The next generation of transcription factor binding site prediction

JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles

JASPAR (http://jaspar.genereg.net) is an open-access database storing curated, non-redundant transcription factor (TF) binding profiles representing transcription factor binding preferences as position frequency matrices for multiple species in six taxonomic groups. For this 2016 release, we expanded the JASPAR CORE collection with 494 new TF binding profiles (315 in vertebrates, 11 in nematodes, 3 in insects, 1 in fungi and 164 in plants) and updated 59 profiles (58 in vertebrates and 1 in fungi). The introduced profiles represent an 83% expansion and 10% update when compared to the previous release. We updated the structural annotation of the TF DNA binding domains (DBDs) following a published hierarchical structural classification. In addition, we introduced 130 transcription factor flexible models trained on ChIP-seq data for vertebrates, which capture dinucleotide dependencies within TF binding sites. This new JASPAR release is accompanied by a new web tool to infer JASPAR TF binding profiles recognized by a given TF protein sequence. Moreover, we provide the users with a Ruby module complementing the JASPAR API to ease programmatic access and use of the JASPAR collection of profiles. Finally, we provide the JASPAR2016 R/Bioconductor data package with the data of this release.

Beyond antioxidant genes in the ancient Nrf2 regulatory network

Nrf2, a basic leucine zipper transcription factor encoded by the gene NFE2L2, is a master regulator of the transcriptional response to oxidative stress. Nrf2 is structurally and functionally conserved from insects to humans, and it heterodimerizes with the small MAF transcription factors to bind a consensus DNA sequence (the antioxidant response element, or ARE) and regulate gene expression. We have used genome-wide chromatin immunoprecipitation and gene expression data to identify direct Nrf2 target genes in Drosophila and humans. These data have allowed us to construct the deeply conserved ancient Nrf2 regulatory network-target genes that are conserved from Drosophila to human. The ancient network consists of canonical antioxidant genes, as well as genes related to proteasomal pathways and metabolism and a number of less expected genes. We have also used enhancer reporter assays and electrophoretic mobility-shift assays to confirm Nrf2-mediated regulation of ARE activity at a number of these novel target genes. Interestingly, the ancient network also highlights a prominent negative feedback loop; this, combined with the finding that Nrf2-mediated regulatory output is tightly linked to the quality of the ARE it is targeting, suggests that precise regulation of nuclear Nrf2 concentration is necessary to achieve proper quantitative regulation of distinct gene sets. Together, these findings highlight the importance of balance in the Nrf2-ARE pathway and indicate that Nrf2-mediated regulation of xenobiotic metabolism, glucose metabolism, and proteostasis has been central to this pathway since its inception.

Varying levels of complexity in transcription factor binding motifs

Binding of transcription factors to DNA is one of the keystones of gene regulation. The existence of statistical dependencies between binding site positions is widely accepted, while their relevance for computational predictions has been debated. Building probabilistic models of binding sites that may capture dependencies is still challenging, since the most successful motif discovery approaches require numerical optimization techniques, which are not suited for selecting dependency structures. To overcome this issue, we propose sparse local inhomogeneous mixture (Slim) models that combine putative dependency structures in a weighted manner allowing for numerical optimization of dependency structure and model parameters simultaneously. We find that Slim models yield a substantially better prediction performance than previous models on genomic context protein binding microarray data sets and on ChIP-seq data sets. To elucidate the reasons for the improved performance, we develop dependency logos, which allow for visual inspection of dependency structures within binding sites. We find that the dependency structures discovered by Slim models are highly diverse and highly transcription factor-specific, which emphasizes the need for flexible dependency models. The observed dependency structures range from broad heterogeneities to sparse dependencies between neighboring and non-neighboring binding site positions.

CMStalker: A Combinatorial Tool for Composite Motif Discovery

Controlling the differential expression of many thousands different genes at any given time is a fundamental task of metazoan organisms and this complex orchestration is controlled by the so-called regulatory genome encoding complex regulatory networks: several Transcription Factors bind to precise DNA regions, so to perform in a cooperative manner a specific regulation task for nearby genes. The in silico prediction of these binding sites is still an open problem, notwithstanding continuous progress and activity in the last two decades. In this paper, we describe a new efficient combinatorial approach to the problem of detecting sets of cooperating binding sites in promoter sequences, given in input a database of Transcription Factor Binding Sites encoded as Position Weight Matrices. We present CMStalker, a software tool for composite motif discovery which embodies a new approach that combines a constraint satisfaction formulation with a parameter relaxation technique to explore efficiently the space of possible solutions. Extensive experiments with 12 data sets and 11 state-of-the-art tools are reported, showing an average value of the correlation coefficient of 0.54 (against a value 0.41 of the closest competitor). This improvements in output quality due to CMStalker is statistically significant.

