approach 2:
The Hidden Markov Model serves as an intermediary layer between the raw signal and the segmentation decision. The HMM introduces hidden states that mediate the relationship between observed signals and event boundaries, enabling the system to infer event segments through probabilistic state sequences rather than direct signal comparison, thus improving accuracy for signals with similar levels
2Reliability
If traditional methods are applied to signals with complex noise profiles, then the processing is computationally simple, but the reliability of event identification deteriorates due to spiky noise and correlated non-Gaussian characteristics
Solution Approach 1:
The patent changes the modeling approach from assuming simple noise characteristics to explicitly modeling complex noise profiles through the HMM framework. The model incorporates correlated non-Gaussian noise by defining state transition probabilities and emission distributions that capture the temporal correlations and non-Gaussian characteristics of the noise, thereby improving event identification reliability in noisy environments
Solution Approach 2:
The HMM implementation includes feedback mechanisms where the model continuously updates state probabilities based on observed signals and previous state sequences. This feedback loop allows the system to adapt to complex noise patterns by refining its state estimates iteratively, improving the reliability of event identification even when signals are obscured by spiky noise and correlated non-Gaussian characteristics
3Length of moving object
If conventional sequencing methods are used for longer molecular sequences, then the process remains straightforward, but the capability to sequence beyond short fragments deteriorates due to signal degradation over length
Solution Approach 1:
The patent applies segmentation by dividing long molecular sequences into discrete event segments through HMM-based detection. Each event represents a distinguishable unit within the longer sequence, and the HMM models the transitions between these events. This segmentation approach enables the system to process and accurately identify individual events within long sequences by breaking down the continuous signal into manageable, distinguishable segments, thereby extending sequencing capability beyond short fragments
Solution Approach 2:
The system performs preliminary action by training the HMM model on characteristic patterns before applying it to long sequence analysis. The model learns expected signal patterns, state transitions, and event characteristics in advance, enabling it to maintain discrimination precision throughout long sequences by continuously referencing these pre-established patterns rather than relying on absolute signal level comparisons that degrade over length
Data Source
AI summary
Systems and methods are disclosed for performing segmentation and labeling of signals generated by single molecule sequencing. In certain embodiments, a method may comprise receiving a training signal generated by molecular detection, segmenting the training signal into a set of events, determining signal characteristics for the set of events, generating a Hidden Markov Model (HMM) based on the set of events and the signal characteristics. The HMM may also be applied to a second signal and may responsively segment the second signal into a second set of events and label the second set of events based on the signal characteristics. A labeled sequence signal output may be provided that includes the second set of events and corresponding labels generated by the HMM.