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Simple Summary Insects comprise the largest and most diverse class of animals on earth, and have major impacts on human health and agriculture. The effort to better understand insect biology has led to the sequencing of hundreds of insect genomes. However, the usefulness of having a genome sequence is limited in the absence of a comprehensive annotation—a description of the function of each part of the sequence. Functional parts of the genome include not only genes, but also regulatory sequences that mediate gene expression. We discuss here methods used to identify regulatory sequences within the genome, with the emphasis on a pair of tools we have developed, REDfly and SCRMshaw, that can be used in tandem to carry out this task in an efficient and economical manner. Abstract An ever-growing number of insect genomes is being sequenced across the evolutionary spectrum. Comprehensive annotation of not only genes but also regulatory regions is critical for reaping the full benefits of this sequencing. Driven by developments in sequencing technologies and in both empirical and computational discovery strategies, the past few decades have witnessed dramatic progress in our ability to identify cis-regulatory modules (CRMs), sequences such as enhancers that play a major role in regulating transcription. Nevertheless, providing a timely and comprehensive regulatory annotation of newly sequenced insect genomes is an ongoing challenge. We review here the methods being used to identify CRMs in both model and non-model insect species, and focus on two tools that we have developed, REDfly and SCRMshaw. These resources can be paired together in a powerful combination to facilitate insect regulatory annotation over a broad range of species, with an accuracy equal to or better than that of other state-of-the-art methods.

The REDfly database provides a comprehensive curation of experimentally-validated Drosophila transcriptional cis-regulatory elements and includes information on DNA sequence, experimental evidence, patterns of regulated gene expression, and more. Now in its thirteenth year, REDfly has grown to over 23 000 records of tested reporter gene constructs and 2200 tested transcription factor binding sites. Recent developments include the start of curation of predicted cis-regulatory modules in addition to experimentally-verified ones, improved search and filtering, and increased interaction with the authors of curated papers. An expanded data model that will capture information on temporal aspects of gene regulation, regulation in response to environmental and other non-developmental cues, sexually dimorphic gene regulation, and non-endogenous (ectopic) aspects of reporter gene expression is under development and expected to be in place within the coming year. REDfly is freely accessible at http://redfly.ccr.buffalo.edu, and news about database updates and new features can be followed on Twitter at @REDfly_database.

The fruit fly, Drosophila melanogaster, has been a favorite experimental system of developmental biologists for more than a century. One of the most attractive features of this model system is the clarity by which one can analyze mutant phenotypes. Most genes are found in single copies, and loss-of-function mutants often have obvious phenotypes that can be analyzed during development and in adulthood. As with all metazoans, a significant fraction of Drosophila genes are used during both embryonic and postembryonic development, and null mutants often die during embryogenesis thereby precluding the analysis of postembryonic tissues. For several decades researchers worked around this problem by either studying gynandromorphs or irradiating chromosomes carrying mutations in the hope of inducing mitotic recombination which would then allow for the analysis of mutant phenotypes in smaller populations of cells. The former method suffers from the fact that mutations in the gene of interest are often lethal when generated in large sectors, which is a hallmark of gynandromorphs. Clonal induction with the latter method occurs at relatively low frequencies making this method laborious. The introduction of the yeast FRT System/FRT site-directed recombination system to Drosophila has made generating loss-of-function mosaic clones simple and easy. Over the years several variants of this method have allowed developmental biologists to remove genes, overexpress genes, and even express one gene in patches of cells that are mutant for a second gene. In this review we will briefly discuss some of various FRT System/FRT-based approaches that are being used to manipulate gene expression in Drosophila. The individual FRT System/FRT-based methods are described in the papers that are cited herein. We will outline the procedure that our lab uses to prepare and analyze mosaic clones in Drosophila eye-antennal imaginal discs.

Chromatin domain boundary elements prevent inappropriate interaction between distant or closely spaced regulatory elements and restrict enhancers and silencers to correct target promoters. In spite of having such a general role and expected frequent occurrence genome wide, there is no DNA sequence analysis based tool to identify boundary elements. Here, we report chromatin domain Boundary Element Search Tool (cdBEST), to identify boundary elements. cdBEST uses known recognition sequences of boundary interacting proteins and looks for ‘motif clusters’. Using cdBEST, we identified boundary sequences across 12 Drosophila species. Of the 4576 boundary sequences identified in Drosophila melanogaster genome, >170 sequences are repetitive in nature and have sequence homology to transposable elements. Analysis of such sequences across 12 Drosophila genomes showed that the occurrence of repetitive sequences in the context of boundaries is a common feature of drosophilids. We use a variety of genome organization criteria and also experimental test on a subset of the cdBEST boundaries in an enhancer-blocking assay and show that 80% of them indeed function as boundaries in vivo. These observations highlight the role of cdBEST in better understanding of chromatin domain boundaries in Drosophila and setting the stage for comparative analysis of boundaries across closely related species.