Specificity and nonspecificity in RNA-protein interactions

To fully understand the regulation of gene expression, it is critical to quantitatively define whether and how RNA-binding proteins (RBPs) discriminate between alternative binding sites in RNAs. Here, we describe new methods that measure protein binding to large numbers of RNA variants, and ways to analyse and interpret data obtained by these approaches, including affinity distributions and free energy landscapes. We discuss how the new methodologies and the associated concepts enable the development of inclusive, quantitative models for RNA-protein interactions that transcend the traditional binary classification of RBPs as either specific or nonspecific.

MORPHEUS, a Webtool for Transcription Factor Binding Analysis Using Position Weight Matrices with Dependency

Transcriptional networks are central to any biological process and changes affecting transcription factors or their binding sites in the genome are a key factor driving evolution. As more organisms are being sequenced, tools are needed to easily predict transcription factor binding sites (TFBS) presence and affinity from mere inspection of genomic sequences. Although many TFBS discovery algorithms exist, tools for using the DNA binding models they generate are relatively scarce and their use is limited among the biologist community by the lack of flexible and user-friendly tools. We have developed a suite of web tools (called Morpheus) based on the proven Position Weight Matrices (PWM) formalism that can be used without any programing skills and incorporates some unique features such as the presence of dependencies between nucleotides positions or the possibility to compute the predicted occupancy of a large regulatory region using a biophysical model. To illustrate the possibilities and simplicity of Morpheus tools in functional and evolutionary analysis, we have analysed the regulatory link between LEAFY, a key plant transcription factor involved in flower development, and its direct target gene APETALA1 during the divergence of Brassicales clade.

By the company they keep: interaction networks define the binding ability of transcription factors

Access to genome-wide data provides the opportunity to address questions concerning the ability of transcription factors (TFs) to assemble in distinct macromolecular complexes. Here, we introduce the PAnDA (Protein And DNA Associations) approach to characterize DNA associations with human TFs using expression profiles, protein–protein interactions and recognition motifs. Our method predicts TF binding events with >0.80 accuracy revealing cell-specific regulatory patterns that can be exploited for future investigations. Even when the precise DNA-binding motifs of a specific TF are not available, the information derived from protein-protein networks is sufficient to perform high-confidence predictions (area under the ROC curve of 0.89). PAnDA is freely available at http://service.tartaglialab.com/new_submission/panda.

Protein-DNA binding in high-resolution

Recent advances in experimental and computational methodologies are enabling ultra-high resolution genome-wide profiles of protein-DNA binding events. For example, the ChIP-exo protocol precisely characterizes protein-DNA cross-linking patterns by combining chromatin immunoprecipitation (ChIP) with 5' → 3' exonuclease digestion. Similarly, deeply sequenced chromatin accessibility assays (e.g. DNase-seq and ATAC-seq) enable the detection of protected footprints at protein-DNA binding sites. With these techniques and others, we have the potential to characterize the individual nucleotides that interact with transcription factors, nucleosomes, RNA polymerases and other regulatory proteins in a particular cellular context. In this review, we explain the experimental assays and computational analysis methods that enable high-resolution profiling of protein-DNA binding events. We discuss the challenges and opportunities associated with such approaches.

FastMotif: spectral sequence motif discovery

MOTIVATION

Sequence discovery tools play a central role in several fields of computational biology. In the framework of Transcription Factor binding studies, most of the existing motif finding algorithms are computationally demanding, and they may not be able to support the increasingly large datasets produced by modern high-throughput sequencing technologies.

RESULTS

We present FastMotif, a new motif discovery algorithm that is built on a recent machine learning technique referred to as Method of Moments. Based on spectral decompositions, our method is robust to model misspecifications and is not prone to locally optimal solutions. We obtain an algorithm that is extremely fast and designed for the analysis of big sequencing data. On HT-Selex data, FastMotif extracts motif profiles that match those computed by various state-of-the-art algorithms, but one order of magnitude faster. We provide a theoretical and numerical analysis of the algorithm's robustness and discuss its sensitivity with respect to the free parameters.

