A Dynamic Programming Algorithm for Binning Microbial Community Profiles

Quansong Ruan, Joshua A. Steele, Michael S. Schwalbach, Jed A. Fuhrman, Fengzhu Sun

Abstract: 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 of the widely used techniques for microbial community profile analysis. ARISA has been used to study the microbial profiles using 16S-23S intergenic spacer length heterogeneity at different times and spaces. Due to reading errors, the data read directly from the machine cannot be used for down stream statistical analysis. No optimal data pre-processing methods are available. A commonly used approach is to bin the reading length 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 spot. Data preprocessing identifies several outliers which are later found to be due to systematic errors. Clustering analysis of the time spots based on the binned data reveals important features of the biodiversity of the microbial communities.

Supplementary Information:

   Quansong Ruan, Joshua A. Steele, Michael S. Schwalbach, Jed A. Fuhrman, Fengzhu Sun (2006), A Dynamic Programming Algorithm for Binning Microbial Community Profiles. Bioinformatics 22:1508-1514

   Software. The algorithm is implemented in R and the code can be found: here.

Contact Information: Please send your questions/suggestions to Prof. F. Sun (fsun AT usc DOT edu).

Return to Home

Last updated: 03/16/2006.