Drosophila melanogaster has one of the best characterized metazoan genomes in terms of functionally annotated regulatory elements. To explore how these elements contribute to gene regulation in the context of gene regulatory networks, we need convenient tools to identify the proteins that bind to them. Here, we present the development and validation of a highly automated protein-DNA interaction detection method, enabling the high-throughput yeast one-hybrid-based screening of DNA elements versus an array of full-length, sequence-verified clones containing 647 (over 85%) of predicted Drosophila transcription factors (TFs). Using six well-characterized regulatory elements (82 bp – 1kb), we identified 33 TF-DNA interactions of which 27 are novel. To simultaneously validate these interactions and locate their binding sites of involved TFs, we implemented a novel microfluidics-based approach that enables us to conduct hundreds of gel shift-like assays at once, thus allowing the retrieval of DNA occupancy data for each TF throughout the respective target DNA elements. Finally, we biologically validate several interactions and specifically identify two novel regulators of sine oculis gene expression and hence eye development.

Genome Surveyor 2.0 is a web-based tool for discovery and analysis of cis-regulatory elements in Drosophila, built on top of the GBrowse genome browser for convenient visualization. Genome Surveyor was developed as a tool for predicting transcription factor (TF) binding targets and cis-regulatory modules (CRMs/enhancers), based on motifs representing experimentally determined DNA binding specificities. Since its first publication, we have added substantial new functionality (e.g. phylogenetic averaging of motif scores from multiple species, and a novel CRM discovery technique), increased the number of supported motifs about 4-fold (from ∼100 to ∼400), added provisions for evolutionary comparison across many more Drosophila species (from 2 to 12), and improved the user-interface. The server is free and open to all users, and there is no login requirement. Address: http://veda.cs.uiuc.edu/gs.

From Genome to Regulatory Networks For biologists, having a genome in hand is only the beginning—much more investigation is still needed to characterize how the genome is used to help to produce a functional organism (see the Perspective by Blaxter ). In this vein, Gerstein et al. (p. 1775 ) summarize for the Caenorhabditis elegans genome, and The modENCODE Consortium (p. 1787 ) summarize for the Drosophila melanogaster genome, full transcriptome analyses over developmental stages, genome-wide identification of transcription factor binding sites, and high-resolution maps of chromatin organization. Both studies identified regions of the nematode and fly genomes that show highly occupied targets (or HOT) regions where DNA was bound by more than 15 of the transcription factors analyzed and the expression of related genes were characterized. Overall, the studies provide insights into the organization, structure, and function of the two genomes and provide basic information needed to guide and correlate both focused and genome-wide studies.

The REDfly database of Drosophila transcriptional cis-regulatory elements provides the broadest and most comprehensive available resource for experimentally validated cis-regulatory modules and transcription factor binding sites among the metazoa. The third major release of the database extends the utility of REDfly as a powerful tool for both computational and experimental studies of transcription regulation. REDfly v3.0 includes the introduction of new data classes to expand the types of regulatory elements annotated in the database along with a roughly 40% increase in the number of records. A completely redesigned interface improves access for casual and power users alike; among other features it now automatically provides graphical views of the genome, displays images of reporter gene expression and implements improved capabilities for database searching and results filtering. REDfly is freely accessible at http://redfly.ccr.buffalo.edu.

Arabidopsis TCH4 gene plays an important role in the biological processes related to plant secondary growth, resistance to pathogen, and adaptation to environmental stresses. It is up-regulated by various hormonal, environmental, and mechanical stimuli. Here, we identified 9 transcriptional regulatory elements from TCH4 promoter by bioinformatics approach. In which, 4 elements have been reported previously, and 5 elements are newly identified in this study. All of the identified elements contain the sequences of known cis-elements. Especially, their distribution along some co-expressed gene promoters and the orthologous promoters is typically clustered and syntenic. Based on our predictions and the information of known cis-elements, a model representing the transcriptional regulation mechanism was proposed for TCH4 gene in response to hormonal, mechanical, and environmental stimuli.

