A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites

Naughton, Brian; Fratkin, Eugene; Batzoglou, Serafim; Brutlag, Douglas L.

doi:10.1093/nar/gkl585

Cited by 21 publications

(15 citation statements)

References 32 publications

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“…There are many methods, as well as software tools, for modeling TFBSs in terms of PWMs (11–17), as well as more advanced techniques that consider dependencies between nucleotides in different positions (18), but the vast majority are based on the assumption that letter representations of DNA sequences suitably capture the physicochemical properties of DNA (and proteins) that govern the specificity of protein–DNA interactions. However, the general validity of this assumption is questionable (19).…”

Section: Introductionmentioning

confidence: 99%

Improved predictions of transcription factor binding sites using physicochemical features of DNA

Maienschein‐Cline

Dinner

Hlavacek

et al. 2012

Nucleic Acids Research

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Typical approaches for predicting transcription factor binding sites (TFBSs) involve use of a position-specific weight matrix (PWM) to statistically characterize the sequences of the known sites. Recently, an alternative physicochemical approach, called SiteSleuth, was proposed. In this approach, a linear support vector machine (SVM) classifier is trained to distinguish TFBSs from background sequences based on local chemical and structural features of DNA. SiteSleuth appears to generally perform better than PWM-based methods. Here, we improve the SiteSleuth approach by considering both new physicochemical features and algorithmic modifications. New features are derived from Gibbs energies of amino acid–DNA interactions and hydroxyl radical cleavage profiles of DNA. Algorithmic modifications consist of inclusion of a feature selection step, use of a nonlinear kernel in the SVM classifier, and use of a consensus-based post-processing step for predictions. We also considered SVM classification based on letter features alone to distinguish performance gains from use of SVM-based models versus use of physicochemical features. The accuracy of each of the variant methods considered was assessed by cross validation using data available in the RegulonDB database for 54 Escherichia coli TFs, as well as by experimental validation using published ChIP-chip data available for Fis and Lrp.

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Section: Introductionmentioning

confidence: 99%

Improved predictions of transcription factor binding sites using physicochemical features of DNA

Maienschein‐Cline

Dinner

Hlavacek

et al. 2012

Nucleic Acids Research

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“…While requiring perfect conservation across many genomes is of limited use, the increased power enables approaches that account for artifacts in sequencing, assembly and alignment, and tolerate diverged, missing, or moved motif instances. Our BLS measure is more generally applicable to PWMs (Stormo 2000), to more complex models of regulatory motifs that account for dependencies between individual motif positions (Yada et al 1998;Naughton et al 2006), and to more advanced rules for miRNAtarget recognition that for example score the contribution of the 3Јpairing energy (Stark et al 2003;Brennecke et al 2005).…”

Section: Discussionmentioning

confidence: 99%

Reliable prediction of regulator targets using 12 Drosophila genomes

Kheradpour

Stark²,

Roy³

et al. 2007

Genome Res.

148

183

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Gene expression is regulated pre-and post-transcriptionally via cis-regulatory DNA and RNA motifs. Identification of individual functional instances of such motifs in genome sequences is a major goal for inferring regulatory networks yet has been hampered due to the motifs' short lengths that lead to many chance matches and poor signal-to-noise ratios. In this paper, we develop a general methodology for the comparative identification of functional motif instances across many related species, using a phylogenetic framework that accounts for the evolutionary relationships between species, allows for motif movements, and is robust against missing data due to artifacts in sequencing, assembly, or alignment. We also provide a robust statistical framework for evaluating motif confidence, which enables us to translate evolutionary conservation into a confidence measure for each motif instance, correcting for varying motif length, composition, and background conservation of the target regions. We predict targets of fly transcription factors and miRNAs in alignments of 12 recently sequenced Drosophila species. When compared to extensive genome-wide experimental data, predicted targets are of high quality, matching and surpassing ChIP-chip microarrays and recovering miRNA targets with high sensitivity. The resulting regulatory network suggests significant redundancy between pre-and post-transcriptional regulation of gene expression.[Supplemental material is available online at www.genome.org. All data and predicted transcription factor and miRNA targets are freely available at

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“…We also note that lasso regression has been used elsewhere for learning regulatory networks in bacteria using time course expression data [21] , and standard PLS has been used with a collection of known motifs in linear modeling of expression data in yeast and bacteria [22] . Finally, graph-based motif representations have been used previously by other groups, for example Naughton et al [23] , but this work again falls into the “cluster-first” category in that it seeks to find overrepresented motifs for a predefined gene set. By contrast, we learn motifs via a global regression problem, and the graph structure is encoded as a constraint on the solution.…”

Section: Discussionmentioning

confidence: 99%

Learning “graph-mer” Motifs that Predict Gene Expression Trajectories in Development

Panea

Wiggins

et al. 2010

PLoS Comput Biol

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A key problem in understanding transcriptional regulatory networks is deciphering what cis regulatory logic is encoded in gene promoter sequences and how this sequence information maps to expression. A typical computational approach to this problem involves clustering genes by their expression profiles and then searching for overrepresented motifs in the promoter sequences of genes in a cluster. However, genes with similar expression profiles may be controlled by distinct regulatory programs. Moreover, if many gene expression profiles in a data set are highly correlated, as in the case of whole organism developmental time series, it may be difficult to resolve fine-grained clusters in the first place. We present a predictive framework for modeling the natural flow of information, from promoter sequence to expression, to learn cis regulatory motifs and characterize gene expression patterns in developmental time courses. We introduce a cluster-free algorithm based on a graph-regularized version of partial least squares (PLS) regression to learn sequence patterns—represented by graphs of k-mers, or “graph-mers”—that predict gene expression trajectories. Applying the approach to wildtype germline development in Caenorhabditis elegans, we found that the first and second latent PLS factors mapped to expression profiles for oocyte and sperm genes, respectively. We extracted both known and novel motifs from the graph-mers associated to these germline-specific patterns, including novel CG-rich motifs specific to oocyte genes. We found evidence supporting the functional relevance of these putative regulatory elements through analysis of positional bias, motif conservation and in situ gene expression. This study demonstrates that our regression model can learn biologically meaningful latent structure and identify potentially functional motifs from subtle developmental time course expression data.

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A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites

Cited by 21 publications

References 32 publications

Improved predictions of transcription factor binding sites using physicochemical features of DNA

Improved predictions of transcription factor binding sites using physicochemical features of DNA

Reliable prediction of regulator targets using 12 Drosophila genomes

Learning “graph-mer” Motifs that Predict Gene Expression Trajectories in Development

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