title: Enhanced regulatory sequence prediction using gapped k-mer features.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Mahmoud
authors.initials: M
authors.lastname: Ghandi
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Dongwon
authors.initials: D
authors.lastname: Lee
authors.affiliation: School of Mathematics, Statistics and Computer Science, University of Tehran, Tehran, Iran; School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
authors.forename: Morteza
authors.initials: M
authors.lastname: Mohammad-Noori
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Beer
mesh_terms: D001483:Base Sequence; D001499:Bayes Theorem; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology / Q000379:methods*; D008957:Models, Genetic; D009841:Oligonucleotides / Q000235:genetics; D009928:Organ Specificity / Q000235:genetics; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D009841:Oligonucleotides