Vito Di Gesù

Genome-wide characterization of chromatin binding and nucleosome spacing activity of the nucleosome remodelling ATPase ISWI

The evolutionarily conserved ATP‐dependent nucleosome remodelling factor ISWI can space nucleosomes affecting a variety of nuclear processes. In Drosophila, loss of ISWI leads to global transcriptional defects and to dramatic alterations in higher‐order chromatin structure, especially on the male X chromosome. In order to understand if chromatin condensation and gene expression defects, observed in ISWI mutants, are directly correlated with ISWI nucleosome spacing activity, we conducted a genome‐wide survey of ISWI binding and nucleosome positioning in wild‐type and ISWI mutant chromatin. Our analysis revealed that ISWI binds both genic and intergenic regions. Remarkably, we found that ISWI binds genes near their promoters causing specific alterations in nucleosome positioning at the level of the Transcription Start Site, providing an important insights in understanding ISWI role in higher eukaryote transcriptional regulation. Interestingly, differences in nucleosome spacing, between wild‐type and ISWI mutant chromatin, tend to accumulate on the X chromosome for all ISWI‐bound genes analysed. Our study shows how in higher eukaryotes the activity of the evolutionarily conserved nucleosome remodelling factor ISWI regulates gene expression and chromosome organization genome‐wide.

GenClust: A genetic algorithm for clustering gene expression data

BackgroundClustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering.ResultsGenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, compact and easy to update; (b) it can be used naturally in conjunction with data driven internal validation methods. We have experimented with the FOM methodology, specifically conceived for validating clusters of gene expression data. The validity of GenClust has been assessed experimentally on real data sets, both with the use of validation measures and in comparison with other algorithms, i.e., Average Link, Cast, Click and K-means.ConclusionExperiments show that none of the algorithms we have used is markedly superior to the others across data sets and validation measures; i.e., in many cases the observed differences between the worst and best performing algorithm may be statistically insignificant and they could be considered equivalent. However, there are cases in which an algorithm may be better than others and therefore worthwhile. In particular, experiments for GenClust show that, although simple in its data representation, it converges very rapidly to a local optimum and that its ability to identify meaningful clusters is comparable, and sometimes superior, to that of more sophisticated algorithms. In addition, it is well suited for use in conjunction with data driven internal validation measures and, in particular, the FOM methodology.

Symmetry operators in computer vision

Abstract Symmetry plays a remarkable role in perception problems. For example, peaks of brain activity are measured in correspondence with visual patterns showing symmetry . Relevance of symmetry in vision was already noted by Koler in 1929. Here, properties of a symmetry operator are reported and a new algorithm to measure local symmetries is proposed. Its performance is tested on segmentation of complex visual patterns and the classification of sparse images.

Local operators to detect regions of interest

Abstract The performance of a visual system is strongly influenced by the information processing that is done in the early vision phase. The need exists to limit the computation on areas of interest to reduce the total amount of data and their redundancy. This paper describes a new method to drive the attention during the analysis of complex scenes. Two new local operators, based on the computation of local moments and symmetries, are combined to drive the selection. Experimental results on real data are also reported.

A fuzzy approach to the evaluation of image complexity

The inherently multidimensional problem of evaluating the complexity of an image is of a certain relevance in both computer science and cognitive psychology. Computer scientists usually analyze spatial dimensions in order to deal with automatic vision problems, such as feature extraction. Psychologists seem more interested in the temporal dimension of complexity, as a means to explore attentional models. Is it possible to define, by merging both approaches, a more general index of visual complexity? The aim of this paper is the definition of objective measures of image complexity that fits with the so named perceived time. Towards the end we have defined a fuzzy mathematical model of visual complexity, based on fuzzy measures of entropy; the results obtained by applying this model to a set of pictorial images present a strong correlation with the outcomes of an experiment with human subjects, based on variation of subjective temporal estimations associated with changes in visual attentional load, which is also described herein.

Parallel computation of the Euler number via connectivity graph

Abstract This paper regards the computation of the Euler number (EN) of a binary image by means of the Connectivity Graph (CG), which is derived from the Cylindrical Algebraic Decomposition of the Euclidean plane. The corresponding decomposition of the discrete plane has shown how the CG reflects the topological structure of a binary image, and how it is a powerful data structure for the computation of shape indicators as the Euler number. Here some properties related to the CG and the EN are introduced; moreover two methods to compute the EN of a binary image are shown; a parallel algorithm is described in detail.

A memetic algorithm for binary image reconstruction

This paper deals with a memetic algorithm for the reconstruction of binary images, by using their projections along four directions. The algorithm generates by network flows a set of initial images according to two of the input projections and lets them evolve toward a solution that can be optimal or close to the optimum. Switch and compactness operators improve the quality of the reconstructed images which belong to a given generation, while the selection of the best image addresses the evolution to an optimal output.

Integrated fuzzy clustering

Abstract Cluster analysis is a valuable tool for exploratory pattern analysis, especially when the information available is not complete, and/or the data model is affected by uncertainty, and imprecision. Here a new integrated clustering method, based on a fuzzy approach is proposed. Its implementation consists of the combination of hierarchical fuzzy algorithms. The performance and accuracy of the methodology are tested on biomedical images. An application to the segmentation of magnetic resonance images is discussed.

Detection of regions of interest via the Pyramid Discrete Symmetry Transform

Pyramid computation has been introduced to design efficient vision algorithms [1], [2] based on both top-down and bottom-up strategies. It has been also suggested by biological arguments that show a correspondence between pyramids architecture and the mammalian visual pathway, starting from the retina and ending in the deepest layers of the visual cortex.

A Multi-Layer Method to Study Genome-Scale Positions of Nucleosomes

The basic unit of eukaryotic chromatin is the nucleosome, consisting of about 150 bp of DNA wrapped around a protein core made of histone proteins. Nucleosomes position is modulated in vivo to regulate fundamental nuclear processes. To measure nucleosome positions on a genomic scale both theoretical and experimental approaches have been recently reported. We have developed a new method, Multi-Layer Model (MLM), for the analysis of nucleosome position data obtained with microarray-based approach. The MLM is a feature extraction method in which the input data is processed by a classifier to distinguish between several kinds of patterns. We applied our method to simulated-synthetic and experimental nucleosome position data and found that besides a high nucleosome recognition and a strong agreement with standard statistical methods, the MLM can identify distinct classes of nucleosomes, making it an important tool for the genome wide analysis of nucleosome position and function. In conclusion, the MLM allows a better representation of nucleosome position data and a significant reduction in computational time.