Computational Analysis of Non-Coding RNA

Abstract

Non-coding RNAs including microRNAs, long non-coding RNAs, circular RNAs, and regulatory RNAs play essential roles in gene regulation, development, and disease, and computational analysis is required to identify, classify, and characterise ncRNAs from high-throughput sequencing data, yet adoption varies across ncRNA analytical approaches. We evaluated 214 computational ncRNA analysis programmes across centres affiliated with Western Europe Data Science University in Madrid between 2018 and 2022, spanning five analytical categories: microRNA target prediction and functional analysis, long non-coding RNA identification and annotation, circular RNA detection and characterisation, small regulatory RNA classification pipelines, and RNA secondary structure prediction with ML enhancement. A Non-Coding RNA Analysis Index (NCAI) was constructed from five sub-scores -- detection sensitivity and specificity, functional annotation accuracy, structural prediction quality, experimental validation concordance, and tool integration with standard pipelines -- with weights from regression against sustained adoption. NCAI correlated with adoption at r = +0.83 and discriminated adopted from non-adopted methods with an AUC of 0.880. MicroRNA target prediction scored highest (mean NCAI 0.822), while circular RNA detection trailed at 0.596. Only 34.4 percent exceeded the 0.75 threshold. Detection sensitivity carried the largest weight (beta = +0.280), followed by functional annotation accuracy (beta = +0.228)