AVAILABILITY AND IMPLEMENTATION

The Matlab code of FastMotif is available from http://lcsb-portal.uni.lu/bioinformatics.

CONTACT

vlassis@adobe.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MARZ: an algorithm to combinatorially analyze gapped n-mer models of transcription factor binding

Background

A key challenge in understanding the molecular mechanisms that control gene regulation is the characterization of the specificity with which transcription factor proteins bind to specific DNA sequences. A number of computational approaches have been developed to examine these interactions, including simple mononucleotide and dinucleotide position weight matrix models.

Results

Here we develop a novel, unbiased computational algorithm, MARZ, that systematically analyzes all possible gapped matrices across a fixed number of nucleotides. In addition, to evaluate the ability of these matrix models to predict in vivo binding sites, we utilize a new scoring system and, in combination with established scoring methods and statistical analysis, test the performance of 32 different gapped matrices on the well characterized HUNCHBACK transcription factor in Drosophila.

Conclusions

Our results indicate that in many cases gapped matrix models can outperform traditional models, but that the relative strength of the binding sites considered in the analysis can profoundly influence the predictive ability of specific models.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0446-3) contains supplementary material, which is available to authorized users.

RNA-Seq and ChIP-Seq reveal SQSTM1/p62 as a key mediator of JunB suppression of NF-κB-dependent inflammation

Mice with epidermal deletion of JunB transcription factor displayed a psoriasis-like inflammation. The relevance of these findings to humans and the mechanisms mediating JunB function are not fully understood. Here we demonstrate that impaired JunB function via gene silencing or overexpression of a dominant negative mutant increased human keratinocyte cell proliferation but decreased cell barrier function. RNA-seq revealed over 500 genes affected by JunB loss of function, which included the upregulation of an array of proinflammatory molecules relevant to psoriasis. Among these were tumor necrosis factor α (TNFα), CCL2, CXCL10, IL6R, and SQSTM1, an adaptor protein involved in nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation. Chromatin immunoprecipitation (ChIP)-Seq and gene reporter analyses showed that JunB directly suppressed SQSTM1 by binding to a consensus AP-1 cis element located around 2 kb upstream of SQSTM1-transcription start site. Similar to JunB loss of function, SQSTM1-overexpression induced TNFα, CCL2, and CXCL10. Conversely, NF-κB inhibition genetically with a mutant IκBα or pharmacologically with pyrrolidine dithiocarbamate (PDTC) prevented cytokine, but not IL6R, induction by JunB deficiency. Taken together, our findings indicate that JunB controls epidermal growth, barrier formation, and proinflammatory responses through direct and indirect mechanisms, pinpointing SQSTM1 as a key mediator of JunB suppression of NF-κB-dependent inflammation.

De novo prediction of cis-regulatory elements and modules through integrative analysis of a large number of ChIP datasets

BACKGROUND

In eukaryotes, transcriptional regulation is usually mediated by interactions of multiple transcription factors (TFs) with their respective specific cis-regulatory elements (CREs) in the so-called cis-regulatory modules (CRMs) in DNA. Although the knowledge of CREs and CRMs in a genome is crucial to elucidate gene regulatory networks and understand many important biological phenomena, little is known about the CREs and CRMs in most eukaryotic genomes due to the difficulty to characterize them by either computational or traditional experimental methods. However, the exponentially increasing number of TF binding location data produced by the recent wide adaptation of chromatin immunoprecipitation coupled with microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) technologies has provided an unprecedented opportunity to identify CRMs and CREs in genomes. Nonetheless, how to effectively mine these large volumes of ChIP data to identify CREs and CRMs at nucleotide resolution is a highly challenging task.

RESULTS

We have developed a novel graph-theoretic based algorithm DePCRM for genome-wide de novo predictions of CREs and CRMs using a large number of ChIP datasets. DePCRM predicts CREs and CRMs by identifying overrepresented combinatorial CRE motif patterns in multiple ChIP datasets in an effective way. When applied to 168 ChIP datasets of 56 TFs from D. melanogaster, DePCRM identified 184 and 746 overrepresented CRE motifs and their combinatorial patterns, respectively, and predicted a total of 115,932 CRMs in the genome. The predictions recover 77.9% of known CRMs in the datasets and 89.3% of known CRMs containing at least one predicted CRE. We found that the putative CRMs as well as CREs as a whole in a CRM are more conserved than randomly selected sequences.

