Dataset Information

Genome-wide prediction, display and refinement of binding sites with information theory-based models.

ABSTRACT: We present Delila-genome, a software system for identification, visualization and analysis of protein binding sites in complete genome sequences. Binding sites are predicted by scanning genomic sequences with information theory-based (or user-defined) weight matrices. Matrices are refined by adding experimentally-defined binding sites to published binding sites. Delila-Genome was used to examine the accuracy of individual information contents of binding sites detected with refined matrices as a measure of the strengths of the corresponding protein-nucleic acid interactions. The software can then be used to predict novel sites by rescanning the genome with the refined matrices.Parameters for genome scans are entered using a Java-based GUI interface and backend scripts in Perl. Multi-processor CPU load-sharing minimized the average response time for scans of different chromosomes. Scans of human genome assemblies required 4-6 hours for transcription factor binding sites and 10-19 hours for splice sites, respectively, on 24- and 3-node Mosix and Beowulf clusters. Individual binding sites are displayed either as high-resolution sequence walkers or in low-resolution custom tracks in the UCSC genome browser. For large datasets, we applied a data reduction strategy that limited displays of binding sites exceeding a threshold information content to specific chromosomal regions within or adjacent to genes. An HTML document is produced listing binding sites ranked by binding site strength or chromosomal location hyperlinked to the UCSC custom track, other annotation databases and binding site sequences. Post-genome scan tools parse binding site annotations of selected chromosome intervals and compare the results of genome scans using different weight matrices. Comparisons of multiple genome scans can display binding sites that are unique to each scan and identify sites with significantly altered binding strengths.Delila-Genome was used to scan the human genome sequence with information weight matrices of transcription factor binding sites, including PXR/RXRalpha, AHR and NF-kappaB p50/p65, and matrices for RNA binding sites including splice donor, acceptor, and SC35 recognition sites. Comparisons of genome scans with the original and refined PXR/RXRalpha information weight matrices indicate that the refined model more accurately predicts the strengths of known binding sites and is more sensitive for detection of novel binding sites.

SUBMITTER: Gadiraju S

PROVIDER: S-EPMC200970 | biostudies-literature | 2003 Sep

REPOSITORIES: biostudies-literature

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Genome-wide prediction, display and refinement of binding sites with information theory-based models.

Gadiraju Sashidhar S Vyhlidal Carrie A CA Leeder J Steven JS Rogan Peter K PK

BMC bioinformatics 20030908

<h4>Background</h4>We present Delila-genome, a software system for identification, visualization and analysis of protein binding sites in complete genome sequences. Binding sites are predicted by scanning genomic sequences with information theory-based (or user-defined) weight matrices. Matrices are refined by adding experimentally-defined binding sites to published binding sites. Delila-Genome was used to examine the accuracy of individual information contents of binding sites detected with ref ...[more]

PMID: 12962546

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Project description:The effective reproduction number, Rt, is a key time-varying prognostic for the growth rate of any infectious disease epidemic. Significant changes in Rt can forewarn about new transmissions within a population or predict the efficacy of interventions. Inferring Rt reliably and in real-time from observed time-series of infected (demographic) data is an important problem in population dynamics. The renewal or branching process model is a popular solution that has been applied to Ebola and Zika virus disease outbreaks, among others, and is currently being used to investigate the ongoing COVID-19 pandemic. This model estimates Rt using a heuristically chosen piecewise function. While this facilitates real-time detection of statistically significant Rt changes, inference is highly sensitive to the function choice. Improperly chosen piecewise models might ignore meaningful changes or over-interpret noise-induced ones, yet produce visually reasonable estimates. No principled piecewise selection scheme exists. We develop a practical yet rigorous scheme using the accumulated prediction error (APE) metric from information theory, which deems the model capable of describing the observed data using the fewest bits as most justified. We derive exact posterior prediction distributions for infected population size and integrate these within an APE framework to obtain an exact and reliable method for identifying the piecewise function best supported by available epidemic data. We find that this choice optimises short-term prediction accuracy and can rapidly detect salient fluctuations in Rt, and hence the infected population growth rate, in real-time over the course of an unfolding epidemic. Moreover, we emphasise the need for formal selection by exposing how common heuristic choices, which seem sensible, can be misleading. Our APE-based method is easily computed and broadly applicable to statistically similar models found in phylogenetics and macroevolution, for example. Our results explore the relationships among estimate precision, forecast reliability and model complexity.

Project description:BackgroundRAD51 proteins, which are conserved in all eukaryotes, repair DNA double-strand breaks. This is critical to homologous chromosome pairing and recombination enabling successful reproduction. Work in Arabidopsis suggests that RAD51 also plays a role in plant defense; the Arabidopsis rad51 mutant is more susceptible to Pseudomonas syringae. However, the defense functions of RAD51 and the proteins interacting with RAD51 have not been thoroughly investigated in maize. Uncovering ligands of RAD51 would help to understand meiotic recombination and possibly the role of RAD51 in defense. This study used phage display, a tool for discovery of protein-protein interactions, to search for proteins interacting with maize RAD51A1.ResultsMaize RAD51A1 was screened against a random phage library. Eleven short peptide sequences were recovered from 15 phages which bound ZmRAD51A1 in vitro; three sequences were found in multiple successfully binding phages. Nine of these phage interactions were verified in vitro through ELISA and/or dot blotting. BLAST searches did not reveal any maize proteins which contained the exact sequence of any of the selected phage peptides, although one of the selected phages had a strong alignment (E-value = 0.079) to a binding domain of maize BRCA2. Therefore, we designed 32 additional short peptides using amino acid sequences found in the predicted maize proteome. These peptides were not contained within phages. Of these synthesized peptides, 14 bound to ZmRAD51A1 in a dot blot experiment. These 14 sequences are found in known maize proteins including transcription factors putatively involved in defense.ConclusionsThese results reveal several peptides which bind ZmRAD51A1 and support a potential role for ZmRAD51A1 in transcriptional regulation and plant defense. This study also demonstrates the applicability of phage display to basic science questions, such as the search for binding partners of a known protein, and raises the possibility of an iterated approach to test peptide sequences that closely but imperfectly align with the selected phages.

Dataset Information

Genome-wide prediction, display and refinement of binding sites with information theory-based models.

Publications

Genome-wide prediction, display and refinement of binding sites with information theory-based models.

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