RNA-ModX: a multilabel prediction and interpretation framework for RNA modifications.

Abstract

Accurate prediction of RNA modifications holds profound implications for elucidating RNA function and mechanism, with potential applications in drug development. Here, the RNA-ModX presents a highly precise predictive model designed to forecast post-transcriptional RNA modifications, complemented by a user-friendly web application tailored for seamless utilization by future researchers. To achieve exceptional accuracy, the RNA-ModX systematically explored a range of machine learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit, and Transformer-based architectures. The model underwent rigorous testing using a dataset comprising RNA sequences containing the four fundamental nucleotides (A, C, G, U) and spanning 12 prevalent modification classes (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um), with sequences of length 1001 nucleotides. Notably, the LSTM model, augmented with 3-mer encoding, demonstrated the highest level of model accuracy. Furthermore, Local Interpretable Model-Agnostic Explanations were employed to facilitate result interpretation, enhancing the transparency and interpretability of the model's predictions. In conjunction with the model development, a user-friendly web application was meticulously crafted, featuring an intuitive interface for researchers to effortlessly upload RNA sequences. Upon submission, the model executes in the backend, generating predictions which are seamlessly presented to the user in a coherent manner. This integration of cutting-edge predictive modeling with a user-centric interface signifies a significant step forward in facilitating the exploration and utilization of RNA modification prediction technologies by the broader research community.