Recent metagenomics studies of environmental samples suggest 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, classifying large collections of 16S ribosomal sequences poses a serious computational challenge for existing algorithms. We proposed a new algorithm, referred to as ESPRIT, which addresses several computational limitations of prior methods. We developed two versions of ESPRIT, one for personal computers and one for computer clusters. The personal-computer version is used for small and medium-scale datasets and can process several tens of thousands sequences within a few minutes, while the computer-cluster version is for large-scale problems and is able to analyze several hundreds of thousands of sequences within one day.
If you publish research that uses esprit you have to cite it as follows:
Y. Sun*, Y. Cai*, L. Liu, F. Yu, M. L. Farrell, W. McKendree, and W. Farmerie, (*equal contribution) ESPRIT: Estimating Species Richness Using Large Collections of 16S rRNA Pyrosequences, Nucleic Acids Research, vol. 37, no. 10, e76, 2009.
- Validated 4/5/2018