Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features
Identifieur interne : 001C79 ( Main/Merge ); précédent : 001C78; suivant : 001C80Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features
Auteurs : Mahmoud Ghandi [États-Unis] ; Dongwon Lee [États-Unis] ; Morteza Mohammad-Noori [Iran] ; Michael A. Beer [États-Unis]Source :
- PLoS Computational Biology [ 1553-734X ] ; 2014.
Descripteurs français
- KwdFr :
- Analyse de séquence d'ADN (), Biologie informatique (), Immunoprécipitation de la chromatine, Machine à vecteur de support, Modèles génétiques, Oligonucléotides (génétique), Spécificité d'organe (génétique), Séquence nucléotidique, Séquences d'acides nucléiques régulatrices (génétique), Théorème de Bayes.
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : Oligonucleotides.
- genetics : Organ Specificity, Regulatory Sequences, Nucleic Acid.
- methods : Computational Biology, Sequence Analysis, DNA.
- Base Sequence, Bayes Theorem, Chromatin Immunoprecipitation, Models, Genetic, Support Vector Machine.
Abstract
Oligomers of length
Url:
DOI: 10.1371/journal.pcbi.1003711
PubMed: 25033408
PubMed Central: 4102394
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PMC:4102394Le document en format XML
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<term>Modèles génétiques</term>
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<front><div type="abstract" xml:lang="en"><title>Abstract</title>
<p>Oligomers of length <italic>k</italic>
, or <italic>k</italic>
-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, <italic>k</italic>
-mers suffer from the inherent limitation that if the parameter <italic>k</italic>
is increased to resolve longer features, the probability of observing any specific <italic>k</italic>
-mer becomes very small, and <italic>k-</italic>
mer counts approach a binary variable, with most <italic>k</italic>
-mers absent and a few present once. Thus, any statistical learning approach using <italic>k</italic>
-mers as features becomes susceptible to noisy training set <italic>k</italic>
-mer frequencies once <italic>k</italic>
becomes large. To address this problem, we introduce alternative feature sets using gapped <italic>k</italic>
-mers, a new classifier, gkm-SVM, and a general method for robust estimation of <italic>k</italic>
-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.</p>
</div>
</front>
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