Flow Cell Image Color Correction for Channel Cross-Talk
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Solution Overview
Problem
Next-generation sequencing technologies face challenges in accurate base calling due to channel cross-talk interference in flow cell images, leading to deteriorated signal intensities and unreliable nucleotide base identification.
Innovation Solution
Implement color correction algorithms that utilize channel cross-talk parameters and histogram analysis to correct flow cell images, enabling accurate and reliable base calling even with unbalanced nucleotide diversity or low plexity samples, and allowing for real-time processing using dedicated processors and FPGAs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If color correction algorithms are applied to correct channel cross-talk interference, then base calling accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent performs color correction calculations in advance during reference cycles before the actual sequencing reads. By pre-calculating and storing color correction parameters (such as transformation matrices or lookup tables) during reference cycles, the system avoids performing complex color correction computations during the actual base calling process, thus improving base calling accuracy while reducing real-time computational complexity
Solution Approach 2:
The patent creates a reference model from reference cycles that captures the channel cross-talk characteristics. This reference model serves as a template that can be applied to subsequent sequencing cycles without reperforming the full color correction analysis. By copying and reusing the reference color correction parameters, the system maintains high base calling accuracy while significantly reducing computational requirements during actual sequencing reads
2Measurement precision
If color correction is performed on all flow cell images, then base calling accuracy improves, but storage requirements and data processing load increase
Solution Approach 1:
The patent extracts and stores only the essential color correction parameters (such as transformation matrices, lookup tables, or correction factors) rather than storing the complete corrected images. By taking out only the necessary correction information and applying it during base calling, the system reduces storage requirements while maintaining the ability to achieve high base calling accuracy across all flow cell images
3Speed
If dedicated processors and FPGAs are used for real-time color correction, then processing speed increases, but device complexity and cost increase
Solution Approach 1:
The patent performs color correction calculations in advance during reference cycles before actual sequencing reads occur. By pre-computing and storing color correction parameters during reference cycles, the system eliminates the need for complex real-time color correction processing during actual base calling, thus achieving fast processing speeds without requiring dedicated processors or FPGAs, thereby reducing device complexity and cost
Data Source
AI summary
Described herein are aspects for color correction of flow cell images acquired from different channels for making accurate base-calling during DNA sequencing. An aspect begins by receiving a plurality of flow cell images and determining coordinates of polonies in the flow cell images in a reference coordinate system. The image intensity of the polonies is then determined. Channel cross-talk parameters are determined based in the image intensity of the polonies. Using the channel cross-talk parameters, the processor generates color-corrected flow cell images.