Self-organizing and self-correcting classifications of biological data.
BACKGROUND
Rapid, automated means of organizing biological data are required if we hope to keep abreast of the flood of data emanating from sequencing, microarray and similar high-throughput analyses. Faced with the need to validate the annotation of thousands of sequences and to generate biologically meaningful classifications based on the sequence data, we turned to statistical methods in order to automate these processes.


RESULTS
An algorithm for automated classification based on evolutionary distance data was written in S. The algorithm was tested on a dataset of 1436 small subunit ribosomal RNA sequences and was able to classify the sequences according to an extant scheme, use statistical measurements of group membership to detect sequences that were misclassified within this scheme and produce a new classification. In this study, the use of the algorithm to address problems in prokaryotic taxonomy is discussed.


BACKGROUND
S-Plus is available from Insightful, Inc. An S-Plus implementation of the algorithm and the associated data are available at http://taxoweb.mmg.msu.edu/datasets
Garrity
George M
GM
Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA. garrity@msu.edu
Lilburn
Timothy G
TG
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003196:Computer Graphics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020563:Gammaproteobacteria / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods; D012335:RNA, Ribosomal / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software; D014584:User-Computer Interface
D012335:RNA, Ribosomal