Sequence information gain based motif analysis.

Background: The detection of regulatory regions in candidate sequences is essential for the understanding of the regulation of a particular gene and the mechanisms involved. This paper proposes a novel methodology based on information theoretic metrics for finding regulatory sequences in promoter regions.

Results: This methodology (SIGMA) has been tested on genomic sequence data for Homo sapiens and Mus musculus.

A subspace method for the detection of transcription factor binding sites.

Motivation: The identification of the sites at which transcription factors (TFs) bind to Deoxyribonucleic acid (DNA) is an important problem in molecular biology. Many computational methods have been developed for motif finding, most of them based on position-specific scoring matrices (PSSMs) which assume the independence of positions within a binding site. However, some experimental and computational studies demonstrate that interdependences within the positions exist.

Classification of Sherry vinegars by combining multidimensional fluorescence, parafac and different classification approaches.

Sherry vinegar is a much appreciated product from Jerez-Xérès-Sherry, Manzanilla de Sanlúcar and Vinagre de Jerez Protected Designation in southwestern Spain. Its complexity and the extraordinary organoleptic properties are acquired thanks to the method of production followed, the so-called "criaderas y solera" ageing system. Three qualities for Sherry vinegar are considered according to ageing time in oak barrels: "Vinagre de Jerez" (minimum of 6 months), "Reserva" (at least 2 years) and "Gran Reserva" (at least 10 years).

MEET: motif elements estimation toolkit.

MEET is an R package that integrates a set of algorithms for the detection of transcription factor binding sites (TFBS). The MEET R package includes five motif searching algorithms: MEME/MAST(Multiple Expectation-Maximization for Motif Elicitation), Q-residuals, MDscan (Motif Discovery scan), ITEME (Information Theory Elements for Motif Estimation) and MATCH. In addition MEET allows the user to work with different alignment algorithms: MUSCLE (Multiple Sequence Comparison by Log-Expectation), ClustalW and MEME.