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PyNAST specifications


Unique identifier OMICS_15419
Alternative names Python Nearest Alignment Space Termination, NAST, Nearest Alignment Space Termination
Software type Package/Module
Interface Command line interface, Graphical user interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Python
License BSD 3-clause “New” or “Revised” License
Computer skills Advanced
Version 1.2.2
Stability Stable
Maintained Yes




No version available


  • person_outline Rob Knight

Publication for Python Nearest Alignment Space Termination

PyNAST citations


Food Starch Structure Impacts Gut Microbiome Composition

PMCID: 5956147
PMID: 29769378
DOI: 10.1128/mSphere.00086-18
call_split See protocol

[…] of lower than 20 were removed. Chimeric sequences were checked and removed using USEARCH version 6.1.544 (). The remaining high-quality reads were clustered into operational taxonomic units (OTUs) by PyNAST () with a 97% sequence identity threshold against the Greengenes core set database version 13.8 (). The generated biome table was normalized using an equal subsampling size of 2,938 sequences. […]


Diversity and antimicrobial potential in sea anemone and holothurian microbiomes

PLoS One
PMCID: 5942802
PMID: 29742123
DOI: 10.1371/journal.pone.0196178

[…] For quality filtering, QIIME default parameters were used, with a minimun Phred quality score <20 and ambiguous nucleotides were removed. Sequence alignment was made using Greengenes database [] and PyNast alignment algorithm []. UCLUST software [] was used to assign similar sequences to operational taxonomic units (OTUs) by clustering sequences based on a 97% similarity threshold. The taxonomic […]


Near full length 16S rRNA gene next generation sequencing revealed Asaia as a common midgut bacterium of wild and domesticated Queensland fruit fly larvae

PMCID: 5935925
PMID: 29729663
DOI: 10.1186/s40168-018-0463-y

[…] _novo. As part of the workflow, representative sequences from each OTU were selected and aligned against the Greengenes database gg_13_8 preclustered at 85% identity using PyNAST [] and used to build a tree using FastTree []. The OTU matching to chloroplasts of plant origin (“Streptophyta”) based on Greengenes taxonomic classification was manually removed. To improve ta […]


Feminizing Wolbachia influence microbiota composition in the terrestrial isopod Armadillidium vulgare

Sci Rep
PMCID: 5934373
PMID: 29725059
DOI: 10.1038/s41598-018-25450-4

[…] remaining reads were clustered into Operational Taxonomic Units (OTUs) at 97% similarity using uclust. Representative sequences were aligned against the Silva reference alignment (release 108,) using PyNAST and identified using the RDP Classifier. Rare OTUs (i.e. singletons and doubletons) were discarded, resulting in 1380 OTUs represented by ≥3 reads (see Supplementary Table  for details). All re […]


Gut microbiomes of wild great apes fluctuate seasonally in response to diet

Nat Commun
PMCID: 5934369
PMID: 29725011
DOI: 10.1038/s41467-018-04204-w

[…] a similarity threshold of 97% (roughly corresponding to species-level OTUs). Representative sequences from the OTUs were aligned to a pre-aligned database of sequences (the Greengenes core set) using PyNAST with quality thresholds set with a minimum sequence length of 150 nucleotides and a minimum percent identity of 75%. PyNAST alignment failures were investigated by blasting all sequences that f […]


Microbiome Dynamics in a Large Artificial Seawater Aquarium

Appl Environ Microbiol
PMCID: 5930379
PMID: 29523545
DOI: 10.1128/AEM.00179-18

[…] using the SILVA 128 database (99% identity level), using BLAST. Sequences classified as chloroplasts were removed from the final table, and samples were rarefied to a depth of 3,748 and aligned with Pynast (). Uninformative base positions based on the default lane mask were removed, and this alignment was used to generate a phylogeny by using the FastTree algorithm (). Alpha and beta diversity me […]


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PyNAST institution(s)
Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder, CO, USA; Department of Microbiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA; Center for Environmental Biotechnology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
PyNAST funding source(s)
This work was funded in part by grants T15LM009451; a Bill and Melinda Gates Foundation Mal-ED Network Discovery Project; 1U01HG004866-01; Human Microbiome Demonstration project grant UH2DK083981; from Human Microbiome Project of the NIH Roadmap Initiative and National Cancer Institute grant UH2CA140233.

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