Structural analysis of biodiversity.
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.
Sirovich
Lawrence
L
Laboratory of Applied Mathematics, Mount Sinai School of Medicine, New York, New York, United States of America. lsirovich@rockefeller.edu
Stoeckle
Mark Y
MY
Zhang
Yu
Y
D000465:Algorithms; D000818:Animals; D044822:Biodiversity; D003576:Electron Transport Complex IV / Q000235:genetics; D014644:Genetic Variation; D007448:Invertebrates / Q000145:classification* / Q000235:genetics / Q000254:growth & development; D010802:Phylogeny; D017422:Sequence Analysis, DNA; D014714:Vertebrates / Q000145:classification* / Q000235:genetics / Q000254:growth & development
D003576:Electron Transport Complex IV