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denoise-pyro: Denoise and dereplicate single-end pyrosequencesΒΆ

Docstring:

Usage: qiime dada2 denoise-pyro [OPTIONS]

  This method denoises single-end pyrosequencing sequences, dereplicates
  them, and filters chimeras.

Inputs:
  --i-demultiplexed-seqs ARTIFACT SampleData[SequencesWithQuality]
                         The single-end demultiplexed pyrosequencing
                         sequences (e.g. 454, IonTorrent) to be denoised.
                                                                    [required]
Parameters:
  --p-trunc-len INTEGER  Position at which sequences should be truncated due
                         to decrease in quality. This truncates the 3' end of
                         the of the input sequences, which will be the bases
                         that were sequenced in the last cycles. Reads that
                         are shorter than this value will be discarded. If 0
                         is provided, no truncation or length filtering will
                         be performed                               [required]
  --p-trim-left INTEGER  Position at which sequences should be trimmed due to
                         low quality. This trims the 5' end of the of the
                         input sequences, which will be the bases that were
                         sequenced in the first cycles.           [default: 0]
  --p-max-ee NUMBER      Reads with number of expected errors higher than
                         this value will be discarded.          [default: 2.0]
  --p-trunc-q INTEGER    Reads are truncated at the first instance of a
                         quality score less than or equal to this value. If
                         the resulting read is then shorter than `trunc-len`,
                         it is discarded.                         [default: 2]
  --p-max-len INTEGER    Remove reads prior to trimming or truncation which
                         are longer than this value. If 0 is provided no reads
                         will be removed based on length.         [default: 0]
  --p-chimera-method TEXT Choices('consensus', 'none', 'pooled')
                         The method used to remove chimeras. "none": No
                         chimera removal is performed. "pooled": All reads are
                         pooled prior to chimera detection. "consensus":
                         Chimeras are detected in samples individually, and
                         sequences found chimeric in a sufficient fraction of
                         samples are removed.           [default: 'consensus']
  --p-min-fold-parent-over-abundance NUMBER
                         The minimum abundance of potential parents of a
                         sequence being tested as chimeric, expressed as a
                         fold-change versus the abundance of the sequence
                         being tested. Values should be greater than or equal
                         to 1 (i.e. parents should be more abundant than the
                         sequence being tested). This parameter has no effect
                         if chimera-method is "none".           [default: 1.0]
  --p-n-threads INTEGER  The number of threads to use for multithreaded
                         processing. If 0 is provided, all available cores
                         will be used.                            [default: 1]
  --p-n-reads-learn INTEGER
                         The number of reads to use when training the error
                         model. Smaller numbers will result in a shorter run
                         time but a less reliable error model.
                                                             [default: 250000]
  --p-hashed-feature-ids / --p-no-hashed-feature-ids
                         If true, the feature ids in the resulting table will
                         be presented as hashes of the sequences defining each
                         feature. The hash will always be the same for the
                         same sequence so this allows feature tables to be
                         merged across runs of this method. You should only
                         merge tables if the exact same parameters are used
                         for each run.                         [default: True]
Outputs:
  --o-table ARTIFACT FeatureTable[Frequency]
                         The resulting feature table.               [required]
  --o-representative-sequences ARTIFACT FeatureData[Sequence]
                         The resulting feature sequences. Each feature in the
                         feature table will be represented by exactly one
                         sequence.                                  [required]
  --o-denoising-stats ARTIFACT SampleData[DADA2Stats]
                                                                    [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).
  --citations            Show citations and exit.
  --help                 Show this message and exit.

Import:

from qiime2.plugins.dada2.methods import denoise_pyro

Docstring:

Denoise and dereplicate single-end pyrosequences

This method denoises single-end pyrosequencing sequences, dereplicates
them, and filters chimeras.

Parameters
----------
demultiplexed_seqs : SampleData[SequencesWithQuality]
    The single-end demultiplexed pyrosequencing sequences (e.g. 454,
    IonTorrent) to be denoised.
trunc_len : Int
    Position at which sequences should be truncated due to decrease in
    quality. This truncates the 3' end of the of the input sequences, which
    will be the bases that were sequenced in the last cycles. Reads that
    are shorter than this value will be discarded. If 0 is provided, no
    truncation or length filtering will be performed
trim_left : Int, optional
    Position at which sequences should be trimmed due to low quality. This
    trims the 5' end of the of the input sequences, which will be the bases
    that were sequenced in the first cycles.
max_ee : Float, optional
    Reads with number of expected errors higher than this value will be
    discarded.
trunc_q : Int, optional
    Reads are truncated at the first instance of a quality score less than
    or equal to this value. If the resulting read is then shorter than
    `trunc_len`, it is discarded.
max_len : Int, optional
    Remove reads prior to trimming or truncation which are longer than this
    value. If 0 is provided no reads will be removed based on length.
chimera_method : Str % Choices('consensus', 'pooled', 'none'), optional
    The method used to remove chimeras. "none": No chimera removal is
    performed. "pooled": All reads are pooled prior to chimera detection.
    "consensus": Chimeras are detected in samples individually, and
    sequences found chimeric in a sufficient fraction of samples are
    removed.
min_fold_parent_over_abundance : Float, optional
    The minimum abundance of potential parents of a sequence being tested
    as chimeric, expressed as a fold-change versus the abundance of the
    sequence being tested. Values should be greater than or equal to 1
    (i.e. parents should be more abundant than the sequence being tested).
    This parameter has no effect if chimera_method is "none".
n_threads : Int, optional
    The number of threads to use for multithreaded processing. If 0 is
    provided, all available cores will be used.
n_reads_learn : Int, optional
    The number of reads to use when training the error model. Smaller
    numbers will result in a shorter run time but a less reliable error
    model.
hashed_feature_ids : Bool, optional
    If true, the feature ids in the resulting table will be presented as
    hashes of the sequences defining each feature. The hash will always be
    the same for the same sequence so this allows feature tables to be
    merged across runs of this method. You should only merge tables if the
    exact same parameters are used for each run.

Returns
-------
table : FeatureTable[Frequency]
    The resulting feature table.
representative_sequences : FeatureData[Sequence]
    The resulting feature sequences. Each feature in the feature table will
    be represented by exactly one sequence.
denoising_stats : SampleData[DADA2Stats]