Magnetic history dependence of metastable states in systems with dipolar interactions

We present the results of a Monte Carlo simulation of the ground state and magnetic relaxation of a model of a thin film consisting on a two-dimensional square lattice of Heisenberg spins with perpendicular anisotropy K, exchange J and long-range dipolar interactions g. We have studied the ground state configurations of this system for a wide range of the interaction parameters J/g, K/g by means of the simulated annealing procedure, showing that the model is able to reproduce the different magnetic configurations found in real samples. We have found the existence of a certain range of K/g, J/g values for which in-plane and out-of-plane configurations are quasi-degenerated in energy. We show that when a system in this region of parameters is perturbed by an external force that is subsequently removed different kinds of ordering may be induced depending on the followed procedure. In particular, simulations of relaxations from saturation under an a.c. demagnetizing field or in zero field are in qualitative agreement with recent experiments on epitaxial and granular alloy thin films, which show a wide variety of magnetic patterns depending on their magnetic history.Comment: Invited paper to 3rd EuroConference on Magnetic Properties of Fine Nanoparticles, Barcelona, October 99. To be published in JMMM (References included 22-Dec-1999

Determinants of the usage of public computer lab facilities in rural communities

For those without information technology in the household, the importance of public computer lab facilities which can offer access to such technology free of charge is very great. However, the sustainability of such facilities is a difficult challenge for rural communities which do not have a large population to uphold such an endeavour. This research examines those factors influencing the usage of public computer lab facilities in rural communities. This research objective is achieved by examining such a facility located in a small rural community in Nova Scotia. Interviews with various stakeholders indicate that the lack of computer technology within the home, desire for social interaction, desire to increase knowledge of information technology, accessibility of the site, sophistication of the site, services offered, and technical support all influence the perceived usefulness of the facility, which in turn, influences the intention to use the facility and ultimately self-reported system usage

Chromatin particle spectrum analysis: a method for comparative chromatin structure analysis using paired-end mode next-generation DNA sequencing

Microarray and next-generation sequencing techniques which allow whole genome analysis of chromatin structure and sequence-specific protein binding are revolutionizing our view of chromosome architecture and function. However, many current methods in this field rely on biochemical purification of highly specific fractions of DNA prepared from chromatin digested with either micrococcal nuclease or DNaseI and are restricted in the parameters they can measure. Here, we show that a broad size-range of genomic DNA species, produced by partial micrococcal nuclease digestion of chromatin, can be sequenced using paired-end mode next-generation technology. The paired sequence reads, rather than DNA molecules, can then be size-selected and mapped as particle classes to the target genome. Using budding yeast as a model, we show that this approach reveals position and structural information for a spectrum of nuclease resistant complexes ranging from transcription factor-bound DNA elements up to mono- and poly-nucleosomes. We illustrate the utility of this approach in visualizing the MNase digestion landscape of protein-coding gene transcriptional start sites, and demonstrate a comparative analysis which probes the function of the chromatin-remodelling transcription factor Cbf1p

c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression

<p>Abstract</p> <p>Background</p> <p>Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes.</p> <p>Results</p> <p>In this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in <it>Drosophila</it>.</p> <p>Conclusion</p> <p>Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs.</p

Cancer somatic mutations cluster in a subset of regulatory sites predicted from the ENCODE data

Background: Transcriptional regulation of gene expression is essential for cellular differentiation and function, and defects in the process are associated with cancer. The ENCODE project has mapped potential regulatory sites across the complete genome in many cell types, and these regions have been shown to harbour many of the somatic mutations that occur in cancer cells, suggesting that their effects may drive cancer initiation and development. The ENCODE data suggests a very large number of regulatory sites, and methods are needed to identify those that are most relevant and to connect them to the genes that they control. Methods: Predictive models of gene expression were developed by integrating the ENCODE data for regulation, including transcription factor binding and DNase1 hypersensitivity, with RNA-seq data for gene expression. A penalized regression method was used to identify the most predictive potential regulatory sites for each transcript. Known cancer somatic mutations from the COSMIC database were mapped to potential regulatory sites, and we examined differences in the mapping frequencies associated with sites chosen in regulatory models and other (rejected) sites. The effects of potential confounders, for example replication timing, were considered. Results: Cancer somatic mutations preferentially occupy those regulatory regions chosen in our models as most predictive of gene expression. Conclusion: Our methods have identified a significantly reduced set of regulatory sites that are enriched in cancer somatic mutations and are more predictive of gene expression. This has significance for the mechanistic interpretation of cancer mutations, and the understanding of genetic regulation

Is Transcription Factor Binding Site Turnover a Sufficient Explanation for Cis-Regulatory Sequence Divergence?

