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1.  RAP: Accurate and Fast Motif Finding Based on Protein-Binding Microarray Data 
Journal of Computational Biology  2013;20(5):375-382.
Abstract
The novel high-throughput technology of protein-binding microarrays (PBMs) measures binding intensity of a transcription factor to thousands of DNA probe sequences. Several algorithms have been developed to extract binding-site motifs from these data. Such motifs are commonly represented by positional weight matrices. Previous studies have shown that the motifs produced by these algorithms are either accurate in predicting in vitro binding or similar to previously published motifs, but not both. In this work, we present a new simple algorithm to infer binding-site motifs from PBM data. It outperforms prior art both in predicting in vitro binding and in producing motifs similar to literature motifs. Our results challenge previous claims that motifs with lower information content are better models for transcription-factor binding specificity. Moreover, we tested the effect of motif length and side positions flanking the “core” motif in the binding site. We show that side positions have a significant effect and should not be removed, as commonly done. A large drop in the results quality of all methods is observed between in vitro and in vivo binding prediction. The software is available on acgt.cs.tau.ac.il/rap.
doi:10.1089/cmb.2012.0253
PMCID: PMC3646338  PMID: 23464877
motif finding; protein-binding microarray; protein-binding site
2.  Identification of co-occurring transcription factor binding sites from DNA sequence using clustered position weight matrices 
Nucleic Acids Research  2011;40(5):e38.
Accurate prediction of transcription factor binding sites (TFBSs) is a prerequisite for identifying cis-regulatory modules that underlie transcriptional regulatory circuits encoded in the genome. Here, we present a computational framework for detecting TFBSs, when multiple position weight matrices (PWMs) for a transcription factor are available. Grouping multiple PWMs of a transcription factor (TF) based on their sequence similarity improves the specificity of TFBS prediction, which was evaluated using multiple genome-wide ChIP-Seq data sets from 26 TFs. The Z-scores of the area under a receiver operating characteristic curve (AUC) values of 368 TFs were calculated and used to statistically identify co-occurring regulatory motifs in the TF bound ChIP loci. Motifs that are co-occurring along with the empirical bindings of E2F, JUN or MYC have been evaluated, in the basal or stimulated condition. Results prove our method can be useful to systematically identify the co-occurring motifs of the TF for the given conditions.
doi:10.1093/nar/gkr1252
PMCID: PMC3300004  PMID: 22187154
3.  A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval 
PLoS Computational Biology  2008;4(2):e1000010.
Characterizing the DNA-binding specificities of transcription factors is a key problem in computational biology that has been addressed by multiple algorithms. These usually take as input sequences that are putatively bound by the same factor and output one or more DNA motifs. A common practice is to apply several such algorithms simultaneously to improve coverage at the price of redundancy. In interpreting such results, two tasks are crucial: clustering of redundant motifs, and attributing the motifs to transcription factors by retrieval of similar motifs from previously characterized motif libraries. Both tasks inherently involve motif comparison. Here we present a novel method for comparing and merging motifs, based on Bayesian probabilistic principles. This method takes into account both the similarity in positional nucleotide distributions of the two motifs and their dissimilarity to the background distribution. We demonstrate the use of the new comparison method as a basis for motif clustering and retrieval procedures, and compare it to several commonly used alternatives. Our results show that the new method outperforms other available methods in accuracy and sensitivity. We incorporated the resulting motif clustering and retrieval procedures in a large-scale automated pipeline for analyzing DNA motifs. This pipeline integrates the results of various DNA motif discovery algorithms and automatically merges redundant motifs from multiple training sets into a coherent annotated library of motifs. Application of this pipeline to recent genome-wide transcription factor location data in S. cerevisiae successfully identified DNA motifs in a manner that is as good as semi-automated analysis reported in the literature. Moreover, we show how this analysis elucidates the mechanisms of condition-specific preferences of transcription factors.
Author Summary
Regulation of gene expression plays a central role in the activity of living cells and in their response to internal (e.g., cell division) or external (e.g., stress) stimuli. Key players in determining gene-specific regulation are transcription factors that bind sequence-specific sites on the DNA, modulating the expression of nearby genes. To understand the regulatory program of the cell, we need to identify these transcription factors, when they act, and on which genes. Transcription regulatory maps can be assembled by computational analysis of experimental data, by discovering the DNA recognition sequences (motifs) of transcription factors and their occurrences along the genome. Such an analysis usually results in a large number of overlapping motifs. To reconstruct regulatory maps, it is crucial to combine similar motifs and to relate them to transcription factors. To this end we developed an accurate fully-automated method, termed BLiC, based upon an improved similarity measure for comparing DNA motifs. By applying it to genome-wide data in yeast, we identified the DNA motifs of transcription factors and their putative target genes. Finally, we analyze motifs of transcription factor that alter their target genes under different conditions, and show how cells adjust their regulatory program in response to environmental changes.
doi:10.1371/journal.pcbi.1000010
PMCID: PMC2265534  PMID: 18463706
4.  Improved benchmarks for computational motif discovery 
BMC Bioinformatics  2007;8:193.
