AI Base Calling for Overlapping Sequencing Clusters
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Solution Overview
Problem
Existing nucleic acid sequencing technologies face limitations in resolving data from closely proximate or spatially overlapping clusters, leading to reduced throughput and increased computational resource requirements for base calling.
Innovation Solution
An artificial intelligence-based method using deep neural networks, specifically convolutional and recurrent neural networks, processes sequencing images to segregate and base call nucleotides by employing spatial and temporal convolution layers to handle misalignment and sequence-specific context, enabling many-to-many base calling for improved resolution and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional base calling methods are used to process sequencing images, then the computational resource requirements increase and processing time increases, but the resolution and quality of sequencing data decrease
Solution Approach 1:
The patent transforms the base calling problem by changing the parameter representation from traditional one-to-one mapping to many-to-many relationships. The neural network learns to associate multiple image patches with multiple base calls, enabling parallel processing that reduces computational time while maintaining or improving sequencing data quality through pattern recognition across multiple sequences simultaneously
2Productivity
If traditional base calling methods are used to process sequencing images, then the computational resource requirements increase, but the throughput decreases
Solution Approach 1:
The patent merges the processing of multiple sequencing images and multiple base calling tasks into a single unified neural network operation. By combining multiple image patches and their corresponding base calls into one training and inference operation, the system achieves higher throughput while managing computational resources more efficiently through shared feature extraction and parameter learning
3Productivity
If deep neural networks with many-to-many base calling are used, then the throughput increases and computational efficiency improves, but the algorithm complexity increases
Solution Approach 1:
The patent segments the complex many-to-many base calling problem into manageable components: image patch extraction, feature encoding through convolutional layers, sequence generation through recurrent layers, and base calling prediction. This segmentation allows the complex algorithm to be implemented as a modular neural network architecture that can be trained end-to-end while maintaining computational efficiency and enabling higher throughput
Data Source
Figure 1A~1B
Figure 1C
Figure 1D
AI summary
The technology disclosed relates to artificial intelligence-based base calling. The technology disclosed relates to accessing a progression of per-cycle analyte channel sets generated for sequencing cycles of a sequencing run, processing, through a neural network-based base caller (NNBC), windows of per-cycle analyte channel sets in the progression for the windows of sequencing cycles of the sequencing run such that the NNBC processes a subject window of per-cycle analyte channel sets in the progression for the subject window of sequencing cycles of the sequencing run and generates provisional base call predictions for three or more sequencing cycles in the subject window of sequencing cycles, from multiple windows in which a particular sequencing cycle appeared at different positions, using the NNBC to generate provisional base call predictions for the particular sequencing cycle, and determining a base call for the particular sequencing cycle based on the plurality of base call predictions.