Putting numbers on the network connections

Reliable scaling of position weight matrices for binding strength comparisons between transcription factors

Background

Scoring DNA sequences against Position Weight Matrices (PWMs) is a widely adopted method to identify putative transcription factor binding sites. While common bioinformatics tools produce scores that can reflect the binding strength between a specific transcription factor and the DNA, these scores are not directly comparable between different transcription factors. Other methods, including p-value associated approaches (Touzet H, Varré J-S. Efficient and accurate p-value computation for position weight matrices. Algorithms Mol Biol. 2007;2(1510.1186):1748–7188), provide more rigorous ways to identify potential binding sites, but their results are difficult to interpret in terms of binding energy, which is essential for the modeling of transcription factor binding dynamics and enhancer activities.

Results

Here, we provide two different ways to find the scaling parameter λ that allows us to infer binding energy from a PWM score. The first approach uses a PWM and background genomic sequence as input to estimate λ for a specific transcription factor, which we applied to show that λ distributions for different transcription factor families correspond with their DNA binding properties. Our second method can reliably convert λ between different PWMs of the same transcription factor, which allows us to directly compare PWMs that were generated by different approaches.

Conclusion

These two approaches provide computationally efficient ways to scale PWM scores and estimate the strength of transcription factor binding sites in quantitative studies of binding dynamics. Their results are consistent with each other and previous reports in most of cases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0666-1) contains supplementary material, which is available to authorized users.

High-resolution specificity from DNA sequencing highlights alternative modes of Lac repressor binding

Knowing the specificity of transcription factors is critical to understanding regulatory networks in cells. The lac repressor-operator system has been studied for many years, but not with high-throughput methods capable of determining specificity comprehensively. Details of its binding interaction and its selection of an asymmetric binding site have been controversial. We employed a new method to accurately determine relative binding affinities to thousands of sequences simultaneously, requiring only sequencing of bound and unbound fractions. An analysis of 2560 different DNA sequence variants, including both base changes and variations in operator length, provides a detailed view of lac repressor sequence specificity. We find that the protein can bind with nearly equal affinities to operators of three different lengths, but the sequence preference changes depending on the length, demonstrating alternative modes of interaction between the protein and DNA. The wild-type operator has an odd length, causing the two monomers to bind in alternative modes, making the asymmetric operator the preferred binding site. We tested two other members of the LacI/GalR protein family and find that neither can bind with high affinity to sites with alternative lengths or shows evidence of alternative binding modes. A further comparison with known and predicted motifs suggests that the lac repressor may be unique in this ability and that this may contribute to its selection.

Modeling the specificity of protein-DNA interactions

The specificity of protein-DNA interactions is most commonly modeled using position weight matrices (PWMs). First introduced in 1982, they have been adapted to many new types of data and many different approaches have been developed to determine the parameters of the PWM. New high-throughput technologies provide a large amount of data rapidly and offer an unprecedented opportunity to determine accurately the specificities of many transcription factors (TFs). But taking full advantage of the new data requires advanced algorithms that take into account the biophysical processes involved in generating the data. The new large datasets can also aid in determining when the PWM model is inadequate and must be extended to provide accurate predictions of binding sites. This article provides a general mathematical description of a PWM and how it is used to score potential binding sites, a brief history of the approaches that have been developed and the types of data that are used with an emphasis on algorithms that we have developed for analyzing high-throughput datasets from several new technologies. It also describes extensions that can be added when the simple PWM model is inadequate and further enhancements that may be necessary. It briefly describes some applications of PWMs in the discovery and modeling of in vivo regulatory networks.

Improved models for transcription factor binding site identification using nonindependent interactions

Identifying transcription factor (TF) binding sites is essential for understanding regulatory networks. The specificity of most TFs is currently modeled using position weight matrices (PWMs) that assume the positions within a binding site contribute independently to binding affinity for any site. Extensive, high-throughput quantitative binding assays let us examine, for the first time, the independence assumption for many TFs. We find that the specificity of most TFs is well fit with the simple PWM model, but in some cases more complex models are required. We introduce a binding energy model (BEM) that can include energy parameters for nonindependent contributions to binding affinity. We show that in most cases where a PWM is not sufficient, a BEM that includes energy parameters for adjacent dinucleotide contributions models the specificity very well. Having more accurate models of specificity greatly improves the interpretation of in vivo TF localization data, such as from chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments.

Maximally efficient modeling of DNA sequence motifs at all levels of complexity

Identification of transcription factor binding sites is necessary for deciphering gene regulatory networks. Several new methods provide extensive data about the specificity of transcription factors but most methods for analyzing these data to obtain specificity models are limited in scope by, for example, assuming additive interactions or are inefficient in their exploration of more complex models. This article describes an approach--encoding of DNA sequences as the vertices of a regular simplex--that allows simultaneous direct comparison of simple and complex models, with higher-order parameters fit to the residuals of lower-order models. In addition to providing an efficient assessment of all model parameters, this approach can yield valuable insight into the mechanism of binding by highlighting features that are critical to accurate models.

