RNA is vital in the central dogma, transmitting genetic information from DNA to proteins, essential for eukaryotic cell survival. In the intricate cellular environment, with only a small number functioning independently as enzymes, most RNAs associate with proteins to form RNA-protein complexes that are crucial for cellular function.
Within cells, RNA-binding proteins (RBPs) form critical complexes and engage in various stages of RNA processing, regulating essential activities such as RNA selective splicing, and translation. The binding of RBPs to RNA is not only associated with RNA sequences but also influenced by factors such as gene structural types at binding sites and RNA modifications. This regulation not only influences protein diversity, but also significantly enhances the complexity of gene expression.
Currently, CLIP-derived technologies have successfully identified RNA-binding protein (RBP) binding sites at the single-nucleotide level. These methods enable researchers to pinpoint RNA crosslinking sites, thereby facilitating the discovery of RBP target RNAs and elucidating RBP binding motifs. Such advancements contribute significantly to our understanding of the roles of RBPs in post-transcriptional regulation.
By utilizing deep learning algorithms and aforementioned biological knowledge, iDeepB integrates sequence information with known binding patterns, allowing for more accurate predictions of protein-RNA interactions. The ability to predict binding profiles at base resolution not only aids in the identification of specific RNA targets but also provides insights into the dynamic regulatory networks influenced by RBPs.
