Flow Cell Cluster Center Detection With Sub-Pixel Base Calling
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Solution Overview
Problem
Existing image analysis algorithms for DNA sequencing struggle with accurately identifying cluster centers in flow cells, leading to improper sequence identification and increased processing time due to precision issues and registration problems.
Innovation Solution
Implement computational methods to improve image resolution beyond physical limits by identifying candidate cluster centers at a sub-pixel level, determining purities, and using dedicated processors and FPGAs for real-time processing to enhance cluster center detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image analysis algorithms are used to identify cluster centers, then the system can operate with standard imaging hardware, but the accuracy of cluster center identification deteriorates due to physical resolution limits and registration problems
Solution Approach 1:
The patent transitions from pixel-level (2D discrete) analysis to sub-pixel continuous coordinate analysis by introducing floating-point coordinates and intensity interpolation. This dimensional transformation allows cluster centers to be located with precision beyond the physical pixel grid, effectively resolving the resolution limit without requiring higher-resolution hardware.
Solution Approach 2:
The patent replaces traditional image processing algorithms with a physics-based intensity interpolation model. Instead of using complex computational algorithms to locate cluster centers, the system uses continuous coordinate mathematics and intensity distribution modeling to achieve sub-pixel precision, substituting mechanical/image processing complexity with mathematical modeling.
2Productivity
If traditional image processing methods are used, then processing hardware can be simplified, but processing time increases and productivity decreases
Solution Approach 1:
The patent extracts only the essential information needed for cluster center identification by using intensity values at specific coordinate points rather than processing entire images or complex feature sets. This extraction approach reduces computational load while maintaining accuracy, enabling faster processing.
Solution Approach 2:
The patent performs preliminary coordinate determination in the first flow cycle and reuses these coordinates in subsequent cycles, avoiding repeated full-image analysis. This preliminary action significantly reduces processing time for multi-cycle sequencing while maintaining identification accuracy through coordinate registration.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately identifies more cluster centers with reduced processing time and cost, improving base-calling accuracy and efficiency in DNA sequencing systems.
Implementation Method 1
the fluorescent signal for any one fragment is amplified by the signal from its cloned counterparts, such that the fluorescence for a cluster may be recorded by an imager
Implementation Method 2
capturing flow cell images after each flow cycle
Data Source
AI summary
Methods and systems for image analysis are provided, and in particular for identifying a set of base-calling locations in a flow cell for DNA sequencing. These include capturing flow cell images after each sequencing step performed on the flow cell, and identifying candidate cluster centers in at least one of the flow cell images. Intensities are determined for each candidate cluster center in a set of flow cell images. Purities are determined for each candidate cluster center based on the intensities. Each candidate cluster center with a purity greater than the purity of the surrounding candidate cluster centers within a distance threshold is added to a template set of base-calling locations.