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1.  Improving MEME via a two-tiered significance analysis 
Bioinformatics  2014;30(14):1965-1973.
Motivation: With over 9000 unique users recorded in the first half of 2013, MEME is one of the most popular motif-finding tools available. Reliable estimates of the statistical significance of motifs can greatly increase the usefulness of any motif finder. By analogy, it is difficult to imagine evaluating a BLAST result without its accompanying E-value. Currently MEME evaluates its EM-generated candidate motifs using an extension of BLAST’s E-value to the motif-finding context. Although we previously indicated the drawbacks of MEME’s current significance evaluation, we did not offer a practical substitute suited for its needs, especially because MEME also relies on the E-value internally to rank competing candidate motifs.
Results: Here we offer a two-tiered significance analysis that can replace the E-value in selecting the best candidate motif and in evaluating its overall statistical significance. We show that our new approach could substantially improve MEME’s motif-finding performance and would also provide the user with a reliable significance analysis. In addition, for large input sets, our new approach is in fact faster than the currently implemented E-value analysis.
Contact: or
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC4080741  PMID: 24665130
2.  Triplex-Inspector: an analysis tool for triplex-mediated targeting of genomic loci 
Bioinformatics  2013;29(15):1895-1897.
Summary: At the heart of many modern biotechnological and therapeutic applications lies the need to target specific genomic loci with pinpoint accuracy. Although landmark experiments demonstrate technological maturity in manufacturing and delivering genetic material, the genomic sequence analysis to find suitable targets lags behind. We provide a computational aid for the sophisticated design of sequence-specific ligands and selection of appropriate targets, taking gene location and genomic architecture into account.
Availability: Source code and binaries are downloadable from
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3712220  PMID: 23740745
3.  Genome-wide in silico prediction of gene expression 
Bioinformatics  2012;28(21):2789-2796.
Motivation: Modelling the regulation of gene expression can provide insight into the regulatory roles of individual transcription factors (TFs) and histone modifications. Recently, Ouyang et al. in 2009 modelled gene expression levels in mouse embryonic stem (mES) cells using in vivo ChIP-seq measurements of TF binding. ChIP-seq TF binding data, however, are tissue-specific and relatively difficult to obtain. This limits the applicability of gene expression models that rely on ChIP-seq TF binding data.
Results: In this study, we build regression-based models that relate gene expression to the binding of 12 different TFs, 7 histone modifications and chromatin accessibility (DNase I hypersensitivity) in two different tissues. We find that expression models based on computationally predicted TF binding can achieve similar accuracy to those using in vivo TF binding data and that including binding at weak sites is critical for accurate prediction of gene expression. We also find that incorporating histone modification and chromatin accessibility data results in additional accuracy. Surprisingly, we find that models that use no TF binding data at all, but only histone modification and chromatin accessibility data, can be as (or more) accurate than those based on in vivo TF binding data.
Availability and implementation: All scripts, motifs and data presented in this article are available online at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3476338  PMID: 22954627
4.  Epigenetic priors for identifying active transcription factor binding sites 
Bioinformatics  2011;28(1):56-62.
Motivation Accurate knowledge of the genome-wide binding of transcription factors in a particular cell type or under a particular condition is necessary for understanding transcriptional regulation. Using epigenetic data such as histone modification and DNase I, accessibility data has been shown to improve motif-based in silico methods for predicting such binding, but this approach has not yet been fully explored.
Results We describe a probabilistic method for combining one or more tracks of epigenetic data with a standard DNA sequence motif model to improve our ability to identify active transcription factor binding sites (TFBSs). We convert each data type into a position-specific probabilistic prior and combine these priors with a traditional probabilistic motif model to compute a log-posterior odds score. Our experiments, using histone modifications H3K4me1, H3K4me3, H3K9ac and H3K27ac, as well as DNase I sensitivity, show conclusively that the log-posterior odds score consistently outperforms a simple binary filter based on the same data. We also show that our approach performs competitively with a more complex method, CENTIPEDE, and suggest that the relative simplicity of the log-posterior odds scoring method makes it an appealing and very general method for identifying functional TFBSs on the basis of DNA and epigenetic evidence.
