Basecaller, called Ravvent, using joint raw and event data sequence-to-sequence processing. Incorporating an encoder-decoder architecture with attention mechanism and LSTMs as RNNs.

• data_loader.py - includes DataGenerator class responsible for loading simulated and real data, then preprocessing it into final batches, consumable by model
• basecaller.py - includes Encoder, Decoder classes, and general Basecaller class, combining all layers into single keras Model responsible for learning and performing basecalling task
• utils.py - includes various utilities functions
• ravvent.py - sample script for running learning model pipeline
• ravvent_mapping_evaluator.py, ravvent_performance_evaluator.py - scripts for performing evaluation (read accuracy, speed)

Simulated datasets were generated using DeepSimulator tool. Script used in this purpose is generate_simulator_reduced.py, where the parameters for execution of DeepSimulator, as well as event_detection, can be found. Fasta files are stored in data/simulator/reduced directory.

Real data source is supporting data for Chiron basecaller, that is available here.

• tensorflow >= 2.7
• ont_fast5_api
• numpy