Motivation: Predicting cis-regulatory modules (CRMs) in higher eukaryotes is a challenging computational task. Commonly used methods to predict CRMs based on the signal of transcription factor binding sites (TFBS) are limited by prior information about transcription factor specificity. More general methods that bypass the reliance on TFBS models are needed for comprehensive CRM prediction. Results: We have developed a method to predict CRMs called CisPlusFinder that identifies high density regions of perfect local ungapped sequences (PLUSs) based on multiple species conservation. By assuming that PLUSs contain core TFBS motifs that are locally overrepresented, the method attempts to capture the expected features of CRM structure and evolution. Applied to a benchmark dataset of CRMs involved in early Drosophila development, CisPlusFinder predicts more annotated CRMs than all other methods tested. Using the REDfly database, we find that some ‘false positive’ predictions in the benchmark dataset correspond to recently annotated CRMs. Our work demonstrates that CRM prediction methods that combine comparative genomic data with statistical properties of DNA may achieve reasonable performance when applied genome-wide in the absence of an a priori set of known TFBS motifs. Availability: The program CisPlusFinder can be downloaded at . All software is licensed under the Lesser GNU Public License (LGPL). Contact: nora.pierstorff@uni-koeln.de. Supplementary information: Supplementary data are available at Bioinformatics online.

Transcriptional regulation is a major tier in the periodic engine that mobilizes cell cycle progression. The availability of complete genome sequences of multiple organisms holds promise for significantly improving the specificity of computational identification of functional elements. Here, we applied a comparative genomics analysis to decipher transcriptional regulatory elements that control cell cycle phasing. We analyzed genome-wide promoter sequences from 12 organisms, including worm, fly, fish, rodents and human, and identified conserved transcriptional modules that determine the expression of genes in specific cell cycle phases. We demonstrate that a canonical E2F signal encodes for expression highly specific to the G1/S phase, and that a cis-regulatory module comprising CHR-NF-Y elements dictates expression that is restricted to the G2 and G2/M phases. B-Myb binding site signatures occur in many of the CHR-NF-Y target genes, suggesting a specific role for this triplet in the regulation of the cell cycle transcriptional program. Remarkably, E2F signals are conserved in promoters of G1/S genes in all organisms from worm to human. The CHR-NF-Y module is conserved in promoters of G2/M regulated genes in all analyzed vertebrates. Our results reveal novel modules that determine specific cell cycle phasing, and identify their respective putative target genes with remarkably high specificity.

While the number of genome-wide, in vivo transcription factor binding datasets is growing, yielding greater insight into the role of regulatory DNA in development, evolution and disease, it is difficult to tease apart signal from noise and identify truly functional binding events. Comparative studies of transcription factor binding between closely related species offer one way to combat this problem, as functionally important aspects of enhancer architecture tend to be constrained by natural selection. Here we review the current field in the area of in vivo transcription factor binding in Drosophila, illustrating how evolutionary studies within the drosophilids are helping to unravel the complexity of the genomic regulatory code. A number of techniques exist for studying transcription factor binding on a genome-wide scale, including ChIP-chip, ChIP-seq and DamID; we touch on these and address the challenges and advantages of each with regard to working on non-model species. We also describe major findings in the field so far, focusing on comparative studies of the developmental regulatory network, the logic of combinatorial binding and the evolutionary properties of noncoding DNA. Finally, we examine how insights from Drosophila compare with similar studies in the vertebrates and address some open questions that have been raised by studies conducted thus far.

Extensive genetic and biochemical analysis of Drosophila melanogaster has made this system an important model for characterization of transcriptional regulatory elements and factors. Given the striking conservation of transcriptional controls in metazoans, general principles derived from studies of Drosophila are expected to continue to illuminate transcriptional regulation in other systems, including vertebrates. With improvement in technologies for genetic manipulation of insects, research in Drosophila will also aid the design of systems for controlled expression of genes in other hosts. This review focuses on recent advances from Drosophila in analysis of the functional components of transcriptional switches, including basal promoters, enhancers, boundary elements, and maintenance elements.

