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denoise-paired: Denoise and dereplicate paired-end sequencesΒΆ

Docstring:

Usage: qiime dada2 denoise-paired [OPTIONS]

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

Inputs:
  --i-demultiplexed-seqs ARTIFACT SampleData[PairedEndSequencesWithQuality]
                          The paired-end demultiplexed sequences to be
                          denoised.                                 [required]
Parameters:
  --p-trunc-len-f INTEGER Position at which forward read 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. After this parameter is applied there
                          must still be at least a 12 nucleotide overlap
                          between the forward and reverse reads. If 0 is
                          provided, no truncation or length filtering will be
                          performed                                 [required]
  --p-trunc-len-r INTEGER Position at which reverse read 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. After this parameter is applied there
                          must still be at least a 12 nucleotide overlap
                          between the forward and reverse reads. If 0 is
                          provided, no truncation or length filtering will be
                          performed                                 [required]
  --p-trim-left-f INTEGER Position at which forward read sequences should be
                          trimmed due to low quality. This trims the 5' end of
                          the input sequences, which will be the bases that
                          were sequenced in the first cycles.     [default: 0]
  --p-trim-left-r INTEGER Position at which reverse read sequences should be
                          trimmed due to low quality. This trims the 5' end of
                          the input sequences, which will be the bases that
                          were sequenced in the first cycles.     [default: 0]
  --p-max-ee-f NUMBER     Forward reads with number of expected errors higher
                          than this value will be discarded.    [default: 2.0]
  --p-max-ee-r NUMBER     Reverse 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-f` or `trunc-len-r` (depending on the
                          direction of the read) it is discarded. [default: 2]
  --p-min-overlap INTEGER The minimum length of the overlap required for
    Range(4, None)        merging the forward and reverse reads. [default: 12]
  --p-pooling-method TEXT Choices('independent', 'pseudo')
                          The method used to pool samples for denoising.
                          "independent": Samples are denoised indpendently.
                          "pseudo": The pseudo-pooling method is used to
                          approximate pooling of samples. In short, samples
                          are denoised independently once, ASVs detected in at
                          least 2 samples are recorded, and samples are
                          denoised independently a second time, but this time
                          with prior knowledge of the recorded ASVs and thus
                          higher sensitivity to those ASVs.
                                                      [default: 'independent']
  --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-allow-one-off / --p-no-allow-one-off
                          Bimeras that are one-off from exact are also
                          identified if the `allow-one-off` argument is TrueIf
                          True, a sequence will be identified as bimera if it
                          is one mismatch or indel away from an exact bimera.
                                                              [default: False]
  --p-n-threads NTHREADS  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: 1000000]
  --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]
  --p-retain-all-samples / --p-no-retain-all-samples
                          If True all samples input to dada2 will be retained
                          in the output of dada2, if false samples with zero
                          total frequency are removed from the table.
                                                               [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, and these sequences will be the joined
                          paired-end sequences.                     [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).
  --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.

Examples:
  # ### example: denoise paired
  qiime dada2 denoise-paired \
    --i-demultiplexed-seqs demux-paired.qza \
    --p-trunc-len-f 150 \
    --p-trunc-len-r 140 \
    --o-representative-sequences representative-sequences.qza \
    --o-table table.qza \
    --o-denoising-stats denoising-stats.qza

Import:

from qiime2.plugins.dada2.methods import denoise_paired

Docstring:

Denoise and dereplicate paired-end sequences

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

Parameters
----------
demultiplexed_seqs : SampleData[PairedEndSequencesWithQuality]
    The paired-end demultiplexed sequences to be denoised.
trunc_len_f : Int
    Position at which forward read 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. After
    this parameter is applied there must still be at least a 12 nucleotide
    overlap between the forward and reverse reads. If 0 is provided, no
    truncation or length filtering will be performed
trunc_len_r : Int
    Position at which reverse read 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. After
    this parameter is applied there must still be at least a 12 nucleotide
    overlap between the forward and reverse reads. If 0 is provided, no
    truncation or length filtering will be performed
trim_left_f : Int, optional
    Position at which forward read sequences should be trimmed due to low
    quality. This trims the 5' end of the input sequences, which will be
    the bases that were sequenced in the first cycles.
trim_left_r : Int, optional
    Position at which reverse read sequences should be trimmed due to low
    quality. This trims the 5' end of the input sequences, which will be
    the bases that were sequenced in the first cycles.
max_ee_f : Float, optional
    Forward reads with number of expected errors higher than this value
    will be discarded.
max_ee_r : Float, optional
    Reverse 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_f` or `trunc_len_r` (depending on the direction of the read)
    it is discarded.
min_overlap : Int % Range(4, None), optional
    The minimum length of the overlap required for merging the forward and
    reverse reads.
pooling_method : Str % Choices('independent', 'pseudo'), optional
    The method used to pool samples for denoising. "independent": Samples
    are denoised indpendently. "pseudo": The pseudo-pooling method is used
    to approximate pooling of samples. In short, samples are denoised
    independently once, ASVs detected in at least 2 samples are recorded,
    and samples are denoised independently a second time, but this time
    with prior knowledge of the recorded ASVs and thus higher sensitivity
    to those ASVs.
chimera_method : Str % Choices('consensus', 'none', 'pooled'), 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".
allow_one_off : Bool, optional
    Bimeras that are one-off from exact are also identified if the
    `allow_one_off` argument is TrueIf True, a sequence will be identified
    as bimera if it is one mismatch or indel away from an exact bimera.
n_threads : Threads, 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.
retain_all_samples : Bool, optional
    If True all samples input to dada2 will be retained in the output of
    dada2, if false samples with zero total frequency are removed from the
    table.

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, and these sequences will be the
    joined paired-end sequences.
denoising_stats : SampleData[DADA2Stats]