The molecular evolution of cis-regulatory sequences is not well understood. Comparisons of closely related species show that cis-regulatory sequences contain a large number of sites constrained by purifying selection. In contrast, there are a number of examples from distantly related species where cis-regulatory sequences retain little to no sequence similarity but drive similar patterns of gene expression. Binding site turnover, whereby the gain of a redundant binding site enables loss of a previously functional site, is one model by which cis-regulatory sequences can diverge without a concurrent change in function. To determine whether cis-regulatory sequence divergence is consistent with binding site turnover, we examined binding site evolution within orthologous intergenic sequences from 14 yeast species defined by their syntenic relationships with adjacent coding sequences. Both local and global alignments show that nearly all distantly related orthologous cis-regulatory sequences have no significant level of sequence similarity but are enriched for experimentally identified binding sites. Yet, a significant proportion of experimentally identified binding sites that are conserved in closely related species are absent in distantly related species and so cannot be explained by binding site turnover. Depletion of binding sites depends on the transcription factor but is detectable for a quarter of all transcription factors examined. Our results imply that binding site turnover is not a sufficient explanation for cis-regulatory sequence evolution

The value of position-specific priors in motif discovery using MEME

<p>Abstract</p> <p>Background</p> <p>Position-specific priors have been shown to be a flexible and elegant way to extend the power of Gibbs sampler-based motif discovery algorithms. Information of many types–including sequence conservation, nucleosome positioning, and negative examples–can be converted into a prior over the location of motif sites, which then guides the sequence motif discovery algorithm. This approach has been shown to confer many of the benefits of conservation-based and discriminative motif discovery approaches on Gibbs sampler-based motif discovery methods, but has not previously been studied with methods based on expectation maximization (EM).</p> <p>Results</p> <p>We extend the popular EM-based MEME algorithm to utilize position-specific priors and demonstrate their effectiveness for discovering transcription factor (TF) motifs in yeast and mouse DNA sequences. Utilizing a discriminative, conservation-based prior dramatically improves MEME's ability to discover motifs in 156 yeast TF ChIP-chip datasets, more than doubling the number of datasets where it finds the correct motif. On these datasets, MEME using the prior has a higher success rate than eight other conservation-based motif discovery approaches. We also show that the same type of prior improves the accuracy of motifs discovered by MEME in mouse TF ChIP-seq data, and that the motifs tend to be of slightly higher quality those found by a Gibbs sampling algorithm using the same prior.</p> <p>Conclusions</p> <p>We conclude that using position-specific priors can substantially increase the power of EM-based motif discovery algorithms such as MEME algorithm.</p

Positional variations among heterogeneous nucleosome maps give dynamical information on chromatin

Although nucleosome remodeling is essential to transcriptional regulation in eukaryotes, little is known about its genome-wide behavior. Since a number of nucleosome positioning maps in vivo have been recently determined, we examined if their comparisons might be used for obtaining a genome-wide profile of nucleosome remodeling. Using seven yeast maps, the local variability of nucleosomes, measured by the entropy, was significantly higher in a set of reported unstable nucleosomes. The binding sites of four transcription factors, known as the remodeling factors, were distinctively high both in entropy and linker ratio, whereas those of Yhp1, their potential inhibitor, showed the lowest values in both of them. Taken together, our map shows the general information of nucleosome dynamics reasonably well. The “nucleosome dynamics” map provides the new significant correlation with the degree of expression variety instead of their intensity. Furthermore, the associations with gene function and histone modification were also discussed here

Probabilistic Inference of Transcription Factor Binding from Multiple Data Sources

An important problem in molecular biology is to build a complete understanding of transcriptional regulatory processes in the cell. We have developed a flexible, probabilistic framework to predict TF binding from multiple data sources that differs from the standard hypothesis testing (scanning) methods in several ways. Our probabilistic modeling framework estimates the probability of binding and, thus, naturally reflects our degree of belief in binding. Probabilistic modeling also allows for easy and systematic integration of our binding predictions into other probabilistic modeling methods, such as expression-based gene network inference. The method answers the question of whether the whole analyzed promoter has a binding site, but can also be extended to estimate the binding probability at each nucleotide position. Further, we introduce an extension to model combinatorial regulation by several TFs. Most importantly, the proposed methods can make principled probabilistic inference from multiple evidence sources, such as, multiple statistical models (motifs) of the TFs, evolutionary conservation, regulatory potential, CpG islands, nucleosome positioning, DNase hypersensitive sites, ChIP-chip binding segments and other (prior) sequence-based biological knowledge. We developed both a likelihood and a Bayesian method, where the latter is implemented with a Markov chain Monte Carlo algorithm. Results on a carefully constructed test set from the mouse genome demonstrate that principled data fusion can significantly improve the performance of TF binding prediction methods. We also applied the probabilistic modeling framework to all promoters in the mouse genome and the results indicate a sparse connectivity between transcriptional regulators and their target promoters. To facilitate analysis of other sequences and additional data, we have developed an on-line web tool, ProbTF, which implements our probabilistic TF binding prediction method using multiple data sources. Test data set, a web tool, source codes and supplementary data are available at: http://www.probtf.org