Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features

Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem. Author Summary: Genomic regulatory elements (enhancers, promoters, and insulators) control the expression of their target genes and are widely believed to play a key role in human development and disease by altering protein concentrations. A fundamental step in understanding enhancers is the development of DNA sequence-based models to predict the tissue specific activity of regulatory elements. Such models facilitate both the identification of the molecular pathways which impinge on enhancer activity through direct transcription factor binding, and the direct evaluation of the impact of specific common or rare genetic variants on enhancer function. We have previously developed a successful sequence-based model for enhancer prediction using a k-mer support vector machine (kmer-SVM). Here, we address a significant limitation of the kmer-SVM approach and present an alternative method using gapped k-mers (gkm-SVM) which exhibits dramatically improved accuracy in all test cases. While we focus on enhancers and transcription factor binding, our method can be applied to improve a much broader class of sequence analysis problems, including proteins and RNA. In addition, we expect that most k-mer based methods can be significantly improved by simply using the generalized k-mer count method that we present in this paper. We believe this improved model will enable significant contributions to our understanding of the human regulatory system.