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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 spec...

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Autores principales: Ghandi, Mahmoud, Lee, Dongwon, Mohammad-Noori, Morteza, Beer, Michael A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4102394/
https://www.ncbi.nlm.nih.gov/pubmed/25033408
http://dx.doi.org/10.1371/journal.pcbi.1003711
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author Ghandi, Mahmoud
Lee, Dongwon
Mohammad-Noori, Morteza
Beer, Michael A.
author_facet Ghandi, Mahmoud
Lee, Dongwon
Mohammad-Noori, Morteza
Beer, Michael A.
author_sort Ghandi, Mahmoud
collection PubMed
description 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.
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spelling pubmed-41023942014-07-21 Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features Ghandi, Mahmoud Lee, Dongwon Mohammad-Noori, Morteza Beer, Michael A. PLoS Comput Biol Research Article 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. Public Library of Science 2014-07-17 /pmc/articles/PMC4102394/ /pubmed/25033408 http://dx.doi.org/10.1371/journal.pcbi.1003711 Text en © 2014 Ghandi et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Ghandi, Mahmoud
Lee, Dongwon
Mohammad-Noori, Morteza
Beer, Michael A.
Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features
title Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features
title_full Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features
title_fullStr Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features
title_full_unstemmed Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features
title_short Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features
title_sort enhanced regulatory sequence prediction using gapped k-mer features
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4102394/
https://www.ncbi.nlm.nih.gov/pubmed/25033408
http://dx.doi.org/10.1371/journal.pcbi.1003711
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