Background
An important step in annotation of sequenced genomes is the identification of transcription factor binding sites. More than a hundred different computational methods have been proposed, and it is difficult to make an informed choice. Therefore, robust assessment of motif discovery methods becomes important, both for validation of existing tools and for identification of promising directions for future research.
Results
We use a machine learning perspective to analyze collections of transcription factors with known binding sites. Algorithms are presented for finding position weight matrices (PWMs), IUPAC-type motifs and mismatch motifs with optimal discrimination of binding sites from remaining sequence. We show that for many data sets in a recently proposed benchmark suite for motif discovery, none of the common motif models can accurately discriminate the binding sites from remaining sequence. This may obscure the distinction between the potential performance of the motif discovery tool itself versus the intrinsic complexity of the problem we are trying to solve. Synthetic data sets may avoid this problem, but we show on some previously proposed benchmarks that there may be a strong bias towards a presupposed motif model. We also propose a new approach to benchmark data set construction. This approach is based on collections of binding site fragments that are ranked according to the optimal level of discrimination achieved with our algorithms. This allows us to select subsets with specific properties. We present one benchmark suite with data sets that allow good discrimination between positive and negative instances with the common motif models. These data sets are suitable for evaluating algorithms for motif discovery that rely on these models. We present another benchmark suite where PWM, IUPAC and mismatch motif models are not able to discriminate reliably between positive and negative instances. This suite could be used for evaluating more powerful motif models.
Conclusion
Our improved benchmark suites have been designed to differentiate between the performance of motif discovery algorithms and the power of motif models. We provide a web server where users can download our benchmark suites, submit predictions and visualize scores on the benchmarks.
doi:10.1186/1471-2105-8-193
PMCID: PMC1903367  PMID: 17559676
5.  The Next Generation of Transcription Factor Binding Site Prediction 
PLoS Computational Biology  2013;9(9):e1003214.
Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regulation at a transcriptional level. Classically, computational prediction of TF binding sites (TFBSs) is based on basic position weight matrices (PWMs) which quantitatively score binding motifs based on the observed nucleotide patterns in a set of TFBSs for the corresponding TF. Such models make the strong assumption that each nucleotide participates independently in the corresponding DNA-protein interaction and do not account for flexible length motifs. We introduce transcription factor flexible models (TFFMs) to represent TF binding properties. Based on hidden Markov models, TFFMs are flexible, and can model both position interdependence within TFBSs and variable length motifs within a single dedicated framework. The availability of thousands of experimentally validated DNA-TF interaction sequences from ChIP-seq allows for the generation of models that perform as well as PWMs for stereotypical TFs and can improve performance for TFs with flexible binding characteristics. We present a new graphical representation of the motifs that convey properties of position interdependence. TFFMs have been assessed on ChIP-seq data sets coming from the ENCODE project, revealing that they can perform better than both PWMs and the dinucleotide weight matrix extension in discriminating ChIP-seq from background sequences. Under the assumption that ChIP-seq signal values are correlated with the affinity of the TF-DNA binding, we find that TFFM scores correlate with ChIP-seq peak signals. Moreover, using available TF-DNA affinity measurements for the Max TF, we demonstrate that TFFMs constructed from ChIP-seq data correlate with published experimentally measured DNA-binding affinities. Finally, TFFMs allow for the straightforward computation of an integrated TF occupancy score across a sequence. These results demonstrate the capacity of TFFMs to accurately model DNA-protein interactions, while providing a single unified framework suitable for the next generation of TFBS prediction.
Author Summary
Transcription factors are critical proteins for sequence-specific control of transcriptional regulation. Finding where these proteins bind to DNA is of key importance for global efforts to decipher the complex mechanisms of gene regulation. Greater understanding of the regulation of transcription promises to improve human genetic analysis by specifying critical gene components that have eluded investigators. Classically, computational prediction of transcription factor binding sites (TFBS) is based on models giving weights to each nucleotide at each position. We introduce a novel statistical model for the prediction of TFBS tolerant of a broader range of TFBS configurations than can be conveniently accommodated by existing methods. The new models are designed to address the confounding properties of nucleotide composition, inter-positional sequence dependence and variable lengths (e.g. variable spacing between half-sites) observed in the more comprehensive experimental data now emerging. The new models generate scores consistent with DNA-protein affinities measured experimentally and can be represented graphically, retaining desirable attributes of past methods. It demonstrates the capacity of the new approach to accurately assess DNA-protein interactions. With the rich experimental data generated from chromatin immunoprecipitation experiments, a greater diversity of TFBS properties has emerged that can now be accommodated within a single predictive approach.
doi:10.1371/journal.pcbi.1003214
PMCID: PMC3764009  PMID: 24039567
6.  Toucan: deciphering the cis-regulatory logic of coregulated genes 
Nucleic Acids Research  2003;31(6):1753-1764.