Availability and implementation: FIMO, part of the MEME Suite software toolkit, now supports log-posterior odds scoring using position-specific priors for motif search. A web server and source code are available at Utilities for creating priors are at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3244768  PMID: 22072382
5.  Tissue-specific prediction of directly regulated genes 
Bioinformatics  2011;27(17):2354-2360.
Direct binding by a transcription factor (TF) to the proximal promoter of a gene is a strong evidence that the TF regulates the gene. Assaying the genome-wide binding of every TF in every cell type and condition is currently impractical. Histone modifications correlate with tissue/cell/condition-specific (‘tissue specific’) TF binding, so histone ChIP-seq data can be combined with traditional position weight matrix (PWM) methods to make tissue-specific predictions of TF–promoter interactions.
Results: We use supervised learning to train a naïve Bayes predictor of TF–promoter binding. The predictor's features are the histone modification levels and a PWM-based score for the promoter. Training and testing uses sets of promoters labeled using TF ChIP-seq data, and we use cross-validation on 23 such datasets to measure the accuracy. A PWM+histone naïve Bayes predictor using a single histone modification (H3K4me3) is substantially more accurate than a PWM score or a conservation-based score (phylogenetic motif model). The naïve Bayes predictor is more accurate (on average) at all sensitivity levels, and makes only half as many false positive predictions at sensitivity levels from 10% to 80%. On average, it correctly predicts 80% of bound promoters at a false positive rate of 20%. Accuracy does not diminish when we test the predictor in a different cell type (and species) from training. Accuracy is barely diminished even when we train the predictor without using TF ChIP-seq data.
Availability: Our tissue-specific predictor of promoters bound by a TF is called Dr Gene and is available at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3157924  PMID: 21724591
6.  Sorting the nuclear proteome 
Bioinformatics  2011;27(13):i7-i14.
Motivation: Quantitative experimental analyses of the nuclear interior reveal a morphologically structured yet dynamic mix of membraneless compartments. Major nuclear events depend on the functional integrity and timely assembly of these intra-nuclear compartments. Yet, unknown drivers of protein mobility ensure that they are in the right place at the time when they are needed.
Results: This study investigates determinants of associations between eight intra-nuclear compartments and their proteins in heterogeneous genome-wide data. We develop a model based on a range of candidate determinants, capable of mapping the intra-nuclear organization of proteins. The model integrates protein interactions, protein domains, post-translational modification sites and protein sequence data. The predictions of our model are accurate with a mean AUC (over all compartments) of 0.71.
We present a complete map of the association of 3567 mouse nuclear proteins with intra-nuclear compartments. Each decision is explained in terms of essential interactions and domains, and qualified with a false discovery assessment. Using this resource, we uncover the collective role of transcription factors in each of the compartments. We create diagrams illustrating the outcomes of a Gene Ontology enrichment analysis. Associated with an extensive range of transcription factors, the analysis suggests that PML bodies coordinate regulatory immune responses.
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3117375  PMID: 21685104
7.  DREME: motif discovery in transcription factor ChIP-seq data 
Bioinformatics  2011;27(12):1653-1659.
Motivation: Transcription factor (TF) ChIP-seq datasets have particular characteristics that provide unique challenges and opportunities for motif discovery. Most existing motif discovery algorithms do not scale well to such large datasets, or fail to report many motifs associated with cofactors of the ChIP-ed TF.
Results: We present DREME, a motif discovery algorithm specifically designed to find the short, core DNA-binding motifs of eukaryotic TFs, and optimized to analyze very large ChIP-seq datasets in minutes. Using DREME, we discover the binding motifs of the the ChIP-ed TF and many cofactors in mouse ES cell (mESC), mouse erythrocyte and human cell line ChIP-seq datasets. For example, in mESC ChIP-seq data for the TF Esrrb, we discover the binding motifs for eight cofactor TFs important in the maintenance of pluripotency. Several other commonly used algorithms find at most two cofactor motifs in this same dataset. DREME can also perform discriminative motif discovery, and we use this feature to provide evidence that Sox2 and Oct4 do not bind in mES cells as an obligate heterodimer. DREME is much faster than many commonly used algorithms, scales linearly in dataset size, finds multiple, non-redundant motifs and reports a reliable measure of statistical significance for each motif found. DREME is available as part of the MEME Suite of motif-based sequence analysis tools (
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3106199  PMID: 21543442
8.  MEME-ChIP: motif analysis of large DNA datasets 
Bioinformatics  2011;27(12):1696-1697.
