Noisy Read Signal Framework for Low-Purity HRD Detection
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Solution Overview
Problem
Existing methods for analyzing sequenced reads in low-purity samples, such as those derived from minimally invasive collection methods or degraded sources, lack adaptability and sensitivity, particularly in detecting homologous recombination deficiency (HRD) from noisy datasets.
Innovation Solution
A computational framework, DirectHRD, that employs signal processing heuristics and a multinomial mixture model to classify and score localized structural disruptions, specifically microhomology deletions, in low-tumor purity samples, enhancing the signal-to-noise ratio and enabling accurate HRD detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal processing methods are used on low-purity samples, then the analysis can be performed, but the sensitivity and adaptability are insufficient to detect HRD accurately
Solution Approach 1:
The patent segments the signal processing into distinct functional modules: a detector module that identifies localized structural disruptions, a classifier that assigns signature classes based on features, and a modeler that applies trained models to generate scores. This segmentation allows each module to specialize in specific tasks, improving overall detection sensitivity while managing computational complexity.
Solution Approach 2:
The patent introduces an intermediary classification layer between raw signal detection and final HRD scoring. The classifier assigns signature classes to localized disruptions based on their features, creating an intermediate representation that bridges raw data and interpretive scores. This intermediary step enhances the signal-to-noise ratio by filtering and organizing detected disruptions before final analysis.
2Measurement precision
If signal processing heuristics are applied to exclude localized disruption candidates, then the signal-to-noise ratio increases, but the computational complexity increases
Solution Approach 1:
The patent applies signal processing heuristics as a preliminary filtering step before final HRD scoring. By pre-excluding localized disruption candidates based on relative positioning and other heuristics, the system reduces the number of false positives early in the pipeline. This preliminary action improves the signal-to-noise ratio while the modular architecture manages computational complexity through efficient filtering.
3Measurement precision
If a trained model is applied to classified localized disruptions to generate scores, then HRD detection accuracy improves, but the computational resources required increase
Solution Approach 1:
The patent divides the computational process into segmented modules: detection, classification, and modeling. Each module processes data independently and passes results to the next stage. This segmentation allows the trained model to operate on pre-classified disruptions rather than raw data, reducing the computational burden while maintaining high detection accuracy.
Solution Approach 2:
The patent applies the trained model selectively to classified localized disruptions that meet certain criteria, rather than processing all possible disruptions. This partial application of the model to relevant candidates improves HRD detection accuracy while reducing overall computational resource consumption by avoiding unnecessary processing of low-probability cases.
Data Source
AI summary
The present disclosure relates to a computational framework for detecting localized disruptions in noisy, low-coverage signals. Signature classes may be assigned to detected localized disruptions based on one or more features. A trained model may be applied to classified disruptions in determining associations with target medical conditions.