CiteULike: emptyhb's motif_searching

Tracing the evolutionary history of Drosophila regulatory regions with models that identify transcription factor binding sites.

ET Dermitzakis — 2007-10-16T21:12:19-00:00

Mol Biol Evol, Vol. 20, No. 5. (May 2003), pp. 703-714.

Much of evolutionary change is mediated at the level of gene expression, yet our understanding of regulatory evolution remains unsatisfying. In light of recent data indicating that transcription factor binding sites undergo substantial turnover between species, we attempt to quantify the process of binding site turnover in regulatory regions of well-studied genes controlling embryonic patterning in Drosophila. We examine polymorphism and divergence data in Drosophila melanogaster and four related species from regulatory regions of five early development genes for which functional binding sites have been identified. This analysis reveals that Drosophila regulatory regions exhibit patterns of variation consistent with functional constraint. We develop a novel approach to binding site prediction which we use to characterize the process of binding site divergence in regulatory regions. This method uses sets of known binding sites to construct a model that predicts transcription factor specificity and bootstrap sampling to derive significance levels. This approach allows appropriate significance levels to be determined even in the face of skewed base composition in the background sequence. Using this approach, we show that, although functional elements exhibit conservation of sequence, there is substantial potential to gain new functional elements within the regulatory regions. Our results show that application of models that predict transcription factor binding sites can yield insights into the process and dynamics of binding site evolution within regulatory regions.

Large-Scale Discovery of Promoter Motifs in Drosophila melanogaster

Thomas Down — 2007-01-23T14:07:06-00:00

PLoS Computational Biology, Vol. 3, No. 1. (1 January 2007), e7.

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.

Precise physical models of protein-DNA interaction from high-throughput data

Justin Kinney — 2007-01-10T08:58:33-00:00

PNAS, Vol. 104, No. 2. (9 January 2007), pp. 501-506.

A cell's ability to regulate gene transcription depends in large part on the energy with which transcription factors (TFs) bind their DNA regulatory sites. Obtaining accurate models of this binding energy is therefore an important goal for quantitative biology. In this article, we present a principled likelihood-based approach for inferring physical models of TF-DNA binding energy from the data produced by modern high-throughput binding assays. Central to our analysis is the ability to assess the relative likelihood of different model parameters given experimental observations. We take a unique approach to this problem and show how to compute likelihood without any explicit assumptions about the noise that inevitably corrupts such measurements. Sampling possible choices for model parameters according to this likelihood function, we can then make probabilistic predictions for the identities of binding sites and their physical binding energies. Applying this procedure to previously published data on the Saccharomyces cerevisiae TF Abf1p, we find models of TF binding whose parameters are determined with remarkable precision. Evidence for the accuracy of these models is provided by an astonishing level of phylogenetic conservation in the predicted energies of putative binding sites. Results from in vivo and in vitro experiments also provide highly consistent characterizations of Abf1p, a result that contrasts with a previous analysis of the same data. 10.1073/pnas.0609908104

Methods for calculating the probabilities of finding patterns in sequences.

R Staden — 2008-03-18T18:09:57-00:00

Comput Appl Biosci, Vol. 5, No. 2. (April 1989), pp. 89-96.

This paper describes the use of probability-generating functions for calculating the probabilities of finding motifs in nucleic acid and protein sequences. Equations and algorithms are given for calculating the probabilities associated with nine different ways of defining motifs. Comparisons are made with searches of random sequences. A higher level structure--the pattern--is defined as a list of motifs. A pattern also specifies the permitted ranges of spacing allowed between its constituent motifs. Equations for calculating the expected numbers of matches to patterns are given.

Close sequence comparisons are sufficient to identify human cis-regulatory elements.

Shyam Prabhakar — 2006-06-16T19:46:25-00:00

Genome Res (12 June 2006)

Cross-species DNA sequence comparison is the primary method used to identify functional noncoding elements in human and other large genomes. However, little is known about the relative merits of evolutionarily close and distant sequence comparisons. To address this problem, we identified evolutionarily conserved noncoding regions in primate, mammalian, and more distant comparisons using a uniform approach (Gumby) that facilitates unbiased assessment of the impact of evolutionary distance on predictive power. We benchmarked computational predictions against previously identified cis-regulatory elements at diverse genomic loci and also tested numerous extremely conserved human-rodent sequences for transcriptional enhancer activity using an in vivo enhancer assay in transgenic mice. Human regulatory elements were identified with acceptable sensitivity (53%-80%) and true-positive rate (27%-67%) by comparison with one to five other eutherian mammals or six other simian primates. More distant comparisons (marsupial, avian, amphibian, and fish) failed to identify many of the empirically defined functional noncoding elements. Our results highlight the practical utility of close sequence comparisons, and the loss of sensitivity entailed by more distant comparisons. We derived an intuitive relationship between ancient and recent noncoding sequence conservation from whole-genome comparative analysis that explains most of the observations from empirical benchmarking. Lastly, we determined that, in addition to strength of conservation, genomic location and/or density of surrounding conserved elements must also be considered in selecting candidate enhancers for in vivo testing at embryonic time points.

Conservation of regulatory elements between two species of Drosophila.

