Summary This study demonstrates that microRNAs can regulate gene expression through binding sites located within protein-coding sequences, not only the canonical 3’ UTR sites. Functional coding sequence targets are identified and validated, broadening the scope of miRNA-mediated gene regulation.
Summary This update to the DIANA-microT web server extends its target prediction capabilities to Drosophila melanogaster and Caenorhabditis elegans microRNAs, and introduces a bibliographic module linking microRNAs to diseases based on the scientific literature.
Summary This study improves the accuracy of microRNA target prediction by modeling the accessibility of miRNA binding sites using detailed RNA secondary structure calculations and training machine learning models on proteomic measurements of miRNA-mediated repression.
Summary This work provides a systematic assessment of the state of computational microRNA target identification, examining the performance of existing tools and discussing the challenges in translating predictions into biological insight. Guidelines are provided for researchers interpreting computational miRNA target predictions.
Summary This study benchmarks microRNA target prediction methods against protein repression data, showing that the DIANA-microT algorithm achieves high correlation with experimentally measured protein repression. The work provides a framework for evaluating and improving computational miRNA target prediction.
Summary The DIANA-microT web server provides access to the DIANA-microT algorithm for microRNA target prediction. The server enables researchers to identify genes regulated by specific microRNAs, query microRNAs targeting specific genes, and perform pathway-level analysis of miRNA regulatory networks.