TOUCAN is a Java application for the rapid discovery of significant cis-regulatory elements from sets of coexpressed or coregulated genes. Biologists can automatically (i) retrieve genes and intergenic regions, (ii) identify putative regulatory regions, (iii) score sequences for known transcription factor binding sites, (iv) identify candidate motifs for unknown binding sites, and (v) detect those statistically over-represented sites that are characteristic for a gene set. Genes or intergenic regions are retrieved from Ensembl or EMBL, together with orthologs and supporting information. Orthologs are aligned and syntenic regions are selected as candidate regulatory regions. Putative sites for known transcription factors are detected using our MotifScanner, which scores position weight matrices using a probabilistic model. New motifs are detected using our MotifSampler based on Gibbs sampling. Binding sites characteristic for a gene set—and thus statistically over-represented with respect to a reference sequence set—are found using a binomial test. We have validated Toucan by analyzing muscle-specific genes, liver-specific genes and E2F target genes; we have easily detected many known binding sites within intergenic DNA and identified new biologically plausible sites for known and unknown transcription factors. Software available at http://www.esat.kuleuven.ac.be/∼dna/BioI/Software.html.
PMCID: PMC152870  PMID: 12626717
7.  SPIC: A novel similarity metric for comparing transcription factor binding site motifs based on information contents 
BMC Systems Biology  2013;7(Suppl 2):S14.
Background
Discovering transcription factor binding sites (TFBS) is one of primary challenges to decipher complex gene regulatory networks encrypted in a genome. A set of short DNA sequences identified by a transcription factor (TF) is known as a motif, which can be expressed accurately in matrix form such as a position-specific scoring matrix (PSSM) and a position frequency matrix. Very frequently, we need to query a motif in a database of motifs by seeking its similar motifs, merge similar TFBS motifs possibly identified by the same TF, separate irrelevant motifs, or filter out spurious motifs. Therefore, a novel metric is required to seize slight differences between irrelevant motifs and highlight the similarity between motifs of the same group in all these applications. While there are already several metrics for motif similarity proposed before, their performance is still far from satisfactory for these applications.
Methods
A novel metric has been proposed in this paper with name as SPIC (Similarity with Position Information Contents) for measuring the similarity between a column of a motif and a column of another motif. When defining this similarity score, we consider the likelihood that the column of the first motif's PFM can be produced by the column of the second motif's PSSM, and multiply the likelihood by the information content of the column of the second motif's PSSM, and vise versa. We evaluated the performance of SPIC combined with a local or a global alignment method having a function for affine gap penalty, for computing the similarity between two motifs. We also compared SPIC with seven existing state-of-the-arts metrics for their capability of clustering motifs from the same group and retrieving motifs from a database on three datasets.
Results
When used jointly with the Smith-Waterman local alignment method with an affine gap penalty function (gap open penalty is equal to1, gap extension penalty is equal to 0.5), SPIC outperforms the seven existing state-of-the-art motif similarity metrics combined with their best alignments for matching motifs in database searches, and clustering the same TF's sub-motifs or distinguishing relevant ones from a miscellaneous group of motifs.
Conclusions
We have developed a novel motif similarity metric that can more accurately match motifs in database searches, and more effectively cluster similar motifs and differentiate irrelevant motifs than do the other seven metrics we are aware of.
doi:10.1186/1752-0509-7-S2-S14
PMCID: PMC3866262  PMID: 24564945
gene regulatory networks; information contents; transcription factor binding site (TFBS); motif; similarity metric
8.  Transcriptional programs: Modelling higher order structure in transcriptional control 
BMC Bioinformatics  2009;10:218.
Background
Transcriptional regulation is an important part of regulatory control in eukaryotes. Even if binding motifs for transcription factors are known, the task of finding binding sites by scanning sequences is plagued by false positives. One way to improve the detection of binding sites from motifs is by taking cooperativity of transcription factor binding into account. We propose a non-parametric probabilistic model, similar to a document topic model, for detecting transcriptional programs, groups of cooperative transcription factors and co-regulated genes. The analysis results in transcriptional programs which generalise both transcriptional modules and TF-target gene incidence matrices and provide a higher-level summary of these structures. The method is independent of prior specification of training sets of genes, for example, via gene expression data. The analysis is based on known binding motifs.
Results
We applied our method to putative regulatory regions of 18,445 Mus musculus genes. We discovered just 68 transcriptional programs that effectively summarised the action of 149 transcription factors on these genes. Several of these programs were significantly enriched for known biological processes and signalling pathways. One transcriptional program has a significant overlap with a reference set of cell cycle specific transcription factors.