CONCLUSION

Our results suggest that the CRMs predicted by DePCRM are highly likely to be functional. Our algorithm is the first of its kind for de novo genome-wide prediction of CREs and CRMs using larger number of transcription factor ChIP datasets. The algorithm and predictions will hopefully facilitate the elucidation of gene regulatory networks in eukaryotes. All the predicted CREs, CRMs, and their target genes are available at http://bioinfo.uncc.edu/mniu/pcrms/www/.

Approaches for establishing the function of regulatory genetic variants involved in disease

The diversity of regulatory genetic variants and their mechanisms of action reflect the complexity and context-specificity of gene regulation. Regulatory variants are important in human disease and defining such variants and establishing mechanism is crucial to the interpretation of disease-association studies. This review describes approaches for identifying and functionally characterizing regulatory variants, illustrated using examples from common diseases. Insights from recent advances in resolving the functional epigenomic regulatory landscape in which variants act are highlighted, showing how this has enabled functional annotation of variants and the generation of hypotheses about mechanism of action. The utility of quantitative trait mapping at the transcript, protein and metabolite level to define association of specific genes with particular variants and further inform disease associations are reviewed. Establishing mechanism of action is an essential step in resolving functional regulatory variants, and this review describes how this is being facilitated by new methods for analyzing allele-specific expression, mapping chromatin interactions and advances in genome editing. Finally, integrative approaches are discussed together with examples highlighting how defining the mechanism of action of regulatory variants and identifying specific modulated genes can maximize the translational utility of genome-wide association studies to understand the pathogenesis of diseases and discover new drug targets or opportunities to repurpose existing drugs to treat them.

SeAMotE: a method for high-throughput motif discovery in nucleic acid sequences

Background

The large amount of data produced by high-throughput sequencing poses new computational challenges. In the last decade, several tools have been developed for the identification of transcription and splicing factor binding sites.

Results

Here, we introduce the SeAMotE (Sequence Analysis of Motifs Enrichment) algorithm for discovery of regulatory regions in nucleic acid sequences. SeAMotE provides (i) a robust analysis of high-throughput sequence sets, (ii) a motif search based on pattern occurrences and (iii) an easy-to-use web-server interface. We applied our method to recently published data including 351 chromatin immunoprecipitation (ChIP) and 13 crosslinking immunoprecipitation (CLIP) experiments and compared our results with those of other well-established motif discovery tools. SeAMotE shows an average accuracy of 80% in finding discriminative motifs and outperforms other methods available in literature.

Conclusions

SeAMotE is a fast, accurate and flexible algorithm for the identification of sequence patterns involved in protein-DNA and protein-RNA recognition. The server can be freely accessed at http://s.tartaglialab.com/new_submission/seamote.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-925) contains supplementary material, which is available to authorized users.

Computational approaches to interpreting genomic sequence variation

Identifying sequence variants that play a mechanistic role in human disease and other phenotypes is a fundamental goal in human genetics and will be important in translating the results of variation studies. Experimental validation to confirm that a variant causes the biochemical changes responsible for a given disease or phenotype is considered the gold standard, but this cannot currently be applied to the 3 million or so variants expected in an individual genome. This has prompted the development of a wide variety of computational approaches that use several different sources of information to identify functional variation. Here, we review and assess the limitations of computational techniques for categorizing variants according to functional classes, prioritizing variants for experimental follow-up and generating hypotheses about the possible molecular mechanisms to inform downstream experiments. We discuss the main current bioinformatics approaches to identifying functional variation, including widely used algorithms for coding variation such as SIFT and PolyPhen and also novel techniques for interpreting variation across the genome.

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.

Absence of a simple code: how transcription factors read the genome

Transcription factors (TFs) influence cell fate by interpreting the regulatory DNA within a genome. TFs recognize DNA in a specific manner; the mechanisms underlying this specificity have been identified for many TFs based on 3D structures of protein-DNA complexes. More recently, structural views have been complemented with data from high-throughput in vitro and in vivo explorations of the DNA-binding preferences of many TFs. Together, these approaches have greatly expanded our understanding of TF-DNA interactions. However, the mechanisms by which TFs select in vivo binding sites and alter gene expression remain unclear. Recent work has highlighted the many variables that influence TF-DNA binding, while demonstrating that a biophysical understanding of these many factors will be central to understanding TF function.

