Dataset Information

ESPRIT: estimating species richness using large collections of 16S rRNA pyrosequences.

ABSTRACT: Recent metagenomics studies of environmental samples suggested that microbial communities are much more diverse than previously reported, and deep sequencing will significantly increase the estimate of total species diversity. Massively parallel pyrosequencing technology enables ultra-deep sequencing of complex microbial populations rapidly and inexpensively. However, computational methods for analyzing large collections of 16S ribosomal sequences are limited. We proposed a new algorithm, referred to as ESPRIT, which addresses several computational issues with prior methods. We developed two versions of ESPRIT, one for personal computers (PCs) and one for computer clusters (CCs). The PC version is used for small- and medium-scale data sets and can process several tens of thousands of sequences within a few minutes, while the CC version is for large-scale problems and is able to analyze several hundreds of thousands of reads within one day. Large-scale experiments are presented that clearly demonstrate the effectiveness of the newly proposed algorithm. The source code and user guide are freely available at http://www.biotech.ufl.edu/people/sun/esprit.html.

SUBMITTER: Sun Y

PROVIDER: S-EPMC2691849 | biostudies-other | 2009 Jun

REPOSITORIES: biostudies-other

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Publications

ESPRIT: estimating species richness using large collections of 16S rRNA pyrosequences.

Sun Yijun Y Cai Yunpeng Y Liu Li L Yu Fahong F Farrell Michael L ML McKendree William W Farmerie William W

Nucleic acids research 20090505 10

Recent metagenomics studies of environmental samples suggested that microbial communities are much more diverse than previously reported, and deep sequencing will significantly increase the estimate of total species diversity. Massively parallel pyrosequencing technology enables ultra-deep sequencing of complex microbial populations rapidly and inexpensively. However, computational methods for analyzing large collections of 16S ribosomal sequences are limited. We proposed a new algorithm, referr ...[more]

PMID: 19417062

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Project description:Pyrosequencing-based 16S rRNA gene surveys are increasingly utilized to study highly diverse bacterial communities, with special emphasis on utilizing the large number of sequences obtained (tens to hundreds of thousands) for species richness estimation. However, it is not yet clear how the number of operational taxonomic units (OTUs) and, hence, species richness estimates determined using shorter fragments at different taxonomic cutoffs correlates with the number of OTUs assigned using longer, nearly complete 16S rRNA gene fragments. We constructed a 16S rRNA clone library from an undisturbed tallgrass prairie soil (1,132 clones) and used it to compare species richness estimates obtained using eight pyrosequencing candidate fragments (99 to 361 bp in length) and the nearly full-length fragment. Fragments encompassing the V1 and V2 (V1+V2) region and the V6 region (generated using primer pairs 8F-338R and 967F-1046R) overestimated species richness; fragments encompassing the V3, V7, and V7+V8 hypervariable regions (generated using primer pairs 338F-530R, 1046F-1220R, and 1046F-1392R) underestimated species richness; and fragments encompassing the V4, V5+V6, and V6+V7 regions (generated using primer pairs 530F-805R, 805F-1046R, and 967F-1220R) provided estimates comparable to those obtained with the nearly full-length fragment. These patterns were observed regardless of the alignment method utilized or the parameter used to gauge comparative levels of species richness (number of OTUs observed, slope of scatter plots of pairwise distance values for short and nearly complete fragments, and nonparametric and parametric species richness estimates). Similar results were obtained when analyzing three other datasets derived from soil, adult Zebrafish gut, and basaltic formations in the East Pacific Rise. Regression analysis indicated that these observed discrepancies in species richness estimates within various regions could readily be explained by the proportions of hypervariable, variable, and conserved base pairs within an examined fragment.

Project description:Estimating the species richness of microorganisms is of great importance in predicting, maintaining and managing microbial communities. Although the roles of environmental heterogeneity and geographical distance in structuring soil microbial communities have been studied intensively, the effects of environmental and spatial variation on the species richness estimation have not been examined. To this end, we have explored their effects on estimating the belowground soil bacterial species richness within a 50 ha forest dynamic plot (FDP) using a published massive sequencing dataset with intensive sampling scheme. Our resampling analyses showed that, for a given sequencing depth, increasing the sample size could significantly enhance the detection of rare species by capturing more of the environmental and spatial variation, thus obtaining higher observed and estimated species richness. Additionally, the estimates of bacterial species richness were significantly and positively correlated with environmental variation among samples, indicating that environmental filtering was the main mechanism driving the processes of community assembly for belowground soil bacterial communities in the plot. Moreover, this effect of environmental variation could be markedly alleviated when the sample size was higher than 450, and thus we predicted that there were at least 42,866 soil bacterial species based on 8,296,878 sequences from 550 samples in the whole 50 ha FDP. Furthermore, we built a power law environmental heterogeneity equation (EHE) as a decision-tool to determine an approximate sample size for comprehensively capturing the environmental gradient within a given habitat. Collectively, this work further links the inherent environmental and spatial variation to the estimation of soil bacterial species richness within a given region, and provides a useful tool of sampling design for a better understanding of microbial biogeographic patterns and estimation of microbial biodiversity.

Dataset Information

ESPRIT: estimating species richness using large collections of 16S rRNA pyrosequences.

Publications

ESPRIT: estimating species richness using large collections of 16S rRNA pyrosequences.

Similar Datasets

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