Conclusion
Our method is able to pick out higher order structure from noisy sequence analyses. The transcriptional programs it identifies potentially represent common mechanisms of regulatory control across the genome. It simultaneously predicts which genes are co-regulated and which sets of transcription factors cooperate to achieve this co-regulation. The programs we discovered enable biologists to choose new genes and transcription factors to study in specific transcriptional regulatory systems.
doi:10.1186/1471-2105-10-218
PMCID: PMC2725141  PMID: 19607663
9.  rMotifGen: random motif generator for DNA and protein sequences 
BMC Bioinformatics  2007;8:292.
Background
Detection of short, subtle conserved motif regions within a set of related DNA or amino acid sequences can lead to discoveries about important regulatory domains such as transcription factor and DNA binding sites as well as conserved protein domains. In order to help assess motif detection algorithms on motifs with varying properties and levels of conservation, we have developed a computational tool, rMotifGen, with the sole purpose of generating a number of random DNA or protein sequences containing short sequence motifs. Each motif consensus can be user-defined, randomly generated, or created from a position-specific scoring matrix (PSSM). Insertions and mutations within these motifs are created according to user-defined parameters and substitution matrices. The resulting sequences can be helpful in mutational simulations and in testing the limits of motif detection algorithms.
Results
Two implementations of rMotifGen have been created, one providing a graphical user interface (GUI) for random motif construction, and the other serving as a command line interface. The second implementation has the added advantages of platform independence and being able to be called in a batch mode. rMotifGen was used to construct sample sets of sequences containing DNA motifs and amino acid motifs that were then tested against the Gibbs sampler and MEME packages.
Conclusion
rMotifGen provides an efficient and convenient method for creating random DNA or amino acid sequences with a variable number of motifs, where the instance of each motif can be incorporated using a position-specific scoring matrix (PSSM) or by creating an instance mutated from its corresponding consensus using an evolutionary model based on substitution matrices. rMotifGen is freely available at: .
doi:10.1186/1471-2105-8-292
PMCID: PMC1963340  PMID: 17683637
10.  The XXmotif web server for eXhaustive, weight matriX-based motif discovery in nucleotide sequences 
Nucleic Acids Research  2012;40(Web Server issue):W104-W109.
The discovery of regulatory motifs enriched in sets of DNA or RNA sequences is fundamental to the analysis of a great variety of functional genomics experiments. These motifs usually represent binding sites of proteins or non-coding RNAs, which are best described by position weight matrices (PWMs). We have recently developed XXmotif, a de novo motif discovery method that is able to directly optimize the statistical significance of PWMs. XXmotif can also score conservation and positional clustering of motifs. The XXmotif server provides (i) a list of significantly overrepresented motif PWMs with web logos and E-values; (ii) a graph with color-coded boxes indicating the positions of selected motifs in the input sequences; (iii) a histogram of the overall positional distribution for selected motifs and (iv) a page for each motif with all significant motif occurrences, their P-values for enrichment, conservation and localization, their sequence contexts and coordinates. Free access: http://xxmotif.genzentrum.lmu.de.
doi:10.1093/nar/gks602
PMCID: PMC3394272  PMID: 22693218
11.  Optimized Position Weight Matrices in Prediction of Novel Putative Binding Sites for Transcription Factors in the Drosophila melanogaster Genome 
PLoS ONE  2013;8(8):e68712.
Position weight matrices (PWMs) have become a tool of choice for the identification of transcription factor binding sites in DNA sequences. DNA-binding proteins often show degeneracy in their binding requirement and thus the overall binding specificity of many proteins is unknown and remains an active area of research. Although existing PWMs are more reliable predictors than consensus string matching, they generally result in a high number of false positive hits. Our previous study introduced a promising approach to PWM refinement in which known motifs are used to computationally mine putative binding sites directly from aligned promoter regions using composition of similar sites. In the present study, we extended this technique originally tested on single examples of transcription factors (TFs) and showed its capability to optimize PWM performance to predict new binding sites in the fruit fly genome. We propose refined PWMs in mono- and dinucleotide versions similarly computed for a large variety of transcription factors of Drosophila melanogaster. Along with the addition of many auxiliary sites the optimization includes variation of the PWM motif length, the binding sites location on the promoters and the PWM score threshold. To assess the predictive performance of the refined PWMs we compared them to conventional TRANSFAC and JASPAR sources. The results have been verified using performed tests and literature review. Overall, the refined PWMs containing putative sites derived from real promoter content processed using optimized parameters had better general accuracy than conventional PWMs.
doi:10.1371/journal.pone.0068712
PMCID: PMC3735551  PMID: 23936309
12.  Discriminative motif discovery in DNA and protein sequences using the DEME algorithm 
BMC Bioinformatics  2007;8:385.
Background
Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to increase the sensitivity and selectivity of motif discovery by utilizing a second set of sequences, and searching only for patterns that can differentiate the two sets of sequences. Potential applications of discriminative motif discovery include discovering transcription factor binding site motifs in ChIP-chip data and finding protein motifs involved in thermal stability using sets of orthologous proteins from thermophilic and mesophilic organisms.
