Identifying protein-binding sites from unaligned DNA fragments

Position-specific evolution in transcription factor binding sites, and a fast likelihood calculation for the F81 model

Transcription factor binding sites (TFBS), like other DNA sequence, evolve via mutation and selection relating to their function. Models of nucleotide evolution describe DNA evolution via single-nucleotide mutation. A stationary vector of such a model is the long-term distribution of nucleotides, unchanging under the model. Neutrally evolving sites may have uniform stationary vectors, but one expects that sites within a TFBS instead have stationary vectors reflective of the fitness of various nucleotides at those positions. We introduce 'position-specific stationary vectors' (PSSVs), the collection of stationary vectors at each site in a TFBS locus, analogous to the position weight matrix (PWM) commonly used to describe TFBS. We infer PSSVs for human TFs using two evolutionary models (Felsenstein 1981 and Hasegawa-Kishino-Yano 1985). We find that PSSVs reflect the nucleotide distribution from PWMs, but with reduced specificity. We infer ancestral nucleotide distributions at individual positions and calculate 'conditional PSSVs' conditioned on specific choices of majority ancestral nucleotide. We find that certain ancestral nucleotides exert a strong evolutionary pressure on neighbouring sequence while others have a negligible effect. Finally, we present a fast likelihood calculation for the F81 model on moderate-sized trees that makes this approach feasible for large-scale studies along these lines.

Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks

BACKGROUND

Transcription factors regulate gene expression by binding to transcription factor binding sites (TFBSs). Most models for predicting TFBSs are based on position weight matrices (PWMs), which require a specific motif to be present in the DNA sequence and do not consider interdependencies of nucleotides. Novel approaches such as Transcription Factor Flexible Models or recurrent neural networks consequently provide higher accuracies. However, it is unclear whether such approaches can uncover novel non-canonical, hitherto unexpected TFBSs relevant to human transcriptional regulation.

RESULTS

In this study, we trained a convolutional recurrent neural network with HT-SELEX data for GRHL1 binding and applied it to a set of GRHL1 binding sites obtained from ChIP-Seq experiments from human cells. We identified 46 non-canonical GRHL1 binding sites, which were not found by a conventional PWM approach. Unexpectedly, some of the newly predicted binding sequences lacked the CNNG core motif, so far considered obligatory for GRHL1 binding. Using isothermal titration calorimetry, we experimentally confirmed binding between the GRHL1-DNA binding domain and predicted GRHL1 binding sites, including a non-canonical GRHL1 binding site. Mutagenesis of individual nucleotides revealed a correlation between predicted binding strength and experimentally validated binding affinity across representative sequences. This correlation was neither observed with a PWM-based nor another deep learning approach.

CONCLUSIONS

Our results show that convolutional recurrent neural networks may uncover unanticipated binding sites and facilitate quantitative transcription factor binding predictions.

De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein-DNA binding

Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences, in vivo genomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained on in vitro TF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific, in vivo binding profiles. Conversely, deep learning models, trained on in vivo TF binding assays, effectively predict and explain genomic occupancy profiles as a function of complex regulatory sequence syntax, albeit without a clear biophysical interpretation. To reconcile these complementary models of in vitro and in vivo TF binding, we developed Affinity Distillation (AD), a method that extracts thermodynamic affinities de-novo from deep learning models of TF chromatin immunoprecipitation (ChIP) experiments by marginalizing away the influence of genomic sequence context. Applied to neural networks modeling diverse classes of yeast and mammalian TFs, AD predicts energetic impacts of sequence variation within and surrounding motifs on TF binding as measured by diverse in vitro assays with superior dynamic range and accuracy compared to motif-based methods. Furthermore, AD can accurately discern affinities of TF paralogs. Our results highlight thermodynamic affinity as a key determinant of in vivo binding, suggest that deep learning models of in vivo binding implicitly learn high-resolution affinity landscapes, and show that these affinities can be successfully distilled using AD. This new biophysical interpretation of deep learning models enables high-throughput in silico experiments to explore the influence of sequence context and variation on both intrinsic affinity and in vivo occupancy.

Minimal synthetic enhancers reveal control of the probability of transcriptional engagement and its timing by a morphogen gradient

How enhancers interpret morphogen gradients to generate gene expression patterns is a central question in developmental biology. Recent studies have proposed that enhancers can dictate whether, when, and at what rate promoters engage in transcription, but the complexity of endogenous enhancers calls for theoretical models with too many free parameters to quantitatively dissect these regulatory strategies. To overcome this limitation, we established a minimal promoter-proximal synthetic enhancer in embryos of Drosophila melanogaster. Here, a gradient of the Dorsal activator is read by a single Dorsal DNA binding site. Using live imaging to quantify transcriptional activity, we found that a single binding site can regulate whether promoters engage in transcription in a concentration-dependent manner. By modulating the binding-site affinity, we determined that a gene's decision to transcribe and its transcriptional onset time can be explained by a simple model where the promoter traverses multiple kinetic barriers before transcription can ensue.

A survey on protein–DNA-binding sites in computational biology

Transcription factors are important cellular components of the process of gene expression control. Transcription factor binding sites are locations where transcription factors specifically recognize DNA sequences, targeting gene-specific regions and recruiting transcription factors or chromatin regulators to fine-tune spatiotemporal gene regulation. As the common proteins, transcription factors play a meaningful role in life-related activities. In the face of the increase in the protein sequence, it is urgent how to predict the structure and function of the protein effectively. At present, protein–DNA-binding site prediction methods are based on traditional machine learning algorithms and deep learning algorithms. In the early stage, we usually used the development method based on traditional machine learning algorithm to predict protein–DNA-binding sites. In recent years, methods based on deep learning to predict protein–DNA-binding sites from sequence data have achieved remarkable success. Various statistical and machine learning methods used to predict the function of DNA-binding proteins have been proposed and continuously improved. Existing deep learning methods for predicting protein–DNA-binding sites can be roughly divided into three categories: convolutional neural network (CNN), recursive neural network (RNN) and hybrid neural network based on CNN–RNN. The purpose of this review is to provide an overview of the computational and experimental methods applied in the field of protein–DNA-binding site prediction today. This paper introduces the methods of traditional machine learning and deep learning in protein–DNA-binding site prediction from the aspects of data processing characteristics of existing learning frameworks and differences between basic learning model frameworks. Our existing methods are relatively simple compared with natural language processing, computational vision, computer graphics and other fields. Therefore, the summary of existing protein–DNA-binding site prediction methods will help researchers better understand this field.

