The authors present a new approach to assess the functional conservation of the RNA editing targeting mechanism using machine learning algorithms. They trained a random forest (RF) model and a bidirectional long short-term memory (biLSTM) neural network with an attention layer to predict RNA editing events in humans, mice, and mackerel.
The RF model was able to achieve over 75% accuracy in predicting editing events, with the global maximum double-strand size and the distance to the closest inner loops being the most important features. The biLSTM model, using both sequence and secondary structure information, achieved almost 95% accuracy in predicting human editing events. Interestingly, the model performed well even when using only the sequence channel, suggesting that secondary structure plays a key role in the RNA editing target selection mechanism.
The authors then tested the models on more unbalanced datasets, mimicking real-world scenarios, and found that while the accuracy remained high, the number of false positives greatly exceeded the true positives. This highlights the challenge of using these models for de novo prediction of editing events.
To investigate the conservation of the RNA editing mechanism, the authors used a cross-training approach, training the models on one species and testing on another. The results showed that the models trained on mammalian data (human and mouse) could predict each other's datasets with reasonable accuracy, but performed poorly on the mackerel dataset. This suggests that while the RNA editing mechanism is largely conserved between mammals, there are likely differences in the targeting mechanism between mammals and the teleost fish mackerel, potentially due to differences in factors like temperature affecting RNA secondary structure.
Overall, this work demonstrates the power of machine learning approaches to study the complex and elusive process of RNA editing, and provides a novel in silico method to infer the conservation of the editing mechanism across species.
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by Zawisza-Alva... at www.biorxiv.org 11-22-2023
https://www.biorxiv.org/content/10.1101/2023.11.21.568001v1Deeper Inquiries