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-pooling-method TEXT Choices('independent', 'pseudo')
The method used to pool samples for denoising.
"independent": Samples are denoised independently.
"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 True.
If 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 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).
--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_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.
pooling_method : Str % Choices('independent', 'pseudo'), optional
The method used to pool samples for denoising. "independent": Samples
are denoised independently. "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 True. If True, a sequence will be
identified as bimera if it is one mismatch or indel away from an exact
bimera.
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]
