Dataset Information

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.

SUBMITTER: Santos AR

PROVIDER: S-EPMC3287580 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Publications

A singular value decomposition approach for improved taxonomic classification of biological sequences.

Santos Anderson R AR Santos Marcos A MA Baumbach Jan J McCulloch John A JA Oliveira Guilherme C GC Silva Artur A Miyoshi Anderson A Azevedo Vasco V

BMC genomics 20111222

<h4>Background</h4>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 anal ...[more]

PMID: 22369633

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Dataset Information

A singular value decomposition approach for improved taxonomic classification of biological sequences.

Background

Results

Conclusions

Publications

A singular value decomposition approach for improved taxonomic classification of biological sequences.

Similar Datasets

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