Novel Transformer Networks for Improved Sequence Labeling in genomics

In genomics, a wide range of machine learning methodologies have been investigated to annotate biological sequences for positions of interest such as transcription start sites, translation initiation sites, methylation sites, splice sites and promoter start sites. In recent years, this area has been dominated by convolutional neural networks, which typically outperform previously-designed methods as a result of automated scanning for influential sequence motifs. However, those architectures do not allow for the efficient processing of the full genomic sequence. As an improvement, we introduce transformer architectures for whole genome sequence labeling tasks. We show that these architectures, recently introduced for natural language processing, are better suited for processing and annotating long DNA sequences. We apply existing networks and introduce an optimized method for the calculation of attention from input nucleotides. To demonstrate this, we evaluate our architecture on several sequence labeling tasks, and find it to achieve state-of-the-art performances when comparing it to specialized models for the annotation of transcription start sites, translation initiation sites and 4mC methylation in E. coli.

Explainability in transformer models for functional genomics

The effectiveness of deep learning methods can be largely attributed to the automated extraction of relevant features from raw data. In the field of functional genomics, this generally concerns the automatic selection of relevant nucleotide motifs from DNA sequences. To benefit from automated learning methods, new strategies are required that unveil the decision-making process of trained models. In this paper, we present a new approach that has been successful in gathering insights on the transcription process in Escherichia coli. This work builds upon a transformer-based neural network framework designed for prokaryotic genome annotation purposes. We find that the majority of subunits (attention heads) of the model are specialized towards identifying transcription factors and are able to successfully characterize both their binding sites and consensus sequences, uncovering both well-known and potentially novel elements involved in the initiation of the transcription process. With the specialization of the attention heads occurring automatically, we believe transformer models to be of high interest towards the creation of explainable neural networks in this field.

A survey on deep learning in DNA/RNA motif mining

DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- or RNA-protein binding site, which helps to understand the mechanism of gene regulation and management. For the past few decades, researchers have been working on designing new efficient and accurate algorithms for mining motif. These algorithms can be roughly divided into two categories: the enumeration approach and the probabilistic method. In recent years, machine learning methods had made great progress, especially the algorithm represented by deep learning had achieved good performance. Existing deep learning methods in motif mining can be roughly divided into three types of models: convolutional neural network (CNN) based models, recurrent neural network (RNN) based models, and hybrid CNN–RNN based models. We introduce the application of deep learning in the field of motif mining in terms of data preprocessing, features of existing deep learning architectures and comparing the differences between the basic deep learning models. Through the analysis and comparison of existing deep learning methods, we found that the more complex models tend to perform better than simple ones when data are sufficient, and the current methods are relatively simple compared with other fields such as computer vision, language processing (NLP), computer games, etc. Therefore, it is necessary to conduct a summary in motif mining by deep learning, which can help researchers understand this field.

THiCweed: fast, sensitive detection of sequence features by clustering big datasets

We present THiCweed, a new approach to analyzing transcription factor binding data from high-throughput chromatin immunoprecipitation-sequencing (ChIP-seq) experiments. THiCweed clusters bound regions based on sequence similarity using a divisive hierarchical clustering approach based on sequence similarity within sliding windows, while exploring both strands. ThiCweed is specially geared toward data containing mixtures of motifs, which present a challenge to traditional motif-finders. Our implementation is significantly faster than standard motif-finding programs, able to process 30 000 peaks in 1–2 h, on a single CPU core of a desktop computer. On synthetic data containing mixtures of motifs it is as accurate or more accurate than all other tested programs. THiCweed performs best with large ‘window’ sizes (≥50 bp), much longer than typical binding sites (7–15 bp). On real data it successfully recovers literature motifs, but also uncovers complex sequence characteristics in flanking DNA, variant motifs and secondary motifs even when they occur in <5% of the input, all of which appear biologically relevant. We also find recurring sequence patterns across diverse ChIP-seq datasets, possibly related to chromatin architecture and looping. THiCweed thus goes beyond traditional motif finding to give new insights into genomic transcription factor-binding complexity.

Review of Different Sequence Motif Finding Algorithms

The DNA motif discovery is a primary step in many systems for studying gene function. Motif discovery plays a vital role in identification of Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Over the past decades, different algorithms were used to design fast and accurate motif discovery tools. These algorithms are generally classified into consensus or probabilistic approaches that many of them are time-consuming and easily trapped in a local optimum. Nature-inspired algorithms and many of combinatorial algorithms are recently proposed to overcome these problems. This paper presents a general classification of motif discovery algorithms with new sub-categories that facilitate building a successful motif discovery algorithm. It also presents a summary of comparison between them.

Comparative Analysis of DNA Motif Discovery Algorithms: A Systemic Review

Background:

Bioinformatics is an interdisciplinary field that combines biology and information technology to study how to deal with the biological data. The DNA motif discovery problem is the main challenge of genome biology and its importance is directly proportional to increasing sequencing technologies which produce large amounts of data. DNA motif is a repeated portion of DNA sequences of major biological interest with important structural and functional features. Motif discovery plays a vital role in the antibody-biomarker identification which is useful for diagnosis of disease and to identify Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Recently, scientists discovered that the TFs have a mutation rate five times higher than the flanking sequences, so motif discovery also has a crucial role in cancer discovery.

Methods:

Over the past decades, many attempts use different algorithms to design fast and accurate motif discovery tools. These algorithms are generally classified into consensus or probabilistic approach.

Results:

Many of DNA motif discovery algorithms are time-consuming and easily trapped in a local optimum.

Conclusion:

Nature-inspired algorithms and many of combinatorial algorithms are recently proposed to overcome the problems of consensus and probabilistic approaches. This paper presents a general classification of motif discovery algorithms with new sub-categories. It also presents a summary comparison between them.

Dempster-Shafer Theory for the Prediction of Auxin-Response Elements (AuxREs) in Plant Genomes

Auxin is a major regulator of plant growth and development; its action involves transcriptional activation. The identification of Auxin-response element (AuxRE) is one of the most important issues to understand the Auxin regulation of gene expression. Over the past few years, a large number of motif identification tools have been developed. Despite these considerable efforts provided by computational biologists, building reliable models to predict regulatory elements has still been a difficult challenge. In this context, we propose in this work a data fusion approach for the prediction of AuxRE. Our method is based on the combined use of Dempster-Shafer evidence theory and fuzzy theory. To evaluate our model, we have scanning the DORNRÖSCHEN promoter by our model. All proven AuxRE present in the promoter has been detected. At the 0.9 threshold we have no false positive. The comparison of the results of our model and some previous motifs finding tools shows that our model can predict AuxRE more successfully than the other tools and produce less false positive. The comparison of the results before and after combination shows the importance of Dempster-Shafer combination in the decrease of false positive and to improve the reliability of prediction. For an overall evaluation we have chosen to present the performance of our approach in comparison with other methods. In fact, the results indicated that the data fusion method has the highest degree of sensitivity (Sn) and Positive Predictive Value (PPV).