Results
We describe DEME, a discriminative motif discovery algorithm for use with protein and DNA sequences. Input to DEME is two sets of sequences; a "positive" set and a "negative" set. DEME represents motifs using a probabilistic model, and uses a novel combination of global and local search to find the motif that optimally discriminates between the two sets of sequences. DEME is unique among discriminative motif finders in that it uses an informative Bayesian prior on protein motif columns, allowing it to incorporate prior knowledge of residue characteristics. We also introduce four, synthetic, discriminative motif discovery problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these synthetic problems and on two biological problems: finding yeast transcription factor binding motifs in ChIP-chip data, and finding motifs that discriminate between groups of thermophilic and mesophilic orthologous proteins.
Conclusion
Using artificial data, we show that DEME is more effective than a non-discriminative approach when there are "decoy" motifs or when a variant of the motif is present in the "negative" sequences. With real data, we show that DEME is as good, but not better than non-discriminative algorithms at discovering yeast transcription factor binding motifs. We also show that DEME can find highly informative thermal-stability protein motifs. Binaries for the stand-alone program DEME is free for academic use and is available at
doi:10.1186/1471-2105-8-385
PMCID: PMC2194741  PMID: 17937785
13.  MATLIGN: a motif clustering, comparison and matching tool 
BMC Bioinformatics  2007;8:189.
Background
Sequence motifs representing transcription factor binding sites (TFBS) are commonly encoded as position frequency matrices (PFM) or degenerate consensus sequences (CS). These formats are used to represent the characterised TFBS profiles stored in transcription factor databases, as well as to represent the potential motifs predicted using computational methods. To fill the gap between the known and predicted motifs, methods are needed for the post-processing of prediction results, i.e. for matching, comparison and clustering of pre-selected motifs. The computational identification of over-represented motifs in sets of DNA sequences is, in particular, a task where post-processing can dramatically simplify the analysis. Efficient post-processing, for example, reduces the redundancy of the motifs predicted and enables them to be annotated.
Results
In order to facilitate the post-processing of motifs, in both PFM and CS formats, we have developed a tool called Matlign. The tool aligns and evaluates the similarity of motifs using a combination of scoring functions, and visualises the results using hierarchical clustering. By limiting the number of distinct gaps created (though, not their length), the alignment algorithm also correctly aligns motifs with an internal spacer. The method selects the best non-redundant motif set, with repetitive motifs merged together, by cutting the hierarchical tree using silhouette values. Our analyses show that Matlign can reliably discover the most similar analogue from a collection of characterised regulatory elements such that the method is also useful for the annotation of motif predictions by PFM library searches.
Conclusion
Matlign is a user-friendly tool for post-processing large collections of DNA sequence motifs. Starting from a large number of potential regulatory motifs, Matlign provides a researcher with a non-redundant set of motifs, which can then be further associated to known regulatory elements. A web-server is available at .
doi:10.1186/1471-2105-8-189
PMCID: PMC1925120  PMID: 17559640
14.  RNP-1, an RNA-binding motif is conserved in the DNA-binding cold shock domain. 
Nucleic Acids Research  1992;20(11):2861-2864.
Sequence analysis has shown that there is a short motif of 8 amino acids, corresponding to the RNP-1 motif found in canonical RNA-binding domains, which is common to two families of apparently unrelated proteins. Many RNA-binding proteins contain the RNP-1 and RNP-2 motifs in an RNA-binding domain. The cold shock domain (CSD) family of proteins, which includes several transcription factors which have been shown to bind to DNA, has now been identified to contain a motif similar to RNP-1. A non-redundant protein sequence database was searched with regular expressions and with a weight/residue position matrix of the RNP-1 motif resulting in the identification of numerous known members of the RNA-binding family of proteins. In addition, the search identified that the CSD-containing family of proteins includes a motif which is almost identical to the RNP-1 motif. A determination of the statistical significance of this analysis showed that the RNP-1 motifs from these two families of proteins are indeed similar.
PMCID: PMC336933  PMID: 1614871
15.  An approach to identify over-represented cis-elements in related sequences 
Nucleic Acids Research  2003;31(7):1995-2005.
Computational identification of transcription factor binding sites is an important research area of computational biology. Positional weight matrix (PWM) is a model to describe the sequence pattern of binding sites. Usually, transcription factor binding sites prediction methods based on PWMs require user-defined thresholds. The arbitrary threshold and also the relatively low specificity of the algorithm prevent the result of such an analysis from being properly interpreted. In this study, a method was developed to identify over-represented cis-elements with PWM-based similarity scores. Three sets of closely related promoters were analyzed, and only over- represented motifs with high PWM similarity scores were reported. The thresholds to evaluate the similarity scores to the PWMs of putative transcription factors binding sites can also be automatically determined during the analysis, which can also be used in further research with the same PWMs. The online program is available on the website: http://www.bioinfo.tsinghua.edu.cn/∼zhengjsh/OTFBS/.
