Abstract: Nowadays, Gene Ontology has been used widely by many researchers for biological data mining and information retrieval, integration of biological databases, finding genes, and incorporating knowledge in the Gene Ontology for gene clustering. However, the increase in size of the Gene Ontology has caused problems in maintaining and processing them. One way to obtain their accessibility is by clustering them into fragmented groups. Clustering the Gene Ontology is a difficult combinatorial problem and can be modeled as a graph partitioning problem. Additionally, deciding the number k of clusters to use is not easily perceived and is a hard algorithmic problem. Therefore, an approach for solving the automatic clustering of the Gene Ontology is proposed by incorporating cohesion-and-coupling metric into a hybrid algorithm consisting of a genetic algorithm and a split-and-merge algorithm. Experimental results and an example of modularized Gene Ontology in RDF/XML format are given to illustrate the effectiveness of the algorithm.
Abstract: The scientific achievements coming from molecular
biology depend greatly on the capability of computational
applications to analyze the laboratorial results. A comprehensive
analysis of an experiment requires typically the simultaneous study
of the obtained dataset with data that is available in several distinct
public databases. Nevertheless, developing a centralized access to
these distributed databases rises up a set of challenges such as: what
is the best integration strategy, how to solve nomenclature clashes,
how to solve database overlapping data and how to deal with huge
datasets. In this paper we present GeNS, a system that uses a simple and yet innovative approach to address several biological data integration issues. Compared with existing systems, the main
advantages of GeNS are related to its maintenance simplicity and to its coverage and scalability, in terms of number of supported
databases and data types. To support our claims we present the current use of GeNS in two concrete applications. GeNS currently contains more than 140 million of biological relations and it can be
publicly downloaded or remotely access through SOAP web services.
Abstract: Approximate tandem repeats in a genomic sequence are
two or more contiguous, similar copies of a pattern of nucleotides.
They are used in DNA mapping, studying molecular evolution
mechanisms, forensic analysis and research in diagnosis of inherited
diseases. All their functions are still investigated and not well
defined, but increasing biological databases together with tools for
identification of these repeats may lead to discovery of their specific
role or correlation with particular features. This paper presents a new
approach for finding approximate tandem repeats in a given sequence,
where the similarity between consecutive repeats is measured using
the Hamming distance. It is an enhancement of a method for finding
exact tandem repeats in DNA sequences based on the Burrows-
Wheeler transform.