Detecting and Filtering Clusters Based on Artificial Intelligence-Predicted Base Calls

A system for improving accuracy and efficiency of neural network-based base calling, the system comprising:

• memory storing, for a plurality of clusters, initial cluster data for initial sequencing cycles of a sequencing run and remainder cluster data for remainder sequencing cycles of the sequencing run;
• a host processor having access to the memory and configured to execute a detection and filtering logic to identify unreliable clusters;
• a configurable processor having access to the memory and configured to execute a neural network to produce base call classification scores;
• and
• a data flow logic having access to the memory, the host processor, and the configurable processor and configured
• to provide the initial cluster data to the neural network and cause the neural network to produce initial base call classification scores for the plurality of clusters and for the initial sequencing cycles based on generating initial intermediate representations from the initial cluster data,
• to provide the initial base call classification scores to the detection and filtering logic and cause the detection and filtering logic to identify unreliable clusters in the plurality of clusters based on generating filter values from the initial base call classification scores,
• to provide the remainder cluster data to the neural network and cause the neural network to generate remainder intermediate representations from the remainder cluster data, and
• to provide data identifying the unreliable clusters to the configurable processor and cause the configurable processor to generate reliable remainder intermediate representations by removing, from the remainder intermediate representations, those portions that result from portions of the remainder cluster data that represent the unreliable clusters.