ChIP-exo interrogation of Crp, DNA, and RNAP holoenzyme interactions

Numerous in vitro studies have yielded a refined picture of the structural and molecular associations between Cyclic-AMP receptor protein (Crp), the DNA motif, and RNA polymerase (RNAP) holoenzyme. In this study, high-resolution ChIP-exonuclease (ChIP-exo) was applied to study Crp binding in vivo and at genome-scale. Surprisingly, Crp was found to provide little to no protection of the DNA motif under activating conditions. Instead, Crp demonstrated binding patterns that closely resembled those generated by σ⁷⁰. The binding patterns of both Crp and σ⁷⁰ are indicative of RNAP holoenzyme DNA footprinting profiles associated with stages during transcription initiation that occur post-recruitment. This is marked by a pronounced advancement of the template strand footprint profile to the +20 position relative to the transcription start site and a multimodal distribution on the nontemplate strand. This trend was also observed in the familial transcription factor, Fnr, but full protection of the motif was seen in the repressor ArcA. Given the time-scale of ChIP studies and that the rate-limiting step in transcription initiation is typically post recruitment, we propose a hypothesis where Crp is absent from the DNA motif but remains associated with RNAP holoenzyme post-recruitment during transcription initiation. The release of Crp from the DNA motif may be a result of energetic changes that occur as RNAP holoenzyme traverses the various stable intermediates towards elongation complex formation.

Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding

Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target interacting with two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NNs) and biophysical models trained on these data, we establish that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more nonadditivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest affinity sites.

Sequence motif finder using memetic algorithm

Background

De novo prediction of Transcription Factor Binding Sites (TFBS) using computational methods is a difficult task and it is an important problem in Bioinformatics. The correct recognition of TFBS plays an important role in understanding the mechanisms of gene regulation and helps to develop new drugs.

Results

We here present Memetic Framework for Motif Discovery (MFMD), an algorithm that uses semi-greedy constructive heuristics as a local optimizer. In addition, we used a hybridization of the classic genetic algorithm as a global optimizer to refine the solutions initially found. MFMD can find and classify overrepresented patterns in DNA sequences and predict their respective initial positions. MFMD performance was assessed using ChIP-seq data retrieved from the JASPAR site, promoter sequences extracted from the ABS site, and artificially generated synthetic data. The MFMD was evaluated and compared with well-known approaches in the literature, called MEME and Gibbs Motif Sampler, achieving a higher f-score in the most datasets used in this work.

Conclusions

We have developed an approach for detecting motifs in biopolymers sequences. MFMD is a freely available software that can be promising as an alternative to the development of new tools for de novo motif discovery. Its open-source software can be downloaded at https://github.com/jadermcg/mfmd.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-2005-1) contains supplementary material, which is available to authorized users.

Inherent limitations of probabilistic models for protein-DNA binding specificity

The specificities of transcription factors are most commonly represented with probabilistic models. These models provide a probability for each base occurring at each position within the binding site and the positions are assumed to contribute independently. The model is simple and intuitive and is the basis for many motif discovery algorithms. However, the model also has inherent limitations that prevent it from accurately representing true binding probabilities, especially for the highest affinity sites under conditions of high protein concentration. The limitations are not due to the assumption of independence between positions but rather are caused by the non-linear relationship between binding affinity and binding probability and the fact that independent normalization at each position skews the site probabilities. Generally probabilistic models are reasonably good approximations, but new high-throughput methods allow for biophysical models with increased accuracy that should be used whenever possible.

Transcription factors (TFs), a class of DNA-binding proteins, play a central role in the regulation of gene expression. TFs control the rate of transcription by binding to the genome in a sequence-specific manner. Thus, one important aspect in the study of gene regulation mechanism is to model the binding specificities of TFs, namely the features of the DNA sequences that a TF prefers to bind. Multiple models have been proposed to characterize the binding specificities of TFs, among which the class of probabilistic models is the most popular. In this study, we point out several major limitations of the well-established probabilistic model by comparing it with the biophysical model. Through simulations we demonstrate that the probabilistic model is only an approximation of the biophysical model. The latter has most of the advantages of the former, and is a more accurate representation of binding specificities. We propose a shift from the probabilistic model to the biophysical model in future studies of protein-DNA interactions.

Validating regulatory predictions from diverse bacteria with mutant fitness data

Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium’s growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomics predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.

iFORM: Incorporating Find Occurrence of Regulatory Motifs

Accurately identifying the binding sites of transcription factors (TFs) is crucial to understanding the mechanisms of transcriptional regulation and human disease. We present incorporating Find Occurrence of Regulatory Motifs (iFORM), an easy-to-use and efficient tool for scanning DNA sequences with TF motifs described as position weight matrices (PWMs). Both performance assessment with a receiver operating characteristic (ROC) curve and a correlation-based approach demonstrated that iFORM achieves higher accuracy and sensitivity by integrating five classical motif discovery programs using Fisher’s combined probability test. We have used iFORM to provide accurate results on a variety of data in the ENCODE Project and the NIH Roadmap Epigenomics Project, and the tool has demonstrated its utility in further elucidating individual roles of functional elements. Both the source and binary codes for iFORM can be freely accessed at https://github.com/wenjiegroup/iFORM. The identified TF binding sites across human cell and tissue types using iFORM have been deposited in the Gene Expression Omnibus under the accession ID GSE53962.

