PyNAST: A flexible tool for aligning sequences to a template alignment

Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder, CO, USA.
Bioinformatics (Impact Factor: 4.98). 11/2009; 26(2):266-7. DOI: 10.1093/bioinformatics/btp636
Source: PubMed


The Nearest Alignment Space Termination (NAST) tool is commonly used in sequence-based microbial ecology community analysis, but due to the limited portability of the original implementation, it has not been as widely adopted as possible. Python Nearest Alignment Space Termination (PyNAST) is a complete reimplementation of NAST, which includes three convenient interfaces: a Mac OS X GUI, a command-line interface and a simple application programming interface (API).
The availability of PyNAST will make the popular NAST algorithm more portable and thereby applicable to datasets orders of magnitude larger by allowing users to install PyNAST on their own hardware. Additionally because users can align to arbitrary template alignments, a feature not available via the original NAST web interface, the NAST algorithm will be readily applicable to novel tasks outside of microbial community analysis.
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Available from: Todd Z DeSantis,
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    • "Alignment was done using PyNAST (Caporaso, Bittinger, et al. 2010).Taxonomy was assigned 156 against Greengenes reference database(DeSantis et al. 2006) (released on May 2013) by using 157 RDP classifier v2.2 (McDonald et al. 2012) (ribosomal database project) (Wang et al. 2007) with 158 a confidence limit of 0.8. Commands included in QIIME were used to obtain rarefaction curves 159 and to calculate alpha diversity metrics, whereas Unifrac(Lozupone and Knight 2005) was used 160 to calculate beta diversity. "
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    • "( The analysis process was as follows: (1) removal of low-quality or ambiguous sequencing reads (reads with lengths <150 bp, >0 ambiguous bases, >6 homopolymers, primer mismatches, or average quality scores <25); (2) assignment of the multiplexed reads to samples through examination of the 12-bp barcode; (3) removal of all putative chimeras, which can be detected by the Usearch tool using a chimera-free reference database according to the Uchime algorithm [28]; (4) assignment of similar sequences to operational taxonomic units (OTUs) via the clustering method at a 97% sequence similarity level [29]; (5) alignment of a representative sequence from each OTU with Python Nearest Alignment Space Termination (PyNAST) [30] "
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    • "); PyNAST and UCLUST were then applied to align the extracted high-quality sequences under 100% clustering of sequence identity to obtain representative sequences (Caporaso et al., 2010a; Edgar, 2010). The unique sequence set was classified into operational taxonomic units (OTU) under the threshold of 99% identity using UCLUST after the selection of the representative sequences (Lozupone and Knight, 2005). "
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