Equalizer Lookup Tables for DNA Base Calling Crosstalk Correction
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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 an equalizer-based approach that generates lookup tables (LUTs) to correct spatial crosstalk by training an equalizer using least square estimation, which adjusts pixel intensities to maximize signal-to-noise ratio and minimize crosstalk interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial crosstalk correction is not applied, then device complexity remains low, but measurement precision deteriorates due to inability to distinguish true light signals from adjacent wells
Solution Approach 1:
The equalizer is trained offline to generate lookup tables (LUTs) before actual sequencing data analysis. This preliminary training phase creates pre-computed correction factors that are then applied during runtime, separating the complex computational work from the actual measurement process and enabling high-speed correction without real-time computational burden
Solution Approach 2:
The patent uses lookup tables (LUTs) that store pre-computed equalizer coefficients for different signal conditions. Instead of performing complex equalization calculations for every data point, the system copies and applies pre-determined correction parameters from the LUTs that match the current signal characteristics, dramatically reducing processing complexity while maintaining measurement precision
2Reliability
If equalizer training is performed comprehensively, then reliability improves through accurate crosstalk correction, but loss of time increases during the training phase
Solution Approach 1:
The equalizer training and LUT generation are performed as a preliminary offline process before actual sequencing operations. This allows comprehensive training to be completed in advance without impacting the time-critical sequencing measurement process, separating the time-consuming training phase from the production phase
Solution Approach 2:
The training process uses synthetic data with known ground truth to achieve sufficient accuracy without requiring exhaustive training on all possible real-world scenarios. This partial action approach achieves adequate reliability for practical purposes while limiting training time
Data Source
AI summary
The technology disclosed relates to equalizer-based intensity correction for base calling. In particular, the technology disclosed relates to accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, selecting a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio, applying the pixel coefficients to intensity values of the pixels in the image to produce an output, and base calling the target cluster based on the output.