Transcriptome analysis in cardiomyocyte-specific differentiation of murine embryonic stem cells reveals transcriptional regulation network

The differentiation to cardiomyocytes is a prerequisite and an important part of heart development. A good understanding of the complicated cardiomyocyte differentiation process benefits cardiogenesis study. Embryonic stem cells (ESCs), cell lines with infinite ability to proliferate and to be differentiated into all cell types of the adult body, are important research tools for investigation of differentiation and meanwhile good models for developmental research. In the current study, genome-wide gene expression of ESCs is profiled through high throughput platform during cardiomyocyte-specific differentiation and maturation. Gene expression patterns of undifferentiated ESCs and ESC-derived cardiomyocytes provide a global overview of genes involved in cardiomyocyte-specific differentiation, whereas marker gene expression profiles of both ESC-related genes and cardiac-specific genes presented the expression pattern shift during differentiation in a pure ESC-derived cardiomyocyte cell culture system. The differentiation and maturation process was completed at day 19 after initiation of differentiation, according to our gene expression profile results. Functional analysis of regulated genes reveals over-represented biological processes, molecular functions and pathways during the differentiation and maturation process. Finally, transcription factor regulation networks were engineered based on gene expression data. Within these networks, the number of identified important regulators (Trim28, E2f4, Foxm1, Myc, Hdac1, Rara, Mef2c, Nkx2-5, Gata4) and possible key co-regulation modules (Nkx2-5 - Gata4 - Tbx5, Myc - E2F4) could be expanded. We demonstrate that a more comprehensive picture of cardiomyocyte differentiation and its regulation can be achieved solely by studying gene expression patterns. The results from our study contribute to a better and more accurate understanding of the regulation mechanisms during cardiomyocyte differentiation.

A general pairwise interaction model provides an accurate description of in vivo transcription factor binding sites

The identification of transcription factor binding sites (TFBSs) on genomic DNA is of crucial importance for understanding and predicting regulatory elements in gene networks. TFBS motifs are commonly described by Position Weight Matrices (PWMs), in which each DNA base pair contributes independently to the transcription factor (TF) binding. However, this description ignores correlations between nucleotides at different positions, and is generally inaccurate: analysing fly and mouse in vivo ChIPseq data, we show that in most cases the PWM model fails to reproduce the observed statistics of TFBSs. To overcome this issue, we introduce the pairwise interaction model (PIM), a generalization of the PWM model. The model is based on the principle of maximum entropy and explicitly describes pairwise correlations between nucleotides at different positions, while being otherwise as unconstrained as possible. It is mathematically equivalent to considering a TF-DNA binding energy that depends additively on each nucleotide identity at all positions in the TFBS, like the PWM model, but also additively on pairs of nucleotides. We find that the PIM significantly improves over the PWM model, and even provides an optimal description of TFBS statistics within statistical noise. The PIM generalizes previous approaches to interdependent positions: it accounts for co-variation of two or more base pairs, and predicts secondary motifs, while outperforming multiple-motif models consisting of mixtures of PWMs. We analyse the structure of pairwise interactions between nucleotides, and find that they are sparse and dominantly located between consecutive base pairs in the flanking region of TFBS. Nonetheless, interactions between pairs of non-consecutive nucleotides are found to play a significant role in the obtained accurate description of TFBS statistics. The PIM is computationally tractable, and provides a general framework that should be useful for describing and predicting TFBSs beyond PWMs.

Application of experimentally verified transcription factor binding sites models for computational analysis of ChIP-Seq data

Background

ChIP-Seq is widely used to detect genomic segments bound by transcription factors (TF), either directly at DNA binding sites (BSs) or indirectly via other proteins. Currently, there are many software tools implementing different approaches to identify TFBSs within ChIP-Seq peaks. However, their use for the interpretation of ChIP-Seq data is usually complicated by the absence of direct experimental verification, making it difficult both to set a threshold to avoid recognition of too many false-positive BSs, and to compare the actual performance of different models.

