Bioinformatics Advance Access originally published online on March 27, 2006
Bioinformatics 2006 22(12):1508-1514; doi:10.1093/bioinformatics/btl114
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
A dynamic programming algorithm for binning microbial community profiles
1 Department of Mathematics, University of Southern California 3620 South Vermont Avenue, KAP 108, Los Angeles, California 90089-253, USA
2 Department of Biological Sciences, University of Southern California 3616 Trousdale Parkway, AHF 107, Los Angeles, CA 90089-0371, USA
3 Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California 1050 Childs Way, MCB 201, Los Angeles, CA 90089-2910, USA
*To whom correspondence should be addressed.
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. Owing 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 preprocessing methods are available. A commonly used approach is to bin the reading lengths of the 16S–23S intergenic spacer. We have developed 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 because of 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.
Contact: fsun{at}usc.edu
Received on January 7, 2006; revised on March 14, 2006; accepted on March 22, 2006
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  Q. Ruan, D. Dutta, M. S. Schwalbach, J. A. Steele, J. A. Fuhrman, and F. Sun Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors Bioinformatics, October 15, 2006; 22(20): 2532 - 2538. [Abstract] [Full Text] [PDF]
|
|