title: A singular value decomposition approach for improved taxonomic classification of biological sequences.
abstract: BACKGROUND
  Singular value decomposition (SVD) is a powerful technique for information retrieval; it helps uncover relationships between elements that are not prima facie related. SVD was initially developed to reduce the time needed for information retrieval and analysis of very large data sets in the complex internet environment. Since information retrieval from large-scale genome and proteome data sets has a similar level of complexity, SVD-based methods could also facilitate data analysis in this research area.
  
  
  RESULTS
  We found that SVD applied to amino acid sequences demonstrates relationships and provides a basis for producing clusters and cladograms, demonstrating evolutionary relatedness of species that correlates well with Linnaean taxonomy. The choice of a reasonable number of singular values is crucial for SVD-based studies. We found that fewer singular values are needed to produce biologically significant clusters when SVD is employed. Subsequently, we developed a method to determine the lowest number of singular values and fewest clusters needed to guarantee biological significance; this system was developed and validated by comparison with Linnaean taxonomic classification.
  
  
  CONCLUSIONS
  By using SVD, we can reduce uncertainty concerning the appropriate rank value necessary to perform accurate information retrieval analyses. In tests, clusters that we developed with SVD perfectly matched what was expected based on Linnaean taxonomy.
authors.affiliation: Department of General Biology, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Av, Antônio Carlos, 6627, MG, 31,270-901, Brazil.
authors.forename: Anderson R
authors.initials: AR
authors.lastname: Santos
authors.forename: Marcos A
authors.initials: MA
authors.lastname: Santos
authors.forename: Jan
authors.initials: J
authors.lastname: Baumbach
authors.forename: John A
authors.initials: JA
authors.lastname: McCulloch
authors.forename: Guilherme C
authors.initials: GC
authors.lastname: Oliveira
authors.forename: Artur
authors.initials: A
authors.lastname: Silva
authors.forename: Anderson
authors.initials: A
authors.lastname: Miyoshi
authors.forename: Vasco
authors.initials: V
authors.lastname: Azevedo
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016247:Information Storage and Retrieval; D012984:Software