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Seqenv specifications


Unique identifier OMICS_17065
Name Seqenv
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
License MIT License
Computer skills Advanced
Version 1.2.9
Stability Stable
Maintained Yes




No version available



  • person_outline Christopher Quince

Publication for Seqenv

Seqenv citations


Benthic Bacterial Community Composition in the Oligohaline Marine Transition of Surface Sediments in the Baltic Sea Based on rRNA Analysis

Front Microbiol
PMCID: 5827536
PMID: 29520255
DOI: 10.3389/fmicb.2018.00236

[…] well estimates their phylogenetic placement. the related full-length sequences were used to calculate a base tree and the most abundant short sequences from this study, were again added. the program seqenv () was used to annotate the most abundant read from an otu to an environmental term based on the environmental ontology (envo) vocabulary ()., richness was estimated using explicet () based […]


Bacterial diversity along a 2600 km river continuum

Environ Microbiol
PMCID: 4918796
PMID: 25922985
DOI: 10.1111/1462-2920.12886

[…] increasing trends downriver (fig. s5a–c). in particular, we could only identify four otus receiving a classification that was dominated by the ‘river’ term., as with every homology‐based assignment, seqenv results are affected by database entries; for example the under‐representation of entries from rivers compared with lakes likely biases against the ‘river’ term. thus, a larger number […]


Metagenomic Sequencing Unravels Gene Fragments with Phylogenetic Signatures of O2 Tolerant NiFe Membrane Bound Hydrogenases in Lacustrine Sediment

PMCID: 4486115
PMID: 26044993
DOI: 10.1007/s00284-015-0846-2

[…] microbial genomes at time of query. searches were conducted with psi-blast []. the default setting was changed to return 500 hits. any query with less than 80 % coverage was eliminated., the seqenv pipeline ( retrieves hits to highly similar reference sequences from ncbi and uses a text-mining module to identify a structured and controlled […]

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Seqenv institution(s)
Department of Ecology and Genetics, Limnology, Uppsala University, Uppsala, Sweden; Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Glasgow, UK; The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Western Australia Organic and Isotope Geochemistry Centre (WA-OIGC), Department of Chemistry, Curtin University of Technology, Bentley, WA, Australia; Institute of Biological & Environmental Sciences, University of Aberdeen, Aberdeen, UK; Institute of Soil Biology, Biology Centre, Czech Academy of Sciences, Ceské Budejovice, Czech Republic; Bioinformatics Group, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus; Institute of Marine Biology Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion Crete, Greece; Department of Molecular Ecology, Microbial Genomics and Bioinformatics Group, Max Planck Institute for Marine Microbiology, Bremen, Germany; Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany; Hawkesbury Institute for the Environment, University of Western Sydney, Sydney, Australia; Warwick Medical School, University of Warwick, Warwick, UK
Seqenv funding source(s)
This work was supported by NERC IRF: NE/L011956/1, Novo Nordisk Foundation: NNF14CC0001, European Commission FP7-REGPOT project MARBIGEN: #264089, LifeWatchGreece Research Infrastructure: 384676-94/GSRT/NSRF C&E, CLIMB project (MR/L015080/1): MR/M50161X/1, Environment Research Council Fellowship: NE/J019151/1.

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