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.

Evidence of global-scale aeolian dispersal and endemism in isolated geothermal microbial communities of Antarctica

“…Sequence predictions from fewer than three individual raw reads or with at least one primer mismatch were discarded. Pairwise alignments/distances between sequence predictions were calculated using ESPRIT 67 and reads were clustered into OTUs using a distance cutoff of 0.03 and the average neighbour clustering algorithm (Table 2) in Mothur 68 . OTUs that correspond to full-length sequences (499% identity) were classified using our calculated phylogeny with a strict phylogenetic nesting criterion while remaining OTUs were classified taxonomically using classify.seqs in Mothur with 80% confidence as a threshold, using the Greengenes alignment and taxonomic outline released in November 2012 (ref.…”

Contrasting denitrifier communities relate to contrasting N2O emission patterns from acidic peat soils in arctic tundra

“…In brief, JAguc2 generates a pairwise sequence alignment before calculation of a distance matrix and clustering with the average similarity method. This approach is more reliable (that is, less sensitive to PCR and pyrosequencing noise, and thus less sensitive to an artifical inflation of diversity (Kunin et al, 2010)) than multiple alignments and/or clustering with complete linkage algorithms (Quince et al, 2009;Sun et al, 2009;Huse et al, 2010). Amplicon sequences obtained by pyrosequencing from defined template mixtures were essentially at most 10% dissimilar to template sequences due to PCR and pyrosequencing noise (Behnke et al, 2011;Quince et al, 2011).…”

Application of Deep Pyrosequencing to the Analysis of Soil Microbial Communities in Different Lotus Fields

“…The deep pyrosequencing technology can be used to study and characterize soil microbes unable to be cultured, and to get more and complete information about soil microbial communities. With pyrosequencing and bioinformatics, soil microorganisms may be more accurately determined based on classification techniques (Sun et al, 2009). Application of microbiological fertilizers to soil may improve beneficial microflora in the soil, thereby gradually enhancing the soil fertility, and therefore this method may help overcome continuous cropping obstacles.…”