Various cis-regulatory module (CRM) predictors have been proposed in the last decade. Several well-established CRM predictors adopted different categories of prediction strategies, including window clustering, probabilistic modeling and phylogenetic footprinting. Appropriate integration of them has a potential to achieve high quality CRM prediction. This study analyzed four existing CRM predictors (ClusterBuster, MSCAN, CisModule and MultiModule) to seek a predictor combination that delivers a higher accuracy than individual CRM predictors. 465 CRMs across 140 Drosophila melanogaster genes from the REDfly database were used to evaluate the integrated CRM predictor proposed in this study. The results show that four predictor combinations achieved superior performance than the best individual CRM predictor.

Summary: Bioinformatics studies of transcriptional regulation in the metazoa are significantly hindered by the absence of readily available data on large numbers of transcriptional cis-regulatory modules (CRMs). Even the richly annotated Drosophila melanogaster genome lacks extensive CRM information. We therefore present here a database of Drosophila CRMs curated from the literature complete with both DNA sequence and a searchable description of the gene expression pattern regulated by each CRM. This resource should greatly facilitate the development of computational approaches to CRM discovery as well as bioinformatics analyses of regulatory sequence properties and evolution. Availability: Contact: mshalfon@buffalo.edu

Transcriptional enhancers are a primary mechanism by which tissue-specific gene expression is achieved. Despite the importance of these regulatory elements in development, responses to environmental stresses, and disease, testing enhancer activity in animals remains tedious, with a minority of enhancers having been characterized. Here, we have developed ‘enhancer-FACS-Seq’ (eFS) technology for highly parallel identification of active, tissue-specific enhancers in Drosophila embryos. Analysis of enhancers identified by eFS to be active in mesodermal tissues revealed enriched DNA binding site motifs of known and putative, novel mesodermal transcription factors (TFs). Naïve Bayes classifiers using TF binding site motifs accurately predicted mesodermal enhancer activity. Application of eFS to other cell types and organisms should accelerate the cataloging of enhancers and understanding how transcriptional regulation is encoded within them.

Researchers have now had access to the fully sequenced Drosophila melanogaster genome for over a decade, and the sequenced genomes of 11 additional Drosophila species have been available for almost 5 years, with more species' genomes becoming available every year [Adams MD, Celniker SE, Holt RA, et al. The genome sequence of Drosophila melanogaster. Science 2000;287:2185-95; Clark AG, Eisen MB, Smith DR, et al. Evolution of genes and genomes on the Drosophila phylogeny. Nature 2007;450:203-18]. Although the best studied of the D. melanogaster transcription factors (TFs) were cloned before sequencing of the genome, the availability of sequence data promised to transform our understanding of TFs and gene regulatory networks. Sequenced genomes have allowed researchers to generate tools for high-throughput characterization of gene expression levels, genome-wide TF localization and analyses of evolutionary constraints on DNA elements across multiple species. With an estimated 700 DNA-binding proteins in the Drosophila genome, it will be many years before each potential sequence-specific TF is studied in detail, yet the last decade of functional genomics research has already impacted our view of gene regulatory networks and TF DNA recognition.

Computational methods attempting to identify instances of cis-regulatory modules (CRMs) in the genome face a challenging problem of searching for potentially interacting transcription factor binding sites while knowledge of the specific interactions involved remains limited. Without a comprehensive comparison of their performance, the reliability and accuracy of these tools remains unclear. Faced with a large number of different tools that address this problem, we summarized and categorized them based on search strategy and input data requirements. Twelve representative methods were chosen and applied to predict CRMs from the Drosophila CRM database REDfly, and across the human ENCODE regions. Our results show that the optimal choice of method varies depending on species and composition of the sequences in question. When discriminating CRMs from non-coding regions, those methods considering evolutionary conservation have a stronger predictive power than methods designed to be run on a single genome. Different CRM representations and search strategies rely on different CRM properties, and different methods can complement one another. For example, some favour homotypical clusters of binding sites, while others perform best on short CRMs. Furthermore, most methods appear to be sensitive to the composition and structure of the genome to which they are applied. We analyze the principal features that distinguish the methods that performed well, identify weaknesses leading to poor performance, and provide a guide for users. We also propose key considerations for the development and evaluation of future CRM-prediction methods., Author Summary Transcriptional regulation involves multiple transcription factors binding to DNA sequences. A limited repertoire of transcription factors performs this complex regulatory step through various spatial and temporal interactions between themselves and their binding sites. These transcription factor binding interactions are clustered as distinct modules: cis-regulatory modules (CRMs). Computational methods attempting to identify instances of CRMs in the genome face a challenging problem because a majority of these interactions between transcription factors remain unknown. To investigate the reliability and accuracy of these methods, we chose twelve representative methods and applied them to predict CRMs on both the fly and human genomes. Our results show that the optimal choice of method varies depending on species and composition of the sequences in question. Different CRM representations and search strategies rely on different CRM properties, and different methods can complement one another. We provide a guide for users and key considerations for developers. We also expect that, along with new technology generating new types of genomic data, future CRM prediction methods will be able to reveal transcription binding interactions in three-dimensional space.

