Flow Cell Image Lookup Tables for Base Calling Quality Prediction
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Solution Overview
Problem
Existing methods for determining the quality of base calling in DNA sequencing lack accurate predictive measurements of error rates, leading to unreliable quality scores without requiring knowledge of the sequence to be read.
Innovation Solution
A system and method using a look-up table based on selected predictors, such as image quality and polony density, to predict quality scores for base calling, allowing for accurate and efficient quality score estimation before actual base calling occurs, without relying on neural networks or downstream alignment information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks or downstream alignment information are used to determine quality of base calling, then measurement precision may be improved, but device complexity and computational burden increase
Solution Approach 1:
The patent pre-calculates and stores quality score predictions in a look-up table based on image quality metrics and polony density before actual base calling occurs. This preliminary action allows accurate quality prediction without requiring complex neural networks or downstream alignment information during the actual sequencing process, thereby reducing computational complexity while maintaining measurement precision.
Solution Approach 2:
The patent creates a simplified copy of the quality prediction process using a look-up table that stores pre-computed quality scores based on image metrics and polony density. This copying approach replaces the need for complex real-time neural network calculations, reducing device complexity while preserving the ability to accurately predict quality scores without requiring downstream alignment information.
2Productivity
If accurate predictive measurement of quality scores is achieved without downstream alignment information, then productivity is improved, but measurement precision may be compromised
Solution Approach 1:
The patent performs all quality score calculations in advance during a calibration phase, storing the results in a look-up table. During actual sequencing, the system simply queries this pre-computed table using image quality metrics and polony density, eliminating the need for time-consuming real-time calculations or downstream alignment information. This preliminary action maintains both high productivity and measurement precision.
Solution Approach 2:
The patent introduces image quality metrics and polony density as intermediary parameters that mediate between the raw imaging data and the final quality score predictions. These intermediaries capture the essential information needed for accurate quality assessment without requiring direct access to downstream alignment information, thus maintaining measurement precision while enabling efficient real-time prediction.
3Adaptability or versatility
If quality measurement is performed without knowledge of the sequence to be read, then adaptability is improved, but measurement precision may be reduced
Solution Approach 1:
The patent enables the sequencing system to self-assess its own quality metrics by measuring image quality parameters and polony density directly from the imaging process. This self-service approach allows the system to predict quality scores independently of the actual sequence content, maintaining adaptability to any sequence while preserving measurement precision through direct observation of physical imaging characteristics that affect base calling accuracy.
Solution Approach 2:
The patent replaces sequence-dependent computational methods with image-based physical measurements. Instead of relying on knowledge of the DNA sequence to assess quality, the system substitutes mechanical/optical measurements of image quality metrics and polony density as proxies for base calling quality. This substitution maintains sequence-independent adaptability while achieving accurate error rate predictions through physical rather than computational assessment.
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
The system provides sensitive and accurate prediction of quality scores across multiple channels and within a single channel, improving computational efficiency and reducing computational burden, while maintaining high accuracy and reliability in base calling.
Implementation Method 1
As single-stranded DNA fragments from a sequencing library are flooded across a flow cell, the fragments may attach to the surface of the flow cell
Implementation Method 2
the blocked nucleotide may also be fluorescently labeled... During the detection step, the flow cell is exposed to excitation light, exciting the labels and causing them to fluoresce
Data Source
AI summary
Aspects of the present disclosure relate to a method for predicting quality of base calling in sequencing. A plurality of predictors may be selected and a corresponding value for each of the plurality of predictors may be determined from one or more flow cell images. A quality score may be determined from a look-up table based on the corresponding value for each of the plurality of predictors, wherein the look-up table comprises a plurality of dimensions corresponding to the plurality of predictors and is generated based on a training data set.