You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: A Feature-Based Approach to Modeling Protein–DNA Interactions}
Article:
Abstract:
Transcription factor( TF) binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position specific scoring matrix( PSSM), which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. Here, we present feature motif models( FMMs), a novel probabilistic method for modeling TF–DNA interactions, based on log-linear models. Our approach uses sequence features to represent TF binding specificities, where each feature may span multiple positions. We develop the mathematical formulation of our model and devise an algorithm for learning its structural features from binding site data. We also developed a discriminative motif finder, which discovers de novo FMMs that are enriched in target sets of sequences compared to background sets. We evaluate our approach on synthetic data and on the widely used TF chromatin immunoprecipitation( ChIP) dataset of Harbison et al. We then apply our algorithm to high-throughput TF ChIP data from mouse and human, reveal sequence features that are present in the binding specificities of mouse and human TFs, and show that FMMs explain TF binding significantly better than PSSMs. Our FMM learning and motif finder software are available at http://genie. weizmann. ac. il/.
Introduction:
Precise control of gene expression lies at the heart of nearly all biological processes. An important layer in such control is the regulation of transcription. This regulation is preformed by a network of interactions between transcription factor proteins( TFs) and the DNA of the genes they regulate. To understand the workings of this network, it is thus crucial to understand the most basic interaction between a TF and its target site on the DNA. Indeed, much effort has been devoted to detecting the TF–DNA binding location and specificities. Experimentally, much of the binding specificity information has been determined using traditional methodologies such as footprinting, gel-shift analysis, Southwestern blotting, or reporter constructs. Recently, a number of high-throughput technologies for identifying TF binding specificities have been developed. These methods can be classified into two major classes, in vitro and in vivo methods. In vitro methods can further be classified to methods that select high-affinity binding sequences for a protein of interest[1],[2]( reviewed in Elnitski et al.[3]), and high-throughput methods that measure the affinities of specific proteins to multiple DNA sequences. Examples of the latter class of methods include protein binding microarrays[4]–[6] and microfluidic platforms[7], which claim to achieve better measurement of transient low affinity interactions. The in vivo methods are mainly based on microarray readout or high throughput sequencing technologies readout of either DNA adenine methyltransferase fusion proteins( DamID) or of chromatin immunoprecipitation DNA-bound proteins( ChIP-chip, ChIP-PET, ChipSeq/Chip-seq)[8]–[16]. The in vivo methods were recently used to characterize the binding specificities of all TFs in the yeast Saccharomyces cerevisiae[8],[9],[17]–[21] and, more recently, to identify genomic targets in mammalian cells[10]–[16],[22]–[26]. However, despite these technological advances, distilling the TF binding specificity from these assays remains a great challenge, since in many cases the in vivo measured targets of a TF do not have common binding sites, and in other cases genes that have the known and experimentally determined site for a TF are not measured as its targets. For these reasons, the problem of identifying transcription factor binding sites( TFBSs) has also been the subject of much computational work( reviewed by Elnitski[3]). The most common approaches start by defining sets of genes that are potentially coregulated, either from clusters of coexpressed genes in microarray data[27], from functional annotations of genes[28], or from TF chromatin immunoprecipitation[17],[29]. They then attempt to identify regulatory elements by searching for common motifs in the promoter regions of the genes in each group[30]–[35]. Recently, Eden et al.[36] developed a discriminative motif finder that is well suited for finding motifs in a ChIP-chip experiment. Their method combines the search for a cutoff that defines the positive set, with the search for a motif that discriminates the positive set from the rest of the chip probes. Other approaches work in the opposite direction, by first reducing the sequence data into some predefined features of the gene( e. g., presence or absence of all DNA-words of length 6–7), and then identifying the putative binding sites, for example, by keeping the features, or combinations thereof, whose genes are coexpressed[37]–[39]. Recently, this latter approach was extended to comparative genomic methods that filter the initial library of features to those that show high conservation in evolutionarily closely related species[40]–[42]. The experimental and computational approaches above revealed that TFBSs are short, typically 6–20 base pairs, and that some degree of variability in the TFBSs is allowed. For these reasons, the binding site specificities of TFs are described by a sequence motif, which should represent the set of multiple allowed TFBSs for a given TF. The most common representation for sequence motifs is the position specific scoring matrix( PSSM), which specifies a separate probability distribution over nucleotides at each position of the TFBS. The goal of computational approaches is then to identify the PSSM associated with each TF. Despite its successes, the PSSM representation makes the strong assumption that the binding specificities of TFs are position-independent. That is, the PSSM assumes that for any given TF and TFBS, the contribution of a nucleotide at one position of the site to the overall binding affinity of the TF to the site does not depend on the nucleotides that appear in other positions of the site. In theory, it is easy to see where this assumption fails. For example, consider the models described in Figure 1. The TFBS( Figure 1A) data contains only “CG” or “GC” in the center positions. Although the PSSM learned from this data( Figure 1B) assigns high probability to these nucleotide pairs, it also