Systematic annotation of gene regulatory elements is a major challenge in genome science. Direct mapping of chromatin modification marks and transcriptional factor binding sites genome-wide1, 2 has successfully identified specific subtypes of regulatory elements3. In Drosophila several pioneering studies have provided genome-wide identification of Polycomb response elements4, chromatin states5, transcription factor binding sites6, 7, 8, 9, RNA polymerase II regulation8 and insulator elements10; however, comprehensive annotation of the regulatory genome remains a significant challenge. Here we describe results from the modENCODE cis-regulatory annotation project. We produced a map of the Drosophila melanogaster regulatory genome on the basis of more than 300 chromatin immunoprecipitation data sets for eight chromatin features, five histone deacetylases and thirty-eight site-specific transcription factors at different stages of development. Using these data we inferred more than 20,000 candidate regulatory elements and validated a subset of predictions for promoters, enhancers and insulators in vivo. We identified also nearly 2,000 genomic regions of dense transcription factor binding associated with chromatin activity and accessibility. We discovered hundreds of new transcription factor co-binding relationships and defined a transcription factor network with over 800 potential regulatory relationships.

The faithful execution of biological processes requires a precise and carefully orchestrated set of steps that depend on the proper spatial and temporal expression of genes. Here we review the various classes of transcriptional regulatory elements (core promoters, proximal promoters, distal enhancers, silencers, insulators/boundary elements, and locus control regions) and the molecular machinery (general transcription factors, activators, and coactivators) that interacts with the regulatory elements to mediate precisely controlled patterns of gene expression. The biological importance of transcriptional regulation is highlighted by examples of how alterations in these transcriptional components can lead to disease. Finally, we discuss the methods currently used to identify transcriptional regulatory elements, and the ability of these methods to be scaled up for the purpose of annotating the entire human genome.

Bioinformatics analysis of transcriptional control is guided by knowledge of the characteristics of cis-regulatory regions or enhancers. Features such as clustering of binding sites and co-occurrence of binding sites have aided enhancer identification, but quantitative predictions of enhancer function are not yet generally feasible. To facilitate the analysis of regulatory sequences in Drosophila melanogaster, we identified quantitative parameters that affect the activity of short-range transcriptional repressors, proteins that play key roles in development. In addition to the previously noted distance dependence, repression is strongly influenced by the stoichiometry, affinity, spacing, and arrangement of activator binding sites. Repression is insensitive to the type of activation domain, suggesting that short-range repression may primarily affect activators at the level of DNA binding. The activity of several short-range, but not long-range, repressors is circumscribed by the same quantitative parameters. This cis-regulatory “grammar” may aid the identification of enhancers regulated by short-range repressors and facilitate bioinformatic prediction of the functional output of transcriptional regulatory sequences.

Summary: Despite increasing numbers of computational tools developed to predict cis-regulatory sequences, the availability of high-quality datasets of transcription factor binding sites limits advances in the bioinformatics of gene regulation. Here we present such a dataset based on a systematic literature curation and genome annotation of DNase I footprints for the fruitfly, Drosophila melanogaster. Using the experimental results of 201 primary references, we annotated 1367 binding sites from 87 transcription factors and 101 target genes in the D.melanogaster genome sequence. These data will provide a rich resource for future bioinformatics analyses of transcriptional regulation in Drosophila such as constructing motif models, training cis-regulatory module detectors, benchmarking alignment tools and continued text mining of the extensive literature on transcriptional regulation in this important model organism. Availability: http://www.flyreg.org/ Contact: cbergman@gen.cam.ac.uk

Keywords: DNA Transposable Elements ; Evolution ; Gene Expression Regulation Reference EPFL-REVIEW-151771View record in PubMedView record in Web of Science Record created on 2010-09-17, modified on 2017-05-12

Background The core promoter region plays a critical role in the regulation of eukaryotic gene expression. We have determined the non-random distribution of DNA sequences relative to the transcriptional start site in Drosophila melanogaster promoters to identify sequences that may be biologically significant. We compare these results with those obtained for human promoters.