PMCID: PMC152803  PMID: 12655017
16.  Fine-Tuning Enhancer Models to Predict Transcriptional Targets across Multiple Genomes 
PLoS ONE  2007;2(11):e1115.
Networks of regulatory relations between transcription factors (TF) and their target genes (TG)- implemented through TF binding sites (TFBS)- are key features of biology. An idealized approach to solving such networks consists of starting from a consensus TFBS or a position weight matrix (PWM) to generate a high accuracy list of candidate TGs for biological validation. Developing and evaluating such approaches remains a formidable challenge in regulatory bioinformatics. We perform a benchmark study on 34 Drosophila TFs to assess existing TFBS and cis-regulatory module (CRM) detection methods, with a strong focus on the use of multiple genomes. Particularly, for CRM-modelling we investigate the addition of orthologous sites to a known PWM to construct phyloPWMs and we assess the added value of phylogenentic footprinting to predict contextual motifs around known TFBSs. For CRM-prediction, we compare motif conservation with network-level conservation approaches across multiple genomes. Choosing the optimal training and scoring strategies strongly enhances the performance of TG prediction for more than half of the tested TFs. Finally, we analyse a 35th TF, namely Eyeless, and find a significant overlap between predicted TGs and candidate TGs identified by microarray expression studies. In summary we identify several ways to optimize TF-specific TG predictions, some of which can be applied to all TFs, and others that can be applied only to particular TFs. The ability to model known TF-TG relations, together with the use of multiple genomes, results in a significant step forward in solving the architecture of gene regulatory networks.
doi:10.1371/journal.pone.0001115
PMCID: PMC2047340  PMID: 17973026
17.  FlyFactorSurvey: a database of Drosophila transcription factor binding specificities determined using the bacterial one-hybrid system 
Nucleic Acids Research  2010;39(Database issue):D111-D117.
FlyFactorSurvey (http://pgfe.umassmed.edu/TFDBS/) is a database of DNA binding specificities for Drosophila transcription factors (TFs) primarily determined using the bacterial one-hybrid system. The database provides community access to over 400 recognition motifs and position weight matrices for over 200 TFs, including many unpublished motifs. Search tools and flat file downloads are provided to retrieve binding site information (as sequences, matrices and sequence logos) for individual TFs, groups of TFs or for all TFs with characterized binding specificities. Linked analysis tools allow users to identify motifs within our database that share similarity to a query matrix or to view the distribution of occurrences of an individual motif throughout the Drosophila genome. Together, this database and its associated tools provide computational and experimental biologists with resources to predict interactions between Drosophila TFs and target cis-regulatory sequences.
doi:10.1093/nar/gkq858
PMCID: PMC3013762  PMID: 21097781
18.  Optimizing the GATA-3 position weight matrix to improve the identification of novel binding sites 
BMC Genomics  2012;13:416.
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.
doi:10.1186/1471-2164-13-416
PMCID: PMC3481455  PMID: 22913572
Transcription factor; Binding sites; GATA-3; Human promoter; Position weight matrix; Optimization
19.  Large-Scale Discovery of Promoter Motifs in Drosophila melanogaster 
A key step in understanding gene regulation is to identify the repertoire of transcription factor binding motifs (TFBMs) that form the building blocks of promoters and other regulatory elements. Identifying these experimentally is very laborious, and the number of TFBMs discovered remains relatively small, especially when compared with the hundreds of transcription factor genes predicted in metazoan genomes. We have used a recently developed statistical motif discovery approach, NestedMICA, to detect candidate TFBMs from a large set of Drosophila melanogaster promoter regions. Of the 120 motifs inferred in our initial analysis, 25 were statistically significant matches to previously reported motifs, while 87 appeared to be novel. Analysis of sequence conservation and motif positioning suggested that the great majority of these discovered motifs are predictive of functional elements in the genome. Many motifs showed associations with specific patterns of gene expression in the D. melanogaster embryo, and we were able to obtain confident annotation of expression patterns for 25 of our motifs, including eight of the novel motifs. The motifs are available through Tiffin, a new database of DNA sequence motifs. We have discovered many new motifs that are overrepresented in D. melanogaster promoter regions, and offer several independent lines of evidence that these are novel TFBMs. Our motif dictionary provides a solid foundation for further investigation of regulatory elements in Drosophila, and demonstrates techniques that should be applicable in other species. We suggest that further improvements in computational motif discovery should narrow the gap between the set of known motifs and the total number of transcription factors in metazoan genomes.