E Emberly — 2006-09-28T20:46:21-00:00

BMC Bioinformatics, Vol. 4 (20 November 2003)

BACKGROUND: One of the important goals in the post-genomic era is to determine the regulatory elements within the non-coding DNA of a given organism's genome. The identification of functional cis-regulatory modules has proven difficult since the component factor binding sites are small and the rules governing their arrangement are poorly understood. However, the genomes of suitably diverged species help to predict regulatory elements based on the generally accepted assumption that conserved blocks of genomic sequence are likely to be functional. To judge the efficacy of strategies that prefilter by sequence conservation it is important to know to what extent the converse assumption holds, namely that functional elements common to both species will fall within these conserved blocks. The recently completed sequence of a second Drosophila species provides an opportunity to test this assumption for one of the experimentally best studied regulatory networks in multicellular organisms, the body patterning of the fly embryo. RESULTS: We find that 50%-70% of known binding sites reside in conserved sequence blocks, but these percentages are not greatly enriched over what is expected by chance. Finally, a computational genome-wide search in both species for regulatory modules based on clusters of binding sites suggests that genes central to the regulatory network are consistently recovered. CONCLUSIONS: Our results indicate that binding sites remain clustered for these "core modules" while not necessarily residing in conserved blocks. This is an important clue as to how regulatory information is encoded in the genome and how modules evolve.

Extensive low-affinity transcriptional interactions in the yeast genome.

Amos Tanay — 2006-07-21T02:19:25-00:00

Genome Res (29 June 2006)

Major experimental and computational efforts are targeted at the characterization of transcriptional networks on a genomic scale. The ultimate goal of many of these studies is to construct networks associating transcription factors with genes via well-defined binding sites. Weaker regulatory interactions other than those occurring at high-affinity binding sites are largely ignored and are not well understood. Here I show that low-affinity interactions are abundant in vivo and quantifiable from current high-throughput ChIP experiments. I develop algorithms that predict DNA-binding energies from sequences and ChIP data across a wide dynamic range of affinities and use them to reveal widespread functionality of low-affinity transcription factor binding. Evolutionary analysis suggests that binding energies of many transcription factors are conserved even in promoters lacking classical binding sites. Gene expression analysis shows that such promoters can generate significant expression. I estimate that while only a small percentage of the genome is strongly regulated by a typical transcription factor, up to an order of magnitude more may be involved in weaker interactions. Low-affinity transcription factor-DNA interaction may therefore be important both evolutionarily and functionally.

Transcriptional Control in the Segmentation Gene Network of Drosophila

Mark Schroeder — 2007-01-01T19:51:52-00:00

PLoS Biology, Vol. 2, No. 9. (1 September 2004), e271.

The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross-) regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules) with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a uniform set of criteria, permitting the definition of basic composition rules. The study demonstrates that computational methods are a powerful complement to experimental approaches in the analysis of transcription networks.

Exploiting transcription factor binding site clustering to identify cis-regulatory modules involved in pattern formation in the Drosophila genome.

BP Berman — 2007-09-17T19:14:17-00:00

Proc Natl Acad Sci U S A, Vol. 99, No. 2. (22 January 2002), pp. 757-762.

A major challenge in interpreting genome sequences is understanding how the genome encodes the information that specifies when and where a gene will be expressed. The first step in this process is the identification of regions of the genome that contain regulatory information. In higher eukaryotes, this cis-regulatory information is organized into modular units [cis-regulatory modules (CRMs)] of a few hundred base pairs. A common feature of these cis-regulatory modules is the presence of multiple binding sites for multiple transcription factors. Here, we evaluate the extent to which the tendency for transcription factor binding sites to be clustered can be used as the basis for the computational identification of cis-regulatory modules. By using published DNA binding specificity data for five transcription factors active in the early Drosophila embryo, we identified genomic regions containing unusually high concentrations of predicted binding sites for these factors. A significant fraction of these binding site clusters overlap known CRMs that are regulated by these factors. In addition, many of the remaining clusters are adjacent to genes expressed in a pattern characteristic of genes regulated by these factors. We tested one of the newly identified clusters, mapping upstream of the gap gene giant (gt), and show that it acts as an enhancer that recapitulates the posterior expression pattern of gt.

MATCH: A tool for searching transcription factor binding sites in DNA sequences.

AE Kel — 2005-08-09T14:17:46-00:00

Nucleic Acids Res, Vol. 31, No. 13. (1 July 2003), pp. 3576-3579.

Match is a weight matrix-based tool for searching putative transcription factor binding sites in DNA sequences. Match is closely interconnected and distributed together with the TRANSFAC database. In particular, Match uses the matrix library collected in TRANSFAC and therefore provides the possibility to search for a great variety of different transcription factor binding sites. Several sets of optimised matrix cut-off values are built in the system to provide a variety of search modes of different stringency. The user may construct and save his/her specific user profiles which are selected subsets of matrices including default or user-defined cut-off values. Furthermore a number of tissue-specific profiles are provided that were compiled by the TRANSFAC team. A public version of the Match tool is available at: http://www.gene-regulation.com/pub/programs.html#match. The same program with a different web interface can be found at http://compel.bionet.nsc.ru/Match/Match.html. An advanced version of the tool called Match Professional is available at http://www.biobase.de.