Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published the raw fluorescent values of Cy3 and Cy5 as a function of the most adjacent 5 nucleotides resulting in 1024 data points. While experimentally tractable, an increase in the sequence space will vastly increase the experimental and time cost. Machine Learning is well suited to addressing the issue of experimental tractability however there is a wide design space in the choice of algorithms. In this work we use classic machine learning models such as Support Vector Machine, Multilayer Perceptrons and Random Forests to both predict the raw intensity value and classify the intensity magnitude of the fluorophore using the sequence as input. We demonstrate that the performance of these models is heavily dependent on the numerical transformation of the sequence and that Random Forest consistently outperforms all other models in both regression and classification tasks irrespective of the sequence transformation.