# 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.

Options:
--i-sequences ARTIFACT PATH FeatureData[Sequence]
The sequences corresponding to the features
in table.  [required]
--i-table ARTIFACT PATH FeatureTable[Frequency]
The feature table to be clustered.
[required]
--p-perc-identity FLOAT         The percent identity at which clustering
should be performed. This parameter maps to
vsearch's --id parameter.  [required]
computation. Passing 0 will launch one
thread per CPU core.  [default: 1]
--o-clustered-table ARTIFACT PATH FeatureTable[Frequency]
The table following clustering of features.
[required if not passing --output-dir]
--o-clustered-sequences ARTIFACT PATH FeatureData[Sequence]
Sequences representing clustered features.
[required if not passing --output-dir]
--output-dir DIRECTORY          Output unspecified results to a directory
--cmd-config FILE               Use config file for command options
--verbose                       Display verbose output to stdout and/or
stderr during execution of this action.
[default: False]
--quiet                         Silence output if execution is successful
(silence is golden).  [default: False]
--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.