TargetRNA3: predicting prokaryotic RNA regulatory targets with machine learning

Abstract

AbstractSmall regulatory RNAs pervade prokaryotes, with the best-studied family of these non-coding genes corresponding to trans-acting regulators that bind via base pairing to their message targets. Given the increasing frequency with which these genes are being identified, it is important that methods for illuminating their regulatory targets keep pace. Using a machine learning approach, we investigate thousands of interactions between small RNAs and their targets, and we interrogate more than a hundred features indicative of these interactions. We present a new method, TargetRNA3, for predicting targets of small RNA regulators and show that it outperforms existing approaches. TargetRNA3 is available at https://cs.wellesley.edu/~btjaden/TargetRNA3.