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uchime-denovo: De novo chimera filtering with vsearch.ΒΆ

Docstring:

Usage: qiime vsearch uchime-denovo [OPTIONS]

  Apply the vsearch uchime_denovo method to identify chimeric feature
  sequences. The results of this method can be used to filter chimeric
  features from the corresponding feature table. For more details, please
  refer to the vsearch documentation.

Inputs:
  --i-sequences ARTIFACT FeatureData[Sequence]
                          The feature sequences to be chimera-checked.
                                                                    [required]
  --i-table ARTIFACT FeatureTable[Frequency]
                          Feature table (used for computing total feature
                          abundances).                              [required]
Parameters:
  --p-dn NUMBER           No vote pseudo-count, corresponding to the
    Range(0.0, None)      parameter n in the chimera scoring function.
                                                                [default: 1.4]
  --p-mindiffs INTEGER    Minimum number of differences per segment.
    Range(1, None)                                                [default: 3]
  --p-mindiv NUMBER       Minimum divergence from closest parent.
    Range(0.0, None)                                            [default: 0.8]
  --p-minh PROPORTION Range(0.0, 1.0, inclusive_end=True)
                          Minimum score (h). Increasing this value tends to
                          reduce the number of false positives and to decrease
                          sensitivity.                         [default: 0.28]
  --p-xn NUMBER Range(1.0, None, inclusive_start=False)
                          No vote weight, corresponding to the parameter beta
                          in the scoring function.              [default: 8.0]
Outputs:
  --o-chimeras ARTIFACT FeatureData[Sequence]
                          The chimeric sequences.                   [required]
  --o-nonchimeras ARTIFACT FeatureData[Sequence]
                          The non-chimeric sequences.               [required]
  --o-stats ARTIFACT      Summary statistics from chimera checking.
    UchimeStats                                                     [required]
Miscellaneous:
  --output-dir PATH       Output unspecified results to a directory
  --verbose / --quiet     Display verbose output to stdout and/or stderr
                          during execution of this action. Or silence output
                          if execution is successful (silence is golden).
  --example-data PATH     Write example data and exit.
  --citations             Show citations and exit.
  --use-cache DIRECTORY   Specify the cache to be used for the intermediate
                          work of this action. If not provided, the default
                          cache under $TMP/qiime2/ will be used.
                          IMPORTANT FOR HPC USERS: If you are on an HPC system
                          and are using parallel execution it is important to
                          set this to a location that is globally accessible
                          to all nodes in the cluster.
  --help                  Show this message and exit.

Import:

from qiime2.plugins.vsearch.methods import uchime_denovo

Docstring:

De novo chimera filtering with vsearch.

Apply the vsearch uchime_denovo method to identify chimeric feature
sequences. The results of this method can be used to filter chimeric
features from the corresponding feature table. For more details, please
refer to the vsearch documentation.

Parameters
----------
sequences : FeatureData[Sequence]
    The feature sequences to be chimera-checked.
table : FeatureTable[Frequency]
    Feature table (used for computing total feature abundances).
dn : Float % Range(0.0, None), optional
    No vote pseudo-count, corresponding to the parameter n in the chimera
    scoring function.
mindiffs : Int % Range(1, None), optional
    Minimum number of differences per segment.
mindiv : Float % Range(0.0, None), optional
    Minimum divergence from closest parent.
minh : Float % Range(0.0, 1.0, inclusive_end=True), optional
    Minimum score (h). Increasing this value tends to reduce the number of
    false positives and to decrease sensitivity.
xn : Float % Range(1.0, None, inclusive_start=False), optional
    No vote weight, corresponding to the parameter beta in the scoring
    function.

Returns
-------
chimeras : FeatureData[Sequence]
    The chimeric sequences.
nonchimeras : FeatureData[Sequence]
    The non-chimeric sequences.
stats : UchimeStats
    Summary statistics from chimera checking.