Sequencing Image Intensity Extraction with Spatial Crosstalk Attenuation
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Solution Overview
Problem
Existing DNA sequencing systems face challenges in accurately distinguishing true light signals from wells of interest due to spatial crosstalk between adjacent wells, leading to sequencing errors and reduced base calling accuracy.
Innovation Solution
Implementing a system that uses deep convolutional neural networks to attenuate spatial crosstalk by applying sharpening masks and adaptive techniques to correct intensity data, followed by convolution and interpolation for precise base calling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep convolutional neural networks are used to attenuate spatial crosstalk, then base calling accuracy is improved, but device complexity increases
Solution Approach 1:
The patent introduces sharpening masks as intermediary components that mediate between the raw intensity data and the base calling process. These masks are applied through convolution operations to attenuate spatial crosstalk before the deep convolutional neural network processes the data, thereby reducing the computational burden on the neural network while maintaining improved base calling accuracy
Solution Approach 2:
The patent performs preliminary signal processing by applying sharpening masks and attenuating spatial crosstalk before the main base calling operation. This preliminary action prepares the intensity data in advance, reducing the complexity requirements for subsequent neural network processing and improving overall system efficiency
2Reliability
If sharpening masks and adaptive techniques are applied to correct intensity data, then sequencing errors are reduced, but processing time increases
Solution Approach 1:
The patent applies different sharpening masks tailored to specific spatial regions and cluster characteristics. Instead of using a uniform processing approach, the system adapts the mask parameters locally based on the intensity profile and position of each cluster, thereby reducing sequencing errors in challenging regions while maintaining fast processing in simpler regions
Solution Approach 2:
The patent implements adaptive techniques that dynamically adjust processing parameters based on real-time analysis of intensity data. The system adapts mask coefficients and processing intensity according to the specific characteristics of each sequencing cycle and cluster, optimizing the balance between error correction and processing speed
Data Source
AI summary
The technology disclosed extracts intensities from sequencing images for base calling target clusters and attenuates spatial crosstalk from neighboring clusters. The technology disclosed accesses a particular section from a plurality of sections of an image output by a sensor, the particular section of the image including at least one pixel depicting intensity emission values from a target cluster and neighboring clusters located across the sensor, and convolves the particular section of the image with a corresponding convolution kernel in a plurality of convolution kernels, to generate a feature map comprising a plurality of feature values. The technology disclosed further assigns a corresponding feature value to the target cluster based on feature values in the plurality of feature values adjoining a center of the target cluster, and processes the corresponding feature value assigned to the target cluster, to base call the target cluster.