Biomedical subjects

Susana Vinga

Publications and source records attributed to Susana Vinga.

6 recordsLinked to original sources

PubMed2006

Computing distribution of scale independent motifs in biological sequences.

The use of Chaos Game Representation (CGR) or its generalization, Universal Sequence Maps (USM), to describe the distribution of biological sequences has been found objectionable because of the fractal structure of that coordinate system. Consequently, the investigation of distribution of symbolic motifs at multiple scales is hampered by an inexact association between distance and sequence dissimilarity. A solution to this problem could unleash the use of iterative maps as phase-state representation of sequences where its statistical properties can be conveniently investigated. In this study a family of kernel density functions is described that accommodates the fractal nature of iterative function representations of symbolic sequences and, consequently, enables the exact investigation of sequence motifs of arbitrary lengths in that scale-independent representation. Furthermore, the proposed kernel density includes both Markovian succession and currently used alignment-free sequence dissimilarity metrics as special solutions. Therefore, the fractal kernel described is in fact a generalization that provides a common framework for a diverse suite of sequence analysis techniques.

Journal Article↗

PubMed2006

Assessment of allelic variation in the ccrAB locus in methicillin-resistant Staphylococcus aureus clones.

OBJECTIVES: To evaluate the allelic variability of ccrA and ccrB among methicillin-resistant Staphylococcus aureus (MRSA) strains belonging to different genetic lineages and carrying different SCCmec types. METHODS: Internal fragments from both genes were obtained by PCR amplification with pairs of degenerate primers and the nucleotide sequence was determined for both strands. Nucleotide sequences were aligned and the phylogenetic trees were reconstructed. RESULTS: The homology scores obtained confirmed the very close relationships among ccrAB alleles associated with each SCCmec type, which further validate this locus as a good target for SCCmec typing strategies. Using this strategy, particularly for the ccrB gene, SCCmec types with the same ccrAB allotype, as detected by PCR, could be discriminated and some strains with non-typeable ccrAB loci were resolved. CONCLUSIONS: We propose that sequencing an internal fragment of ccrB is a strategy for SCCmec typing that may be easily incorporated into other sequence-based MRSA typing strategies, such as multi-locus sequence typing and spa typing.

Alleles↗

PubMed2004

Rényi continuous entropy of DNA sequences.

Entropy measures of DNA sequences estimate their randomness or, inversely, their repeatability. L-block Shannon discrete entropy accounts for the empirical distribution of all length-L words and has convergence problems for finite sequences. A new entropy measure that extends Shannon's formalism is proposed. Renyi's quadratic entropy calculated with Parzen window density estimation method applied to CGR/USM continuous maps of DNA sequences constitute a novel technique to evaluate sequence global randomness without some of the former method drawbacks. The asymptotic behaviour of this new measure was analytically deduced and the calculation of entropies for several synthetic and experimental biological sequences was performed. The results obtained were compared with the distributions of the null model of randomness obtained by simulation. The biological sequences have shown a different p-value according to the kernel resolution of Parzen's method, which might indicate an unknown level of organization of their patterns. This new technique can be very useful in the study of DNA sequence complexity and provide additional tools for DNA entropy estimation. The main MATLAB applications developed and additional material are available at the webpage . Specialized functions can be obtained from the authors.

Animals↗

PubMed2004

Comparative evaluation of word composition distances for the recognition of SCOP relationships.

MOTIVATION: Alignment-free metrics were recently reviewed by the authors, but have not until now been object of a comparative study. This paper compares the classification accuracy of word composition metrics therein reviewed. It also presents a new distance definition between protein sequences, the W-metric, which bridges between alignment metrics, such as scores produced by the Smith-Waterman algorithm, and methods based solely in L-tuple composition, such as Euclidean distance and Information content. RESULTS: The comparative study reported here used the SCOP/ASTRAL protein structure hierarchical database and accessed the discriminant value of alternative sequence dissimilarity measures by calculating areas under the Receiver Operating Characteristic curves. Although alignment methods resulted in very good classification accuracy at family and superfamily levels, alignment-free distances, in particular Standard Euclidean Distance, are as good as alignment algorithms when sequence similarity is smaller, such as for recognition of fold or class relationships. This observation justifies its advantageous use to pre-filter homologous proteins since word statistics techniques are computed much faster than the alignment methods. AVAILABILITY: All MATLAB code used to generate the data is available upon request to the authors. Additional material available at http://bioinformatics.musc.edu/wmetric

Algorithms↗

PubMed2003

Alignment-free sequence comparison-a review.

MOTIVATION: Genetic recombination and, in particular, genetic shuffling are at odds with sequence comparison by alignment, which assumes conservation of contiguity between homologous segments. A variety of theoretical foundations are being used to derive alignment-free methods that overcome this limitation. The formulation of alternative metrics for dissimilarity between sequences and their algorithmic implementations are reviewed. RESULTS: The overwhelming majority of work on alignment-free sequence has taken place in the past two decades, with most reports published in the past 5 years. Two main categories of methods have been proposed-methods based on word (oligomer) frequency, and methods that do not require resolving the sequence with fixed word length segments. The first category is based on the statistics of word frequency, on the distances defined in a Cartesian space defined by the frequency vectors, and on the information content of frequency distribution. The second category includes the use of Kolmogorov complexity and Chaos Theory. Despite their low visibility, alignment-free metrics are in fact already widely used as pre-selection filters for alignment-based querying of large applications. Recent work is furthering their usage as a scale-independent methodology that is capable of recognizing homology when loss of contiguity is beyond the possibility of alignment. AVAILABILITY: Most of the alignment-free algorithms reviewed were implemented in MATLAB code and are available at http://bioinformatics.musc.edu/resources.html

Algorithms↗

PubMed2002

Universal sequence map (USM) of arbitrary discrete sequences.

BACKGROUND: For over a decade the idea of representing biological sequences in a continuous coordinate space has maintained its appeal but not been fully realized. The basic idea is that any sequence of symbols may define trajectories in the continuous space conserving all its statistical properties. Ideally, such a representation would allow scale independent sequence analysis--without the context of fixed memory length. A simple example would consist on being able to infer the homology between two sequences solely by comparing the coordinates of any two homologous units. RESULTS: We have successfully identified such an iterative function for bijective mapping psi of discrete sequences into objects of continuous state space that enable scale-independent sequence analysis. The technique, named Universal Sequence Mapping (USM), is applicable to sequences with an arbitrary length and arbitrary number of unique units and generates a representation where map distance estimates sequence similarity. The novel USM procedure is based on earlier work by these and other authors on the properties of Chaos Game Representation (CGR). The latter enables the representation of 4 unit type sequences (like DNA) as an order free Markov chain transition table. The properties of USM are illustrated with test data and can be verified for other data by using the accompanying web-based tool:http://bioinformatics.musc.edu/~jonas/usm/. CONCLUSIONS: USM is shown to enable a statistical mechanics approach to sequence analysis. The scale independent representation frees sequence analysis from the need to assume a memory length in the investigation of syntactic rules.

Algorithms↗