Dynamics of Transcription Factor Binding Site Evolution

Evolution of gene regulation is crucial for our understanding of the phenotypic differences between species, populations and individuals. Sequence-specific binding of transcription factors to the regulatory regions on the DNA is a key regulatory mechanism that determines gene expression and hence heritable phenotypic variation. We use a biophysical model for directional selection on gene expression to estimate the rates of gain and loss of transcription factor binding sites (TFBS) in finite populations under both point and insertion/deletion mutations. Our results show that these rates are typically slow for a single TFBS in an isolated DNA region, unless the selection is extremely strong. These rates decrease drastically with increasing TFBS length or increasingly specific protein-DNA interactions, making the evolution of sites longer than ∼ 10 bp unlikely on typical eukaryotic speciation timescales. Similarly, evolution converges to the stationary distribution of binding sequences very slowly, making the equilibrium assumption questionable. The availability of longer regulatory sequences in which multiple binding sites can evolve simultaneously, the presence of “pre-sites” or partially decayed old sites in the initial sequence, and biophysical cooperativity between transcription factors, can all facilitate gain of TFBS and reconcile theoretical calculations with timescales inferred from comparative genomics.

Evolution has produced a remarkable diversity of living forms that manifests in qualitative differences as well as quantitative traits. An essential factor that underlies this variability is transcription factor binding sites, short pieces of DNA that control gene expression levels. Nevertheless, we lack a thorough theoretical understanding of the evolutionary times required for the appearance and disappearance of these sites. By combining a biophysically realistic model for how cells read out information in transcription factor binding sites with model for DNA sequence evolution, we explore these timescales and ask what factors crucially affect them. We find that the emergence of binding sites from a random sequence is generically slow under point and insertion/deletion mutational mechanisms. Strong selection, sufficient genomic sequence in which the sites can evolve, the existence of partially decayed old binding sites in the sequence, as well as certain biophysical mechanisms such as cooperativity, can accelerate the binding site gain times and make them consistent with the timescales suggested by comparative analyses of genomic data.

Sequence-based prediction of transcription upregulation by auxin in plants

Auxin is one of the main regulators of growth and development in plants. Prediction of auxin response based on gene sequence is of high importance. We found the TGTCNC consensus of 111 known natural and artificially mutated auxin response elements (AuxREs) with measured auxin-caused relative increase in genes' transcription levels, so-called either "a response to auxin" or "an auxin response." This consensus was identical to the most cited AuxRE motif. Also, we found several DNA sequence features that correlate with auxin-caused increase in genes' transcription levels, namely: number of matches with TGTCNC, homology score based on nucleotide frequencies at the consensus positions, abundances of five trinucleotides and five B-helical DNA features around these known AuxREs. We combined these correlations using a four-step empirical model of auxin response based on a gene's sequence with four steps, namely: (1) search for AuxREs with no auxin; (2) stop at the found AuxRE; (3) repression of the basal transcription of the gene having this AuxRE; and (4) manifold increase of this gene's transcription in response to auxin. Independently measured increases in transcription levels in response to auxin for 70 Arabidopsis genes were found to significantly correlate with predictions of this equation (r = 0.44, p < 0.001) as well as with TATA-binding protein (TBP)'s affinity to promoters of these genes and with nucleosome packing of these promoters (both, p < 0.025). Finally, we improved our equation for prediction of a gene's transcription increase in response to auxin by taking into account TBP-binding and nucleosome packing (r = 0.53, p < 10(-6)). Fisher's F-test validated the significant impact of both TBP/promoter-affinity and promoter nucleosome on auxin response in addition to those of AuxRE, F = 4.07, p < 0.025. It means that both TATA-box and nucleosome should be taken into account to recognize transcription factor binding sites upon DNA sequences: in the case of the TATA-less nucleosome-rich promoters, recognition scores must be higher than in the case of the TATA-containing nucleosome-free promoters at the same transcription activity.

Stochastic EM-based TFBS motif discovery with MITSU

Motivation: The Expectation–Maximization (EM) algorithm has been successfully applied to the problem of transcription factor binding site (TFBS) motif discovery and underlies the most widely used motif discovery algorithms. In the wider field of probabilistic modelling, the stochastic EM (sEM) algorithm has been used to overcome some of the limitations of the EM algorithm; however, the application of sEM to motif discovery has not been fully explored.

Results: We present MITSU (Motif discovery by ITerative Sampling and Updating), a novel algorithm for motif discovery, which combines sEM with an improved approximation to the likelihood function, which is unconstrained with regard to the distribution of motif occurrences within the input dataset. The algorithm is evaluated quantitatively on realistic synthetic data and several collections of characterized prokaryotic TFBS motifs and shown to outperform EM and an alternative sEM-based algorithm, particularly in terms of site-level positive predictive value.

Availability and implementation: Java executable available for download at http://www.sourceforge.net/p/mitsu-motif/, supported on Linux/OS X.

Contact:a.m.kilpatrick@sms.ed.ac.uk

Genome-wide identification of transcription factors and transcription-factor binding sites in oleaginous microalgae Nannochloropsis

Nannochloropsis spp. are a group of oleaginous microalgae that harbor an expanded array of lipid-synthesis related genes, yet how they are transcriptionally regulated remains unknown. Here a phylogenomic approach was employed to identify and functionally annotate the transcriptional factors (TFs) and TF binding-sites (TFBSs) in N. oceanica IMET1. Among 36 microalgae and higher plants genomes, a two-fold reduction in the number of TF families plus a seven-fold decrease of average family-size in Nannochloropsis, Rhodophyta and Chlorophyta were observed. The degree of similarity in TF-family profiles is indicative of the phylogenetic relationship among the species, suggesting co-evolution of TF-family profiles and species. Furthermore, comparative analysis of six Nannochloropsis genomes revealed 68 “most-conserved” TFBS motifs, with 11 of which predicted to be related to lipid accumulation or photosynthesis. Mapping the IMET1 TFs and TFBS motifs to the reference plant TF-“TFBS motif” relationships in TRANSFAC enabled the prediction of 78 TF-“TFBS motif” interaction pairs, which consisted of 34 TFs (with 11 TFs potentially involved in the TAG biosynthesis pathway), 30 TFBS motifs and 2,368 regulatory connections between TFs and target genes. Our results form the basis of further experiments to validate and engineer the regulatory network of Nannochloropsis spp. for enhanced biofuel production.

Saccharomyces genome database provides new regulation data

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is the community resource for genomic, gene and protein information about the budding yeast Saccharomyces cerevisiae, containing a variety of functional information about each yeast gene and gene product. We have recently added regulatory information to SGD and present it on a new tabbed section of the Locus Summary entitled 'Regulation'. We are compiling transcriptional regulator-target gene relationships, which are curated from the literature at SGD or imported, with permission, from the YEASTRACT database. For nearly every S. cerevisiae gene, the Regulation page displays a table of annotations showing the regulators of that gene, and a graphical visualization of its regulatory network. For genes whose products act as transcription factors, the Regulation page also shows a table of their target genes, accompanied by a Gene Ontology enrichment analysis of the biological processes in which those genes participate. We additionally synthesize information from the literature for each transcription factor in a free-text Regulation Summary, and provide other information relevant to its regulatory function, such as DNA binding site motifs and protein domains. All of the regulation data are available for querying, analysis and download via YeastMine, the InterMine-based data warehouse system in use at SGD.