Author Summary
In contrast to the genomic sequences that encode proteins, little is known about the regulatory elements that instruct the cell as to when and where a given gene should be active. Regulatory elements are thought to consist of clusters of short DNA words (motifs), each of which acts as a binding site for sequence-specific DNA binding protein. Thus, building a comprehensive dictionary of such motifs is an important step towards a broader understanding of gene regulation. Using the recently published NestedMICA method for detecting overrepresented motifs in a set of sequences, we build a dictionary of 120 motifs from regulatory sequences in the fruitfly genome, 87 of which are novel. Analysis of positional biases, conservation across species, and association with specific patterns of gene expression in fruitfly embryos suggest that the great majority of these newly discovered motifs represent functional regulatory elements. In addition to providing an initial motif dictionary for one of the most intensively studied model organisms, this work provides an analytical framework for the comprehensive discovery of regulatory motifs in complex animal genomes.
doi:10.1371/journal.pcbi.0030007
PMCID: PMC1779301  PMID: 17238282
20.  Large-Scale Discovery of Promoter Motifs in Drosophila melanogaster 
A key step in understanding gene regulation is to identify the repertoire of transcription factor binding motifs (TFBMs) that form the building blocks of promoters and other regulatory elements. Identifying these experimentally is very laborious, and the number of TFBMs discovered remains relatively small, especially when compared with the hundreds of transcription factor genes predicted in metazoan genomes. We have used a recently developed statistical motif discovery approach, NestedMICA, to detect candidate TFBMs from a large set of Drosophila melanogaster promoter regions. Of the 120 motifs inferred in our initial analysis, 25 were statistically significant matches to previously reported motifs, while 87 appeared to be novel. Analysis of sequence conservation and motif positioning suggested that the great majority of these discovered motifs are predictive of functional elements in the genome. Many motifs showed associations with specific patterns of gene expression in the D. melanogaster embryo, and we were able to obtain confident annotation of expression patterns for 25 of our motifs, including eight of the novel motifs. The motifs are available through Tiffin, a new database of DNA sequence motifs. We have discovered many new motifs that are overrepresented in D. melanogaster promoter regions, and offer several independent lines of evidence that these are novel TFBMs. Our motif dictionary provides a solid foundation for further investigation of regulatory elements in Drosophila, and demonstrates techniques that should be applicable in other species. We suggest that further improvements in computational motif discovery should narrow the gap between the set of known motifs and the total number of transcription factors in metazoan genomes.
Author Summary
In contrast to the genomic sequences that encode proteins, little is known about the regulatory elements that instruct the cell as to when and where a given gene should be active. Regulatory elements are thought to consist of clusters of short DNA words (motifs), each of which acts as a binding site for sequence-specific DNA binding protein. Thus, building a comprehensive dictionary of such motifs is an important step towards a broader understanding of gene regulation. Using the recently published NestedMICA method for detecting overrepresented motifs in a set of sequences, we build a dictionary of 120 motifs from regulatory sequences in the fruitfly genome, 87 of which are novel. Analysis of positional biases, conservation across species, and association with specific patterns of gene expression in fruitfly embryos suggest that the great majority of these newly discovered motifs represent functional regulatory elements. In addition to providing an initial motif dictionary for one of the most intensively studied model organisms, this work provides an analytical framework for the comprehensive discovery of regulatory motifs in complex animal genomes.
doi:10.1371/journal.pcbi.0030007
PMCID: PMC1779301  PMID: 17238282
21.  Tree-Based Position Weight Matrix Approach to Model Transcription Factor Binding Site Profiles 
PLoS ONE  2011;6(9):e24210.
Most of the position weight matrix (PWM) based bioinformatics methods developed to predict transcription factor binding sites (TFBS) assume each nucleotide in the sequence motif contributes independently to the interaction between protein and DNA sequence, usually producing high false positive predictions. The increasing availability of TF enrichment profiles from recent ChIP-Seq methodology facilitates the investigation of dependent structure and accurate prediction of TFBSs. We develop a novel Tree-based PWM (TPWM) approach to accurately model the interaction between TF and its binding site. The whole tree-structured PWM could be considered as a mixture of different conditional-PWMs. We propose a discriminative approach, called TPD (TPWM based Discriminative Approach), to construct the TPWM from the ChIP-Seq data with a pre-existing PWM. To achieve the maximum discriminative power between the positive and negative datasets, the cutoff value is determined based on the Matthew Correlation Coefficient (MCC). The resulting TPWMs are evaluated with respect to accuracy on extensive synthetic datasets. We then apply our TPWM discriminative approach on several real ChIP-Seq datasets to refine the current TFBS models stored in the TRANSFAC database. Experiments on both the simulated and real ChIP-Seq data show that the proposed method starting from existing PWM has consistently better performance than existing tools in detecting the TFBSs. The improved accuracy is the result of modelling the complete dependent structure of the motifs and better prediction of true positive rate. The findings could lead to better understanding of the mechanisms of TF-DNA interactions.
doi:10.1371/journal.pone.0024210
PMCID: PMC3166302  PMID: 21912677
22.  A General Pairwise Interaction Model Provides an Accurate Description of In Vivo Transcription Factor Binding Sites 
PLoS ONE  2014;9(6):e99015.
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.
doi:10.1371/journal.pone.0099015
PMCID: PMC4057186  PMID: 24926895
23.  An intuitionistic approach to scoring DNA sequences against transcription factor binding site motifs 
BMC Bioinformatics  2010;11:551.
