Siamese CNV Caller Guide for Breakpoint Detection
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Solution Overview
Problem
Current de novo copy number variation (CNV) callers have limitations such as redundant detection, size limitations, and inaccuracy due to high variability of breakpoint structural patterns, which hinder precise identification of CNVs in genomic data, impacting precision medicine and targeted drug development.
Innovation Solution
A targeted CNV caller guide model using deep learning techniques, specifically a Siamese CNV caller guide, is trained to predict CNV breakpoints by processing genomic sequences, leveraging homology between start and end point regions, and utilizing a triplet loss function to determine similarity between anchor and candidate sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If de novo CNV callers are used to detect CNV breakpoints, then CNV detection can be performed, but redundant detection and inaccuracy occur due to high variability of breakpoint structural patterns
Solution Approach 1:
The patent applies preliminary action by pre-training a deep neural network model on known CNV breakpoint patterns before actual detection. The model is trained in advance on training data containing CNV breakpoints, enabling it to recognize patterns and make accurate predictions during inference, thereby resolving the accuracy-reliability contradiction through prior knowledge acquisition
Solution Approach 2:
The patent uses copying by creating a computational model that replicates the patterns of CNV breakpoints from training data. The deep neural network learns and copies the structural patterns of breakpoints, allowing consistent and accurate detection across different samples by reproducing the learned patterns rather than relying on variable de novo detection algorithms
2Productivity
If de novo CNV callers are used, then CNV detection is possible, but size limitations prevent comprehensive analysis of CNV regions
Solution Approach 1:
The patent applies segmentation by dividing the CNV detection task into distinct components: a first deep neural network for predicting CNV breakpoints and a second deep neural network for calling CNVs. This segmentation allows each component to specialize in specific aspects of the detection process, improving overall productivity while managing complexity through modular architecture
Solution Approach 2:
The patent introduces an intermediary element - the trained deep neural network model that serves as a guide between raw genomic data and CNV detection results. This intermediary processes the data through learned patterns, enabling comprehensive analysis without requiring overly complex de novo callers, thus resolving the productivity-complexity contradiction
3Measurement precision
If traditional CNV detection methods are used, then detection can be performed, but inaccuracy results from high variability of breakpoint structural patterns
Solution Approach 1:
The patent applies parameter changes by transforming the detection approach from traditional algorithmic methods to a data-driven deep learning model. The model learns optimal parameters and patterns from training data, adapting to the high variability of breakpoint structural patterns through parameter optimization during training, thereby achieving high accuracy while maintaining flexibility through the learned representations
Data Source
AI summary
A method of processing sequence data comprising a known location of the start of a copy number variant breakpoint to generate a prediction for the location of the end of the copy number variant breakpoint. The method comprises an encoder and a copy number variation (CNV) caller guide. The encoder processes an anchor sequence and corresponding subject candidate sequence to generate a learned representation of the anchor sequence and a learned representation of the corresponding subject candidate sequence. The CNV caller guide determines a similarity between the learned representation of the anchor sequence and a learned representation of the corresponding subject candidate sequence. Similarity between anchor sequence and subject candidate sequence is used as a proxy for likelihood that the end of the CNV breakpoint is located on the subject candidate sequence.