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ARISA (Automated Ribosomal Intergenic Spacer Analysis) is a low-costtechnique that allows for the rapid comparison of different microbialenvironments. In this study, we asked if a set of ARISA profiles candistinguish human microbial environments from one another with the sameaccuracy as results generated from 454 high throughput DNA sequencing. Using aset of human microbial communities where the sequencing results cluster bysubject, we tested how choices made during ARISA data processing influenceclustering. We found that choice of clustering methods had a profound effectwith Ward's clustering generating profiles the most similar to 454 sequencing.Factors such as bin size, using presence or absence calls and technicalreplicate manipulation had a negligible effect on clustering. In fact, noestablished bin sizing method reported in the literature performedsignificantly different results than simply picking bin intervals at random. Weconclude that in an analysis of ARISA data from an ecosystem of sufficientcomplexity to saturate bins, a careful choice of clustering algorithm isessential whereas differing strategies for choosing bins are likely to have amuch less pronounced effect on the outcome of the analysis. As a tool fordistinguishing complex microbial communities, ARISA closely approximates theresults obtained from DNA sequencing at a fraction of the cost; however ARISAfails to reproduce the sequencing results perfectly.

ARISA data from the human gut microbiome can detect individual differences observed by 454 sequencing regardless of binning strategy