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Taxy specifications

Information


Unique identifier OMICS_10703
Name Taxy
Software type Toolkit/Suite
Interface Command line interface
Restrictions to use None
Input data As input, DNA sequences in terms of multi-FASTA files of any size can be used for the estimation of metagenomic profiles.
Operating system Unix/Linux, Mac OS, Windows
Programming languages MATLAB
Computer skills Advanced
Stability Stable
Maintained Yes

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Maintainer


  • person_outline Peter Meinicke <>

Publication for Taxy

Taxy in publications

 (3)
PMCID: 4459075
PMID: 26056565
DOI: 10.1186/s40168-015-0086-5

[…] bias occurs post size selection, we next asked if certain taxa were differentially represented across the samples to see if this would point to a possible reason for library sequence bias. we used taxy [] as well as taxy-pro [] as part of the comet web server [] to do a fast preliminary comparison of taxa abundance across the three different samples. taxy calculates k-mer frequencies […]

PMCID: 4139848
PMID: 25026170
DOI: 10.3390/ijms150712364

[…] can be detected by all methods. here, the metaphlan and mop-pro methods show very little variation of the accuracy with respect to the underlying profile distance measure. on the other side, taxy-oligo and go show a relatively low accuracy on average and are much more susceptible with respect to the distance metric. the go slim profile space has the lowest dimensionality and it seems […]

PMCID: 3944733
PMID: 24599149
DOI: 10.1371/journal.pone.0090934

[…] to assign a taxon to each matching read. the two other methods, metaphlan and metaphyler, consisted in searching for clade-specific marker genes. in addition, we tried the composition-based method taxy designed to be used with short reads , but it did not produce convincing results. indeed, different results were obtained whether it was applied on complete or filtered datasets, […]


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Taxy institution(s)
Department of Bioinformatics, Institute for Microbiology and Genetics, Georg-August University, Göttingen, Germany
Taxy funding source(s)
Grants from the Deutsche Forschungsgemeinschaft (ME 3138, ‘Compositional descriptors for large scale comparative metagenome analysis’ and LI 2050, ‘Development of machine learning methods for functional characterization of the peroxisome’)

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