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Bioinformatics Advance Access published online on March 27, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl114
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received January 7, 2006
Revised March 14, 2006
Accepted March 22, 2006

Article

A dynamic programming algorithm for binning microbial community profiles

Quansong Ruan 1, Joshua A. Steele 2, Michael S. Schwalbach 2, Jed A. Fuhrman 2, and Fengzhu Sun 2 *

1 Department of Mathematics, University of Southern California, 3620 South Vermont Ave., KAP 108 Los Angeles, California 90089-253, USA
2 Department of Biological Sciences, University of Southern California, 3616 Trousdale Pkwy, AHF 107, Los Angeles, CA 90089-0371, USA

* To whom correspondence should be addressed.
Fengzhu Sun, E-mail: fsun{at}usc.edu


  Abstract

Motivation: A number of community profiling approaches have been widely used to study the microbial community composition and its variations in environmental ecology. Automated Ribosomal Intergenic Spacer Analysis (ARISA) is one such technique. ARISA has been used to study microbial communities using 16S-23S rRNA intergenic spacer length heterogeneity at different times and places. Due to errors in sampling, random mutations in PCR amplification, and probably mostly variations in readings from the equipment used to analyze fragment sizes, the data read directly from the fragment analyzer should not be used for down stream statistical analysis. No optimal data pre-processing methods are available. A commonly used approach is to bin the reading lengths of the 16S-23S intergenic spacer. We develop a dynamic programming algorithm based binning method for ARISA data analysis which minimizes the overall differences between replicates from the same sampling location and time.

Results: In a test example from an ocean time series sampling program, data preprocessing identified several outliers which upon re-examination were found to be due to systematic errors. Clustering analysis of the ARISA from different times based on the dynamic programming algorithm binned data revealed important features of the biodiversity of the microbial communities.

Availability: The algorithm is implemented in a software package and it is available upon request from the corresponding author.

Associate Editor: Jonathan Wren
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Q. Ruan, D. Dutta, M. S. Schwalbach, J. A. Steele, J. A. Fuhrman, and F. Sun
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[Abstract] [Full Text] [PDF]


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