Genome-Wide Tissue-Specific RNA Editability Estimation Through Convolution Neural Networks

RiboNucleic Acid (RNA) editing is a dynamic and essential biological process that has multifaceted functions in gene regulation, protein diversity, and immune response. Tissue-specific RNA editing is governed by the presence and activity of RNA editing enzymes, such as adenosine deaminase acting on RNA enzymes, and is influenced by the cellular context and regulatory factors in each tissue. As a result, RNA editing can exhibit tissue-specificity. To fully understand the functional implications of RNA editing, it is important to consider its tissue-specific nature and its potential impact on the biology of specific tissues and organs. Utilizing convolutional neural networks, we designed models that can predict RNA editability. The models were validated independently using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR associated protein 9 (Cas9)-based Adenosine Deaminase Acting on RNA (ADAR) knockout in both Jurkat and Human Embryonic Kidney 293T‌ (HEK293T) cells. Although RNA editing can be categorized into Alu and non-Alu RNA editing, with the majority of RNA editing falling within the Alu category, our motif and phylogenetic analyses reveal that the tissue-specific characteristics of RNA editing are primarily attributed to non-Alu-related RNA editing. Based on these results, we developed a web server that incorporates RNA editability prediction models for 30 distinct tissue types in Humans and four other species (mouse, bee, fly, and squid). This tool assists studies that aim to gain a more comprehensive understanding of RNA editing-related gene regulation, cellular diversity, and the molecular basis of tissue-specific diseases.