Using a K-mer Based Approach with Machine Learning Classifiers for Enhancer Identification and Classification

Abstract Background Enhancers are genetic elements that modulate the level of gene expression in cells; because they are essential for cellular function, enhancer dysfunction is associated with many complex diseases including many types of cancers. Furthermore, current research highlights the challenge of enhancer identification due to variability in enhancer definitions and identification approaches. Developing a machine learning pipeline to distinguish enhancers from other DNA elements would greatly aid the ability to study enhancers and their role in disease. In this project, we developed a random forest machine learning model to distinguish between human liver enhancer sequences with low versus high levels of support across eight different enhancer identification methods. Results Enhancers were classified as “shared” if they had support from multiple methods and “unique” otherwise; the threshold for the number of methods that made an enhancer region “shared” or “unique” was adjusted as an independent variable. We also extracted a set of random sequences from the human genome to evaluate potential differences between shared/unique enhancer regions and genomic background. Our machine learning model used the frequency of k length sequence (k-mer) patterns as features to classify the shared versus unique enhancer regions. We hypothesized that the distribution of k-mer frequencies would correlate with transcription factor binding sites on the enhancer and distinguish them from other types of DNA sequence. Conclusions The final shared vs random models perform with 93.8–99.9% accuracy, the final unique vs random models perform with 93.4–99.8% accuracy, but the final shared model performs with only 56.8–61.4% accuracy. These results indicate that the k-mer frequency distributions for shared and unique enhancers are not significantly different although shared/unique sequences differ from the genomic background.