Catálogo de publicaciones - libros

HIV/AIDS pandemic affected 39.4 million people at the end of 2004, spreading at the rate of 15.000 new infections per day [1]. Although Brazil ranks in fourth in number of reported AIDS cases, limited information concerning the molecular diversity of HIV-1 circulating subtypes is known [3]. Southern Brazil has a particular HIV-1 epidemic, whereas subtype B dominates other regions of the country and subtype C reported cases are rare, in southern states the subtypes C and B have equivalent proportions, and the subtype C seems to be growing up since it was first described in Porto Alegre city, capital of Rio Grande do Sul (RS), at 90’s.

We present a preliminary description and results of a system to help the curation of genome assembly and annotation. Standard tools are used for these tasks, and our methodology focuses on user guidance, data visualization and integration, and data browsing aspects.

This paper presents a neural network clustering strategy to identify regularities in a dataset of Mycoplasma promoter sequences. The traditional way that prokaryotic promoters are identified is proven inadequate to the Mycoplasma family. Our clustering approach tries to discover regularities in base pair compositions of the dataset sequences to give clues which indicate the presence or absence of promoters. Several experiments with leave-one-out strategy and a negative dataset revealed a best way to fit model parameters. Preliminary results are promising for creating a computational model able to find promoter regions in Mycoplasmas.

Given the number of available genomic DNA, one now faces the task of identifying the functional parts of such raw sequence data, like the protein-coding regions. The gene prediction problem can be addressed in several ways. The most recently methods make use of the similarities between regions of two unannotated genomic sequences in order to find their genes. In this paper we present a new comparative-based heuristic to the gene prediction problem. It relies on a syntenic alignment of two genomic sequences. We have implemented the proposed heuristic in a computer program and confirmed its validity on a benchmark including 50 pairs of human and mouse genomic sequences.

The aim of this paper is to present a real time immersive visualization and manipulation of the full color visible human dataset. A data glove and stereo shutter glasses were used to provide interactive and stereoscopic 3D visualization of this set of images in a multiprojection immersive environment.