Identification of cis-regulatory modules in promoters of human genes exploiting mutual positioning of transcription factors

In higher organisms, gene regulation is controlled by the interplay of non-random combinations of multiple transcription factors (TFs). Although numerous attempts have been made to identify these combinations, important details, such as mutual positioning of the factors that have an important role in the TF interplay, are still missing. The goal of the present work is in silico mapping of some of such associating factors based on their mutual positioning, using computational screening. We have selected the process of myogenesis as a study case, and we focused on TF combinations involving master myogenic TF Myogenic differentiation (MyoD) with other factors situated at specific distances from it. The results of our work show that some muscle-specific factors occur together with MyoD within the range of ±100 bp in a large number of promoters. We confirm co-occurrence of the MyoD with muscle-specific factors as described in earlier studies. However, we have also found novel relationships of MyoD with other factors not specific for muscle. Additionally, we have observed that MyoD tends to associate with different factors in proximal and distal promoter areas. The major outcome of our study is establishing the genome-wide connection between biological interactions of TFs and close co-occurrence of their binding sites.

An ant colony optimization based algorithm for identifying gene regulatory elements

It is one of the most important tasks in bioinformatics to identify the regulatory elements in gene sequences. Most of the existing algorithms for identifying regulatory elements are inclined to converge into a local optimum, and have high time complexity. Ant Colony Optimization (ACO) is a meta-heuristic method based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of real ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper designs and implements an ACO based algorithm named ACRI (ant-colony-regulatory-identification) for identifying all possible binding sites of transcription factor from the upstream of co-expressed genes. To accelerate the ants' searching process, a strategy of local optimization is presented to adjust the ants' start positions on the searched sequences. By exploiting the powerful optimization ability of ACO, the algorithm ACRI can not only improve precision of the results, but also achieve a very high speed. Experimental results on real world datasets show that ACRI can outperform other traditional algorithms in the respects of speed and quality of solutions.

A heuristic cluster-based EM algorithm for the planted (l, d) problem

The planted motif search problem arises from locating the transcription factor binding sites (TFBSs) which are crucial for understanding the gene regulatory relationship. Many attempts in using expectation maximization for TFBSs discovery are successful in past. However, identifying highly degenerate motifs and reducing the effect of local optima are still an arduous task. To alleviate the vulnerability of EM to local optima trapping, we present a heuristic cluster-based EM algorithm, CEM, which refines the cluster subsets in EM method to explore the best local optimal solution. Based on experiments using both synthetic and real datasets, our algorithm demonstrates significant improvements in identifying the motif instances and performs better than current widely used algorithms. CEM is a novel planted motif finding algorithm, which is able to solve the challenging instances and easy to parallel since the process of solving each cluster subset is independent.

PairMotif+: a fast and effective algorithm for de novo motif discovery in DNA sequences

The planted (l, d) motif search is one of the most widely studied problems in bioinformatics, which plays an important role in the identification of transcription factor binding sites in DNA sequences. However, it is still a challenging task to identify highly degenerate motifs, since current algorithms either output the exact results with a high computational cost or accomplish the computation in a short time but very often fall into a local optimum. In order to make a better trade-off between accuracy and efficiency, we propose a new pattern-driven algorithm, named PairMotif+. At first, some pairs of l-mers are extracted from input sequences according to probabilistic analysis and statistical method so that one or more pairs of motif instances are included in them. Then an approximate strategy for refining pairs of l-mers with high accuracy is adopted in order to avoid the verification of most candidate motifs. Experimental results on the simulated data show that PairMotif+ can solve various (l, d) problems within an hour on a PC with 2.67 GHz processor, and has a better identification accuracy than the compared algorithms MEME, AlignACE and VINE. Also, the validity of the proposed algorithm is tested on multiple real data sets.

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.

Screening of potential pseudo att sites of Streptomyces phage ΦC31 integrase in the human genome

AIM

ΦC31 integrase mediates site-specific recombination between two short sequences, attP and attB, in phage and bacterial genomes, which is a promising tool in gene regulation-based therapy since the zinc finger structure is probably the DNA recognizing domain that can further be engineered. The aim of this study was to screen potential pseudo att sites of ΦC31 integrase in the human genome, and evaluate the risks of its application in human gene therapy.

METHODS

TFBS (transcription factor binding sites) were found on the basis of reported pseudo att sites using multiple motif-finding tools, including AlignACE, BioProspector, Consensus, MEME, and Weeder. The human genome with the proposed motif was scanned to find the potential pseudo att sites of ΦC31 integrase.

RESULTS

The possible recognition motif of ΦC31 integrase was identified, which was composed of two co-occurrence conserved elements that were reverse complement to each other flanking the core sequence TTG. In the human genome, a total of 27924 potential pseudo att sites of ΦC31 integrase were found, which were distributed in each human chromosome with high-risk specificity values in the chromosomes 16, 17, and 19. When the risks of the sites were evaluate more rigorously, 53 hits were discovered, and some of them were just the vital functional genes or regulatory regions, such as ACYP2, AKR1B1, DUSP4, etc.

CONCLUSION

The results provide clues for more comprehensive evaluation of the risks of using ΦC31 integrase in human gene therapy and for drug discovery.

Evidence classification of high-throughput protocols and confidence integration in RegulonDB

RegulonDB provides curated information on the transcriptional regulatory network of Escherichia coli and contains both experimental data and computationally predicted objects. To account for the heterogeneity of these data, we introduced in version 6.0, a two-tier rating system for the strength of evidence, classifying evidence as either ‘weak’ or ‘strong’ (Gama-Castro,S., Jimenez-Jacinto,V., Peralta-Gil,M. et al. RegulonDB (Version 6.0): gene regulation model of Escherichia Coli K-12 beyond transcription, active (experimental) annotated promoters and textpresso navigation. Nucleic Acids Res., 2008;36:D120–D124.). We now add to our classification scheme the classification of high-throughput evidence, including chromatin immunoprecipitation (ChIP) and RNA-seq technologies. To integrate these data into RegulonDB, we present two strategies for the evaluation of confidence, statistical validation and independent cross-validation. Statistical validation involves verification of ChIP data for transcription factor-binding sites, using tools for motif discovery and quality assessment of the discovered matrices. Independent cross-validation combines independent evidence with the intention to mutually exclude false positives. Both statistical validation and cross-validation allow to upgrade subsets of data that are supported by weak evidence to a higher confidence level. Likewise, cross-validation of strong confidence data extends our two-tier rating system to a three-tier system by introducing a third confidence score ‘confirmed’.

