Comparison of discriminative motif optimization using matrix and DNA shape-based models
Identifieur interne : 000A32 ( Main/Exploration ); précédent : 000A31; suivant : 000A33Comparison of discriminative motif optimization using matrix and DNA shape-based models
Auteurs : Shuxiang Ruan ; Gary D. StormoSource :
- BMC Bioinformatics [ 1471-2105 ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : DNA.
- genetics : Nucleotide Motifs.
- Algorithms, Area Under Curve, Binding Sites, Databases, Nucleic Acid, Humans, Models, Molecular, Position-Specific Scoring Matrices, Protein Binding.
Abstract
Transcription factor (TF) binding site specificity is commonly represented by some form of matrix model in which the positions in the binding site are assumed to contribute independently to the site’s activity. The independence assumption is known to be an approximation, often a good one but sometimes poor. Alternative approaches have been developed that use
We describe a program “Discriminative Additive Model Optimization” (DAMO) that uses positive and negative examples, as in ChIP-seq data, and finds the additive position weight matrix (PWM) that maximizes the Area Under the Receiver Operating Characteristic Curve (AUROC). We compare to a recent study where structural parameters, serving as features in a gradient boosting classifier algorithm, are shown to improve the AUROC over JASPAR position frequency matrices (PFMs). In agreement with the previous results, we find that adding structural parameters gives the largest improvement, but most of the gain can be obtained by an optimized PWM and nearly all of the gain can be obtained with a di-nucleotide extension to the PWM.
To appropriately compare different models for TF bind sites, optimized models must be used. PWMs and their extensions are good representations of binding specificity for most TFs, and more complex models, including the incorporation of DNA shape features and gradient boosting classifiers, provide only moderate improvements for a few TFs.
The online version of this article (10.1186/s12859-018-2104-7) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12859-018-2104-7
PubMed: 29510689
PubMed Central: 5840810
Affiliations:
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Le document en format XML
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<term>Humans</term>
<term>Models, Molecular</term>
<term>Nucleotide Motifs (genetics)</term>
<term>Position-Specific Scoring Matrices</term>
<term>Protein Binding</term>
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<term>Aire sous la courbe</term>
<term>Algorithmes</term>
<term>Bases de données d'acides nucléiques</term>
<term>Humains</term>
<term>Liaison aux protéines</term>
<term>Matrices de scores</term>
<term>Modèles moléculaires</term>
<term>Motifs nucléotidiques (génétique)</term>
<term>Sites de fixation</term>
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<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Nucleotide Motifs</term>
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<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Motifs nucléotidiques</term>
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<term>Area Under Curve</term>
<term>Binding Sites</term>
<term>Databases, Nucleic Acid</term>
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<term>Position-Specific Scoring Matrices</term>
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<term>Humains</term>
<term>Liaison aux protéines</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p id="Par1">Transcription factor (TF) binding site specificity is commonly represented by some form of matrix model in which the positions in the binding site are assumed to contribute independently to the site’s activity. The independence assumption is known to be an approximation, often a good one but sometimes poor. Alternative approaches have been developed that use <italic>k</italic>
-mers (DNA “words” of length <italic>k</italic>
) to account for the non-independence, and more recently DNA structural parameters have been incorporated into the models. ChIP-seq data are often used to assess the discriminatory power of motifs and to compare different models. However, to measure the improvement due to using more complex models, one must compare to optimized matrix models.</p>
</sec>
<sec><title>Results</title>
<p id="Par2">We describe a program “Discriminative Additive Model Optimization” (DAMO) that uses positive and negative examples, as in ChIP-seq data, and finds the additive position weight matrix (PWM) that maximizes the Area Under the Receiver Operating Characteristic Curve (AUROC). We compare to a recent study where structural parameters, serving as features in a gradient boosting classifier algorithm, are shown to improve the AUROC over JASPAR position frequency matrices (PFMs). In agreement with the previous results, we find that adding structural parameters gives the largest improvement, but most of the gain can be obtained by an optimized PWM and nearly all of the gain can be obtained with a di-nucleotide extension to the PWM.</p>
</sec>
<sec><title>Conclusion</title>
<p id="Par3">To appropriately compare different models for TF bind sites, optimized models must be used. PWMs and their extensions are good representations of binding specificity for most TFs, and more complex models, including the incorporation of DNA shape features and gradient boosting classifiers, provide only moderate improvements for a few TFs.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (10.1186/s12859-018-2104-7) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
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