Fork me on GitHub

denoise-other: Deblur sequences using a user-specified positive filter.ΒΆ

Docstring:

Usage: qiime deblur denoise-other [OPTIONS]

  Perform sequence quality control for Illumina data using the Deblur
  workflow, including positive alignment-based filtering. Only forward reads
  are supported at this time. This mode of execution is particularly useful
  when operating on non-16S data. For example, to apply Deblur to 18S data,
  you would want to specify a reference composed of 18S sequences in order
  to filter out sequences which do not appear to be 18S. The assessment is
  performed by local alignment using SortMeRNA with a permissive e-value
  threshold.

Options:
  --i-demultiplexed-seqs ARTIFACT PATH SampleData[JoinedSequencesWithQuality | PairedEndSequencesWithQuality | SequencesWithQuality]
                                  The demultiplexed sequences to be denoised.
                                  [required]
  --i-reference-seqs ARTIFACT PATH FeatureData[Sequence]
                                  Positive filtering database. Keep all
                                  sequences aligning to these sequences.
                                  [required]
  --p-trim-length INTEGER         Sequence trim length, specify -1 to disable
                                  trimming.  [required]
  --p-sample-stats / --p-no-sample-stats
                                  If true, gather stats per sample.  [default:
                                  False]
  --p-mean-error FLOAT            The mean per nucleotide error, used for
                                  original sequence estimate.  [default:
                                  0.005]
  --p-indel-prob FLOAT            Insertion/deletion (indel) probability (same
                                  for N indels).  [default: 0.01]
  --p-indel-max INTEGER           Maximum number of insertion/deletions.
                                  [default: 3]
  --p-min-reads INTEGER           Retain only features appearing at least
                                  min_reads times across all samples in the
                                  resulting feature table.  [default: 10]
  --p-min-size INTEGER            In each sample, discard all features with an
                                  abundance less than min_size.  [default: 2]
  --p-jobs-to-start INTEGER       Number of jobs to start (if to run in
                                  parallel).  [default: 1]
  --p-hashed-feature-ids / --p-no-hashed-feature-ids
                                  If true, hash the feature IDs.  [default:
                                  True]
  --o-table ARTIFACT PATH FeatureTable[Frequency]
                                  The resulting denoised feature table.
                                  [required if not passing --output-dir]
  --o-representative-sequences ARTIFACT PATH FeatureData[Sequence]
                                  The resulting feature sequences.  [required
                                  if not passing --output-dir]
  --o-stats ARTIFACT PATH DeblurStats
                                  Per-sample stats if requested.  [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.deblur.methods import denoise_other

Docstring:

Deblur sequences using a user-specified positive filter.

Perform sequence quality control for Illumina data using the Deblur
workflow, including positive alignment-based filtering. Only forward reads
are supported at this time. This mode of execution is particularly useful
when operating on non-16S data. For example, to apply Deblur to 18S data,
you would want to specify a reference composed of 18S sequences in order to
filter out sequences which do not appear to be 18S. The assessment is
performed by local alignment using SortMeRNA with a permissive e-value
threshold.

Parameters
----------
demultiplexed_seqs : SampleData[JoinedSequencesWithQuality | PairedEndSequencesWithQuality | SequencesWithQuality]
    The demultiplexed sequences to be denoised.
reference_seqs : FeatureData[Sequence]
    Positive filtering database. Keep all sequences aligning to these
    sequences.
trim_length : Int
    Sequence trim length, specify -1 to disable trimming.
sample_stats : Bool, optional
    If true, gather stats per sample.
mean_error : Float, optional
    The mean per nucleotide error, used for original sequence estimate.
indel_prob : Float, optional
    Insertion/deletion (indel) probability (same for N indels).
indel_max : Int, optional
    Maximum number of insertion/deletions.
min_reads : Int, optional
    Retain only features appearing at least min_reads times across all
    samples in the resulting feature table.
min_size : Int, optional
    In each sample, discard all features with an abundance less than
    min_size.
jobs_to_start : Int, optional
    Number of jobs to start (if to run in parallel).
hashed_feature_ids : Bool, optional
    If true, hash the feature IDs.

Returns
-------
table : FeatureTable[Frequency]
    The resulting denoised feature table.
representative_sequences : FeatureData[Sequence]
    The resulting feature sequences.
stats : DeblurStats
    Per-sample stats if requested.