Determining the specificity of protein-DNA interactions

Proteins, such as many transcription factors, that bind to specific DNA sequences are essential for the proper regulation of gene expression. Identifying the specific sequences that each factor binds can help to elucidate regulatory networks within cells and how genetic variation can cause disruption of normal gene expression, which is often associated with disease. Traditional methods for determining the specificity of DNA-binding proteins are slow and laborious, but several new high-throughput methods can provide comprehensive binding information much more rapidly. Combined with in vivo determinations of transcription factor binding locations, this information provides more detailed views of the regulatory circuitry of cells and the effects of variation on gene expression.

Inferring binding energies from selected binding sites

We employ a biophysical model that accounts for the non-linear relationship between binding energy and the statistics of selected binding sites. The model includes the chemical potential of the transcription factor, non-specific binding affinity of the protein for DNA, as well as sequence-specific parameters that may include non-independent contributions of bases to the interaction. We obtain maximum likelihood estimates for all of the parameters and compare the results to standard probabilistic methods of parameter estimation. On simulated data, where the true energy model is known and samples are generated with a variety of parameter values, we show that our method returns much more accurate estimates of the true parameters and much better predictions of the selected binding site distributions. We also introduce a new high-throughput SELEX (HT-SELEX) procedure to determine the binding specificity of a transcription factor in which the initial randomized library and the selected sites are sequenced with next generation methods that return hundreds of thousands of sites. We show that after a single round of selection our method can estimate binding parameters that give very good fits to the selected site distributions, much better than standard motif identification algorithms.

The DNA binding sites of transcription factors that control gene expression are often predicted based on a collection of known or selected binding sites. The most commonly used methods for inferring the binding site pattern, or sequence motif, assume that the sites are selected in proportion to their affinity for the transcription factor, ignoring the effect of the transcription factor concentration. We have developed a new maximum likelihood approach, in a program called BEEML, that directly takes into account the transcription factor concentration as well as non-specific contributions to the binding affinity, and we show in simulation studies that it gives a much more accurate model of the transcription factor binding sites than previous methods. We also develop a new method for extracting binding sites for a transcription factor from a random pool of DNA sequences, called high-throughput SELEX (HT-SELEX), and we show that after a single round of selection BEEML can obtain an accurate model of the transcription factor binding sites.

Modeling the quantitative specificity of DNA-binding proteins from example binding sites

BACKGROUND

The binding of transcription factors to their respective DNA sites is a key component of every regulatory network. Predictions of transcription factor binding sites are usually based on models for transcription factor specificity. These models, in turn, are often based on examples of known binding sites.

METHODOLOGY/PRINCIPAL FINDINGS

Collections of binding sites are obtained in simulation experiments where the true model for the transcription factor is known and various sampling procedures are employed. We compare the accuracies of three different and commonly used methods for predicting the specificity of the transcription factor based on example binding sites. Different methods for constructing the models can lead to significant differences in the accuracy of the predictions and we show that commonly used methods can be positively misleading, even at large sample sizes and using noise-free data. Methods that minimize the number of predicted binding sequences are often significantly more accurate than the other methods tested.

CONCLUSIONS/SIGNIFICANCE

Different methods for generating motifs from example binding sites can have significantly different numbers of false positive and false negative predictions. For many different sampling procedures models based on quadratic programming are the most accurate.

The canonical E-box motif: a target for glucocorticoid action that drives rhythmic mouse Pai-1 transcription in vitro

Circadian (approximately 24 h) control impinges on an array of diverse physiological processes in many organisms, ranging from plants to human. Disruption of the mammalian circadian clockwork can lead to severe chronic illnesses such as cardiovascular disease, cancer progression and metabolic disorders. Transcriptional regulation of plasminogen activator inhibitor 1 (PAI-1) is of particular importance because of its crucial role in these pathological conditions. Pai-1 expression is partly regulated by the circadian clock, although direct mechanisms on Pai-1 rhythmicity are unknown. In the present study, we have identified a conserved functional E-box cis-element in the distal part of the mouse Pai-1 gene that is necessary and sufficient to drive circadian expression in Pai-1 activity after dexamethasone synchronisation in vitro. Mutagenesis and in vitro transfection analysis indicated this E-box provides a cognate binding site for cross-talk between clock and hypoxia factors, thus providing a potential cooperation mechanism between circadian and stress pathways, which is conserved in the human Pai-1 gene. Together, these results suggest that the canonical E-box is a target for glucocorticoid action, thus providing the molecular interface between gene transcription and drug action. The mechanism described has global impact on diverse dynamic biological processes governed by the neuroendocrine axis and the circadian clockwork to control complex coordination of gene cascades and biology.