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PLoS Comput Biol. 2014 Jul 17;10(7):e1003711. doi: 10.1371/journal.pcbi.1003711. eCollection 2014 Jul.

Enhanced regulatory sequence prediction using gapped k-mer features.

Author information

1
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
2
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.
3
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.

Erratum in

  • PLoS Comput Biol. 2014 Dec;10(12):e1004035.

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.

PMID:
25033408
PMCID:
PMC4102394
DOI:
10.1371/journal.pcbi.1003711
[Indexed for MEDLINE]
Free PMC Article

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