Database URL:http://regulondb.ccg.unam.mx/

Efficient identification of transcription factor binding sites with a graph theoretic approach

Identifying transcription factor binding sites with experimental methods is often expensive and time consuming. Although many computational approaches and tools have been developed for this problem, the prediction accuracy is not satisfactory. In this paper, we develop a new computational approach that can model the relationships among all short sequence segments in the promoter regions with a graph theoretic model. Based on this model, finding the locations of transcription factor binding site is reduced to computing maximum weighted cliques in a graph with weighted edges. We have implemented this approach and used it to predict the binding sites in two organisms, Caenorhabditis elegans and mus musculus. We compared the prediction accuracy with that of the Gibbs Motif Sampler. We found that the accuracy of our approach is higher than or comparable with that of the Gibbs Motif Sampler for most of tested data and can accurately identify binding sites in cases where the Gibbs Motif Sampler has difficulty to predict their locations.

The twilight zone of cis element alignments

Sequence alignment of proteins and nucleic acids is a routine task in bioinformatics. Although the comparison of complete peptides, genes or genomes can be undertaken with a great variety of tools, the alignment of short DNA sequences and motifs entails pitfalls that have not been fully addressed yet. Here we confront the structural superposition of transcription factors with the sequence alignment of their recognized cis elements. Our goals are (i) to test TFcompare (http://floresta.eead.csic.es/tfcompare), a structural alignment method for protein–DNA complexes; (ii) to benchmark the pairwise alignment of regulatory elements; (iii) to define the confidence limits and the twilight zone of such alignments and (iv) to evaluate the relevance of these thresholds with elements obtained experimentally. We find that the structure of cis elements and protein–DNA interfaces is significantly more conserved than their sequence and measures how this correlates with alignment errors when only sequence information is considered. Our results confirm that DNA motifs in the form of matrices produce better alignments than individual sequences. Finally, we report that empirical and theoretically derived twilight thresholds are useful for estimating the natural plasticity of regulatory sequences, and hence for filtering out unreliable alignments.

Comprehensive human transcription factor binding site map for combinatory binding motifs discovery

To know the map between transcription factors (TFs) and their binding sites is essential to reverse engineer the regulation process. Only about 10%-20% of the transcription factor binding motifs (TFBMs) have been reported. This lack of data hinders understanding gene regulation. To address this drawback, we propose a computational method that exploits never used TF properties to discover the missing TFBMs and their sites in all human gene promoters. The method starts by predicting a dictionary of regulatory "DNA words." From this dictionary, it distills 4098 novel predictions. To disclose the crosstalk between motifs, an additional algorithm extracts TF combinatorial binding patterns creating a collection of TF regulatory syntactic rules. Using these rules, we narrowed down a list of 504 novel motifs that appear frequently in syntax patterns. We tested the predictions against 509 known motifs confirming that our system can reliably predict ab initio motifs with an accuracy of 81%-far higher than previous approaches. We found that on average, 90% of the discovered combinatorial binding patterns target at least 10 genes, suggesting that to control in an independent manner smaller gene sets, supplementary regulatory mechanisms are required. Additionally, we discovered that the new TFBMs and their combinatorial patterns convey biological meaning, targeting TFs and genes related to developmental functions. Thus, among all the possible available targets in the genome, the TFs tend to regulate other TFs and genes involved in developmental functions. We provide a comprehensive resource for regulation analysis that includes a dictionary of "DNA words," newly predicted motifs and their corresponding combinatorial patterns. Combinatorial patterns are a useful filter to discover TFBMs that play a major role in orchestrating other factors and thus, are likely to lock/unlock cellular functional clusters.

Sequence evolution of the intrinsically disordered and globular domains of a model viral oncoprotein

In the present work, we have used the papillomavirus E7 oncoprotein to pursue structure-function and evolutionary studies that take into account intrinsic disorder and the conformational diversity of globular domains. The intrinsically disordered (E7N) and globular (E7C) domains of E7 show similar degrees of conservation and co-evolution. We found that E7N can be described in terms of conserved and coevolving linear motifs separated by variable linkers, while sequence evolution of E7C is compatible with the known homodimeric structure yet suggests other activities for the domain. Within E7N, inter-residue relationships such as residue co-evolution and restricted intermotif distances map functional coupling and co-occurrence of linear motifs that evolve in a coordinate manner. Within E7C, additional cysteine residues proximal to the zinc-binding site may allow redox regulation of E7 function. Moreover, we describe a conserved binding site for disordered domains on the surface of E7C and suggest a putative target linear motif. Both homodimerization and peptide binding activities of E7C are also present in the distantly related host PHD domains, showing that these two proteins share not only structural homology but also functional similarities, and strengthening the view that they evolved from a common ancestor. Finally, we integrate the multiple activities and conformations of E7 into a hierarchy of structure-function relationships.

Systematic prediction of cis-regulatory elements in the Chlamydomonas reinhardtii genome using comparative genomics

Chlamydomonas reinhardtii is one of the most important microalgae model organisms and has been widely studied toward the understanding of chloroplast functions and various cellular processes. Further exploitation of C. reinhardtii as a model system to elucidate various molecular mechanisms and pathways requires systematic study of gene regulation. However, there is a general lack of genome-scale gene regulation study, such as global cis-regulatory element (CRE) identification, in C. reinhardtii. Recently, large-scale genomic data in microalgae species have become available, which enable the development of efficient computational methods to systematically identify CREs and characterize their roles in microalgae gene regulation. Here, we performed in silico CRE identification at the whole genome level in C. reinhardtii using a comparative genomics-based method. We predicted a large number of CREs in C. reinhardtii that are consistent with experimentally verified CREs. We also discovered that a large percentage of these CREs form combinations and have the potential to work together for coordinated gene regulation in C. reinhardtii. Multiple lines of evidence from literature, gene transcriptional profiles, and gene annotation resources support our prediction. The predicted CREs will serve, to our knowledge, as the first large-scale collection of CREs in C. reinhardtii to facilitate further experimental study of microalgae gene regulation. The accompanying software tool and the predictions in C. reinhardtii are also made available through a Web-accessible database (http://hulab.ucf.edu/research/projects/Microalgae/sdcre/motifcomb.html).

