qiime dada2 denoise-single (version 2019.4)

--i-demultiplexed-seqs: ARTIFACT SampleData[SequencesWithQuality | PairedEndSequencesWithQuality] The single-end demultiplexed sequences to be denoised. [required]:

--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-chimera-method: :

--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-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-no-hashed-feature-ids: If false, 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: False]:

Denoise and dereplicate single-end sequences

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

Parameters

demultiplexed_seqs : SampleData[SequencesWithQuality | PairedEndSequencesWithQuality]: The single-end demultiplexed sequences 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.
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_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]