undesirably( and unavoidably) assigns high probability to “CC” and “GG” in the center positions. However, if instead of the PSSM representation, we allow ourselves to assign probabilities to multiple nucleotides at multiple positions, we could use the same number of parameters to specify the desired TF binding specificities( For example, consider the model illustrated in Figure 1C, which uses two parameters that are each associated with two positions to give exact description of the binding specificities over the center positions). This observation lies at the heart of our approach. From the above discussion, it should be clear that the position-independent assumption of PSSMs is rather strong, and that relaxing this assumption may lead to a qualitatively better characterization of TF motifs. Indeed, recent studies revealed specific cases in which dependencies between positions may exist[7],[43]. Several models were developed to capture such dependencies( see[44] for a brief review). These models can be classified into two main classes: Markov chains based models[44],[45] and Bayesian Network based models[46]–[49]. In the first class, the dependencies between neighboring positions are modeled using a Markov model of some order. A recent representative of this class is the permutated variable length Markov model( PVLMM) of Zhao et al., which incorporates two major improvements: it searches for the best permutation of the motif positions, and it reduces the number of parameters by using a context tree representation for the Markov model representation. Although Markov chain based models may perform well in some datasets, they have a limited ability to model dependencies between more distant positions. Since modeling these dependencies by increasing the order of the Markov model exponentially increases the size of the model representation, it was suggested to search for a permutation of the binding site positions that produces the best model. However, this is only a partial solution, as it poses a limitation on the model learned and easily becomes computational intensive for long motifs( Zhao et al. limit their motifs to length 9 bp). The second class of models was proposed by Barash et al.[46], who developed a Bayesian network approach to represent higher order dependencies between motif positions. They showed that these models predict putative TFBSs in ChIP-chip data with higher accuracy than PSSMs. Ben-Gal et al.[47] extended this approach by using a context dependent representation of the conditional probability distributions, which, to some extent, reduces the representation size( depending on the data). Zhou et al. suggested a simpler Bayesian network model( GWM) where only dependencies between nonoverlapping positions are modeled[49]. Pudimat et al. also extended the Bayesian network framework by adding structural DNA features[48]. However, the Bayesian network representation, due to its acyclicity constraints, imposes nonnatural restrictions on the motif structure, and its conditional probability distributions limit the number of dependencies that can be introduced between positions in practice, due to the exponential increase in the number of parameters introduced with each additional dependency. Although some of these issues may be addressed, e. g., using sparse conditional probability distribution representations, Bayesian networks do not seem to be the ideal and most intuitive tool for the task. Another class of TF binding specificities models that is complementary to the above two is a mixture of models. In the above mentioned work, Barash et al. also used a mixture of PSSMs to model TFBSs. In this representation, each motif is modeled as a mixture of PSSMs each defining a different mode of binding. This approach was later extended as a part of the LOGOS[50] and MotifBooster[51] motif finding software. However, this approach does not explicitly represent dependencies between binding site positions. Here, we propose a novel approach for modeling TFBS motifs, termed feature motif models( FMMs). Our approach is based on describing the set of sequence properties, or features, that are relevant to the TF–DNA interactions. Intuitively, the binding affinity of a given site to the TF increases as it contains more of the features that are important for the TF in recognizing its target site. In our framework, features may be binary( e. g., “C at position 2, and G at position 3”) or multivalued( e. g., “the number of G or C nucleotides at positions 1–4”), and global features are also allowed( e. g., “the sequence is palindromic”). Each feature is assigned a statistical weight, representing the degree of its importance to the TF–DNA interaction, and the overall strength of a TFBS can then be computed by summing the contribution of all of its constituent features. We argue that this formulation captures the essence of the TF–DNA interaction more explicitly than PSSMs and other previous approaches. It is easy to see that PSSMs are a special case of FMMs, since a PSSM can be described within our framework using four single nucleotide features per position. Our approach can also naturally represent complex and distant dependencies efficiently, thereby overcoming a limitation of other models that have been proposed. The rest of the paper is organized as follows: The Results section starts with a brief overview of our methodology. We then validate our approach by learning FMMs from synthetic and real aligned TFBS data. Next, we devise a novel motif finder algorithm that finds motifs in a set of unaligned sequences, and validate its performance on yeast TF ChIP data[17]. Using this motif finder, we demonstrate the benefits of using FMMs instead of PSSMs as a basic building block of a motif finder, which represents TF binding motifs. Finally, we present insights that we gained from learning FMMs for two human TFs, CTCF and c-MYC. In the Methods section we discuss the details and the mathematical formulation of our FMM approach. The problem of learning an FMM from TFBS data is quite difficult, as it reduces to structure learning in Markov networks, a paradigm that is still poorly developed. In the Methods section we elaborate on our learning strategy and suggest an improved methodology for optimizing the data likelihood, which we define as our objective function.
