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

Content Provider	Springer Nature : BioMed Central
Author	Santos, Anderson R Santos, Marcos A Baumbach, Jan McCulloch, John A Oliveira, Guilherme C Silva, Artur Miyoshi, Anderson Azevedo, Vasco
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.
Related Links	https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/1471-2164-12-S4-S11.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	14712164
DOI	10.1186/1471-2164-12-S4-S11
Journal	BMC Genomics
Issue Number	4
Volume Number	12
Language	English
Publisher	BioMed Central
Publisher Date	2011-12-22
Access Restriction	Open
Subject Keyword	Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Singular Value Decomposition Positive Control Group Cluster Quality Cosine Distance Agglomerative Cluster Algorithm
Content Type	Text
Resource Type	Article
Subject	Biotechnology Genetics
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.1/2023

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