Motif analysis unveils the possible co-regulation of chloroplast genes and nuclear genes encoding chloroplast proteins

Chloroplasts play critical roles in land plant cells. Despite their importance and the availability of at least 200 sequenced chloroplast genomes, the number of known DNA regulatory sequences in chloroplast genomes are limited. In this paper, we designed computational methods to systematically study putative DNA regulatory sequences in intergenic regions near chloroplast genes in seven plant species and in promoter sequences of nuclear genes in Arabidopsis and rice. We found that -35/-10 elements alone cannot explain the transcriptional regulation of chloroplast genes. We also concluded that there are unlikely motifs shared by intergenic sequences of most of chloroplast genes, indicating that these genes are regulated differently. Finally and surprisingly, we found five conserved motifs, each of which occurs in no more than six chloroplast intergenic sequences, are significantly shared by promoters of nuclear-genes encoding chloroplast proteins. By integrating information from gene function annotation, protein subcellular localization analyses, protein-protein interaction data, and gene expression data, we further showed support of the functionality of these conserved motifs. Our study implies the existence of unknown nuclear-encoded transcription factors that regulate both chloroplast genes and nuclear genes encoding chloroplast protein, which sheds light on the understanding of the transcriptional regulation of chloroplast genes.

Optimizing the GATA-3 position weight matrix to improve the identification of novel binding sites

BACKGROUND

The identifying of binding sites for transcription factors is a key component of gene regulatory network analysis. This is often done using position-weight matrices (PWMs). Because of the importance of in silico mapping of tentative binding sites, we previously developed an approach for PWM optimization that substantially improves the accuracy of such mapping.

RESULTS

The present work implements the optimization algorithm applied to the existing PWM for GATA-3 transcription factor and builds a new di-nucleotide PWM. The existing available PWM is based on experimental data adopted from Jaspar. The optimized PWM substantially improves the sensitivity and specificity of the TF mapping compared to the conventional applications. The refined PWM also facilitates in silico identification of novel binding sites that are supported by experimental data. We also describe uncommon positioning of binding motifs for several T-cell lineage specific factors in human promoters.

CONCLUSION

Our proposed di-nucleotide PWM approach outperforms the conventional mono-nucleotide PWM approach with respect to GATA-3. Therefore our new di-nucleotide PWM provides new insight into plausible transcriptional regulatory interactions in human promoters.

Direct vs 2-stage approaches to structured motif finding

Background

The notion of DNA motif is a mathematical abstraction used to model regions of the DNA (known as Transcription Factor Binding Sites, or TFBSs) that are bound by a given Transcription Factor to regulate gene expression or repression. In turn, DNA structured motifs are a mathematical counterpart that models sets of TFBSs that work in concert in the gene regulations processes of higher eukaryotic organisms. Typically, a structured motif is composed of an ordered set of isolated (or simple) motifs, separated by a variable, but somewhat constrained number of “irrelevant” base-pairs. Discovering structured motifs in a set of DNA sequences is a computationally hard problem that has been addressed by a number of authors using either a direct approach, or via the preliminary identification and successive combination of simple motifs.

Results

We describe a computational tool, named SISMA, for the de-novo discovery of structured motifs in a set of DNA sequences. SISMA is an exact, enumerative algorithm, meaning that it finds all the motifs conforming to the specifications. It does so in two stages: first it discovers all the possible component simple motifs, then combines them in a way that respects the given constraints. We developed SISMA mainly with the aim of understanding the potential benefits of such a 2-stage approach w.r.t. direct methods. In fact, no 2-stage software was available for the general problem of structured motif discovery, but only a few tools that solved restricted versions of the problem. We evaluated SISMA against other published tools on a comprehensive benchmark made of both synthetic and real biological datasets. In a significant number of cases, SISMA outperformed the competitors, exhibiting a good performance also in most of the cases in which it was inferior.

Conclusions

A reflection on the results obtained lead us to conclude that a 2-stage approach can be implemented with many advantages over direct approaches. Some of these have to do with greater modularity, ease of parallelization, and the possibility to perform adaptive searches of structured motifs. As another consideration, we noted that most hard instances for SISMA were easy to detect in advance. In these cases one may initially opt for a direct method; or, as a viable alternative in most laboratories, one could run both direct and 2-stage tools in parallel, halting the computations when the first halts.

Transcription factor binding sites detection by using alignment-based approach

Gene expression is the main cause for the existence of various phenotypes. Through this procedure, the information stored in DNA rises to the phenotype. Essentially, gene expression is dependent upon the successful binding of transcription factors (TFs) - a specific type of proteins - to explicit positions in its upstream, TF binding sites (TFBSs). Unfortunately, finding these TFBSs is costly and laborious; therefore, discovering TFBSs computationally is a significant problem that many researches endeavor to solve. In this paper, a new TFBS discovery method is presented by considering known biological facts about TFBSs. The input to this method includes sequences with arbitrary lengths and the output comprises positions that tend to be TFBS. Through the application of previous methods along with a method that focuses on biological and simulated datasets, it is shown that this method achieves higher accuracy in discovering TFBSs.

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.

Fast, sensitive discovery of conserved genome-wide motifs

Regulatory sites that control gene expression are essential to the proper functioning of cells, and identifying them is critical for modeling regulatory networks. We have developed Magma (Multiple Aligner of Genomic Multiple Alignments), a software tool for multiple species, multiple gene motif discovery. Magma identifies putative regulatory sites that are conserved across multiple species and occur near multiple genes throughout a reference genome. Magma takes as input multiple alignments that can include gaps. It uses efficient clustering methods that make it about 70 times faster than PhyloNet, a previous program for this task, with slightly greater sensitivity. We ran Magma on all non-coding DNA conserved between Caenorhabditis elegans and five additional species, about 70 Mbp in total, in <4 h. We obtained 2,309 motifs with lengths of 6-20 bp, each occurring at least 10 times throughout the genome, which collectively covered about 566 kbp of the genomes, approximately 0.8% of the input. Predicted sites occurred in all types of non-coding sequence but were especially enriched in the promoter regions. Comparisons to several experimental datasets show that Magma motifs correspond to a variety of known regulatory motifs.

