FEMS Microbiology Ecology · 2014 · 161 citations · 49 references
Life SciencesEcologyMicrobiologyCommunity EcologyEnvironmental MicrobiologyMicrobial Ecology16S Ribosomal Ribonucleic Acid AnalysisMicrobial Community ProfilingMicrobial DiversityTaxonomic ProfilingDna FingerprintingFingerprintingMicrobial Community DiversityMicrobial Community StructureMicrobial Diversity AssessmentRestriction Fragment Length Polymorphism AnalysisSoil Microbial Community StructureSpatial Microbial Ecology
Despite decreasing costs, generating large-scale, well-replicated and multivariate microbial ecology investigations with sequencing remains an expensive and time-consuming option. As a result, many microbial ecology investigations continue to suffer from a lack of appropriate replication. We evaluated two fingerprinting approaches - terminal restriction fragment length polymorphism (T-RFLP) and automated ribosomal intergenic spacer analysis (ARISA) against 454 pyrosequencing, by applying them to 225 polar soil samples from East Antarctica and the high Arctic. By incorporating local and global spatial scales into the dataset, our aim was to determine whether various approaches differed in their ability and hence utility, to identify ecological patterns. Through the reduction in the 454 sequencing data to the most dominant OTUs, we revealed that a surprisingly small proportion of abundant OTUs (< 0.25%) was driving the biological patterns observed. Overall, ARISA and T-RFLP had a similar capacity as sequencing to separate samples according to distance at a local scale, and to correlate environmental variables with microbial community structure. Pyrosequencing had a greater resolution at the global scale but all methods were capable of significantly differentiating the polar sites. We conclude fingerprinting remains a legitimate approach to generating large datasets as well as a cost-effective rapid method to identify samples for elucidating taxonomic information or diversity estimates with sequencing methods.
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R: A Language and Environment for Statistical Computing
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