LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment
This work addresses time-critical and privacy-aware DNA sequencing applications, offering incremental improvements in efficiency and adaptability for bacterial sample analysis.
The paper tackled the problem of DNA base-calling from MinION device signals by proposing LRDB, a lightweight LSTM-based model that achieved a 0.35% increase in read-identity for bacterial samples and showed a 1.439% accuracy decline under 15dB noise injection.
The first important step in extracting DNA characters is using the output data of MinION devices in the form of electrical current signals. Various cutting-edge base callers use this data to detect the DNA characters based on the input. In this paper, we discuss several shortcomings of prior base callers in the case of time-critical applications, privacy-aware design, and the problem of catastrophic forgetting. Next, we propose the LRDB model, a lightweight open-source model for private developments with a better read-identity (0.35% increase) for the target bacterial samples in the paper. We have limited the extent of training data and benefited from the transfer learning algorithm to make the active usage of the LRDB viable in critical applications. Henceforth, less training time for adapting to new DNA samples (in our case, Bacterial samples) is needed. Furthermore, LRDB can be modified concerning the user constraints as the results show a negligible accuracy loss in case of using fewer parameters. We have also assessed the noise-tolerance property, which offers about a 1.439% decline in accuracy for a 15dB noise injection, and the performance metrics show that the model executes in a medium speed range compared with current cutting-edge models.