GPUmotif: an ultra-fast and energy-efficient motif analysis program using graphics processing units

Computational detection of TF binding patterns has become an indispensable tool in functional genomics research. With the rapid advance of new sequencing technologies, large amounts of protein-DNA interaction data have been produced. Analyzing this data can provide substantial insight into the mechanisms of transcriptional regulation. However, the massive amount of sequence data presents daunting challenges. In our previous work, we have developed a novel algorithm called Hybrid Motif Sampler (HMS) that enables more scalable and accurate motif analysis. Despite much improvement, HMS is still time-consuming due to the requirement to calculate matching probabilities position-by-position. Using the NVIDIA CUDA toolkit, we developed a graphics processing unit (GPU)-accelerated motif analysis program named GPUmotif. We proposed a "fragmentation" technique to hide data transfer time between memories. Performance comparison studies showed that commonly-used model-based motif scan and de novo motif finding procedures such as HMS can be dramatically accelerated when running GPUmotif on NVIDIA graphics cards. As a result, energy consumption can also be greatly reduced when running motif analysis using GPUmotif. The GPUmotif program is freely available at http://sourceforge.net/projects/gpumotif/

PDZ domain-containing 1 (PDZK1) protein regulates phospholipase C-β3 (PLC-β3)-specific activation of somatostatin by forming a ternary complex with PLC-β3 and somatostatin receptors

Phospholipase C-β (PLC-β) is a key molecule in G protein-coupled receptor (GPCR)-mediated signaling. Many studies have shown that the four PLC-β subtypes have different physiological functions despite their similar structures. Because the PLC-β subtypes possess different PDZ-binding motifs, they have the potential to interact with different PDZ proteins. In this study, we identified PDZ domain-containing 1 (PDZK1) as a PDZ protein that specifically interacts with PLC-β3. To elucidate the functional roles of PDZK1, we next screened for potential interacting proteins of PDZK1 and identified the somatostatin receptors (SSTRs) as another protein that interacts with PDZK1. Through these interactions, PDZK1 assembles as a ternary complex with PLC-β3 and SSTRs. Interestingly, the expression of PDZK1 and PLC-β3, but not PLC-β1, markedly potentiated SST-induced PLC activation. However, disruption of the ternary complex inhibited SST-induced PLC activation, which suggests that PDZK1-mediated complex formation is required for the specific activation of PLC-β3 by SST. Consistent with this observation, the knockdown of PDZK1 or PLC-β3, but not that of PLC-β1, significantly inhibited SST-induced intracellular Ca(2+) mobilization, which further attenuated subsequent ERK1/2 phosphorylation. Taken together, our results strongly suggest that the formation of a complex between SSTRs, PDZK1, and PLC-β3 is essential for the specific activation of PLC-β3 and the subsequent physiologic responses by SST.

Rewiring of PDZ domain-ligand interaction network contributed to eukaryotic evolution

PDZ domain-mediated interactions have greatly expanded during metazoan evolution, becoming important for controlling signal flow via the assembly of multiple signaling components. The evolutionary history of PDZ domain-mediated interactions has never been explored at the molecular level. It is of great interest to understand how PDZ domain-ligand interactions emerged and how they become rewired during evolution. Here, we constructed the first human PDZ domain-ligand interaction network (PDZNet) together with binding motif sequences and interaction strengths of ligands. PDZNet includes 1,213 interactions between 97 human PDZ proteins and 591 ligands that connect most PDZ protein-mediated interactions (98%) in a large single network via shared ligands. We examined the rewiring of PDZ domain-ligand interactions throughout eukaryotic evolution by tracing changes in the C-terminal binding motif sequences of the PDZ ligands. We found that interaction rewiring by sequence mutation frequently occurred throughout evolution, largely contributing to the growth of PDZNet. The rewiring of PDZ domain-ligand interactions provided an effective means of functional innovations in nervous system development. Our findings provide empirical evidence for a network evolution model that highlights the rewiring of interactions as a mechanism for the development of new protein functions. PDZNet will be a valuable resource to further characterize the organization of the PDZ domain-mediated signaling proteome.

Rewiring of interactions is a powerful tool for the evolution of organism complexity. Rewiring among preexisting proteins provides a simple mechanism for the development of new signaling circuits by redirecting information flows without a gain or loss of genes. Particularly, interactions mediated by short linear motifs can be easily changed by mutations during evolution, resulting in a rewiring of interactions. However, how interaction rewiring of linear motif interactions facilitates the emergence of new protein function during evolution is poorly understood. Here, we systematically investigated the rewiring of interactions mediated by PDZ domains, which are one of the most commonly found peptide recognition modules. We found that PDZ domain-ligand interactions are frequently rewired by C-terminal sequence mutations in PDZ ligands during evolution. Especially, rewiring of PDZ domain-ligand interactions was involved in neuronal function development, occurring concurrently with the emergence of vertebrates and suggesting that reorganization of signaling pathways by rewiring PDZ domain-ligand interactions significantly contributed to the evolution of nervous systems in vertebrates. Our findings highlight the rewiring of interactions as an effective means for functional innovation, providing new insight into eukaryotic evolution, which has not been fully explained by only the expansion of protein families.

Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

Identification of RNA-protein interaction networks using PAR-CLIP

All mRNA molecules are subject to some degree of post-transcriptional gene regulation (PTGR) involving sequence-dependent modulation of splicing, cleavage and polyadenylation, editing, transport, stability, and translation. The recent introduction of deep-sequencing technologies enabled the development of new methods for broadly mapping interaction sites between RNA-binding proteins (RBPs) and their RNA target sites. In this article, we review crosslinking and immunoprecipitation (CLIP) methods adapted for large-scale identification of target RNA-binding sites and the respective RNA recognition elements. CLIP methods have the potential to detect hundreds of thousands of binding sites in single experiments although the separation of signal from noise can be challenging. As a consequence, each CLIP method has developed different strategies to distinguish true targets from background. We focus on photoactivatable ribonucleoside-enhanced CLIP, which relies on the intracellular incorporation of photoactivatable ribonucleoside analogs into nascent transcripts, and yields characteristic sequence changes upon crosslinking that facilitate the separation of signal from noise. The precise knowledge of the position and distribution of binding sites across mature and primary mRNA transcripts allows critical insights into cellular localization and regulatory function of the examined RBP. When coupled with other systems-wide approaches measuring transcript and protein abundance, the generation of high-resolution RBP-binding site maps across the transcriptome will broaden our understanding of PTGR and thereby lead to new strategies for therapeutic treatment of genetic diseases perturbing these processes.