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NINJA-OPS specifications


Unique identifier OMICS_14579
Alternative name NINJA Is Not Just Another - OTU Picking Solution
Software type Pipeline/Workflow
Interface Command line interface
Restrictions to use None
Input data Takes a FASTA-formatted input file.
Input format FASTA
Output data Outputs a QIIME-formatted taxonomy-annotated BIOM file.
Operating system Unix/Linux, Mac OS, Windows
Programming languages C, Python
License ISC License
Computer skills Advanced
Version 1.5
Stability Stable
Maintained Yes




No version available


  • person_outline Dan Knights

Publication for NINJA Is Not Just Another - OTU Picking Solution

NINJA-OPS citations


SHI7 Is a Self Learning Pipeline for Multipurpose Short Read DNA Quality Control

PMCID: 5915699
PMID: 29719872
DOI: 10.1128/mSystems.00202-17

[…] procedural parameters in advance. Furthermore, the resulting merged FASTA file (if this output mode is used) is immediately compatible with operational taxonomic unit (OTU) picking solutions such as NINJA-OPS and others (, , ), whose outputs are in turn compatible with statistical analyses in standard metagenomics pipelines, including QIIME (). The intention, then, is for SHI7 to bring users, in […]


Development of the Human Mycobiome over the First Month of Life and across Body Sites

PMCID: 5840654
PMID: 29546248
DOI: 10.1128/mSystems.00140-17
call_split See protocol

[…] st 25 bases of each read and truncating the sequences to a maximum read length of 150 bases. Reads shorter than 150 bases were dropped (see  in the supplemental material). Using a validated protocol, NINJA-OPS was used to align preprocessed reads against the UNITE v7 singleton-exclusive dynamic fungal ITS database release (31 January 2016) for NINJA-OPS using default options (, ). The resulting op […]


Compositional shifts in root associated bacterial and archaeal microbiota track the plant life cycle in field grown rice

PLoS Biol
PMCID: 5841827
PMID: 29474469
DOI: 10.1371/journal.pbio.2003862
call_split See protocol

[…] using the PandaSeq software []. Any sequences containing ambiguous bases or having a length of over 275 were discarded from the analysis. The high-quality sequences were clustered into OTUs using the Ninja-OPS pipeline [] against a “concatesome” composed of the Greengenes 97% OTU representative sequence database (version 13_8) [] and then assembled into an OTU table. This OTU table was filtered to […]


Linking Nitrogen Load to the Structure and Function of Wetland Soil and Rhizosphere Microbial Communities

PMCID: 5790874
PMID: 29404427
DOI: 10.1128/mSystems.00214-17

[…] re filtered for quality (Q > 25) and size (>200 bp) using QIIME v1.9 (). Quality-controlled reads were then clustered into OTU at a 97% identify level and phylogenetically classified by utilizing the NINJA-OPS v1.3 pipeline (). The reference database used for taxonomic assignment was the SILVA database version 123 (). The resulting OTU table was used for downstream analysis in R (). Count data wer […]


The lung tissue microbiota of mild and moderate chronic obstructive pulmonary disease

PMCID: 5759273
PMID: 29316977
DOI: 10.1186/s40168-017-0381-4
call_split See protocol

[…] together using PANDAseq [], and the resulting sequences were subjected to filtering, denoising, and chimera removal within QIIME []. OTU picking at 97% identity was accomplished with a combination of ninja_ops [] closed-reference OTU picking utilizing Greengenes (version 13_8), followed by de novo clustering of unmapped reads. The full data set underwent β-diversity analysis with Bray-Curtis dista […]


Impact of the Mk VI SkinSuit on skin microbiota of terrestrial volunteers and an International Space Station bound astronaut

PMCID: 5589758
PMID: 28894789
DOI: 10.1038/s41526-017-0029-5

[…] Sequences were clustered into OTUs at 97% sequence identity using a closed reference method (NINJA-OPS, v. 1.5). The resulting OTUs were analysed using phyloseq in R (v. 1.20.0). Sequences were filtered of error by keeping only OTUs that were present at greater than two reads in 20% of all sa […]


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NINJA-OPS institution(s)
Biomedical Informatics and Computational Biology, University of Minnesota, Minneapolis, MN, USA; University of Nantes, Nantes, France; Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA; Lawrence University, Appleton, WI, USA
NINJA-OPS funding source(s)
This work was funded by NIH R01AI121383 and the Robert Tournut award of the French National Society of Gastroenterology.

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