Fork me on GitHub

denoise-ccs: Denoise and dereplicate single-end Pacbio CCSΒΆ

Docstring:

Usage: qiime dada2 denoise-ccs [OPTIONS]

  This method denoises single-end Pacbio CCS sequences, dereplicates them,
  and filters chimeras. Tutorial and workflow:
  https://github.com/benjjneb/LRASManuscript

Inputs:
  --i-demultiplexed-seqs ARTIFACT SampleData[SequencesWithQuality]
                         The single-end demultiplexed PacBio CCS sequences to
                         be denoised.                               [required]
Parameters:
  --p-front TEXT         Sequence of an adapter ligated to the 5' end. The
                         adapter and any preceding bases are trimmed. Can
                         contain IUPAC ambiguous nucleotide codes. Note,
                         primer direction is 5' to 3'. Primers are removed
                         before trim and filter step. Reads that do not
                         contain the primer are discarded. Each read is
                         re-oriented if the reverse complement of the read is
                         a better match to the provided primer sequence. This
                         is recommended for PacBio CCS reads, which come in a
                         random mix of forward and reverse-complement
                         orientations.                              [required]
  --p-adapter TEXT       Sequence of an adapter ligated to the 3' end. The
                         adapter and any preceding bases are trimmed. Can
                         contain IUPAC ambiguous nucleotide codes. Note,
                         primer direction is 5' to 3'. Primers are removed
                         before trim and filter step. Reads that do not
                         contain the primer are discarded.          [required]
  --p-max-mismatch INTEGER
                         The number of mismatches to tolerate when matching
                         reads to primer sequences - see
                         http://benjjneb.github.io/dada2/ for complete
                         details.                                 [default: 2]
  --p-indels / --p-no-indels
                         Allow insertions or deletions of bases when matching
                         adapters. Note that primer matching can be
                         significantly slower, currently about 4x slower
                                                              [default: False]
  --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. Note: Since Pacbio CCS sequences were
                         normally with very high quality scores, there is no
                         need to truncate the Pacbio CCS sequences.
                                                                  [default: 0]
  --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-min-len INTEGER    Remove reads with length less than minLen. minLen is
                         enforced after trimming and truncation. For 16S
                         Pacbio CCS, suggest 1000.               [default: 20]
  --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. For 16S Pacbio CCS,
                         suggest 1600.                            [default: 0]
  --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('none', 'consensus', '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). Suggest 3.5. This parameter
                         has no effect if chimera-method is "none".
                                                                [default: 3.5]
  --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: 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]
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).
  --example-data PATH    Write example data and exit.
  --citations            Show citations and exit.
  --help                 Show this message and exit.

Import:

from qiime2.plugins.dada2.methods import denoise_ccs

Docstring:

Denoise and dereplicate single-end Pacbio CCS

This method denoises single-end Pacbio CCS sequences, dereplicates them,
and filters chimeras. Tutorial and workflow:
https://github.com/benjjneb/LRASManuscript

Parameters
----------
demultiplexed_seqs : SampleData[SequencesWithQuality]
    The single-end demultiplexed PacBio CCS sequences to be denoised.
front : Str
    Sequence of an adapter ligated to the 5' end. The adapter and any
    preceding bases are trimmed. Can contain IUPAC ambiguous nucleotide
    codes. Note, primer direction is 5' to 3'. Primers are removed before
    trim and filter step. Reads that do not contain the primer are
    discarded. Each read is re-oriented if the reverse complement of the
    read is a better match to the provided primer sequence. This is
    recommended for PacBio CCS reads, which come in a random mix of forward
    and reverse-complement orientations.
adapter : Str
    Sequence of an adapter ligated to the 3' end. The adapter and any
    preceding bases are trimmed. Can contain IUPAC ambiguous nucleotide
    codes. Note, primer direction is 5' to 3'. Primers are removed before
    trim and filter step. Reads that do not contain the primer are
    discarded.
max_mismatch : Int, optional
    The number of mismatches to tolerate when matching reads to primer
    sequences - see http://benjjneb.github.io/dada2/ for complete details.
indels : Bool, optional
    Allow insertions or deletions of bases when matching adapters. Note
    that primer matching can be significantly slower, currently about 4x
    slower
trunc_len : Int, optional
    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. Note: Since Pacbio
    CCS sequences were normally with very high quality scores, there is no
    need to truncate the Pacbio CCS sequences.
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.
min_len : Int, optional
    Remove reads with length less than minLen. minLen is enforced after
    trimming and truncation. For 16S Pacbio CCS, suggest 1000.
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. For
    16S Pacbio CCS, suggest 1600.
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).
    Suggest 3.5. 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]