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PLoS One. 2010 Feb 24;5(2):e9266. doi: 10.1371/journal.pone.0009266.

Structural analysis of biodiversity.

Author information

  • 1Laboratory of Applied Mathematics, Mount Sinai School of Medicine, New York, New York, United States of America. lsirovich@rockefeller.edu

Abstract

Large, recently-available genomic databases cover a wide range of life forms, suggesting opportunity for insights into genetic structure of biodiversity. In this study we refine our recently-described technique using indicator vectors to analyze and visualize nucleotide sequences. The indicator vector approach generates correlation matrices, dubbed Klee diagrams, which represent a novel way of assembling and viewing large genomic datasets. To explore its potential utility, here we apply the improved algorithm to a collection of almost 17,000 DNA barcode sequences covering 12 widely-separated animal taxa, demonstrating that indicator vectors for classification gave correct assignment in all 11,000 test cases. Indicator vector analysis revealed discontinuities corresponding to species- and higher-level taxonomic divisions, suggesting an efficient approach to classification of organisms from poorly-studied groups. As compared to standard distance metrics, indicator vectors preserve diagnostic character probabilities, enable automated classification of test sequences, and generate high-information density single-page displays. These results support application of indicator vectors for comparative analysis of large nucleotide data sets and raise prospect of gaining insight into broad-scale patterns in the genetic structure of biodiversity.

PMID:
20195371
[PubMed - indexed for MEDLINE]
PMCID:
PMC2827552
Free PMC Article
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