Motivation: Advances in high-throughput sequencing have resulted in rapid growth in large, high-quality datasets including those arising from transcription factor (TF) ChIP-seq experiments. While there are many existing tools for discovering TF binding site motifs in such datasets, most web-based tools cannot directly process such large datasets.
Results: The MEME-ChIP web service is designed to analyze ChIP-seq ‘peak regions’—short genomic regions surrounding declared ChIP-seq ‘peaks’. Given a set of genomic regions, it performs (i) ab initio motif discovery, (ii) motif enrichment analysis, (iii) motif visualization, (iv) binding affinity analysis and (v) motif identification. It runs two complementary motif discovery algorithms on the input data—MEME and DREME—and uses the motifs they discover in subsequent visualization, binding affinity and identification steps. MEME-ChIP also performs motif enrichment analysis using the AME algorithm, which can detect very low levels of enrichment of binding sites for TFs with known DNA-binding motifs. Importantly, unlike with the MEME web service, there is no restriction on the size or number of uploaded sequences, allowing very large ChIP-seq datasets to be analyzed. The analyses performed by MEME-ChIP provide the user with a varied view of the binding and regulatory activity of the ChIP-ed TF, as well as the possible involvement of other DNA-binding TFs.
Availability: MEME-ChIP is available as part of the MEME Suite at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3106185  PMID: 21486936
9.  FIMO: scanning for occurrences of a given motif 
Bioinformatics  2011;27(7):1017-1018.
Summary: A motif is a short DNA or protein sequence that contributes to the biological function of the sequence in which it resides. Over the past several decades, many computational methods have been described for identifying, characterizing and searching with sequence motifs. Critical to nearly any motif-based sequence analysis pipeline is the ability to scan a sequence database for occurrences of a given motif described by a position-specific frequency matrix.
Results: We describe Find Individual Motif Occurrences (FIMO), a software tool for scanning DNA or protein sequences with motifs described as position-specific scoring matrices. The program computes a log-likelihood ratio score for each position in a given sequence database, uses established dynamic programming methods to convert this score to a P-value and then applies false discovery rate analysis to estimate a q-value for each position in the given sequence. FIMO provides output in a variety of formats, including HTML, XML and several Santa Cruz Genome Browser formats. The program is efficient, allowing for the scanning of DNA sequences at a rate of 3.5 Mb/s on a single CPU.
Availability and Implementation: FIMO is part of the MEME Suite software toolkit. A web server and source code are available at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3065696  PMID: 21330290
10.  Assigning roles to DNA regulatory motifs using comparative genomics 
Bioinformatics  2010;26(7):860-866.
Motivation: Transcription factors (TFs) are crucial during the lifetime of the cell. Their functional roles are defined by the genes they regulate. Uncovering these roles not only sheds light on the TF at hand but puts it into the context of the complete regulatory network.
Results: Here, we present an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs. We incorporate our approach into the Gomo algorithm, a computational tool for detecting associations between a user-specified DNA regulatory motif [expressed as a position weight matrix (PWM)] and Gene Ontology (GO) terms. Incorporating multiple species into the analysis significantly improves Gomo's ability to identify GO terms associated with the regulatory targets of TFs. Including three comparative species in the process of predicting TF roles in Saccharomyces cerevisiae and Homo sapiens increases the number of significant predictions by 75 and 200%, respectively. The predicted GO terms are also more specific, yielding deeper biological insight into the role of the TF. Adjusting motif (binding) affinity scores for individual sequence composition proves to be essential for avoiding false positive associations. We describe a novel DNA sequence-scoring algorithm that compensates a thermodynamic measure of DNA-binding affinity for individual sequence base composition. Gomo's prediction accuracy proves to be relatively insensitive to how promoters are defined. Because Gomo uses a threshold-free form of gene set analysis, there are no free parameters to tune. Biologists can investigate the potential roles of DNA regulatory motifs of interest using Gomo via the web (
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2844991  PMID: 20147307
11.  Assessing phylogenetic motif models for predicting transcription factor binding sites 
Bioinformatics  2009;25(12):i339-i347.
