# cluster-features-de-novo: De novo clustering of features.¶

#### Docstring:

Usage: qiime vsearch cluster-features-de-novo [OPTIONS]

Given a feature table and the associated feature sequences, cluster the
features based on user-specified percent identity threshold of their
sequences. This is not a general-purpose de novo clustering method, but
rather is intended to be used for clustering the results of quality-
filtering/dereplication methods, such as DADA2, or for re-clustering a
FeatureTable at a lower percent identity than it was originally clustered
at. When a group of features in the input table are clustered into a
single feature, the frequency of that single feature in a given sample is
the sum of the frequencies of the features that were clustered in that
sample. Feature identifiers and sequences will be inherited from the
centroid feature of each cluster. See the vsearch documentation for
details on how sequence clustering is performed.

Inputs:
--i-sequences ARTIFACT FeatureData[Sequence]
The sequences corresponding to the features in
table.                                    [required]
--i-table ARTIFACT FeatureTable[Frequency]
The feature table to be clustered.        [required]
Parameters:
--p-perc-identity PROPORTION Range(0, 1, inclusive_start=False,
inclusive_end=True)   The percent identity at which clustering should be
performed. This parameter maps to vsearch's --id
parameter.                                [required]
The number of threads to use for computation.
Passing 0 will launch one thread per CPU core.
[default: 1]
Outputs:
--o-clustered-table ARTIFACT FeatureTable[Frequency]
The table following clustering of features.
[required]
--o-clustered-sequences ARTIFACT FeatureData[Sequence]
Sequences representing clustered features.
[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).
--examples              Show usage examples and exit.
--citations             Show citations and exit.
--help                  Show this message and exit.

#### Import:

from qiime2.plugins.vsearch.methods import cluster_features_de_novo


#### Docstring:

De novo clustering of features.

Given a feature table and the associated feature sequences, cluster the
features based on user-specified percent identity threshold of their
sequences. This is not a general-purpose de novo clustering method, but
rather is intended to be used for clustering the results of quality-
filtering/dereplication methods, such as DADA2, or for re-clustering a
FeatureTable at a lower percent identity than it was originally clustered
at. When a group of features in the input table are clustered into a single
feature, the frequency of that single feature in a given sample is the sum
of the frequencies of the features that were clustered in that sample.
Feature identifiers and sequences will be inherited from the centroid
feature of each cluster. See the vsearch documentation for details on how
sequence clustering is performed.

Parameters
----------
sequences : FeatureData[Sequence]
The sequences corresponding to the features in table.
table : FeatureTable[Frequency]
The feature table to be clustered.
perc_identity : Float % Range(0, 1, inclusive_start=False, inclusive_end=True)
The percent identity at which clustering should be performed. This
parameter maps to vsearch's --id parameter.
threads : Int % Range(0, 256, inclusive_end=True), optional
The number of threads to use for computation. Passing 0 will launch one
Sequences representing clustered features.