Artificial Intelligence-Based Many-To-Many Base Calling

An artificial intelligence-based system for base calling, the system comprising:

• a host processor;
• memory accessible by the host processor storing image data for sequencing cycles of a sequencing run, wherein current image data for a current sequencing cycle of the sequencing run depicts intensity emissions of analytes and their surrounding background captured at the current sequencing cycle;
• and
• a configurable processor having access to the memory, the configurable processor including:
• a plurality of execution clusters, the execution clusters in the plurality of execution clusters configured to execute a neural network;
• and
• data flow logic having access to the memory and to the execution clusters in the plurality of execution clusters, configured to provide the current image data, previous image data for one or more previous sequencing cycles preceding the current sequencing cycle, and successive image data for one or more successive sequencing cycles succeeding the current sequencing cycle to available execution clusters in the plurality of execution clusters, cause the execution clusters to apply different groupings of the current image data, the previous image data, and the successive image data to the neural network to produce first, second, and third base call predictions for the current sequencing cycle, and to feedback the first, second, and third base call predictions for the current sequencing cycle to the memory for use in generation of a base call for the current sequencing cycle based on the first, second, and third base call predictions.

An artificial intelligence-based method of base calling, the method including:

• accessing current image data for a current sequencing cycle of a sequencing run, previous image data for one or more previous sequencing cycles preceding the current sequencing cycle, and successive image data for one or more successive sequencing cycles succeeding the current sequencing cycle;
• processing different groupings of the current image data, the previous image data, and the successive image data through a neural network-based base caller and producing a plurality of call predictions for the current sequencing cycle;
• and
• generating a base call for the current sequencing cycle based on the plurality of base call predictions.

An artificial intelligence-based method of base calling, the method including:

• generating respective base calls for a particular sequencing cycle in response to executing respective iterations of a base caller;
• wherein the respective iterations process respective input sets for respective windows of sequences cycles;
• and
• wherein the respective windows of sequencing cycles have the particular sequencing cycle as at least one overlapping cycle, and one or more non-overlapping cycles.