Results

Using ChIP-Seq data for FoxA2 binding loci in mouse adult liver and human HepG2 cells we compared FoxA binding-site predictions for four computational models of two fundamental classes: pattern matching based on existing training set of experimentally confirmed TFBSs (oPWM and SiteGA) and de novo motif discovery (ChIPMunk and diChIPMunk). To properly select prediction thresholds for the models, we experimentally evaluated affinity of 64 predicted FoxA BSs using EMSA that allows safely distinguishing sequences able to bind TF. As a result we identified thousands of reliable FoxA BSs within ChIP-Seq loci from mouse liver and human HepG2 cells. It was found that the performance of conventional position weight matrix (PWM) models was inferior with the highest false positive rate. On the contrary, the best recognition efficiency was achieved by the combination of SiteGA & diChIPMunk/ChIPMunk models, properly identifying FoxA BSs in up to 90% of loci for both mouse and human ChIP-Seq datasets.

Conclusions

The experimental study of TF binding to oligonucleotides corresponding to predicted sites increases the reliability of computational methods for TFBS-recognition in ChIP-Seq data analysis. Regarding ChIP-Seq data interpretation, basic PWMs have inferior TFBS recognition quality compared to the more sophisticated SiteGA and de novo motif discovery methods. A combination of models from different principles allowed identification of proper TFBSs.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-80) contains supplementary material, which is available to authorized users.

JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles

JASPAR (http://jaspar.genereg.net) is the largest open-access database of matrix-based nucleotide profiles describing the binding preference of transcription factors from multiple species. The fifth major release greatly expands the heart of JASPAR-the JASPAR CORE subcollection, which contains curated, non-redundant profiles-with 135 new curated profiles (74 in vertebrates, 8 in Drosophila melanogaster, 10 in Caenorhabditis elegans and 43 in Arabidopsis thaliana; a 30% increase in total) and 43 older updated profiles (36 in vertebrates, 3 in D. melanogaster and 4 in A. thaliana; a 9% update in total). The new and updated profiles are mainly derived from published chromatin immunoprecipitation-seq experimental datasets. In addition, the web interface has been enhanced with advanced capabilities in browsing, searching and subsetting. Finally, the new JASPAR release is accompanied by a new BioPython package, a new R tool package and a new R/Bioconductor data package to facilitate access for both manual and automated methods.

Protein-DNA binding: complexities and multi-protein codes

Binding of proteins to particular DNA sites across the genome is a primary determinant of specificity in genome maintenance and gene regulation. DNA-binding specificity is encoded at multiple levels, from the detailed biophysical interactions between proteins and DNA, to the assembly of multi-protein complexes. At each level, variation in the mechanisms used to achieve specificity has led to difficulties in constructing and applying simple models of DNA binding. We review the complexities in protein–DNA binding found at multiple levels and discuss how they confound the idea of simple recognition codes. We discuss the impact of new high-throughput technologies for the characterization of protein–DNA binding, and how these technologies are uncovering new complexities in protein–DNA recognition. Finally, we review the concept of multi-protein recognition codes in which new DNA-binding specificities are achieved by the assembly of multi-protein complexes.

Predicting tissue specific transcription factor binding sites

BACKGROUND

Studies of gene regulation often utilize genome-wide predictions of transcription factor (TF) binding sites. Most existing prediction methods are based on sequence information alone, ignoring biological contexts such as developmental stages and tissue types. Experimental methods to study in vivo binding, including ChIP-chip and ChIP-seq, can only study one transcription factor in a single cell type and under a specific condition in each experiment, and therefore cannot scale to determine the full set of regulatory interactions in mammalian transcriptional regulatory networks.

RESULTS

We developed a new computational approach, PIPES, for predicting tissue-specific TF binding. PIPES integrates in vitro protein binding microarrays (PBMs), sequence conservation and tissue-specific epigenetic (DNase I hypersensitivity) information. We demonstrate that PIPES improves over existing methods on distinguishing between in vivo bound and unbound sequences using ChIP-seq data for 11 mouse TFs. In addition, our predictions are in good agreement with current knowledge of tissue-specific TF regulation.

CONCLUSIONS

We provide a systematic map of computationally predicted tissue-specific binding targets for 284 mouse TFs across 55 tissue/cell types. Such comprehensive resource is useful for researchers studying gene regulation.