Motivation: A variety of algorithms have been developed to predict transcription factor binding sites (TFBSs) within the genome by exploiting the evolutionary information implicit in multiple alignments of the genomes of related species. One such approach uses an extension of the standard position-specific motif model that incorporates phylogenetic information via a phylogenetic tree and a model of evolution. However, these phylogenetic motif models (PMMs) have never been rigorously benchmarked in order to determine whether they lead to better prediction of TFBSs than obtained using simple position weight matrix scanning.
Results: We evaluate three PMM-based prediction algorithms, each of which uses a different treatment of gapped alignments, and we compare their prediction accuracy with that of a non-phylogenetic motif scanning approach. Surprisingly, all of these algorithms appear to be inferior to simple motif scanning, when accuracy is measured using a gold standard of validated yeast TFBSs. However, the PMM scanners perform much better than simple motif scanning when we abandon the gold standard and consider the number of statistically significant sites predicted, using column-shuffled ‘random’ motifs to measure significance. These results suggest that the common practice of measuring the accuracy of binding site predictors using collections of known sites may be dangerously misleading since such collections may be missing ‘weak’ sites, which are exactly the type of sites needed to discriminate among predictors. We then extend our previous theoretical model of the statistical power of PMM-based prediction algorithms to allow for loss of binding sites during evolution, and show that it gives a more accurate upper bound on scanner accuracy. Finally, utilizing our theoretical model, we introduce a new method for predicting the number of real binding sites in a genome. The results suggest that the number of true sites for a yeast TF is in general several times greater than the number of known sites listed in the Saccharomyces cerevisiae Database (SCPD). Among the three scanning algorithms that we test, the MONKEY algorithm has the highest accuracy for predicting yeast TFBSs.
PMCID: PMC2687955  PMID: 19478008
12.  Optimizing static thermodynamic models of transcriptional regulation 
Bioinformatics  2009;25(13):1640-1646.
Motivation: Modeling transcriptional regulation using thermo-dynamic modeling approaches has become increasingly relevant as a way to gain a detailed understanding of transcriptional regulation. Thermodynamic models are able to model the interactions between transcription factors (TFs) and DNA that lead to a specific transcriptional output of the target gene. Such models can be ‘trained’ by fitting their free parameters to data on the transcription rate of a gene and the concentrations of its regulating factors. However, the parameter fitting process is computationally very expensive and this limits the number of alternative types of model that can be explored.
Results: In this study, we evaluate the ‘optimization landscape’ of a class of static, quantitative models of regulation and explore the efficiency of a range of optimization methods. We evaluate eight optimization methods: two variants of simulated annealing (SA), four variants of gradient descent (GD), a hybrid SA/GD algorithm and a genetic algorithm. We show that the optimization landscape has numerous local optima, resulting in poor performance for the GD methods. SA with a simple geometric cooling schedule performs best among all tested methods. In particular, we see no advantage to using the more sophisticated ‘LAM’ cooling schedule. Overall, a good approximate solution is achievable in minutes using SA with a simple cooling schedule.
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2732318  PMID: 19398449
13.  STREAM: Static Thermodynamic REgulAtory Model of transcription 
Bioinformatics  2008;24(21):2544-2545.
Motivation: Understanding the transcriptional regulation of a gene in detail is a crucial step towards uncovering and ultimately utilizing the regulatory grammar of the genome. Modeling transcriptional regulation using thermodynamic equations has become an increasingly important approach towards this goal.
Here, we present stream, the first publicly available framework for modeling, visualizing and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of transcription factors (TFs), the TF binding sites (TFBSs) in a regulatory DNA region, and the transcription rate of the target gene, stream will optimize its parameters to generate a model that best fits the input data. This trained model can then be used to (a) validate that the given set of TFs is able to regulate the target gene and (b) to predict the transcription rate under different conditions (e.g. different tissues, knockout/additional TFs or mutated/missing TFBSs).
Availability: The platform independent executable of stream, as well as a tutorial and the full documentation, are available at stream requires Java version 5 or higher.
PMCID: PMC2732279  PMID: 18776194

Results 1-13 (13)