Discussion:
In this paper we present feature motif models( FMMs), a novel probabilistic method for modeling the binding specificities of TFs. The current dominant model used is the position specific scoring matrix( PSSM), which assumes independence between binding motif positions. The richer FMM formulation may be viewed as a generalization of the PSSM model, enabling complex position dependencies to be captured, as well as other high-level features( e. g., palindromes). In this work, we used FMM models that extend the PSSM by capturing dinucleotide dependencies. To show that FMM models describe TF binding motifs better than PSSM models, we compared their likelihoods over held-out synthetic and real data. The real biological data included both aligned TFBS data and unaligned TF bound regions data. We showed that for all types of data, the FMM representation of motifs outperforms the PSSM representation. Importantly, FMMs can be presented using a clear and easy to understand logo, where important position dependencies are plainly visible. Examining our FMM results for two human TFs, c-Myc and CTCF, we found intriguing dinucleotide features that may be important for their binding. Some of those features are well-correlated with previously published results, while others may provide hypotheses on the binding specificities of these TFs. These hypotheses can be further studied experimentally to gain better understanding of how the TF recognizes its binding sites. Notably, in those examples, the FMMs hint at the importance of positions that are regarded uninformative by the PSSMs. In order to allow de novo FMM motif finding, we developed a novel motif finder. Our motif finder finds motifs that are discriminatively enriched in a positive set of unaligned sequences over a negative set of unaligned sequences. For each motif, it learns either an FMM or a PSSM representation. An important property of our motif finding algorithm is that it extracts enriched sets of K-mers from the data, thus maintaining dependencies between positions, if such exist. We show that our motif finder performs well and use it to demonstrate how FMMs can easily be integrated as a basic building block of a motif finding software which represents TF binding specificities. As a future direction, we suggest to integrate the FMM into both common and novel state-of-the-art motif finding algorithms. We demonstrated the benefits of using log-linear models( a representation of Markov networks) for representing important features of TF binding specificities, and suggested a methodology to learn such features from both aligned and unaligned input sequences. In the Methods section we also contribute to the general problem of learning log-linear models by suggesting a methodology for optimizing the objective function, which may give better performance under settings that require approximation. There are several directions for refining and extending our FMM approach. First, our rich framework can model many other types of features. Examples of features that can be added are: to what extent is the sequence a palindrome and the structural curvature of the sequence. Another direction is to add to our learning process the ability to learn binding energies associated with a given set of sequences. Finally, using our models as an improved basic building block, we can integrate it into higher level regulatory models( e. g.,[65]) and obtain a much better quantitative understanding of the underlying principles of transcriptional regulatory networks.
Transcription factor ( TF ) protein binding to its DNA target sequences is a fundamental physical interaction underlying gene regulation . Characterizing the binding specificities of TFs is essential for deducing which genes are regulated by which TFs . Recently , several high-throughput methods that measure sequences enriched for TF targets genomewide were developed . Since TFs recognize relatively short sequences , much effort has been directed at developing computational methods that identify enriched subsequences ( motifs ) from these sequences . However , little effort has been directed towards improving the representation of motifs . Practically , available motif finding software use the position specific scoring matrix ( PSSM ) model , which assumes independence between different motif positions . We present an alternative , richer model , called the feature motif model ( FMM ) , that enables the representation of a variety of sequence features and captures dependencies that exist between binding site positions . We show how FMMs explain TF binding data better than PSSMs on both synthetic and real data . We also present a motif finder algorithm that learns FMM motifs from unaligned promoter sequences and show how de novo FMMs , learned from binding data of the human TFs c-Myc and CTCF , reveal intriguing insights about their binding specificities .