Background
Transcription factors (TFs) control transcription by binding to specific regions of DNA called transcription factor binding sites (TFBSs). The identification of TFBSs is a crucial problem in computational biology and includes the subtask of predicting the location of known TFBS motifs in a given DNA sequence. It has previously been shown that, when scoring matches to known TFBS motifs, interdependencies between positions within a motif should be taken into account. However, this remains a challenging task owing to the fact that sequences similar to those of known TFBSs can occur by chance with a relatively high frequency. Here we present a new method for matching sequences to TFBS motifs based on intuitionistic fuzzy sets (IFS) theory, an approach that has been shown to be particularly appropriate for tackling problems that embody a high degree of uncertainty.
Results
We propose SCintuit, a new scoring method for measuring sequence-motif affinity based on IFS theory. Unlike existing methods that consider dependencies between positions, SCintuit is designed to prevent overestimation of less conserved positions of TFBSs. For a given pair of bases, SCintuit is computed not only as a function of their combined probability of occurrence, but also taking into account the individual importance of each single base at its corresponding position. We used SCintuit to identify known TFBSs in DNA sequences. Our method provides excellent results when dealing with both synthetic and real data, outperforming the sensitivity and the specificity of two existing methods in all the experiments we performed.
Conclusions
The results show that SCintuit improves the prediction quality for TFs of the existing approaches without compromising sensitivity. In addition, we show how SCintuit can be successfully applied to real research problems. In this study the reliability of the IFS theory for motif discovery tasks is proven.
doi:10.1186/1471-2105-11-551
PMCID: PMC3098096  PMID: 21059262
24.  Creating PWMs of transcription factors using 3D structure-based computation of protein-DNA free binding energies 
BMC Bioinformatics  2010;11:225.
Background
Knowledge of transcription factor-DNA binding patterns is crucial for understanding gene transcription. Numerous DNA-binding proteins are annotated as transcription factors in the literature, however, for many of them the corresponding DNA-binding motifs remain uncharacterized.
Results
The position weight matrices (PWMs) of transcription factors from different structural classes have been determined using a knowledge-based statistical potential. The scoring function calibrated against crystallographic data on protein-DNA contacts recovered PWMs of various members of widely studied transcription factor families such as p53 and NF-κB. Where it was possible, extensive comparison to experimental binding affinity data and other physical models was made. Although the p50p50, p50RelB, and p50p65 dimers belong to the same family, particular differences in their PWMs were detected, thereby suggesting possibly different in vivo binding modes. The PWMs of p63 and p73 were computed on the basis of homology modeling and their performance was studied using upstream sequences of 85 p53/p73-regulated human genes. Interestingly, about half of the p63 and p73 hits reported by the Match algorithm in the altogether 126 promoters lay more than 2 kb upstream of the corresponding transcription start sites, which deviates from the common assumption that most regulatory sites are located more proximal to the TSS. The fact that in most of the cases the binding sites of p63 and p73 did not overlap with the p53 sites suggests that p63 and p73 could influence the p53 transcriptional activity cooperatively. The newly computed p50p50 PWM recovered 5 more experimental binding sites than the corresponding TRANSFAC matrix, while both PWMs showed comparable receiver operator characteristics.
Conclusions
A novel algorithm was developed to calculate position weight matrices from protein-DNA complex structures. The proposed algorithm was extensively validated against experimental data. The method was further combined with Homology Modeling to obtain PWMs of factors for which crystallographic complexes with DNA are not yet available. The performance of PWMs obtained in this work in comparison to traditionally constructed matrices demonstrates that the structure-based approach presents a promising alternative to experimental determination of transcription factor binding properties.
doi:10.1186/1471-2105-11-225
PMCID: PMC2879287  PMID: 20438625
25.  A niched Pareto genetic algorithm for finding variable length regulatory motifs in DNA sequences 
3 Biotech  2011;2(2):141-148.
The transcription factor binding sites also called as motifs are short, recurring patterns in DNA sequences that are presumed to have a biological function. Identification of the motifs from the promoter region of the genes is an important and unsolved problem specifically in the eukaryotic genomes. In this paper, we present a niched Pareto genetic algorithm to identify the regulatory motifs. This approach is based on the maximization of two objectives of the problem that is the motif length and the consensus similarity score. A long motif means it is less likely to be a false motif. The similarity score represents a motifs probability of conservation in a given set of sequences. Proposed method can find multiple, variable length motifs. In this method, we represented a candidate motif as a combination of length and starting position of the motif in each sequence of the co-regulated genes. This enables the algorithm to identify multiple motifs of variable length. We applied this approach on various data sets and the results show that it can find multiple motifs of variable length in co-regulated genes.
doi:10.1007/s13205-011-0040-6
PMCID: PMC3376862
Motif; TFBS; Binding sites; Multi-objective and genetic algorithm

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