Assessing Computational Methods of Cis-Regulatory Module Prediction

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

Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems.

Despite the efforts that bioengineers have exerted in designing and constructing biological processes that function according to a predetermined set of rules, their operation remains fundamentally circumstantial. The contextual situation in which molecules and single-celled or multi-cellular organisms find themselves shapes the way they interact, respond to the environment and process external information. Since the birth of the field, synthetic biologists have had to grapple with contextual issues, particularly when the molecular and genetic devices inexplicably fail to function as designed when tested in vivo. In this review, we set out to identify and classify the sources of the unexpected divergences between design and actual function of synthetic systems and analyze possible methodologies aimed at controlling, if not preventing, unwanted contextual issues

Genome Biol.

With genome analysis expanding from the study of genes to the study of gene regulation, 'regulatory genomics' utilizes sequence information, evolution and functional genomics measurements to unravel how regulatory information is encoded in the genome

Identifying Cis-Regulatory Sequences by Word Profile Similarity

Recognizing regulatory sequences in genomes is a continuing challenge, despite a wealth of available genomic data and a growing number of experimentally validated examples.We discuss here a simple approach to search for regulatory sequences based on the compositional similarity of genomic regions and known cis-regulatory sequences. This method, which is not limited to searching for predefined motifs, recovers sequences known to be under similar regulatory control. The words shared by the recovered sequences often correspond to known binding sites. Furthermore, we show that although local word profile clustering is predictive for the regulatory sequences involved in blastoderm segmentation, local dissimilarity is a more universal feature of known regulatory sequences in Drosophila.Our method leverages sequence motifs within a known regulatory sequence to identify co-regulated sequences without explicitly defining binding sites. We also show that regulatory sequences can be distinguished from surrounding sequences by local sequence dissimilarity, a novel feature in identifying regulatory sequences across a genome. Source code for WPH-finder is available for download at http://rana.lbl.gov/downloads/wph.tar.gz

REDfly 2.0: an integrated database of cis-regulatory modules and transcription factor binding sites in Drosophila

The identification and study of the cis-regulatory elements that control gene expression are important areas of biological research, but few resources exist to facilitate large-scale bioinformatics studies of cis-regulation in metazoan species. Drosophila melanogaster, with its well-annotated genome, exceptional resources for comparative genomics and long history of experimental studies of transcriptional regulation, represents the ideal system for regulatory bioinformatics. We have merged two existing Drosophila resources, the REDfly database of cis-regulatory modules and the FlyReg database of transcription factor binding sites (TFBSs), into a single integrated database containing extensive annotation of empirically validated cis-regulatory modules and their constituent binding sites. With the enhanced functionality made possible through this integration of TFBS data into REDfly, together with additional improvements to the REDfly infrastructure, we have constructed a one-stop portal for Drosophila cis-regulatory data that will serve as a powerful resource for both computational and experimental studies of transcriptional regulation. REDfly is freely accessible at http://redfly.ccr.buffalo.edu

REDfly 2.0: an integrated database of cis-regulatory modules and transcription factor binding sites in Drosophila

The identification and study of the cis-regulatory elements that control gene expression are important areas of biological research, but few resources exist to facilitate large-scale bioinformatics studies of cis-regulation in metazoan species. Drosophila melanogaster, with its well-annotated genome, exceptional resources for comparative genomics and long history of experimental studies of transcriptional regulation, represents the ideal system for regulatory bioinformatics. We have merged two existing Drosophila resources, the REDfly database of cis-regulatory modules and the FlyReg database of transcription factor binding sites (TFBSs), into a single integrated database containing extensive annotation of empirically validated cis-regulatory modules and their constituent binding sites. With the enhanced functionality made possible through this integration of TFBS data into REDfly, together with additional improvements to the REDfly infrastructure, we have constructed a one-stop portal for Drosophila cis-regulatory data that will serve as a powerful resource for both computational and experimental studies of transcriptional regulation. REDfly is freely accessible at http://redfly.ccr.buffalo.edu

Predicting enhancer regions and transcription factor binding sites in D. melanogaster

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 71-75).Identifying regions in the genome that have regulatory function is important to the fundamental biological problem of understanding the mechanisms through which a regulatory sequence drives specific spatial and temporal patterns of gene expression in early development. The modENCODE project aims to comprehensively identify functional elements in the C. elegans and D. melanogaster genomes. The genome- wide binding locations of all known transcription factors as well as of other DNA- binding proteins are currently being mapped within the context of this project [8]. The large quantity of new data that is becoming available through the modENCODE project and other experimental efforts offers the potential for gaining insight into the mechanisms of gene regulation. Developing improved approaches to identify functional regions and understand their architecture based on available experimental data represents a critical part of the modENCODE effort. Towards this goal, I use a machine learning approach to study the predictive power of experimental and sequence-based combinations of features for predicting enhancers and transcription factor binding sites.by Rachel Sealfon.S.M

When needles look like hay: How to find tissue-specific enhancers in model organism genomes

AbstractA major prerequisite for the investigation of tissue-specific processes is the identification of cis-regulatory elements. No generally applicable technique is available to distinguish them from any other type of genomic non-coding sequence. Therefore, researchers often have to identify these elements by elaborate in vivo screens, testing individual regions until the right one is found.Here, based on many examples from the literature, we summarize how functional enhancers have been isolated from other elements in the genome and how they have been characterized in transgenic animals. Covering computational and experimental studies, we provide an overview of the global properties of cis-regulatory elements, like their specific interactions with promoters and target gene distances. We describe conserved non-coding elements (CNEs) and their internal structure, nucleotide composition, binding site clustering and overlap, with a special focus on developmental enhancers. Conflicting data and unresolved questions on the nature of these elements are highlighted. Our comprehensive overview of the experimental shortcuts that have been found in the different model organism communities and the new field of high-throughput assays should help during the preparation phase of a screen for enhancers. The review is accompanied by a list of general guidelines for such a project

Computational detection of genomic cis-regulatory modules applied to body patterning in the early Drosophila embryo

BACKGROUND: Regulation of gene transcription is crucial for the function and development of all organisms. While gene prediction programs that identify protein coding sequence are used with remarkable success in the annotation of genomes, the development of computational methods to analyze noncoding regions and to delineate transcriptional control elements is still in its infancy. RESULTS: Here we present novel algorithms to detect cis-regulatory modules through genome wide scans for clusters of transcription factor binding sites using three levels of prior information. When binding sites for the factors are known, our statistical segmentation algorithm, Ahab, yields about 150 putative gap gene regulated modules, with no adjustable parameters other than a window size. If one or more related modules are known, but no binding sites, repeated motifs can be found by a customized Gibbs sampler and input to Ahab, to predict genes with similar regulation. Finally using only the genome, we developed a third algorithm, Argos, that counts and scores clusters of overrepresented motifs in a window of sequence. Argos recovers many of the known modules, upstream of the segmentation genes, with no training data. CONCLUSIONS: We have demonstrated, in the case of body patterning in the Drosophila embryo, that our algorithms allow the genome-wide identification of regulatory modules. We believe that Ahab overcomes many problems of recent approaches and we estimated the false positive rate to be about 50%. Argos is the first successful attempt to predict regulatory modules using only the genome without training data. Complete results and module predictions across the Drosophila genome are available at http://uqbar.rockefeller.edu/~siggia/