# ---------------------------------------------------------------------------- # Copyright (c) 2016-2023, QIIME 2 development team. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file LICENSE, distributed with this software. # ---------------------------------------------------------------------------- import importlib import qiime2.plugin from q2_types.per_sample_sequences import ( SequencesWithQuality, PairedEndSequencesWithQuality) from q2_types.sample_data import SampleData from q2_types.feature_data import FeatureData, Sequence from q2_types.feature_table import FeatureTable, Frequency import q2_dada2 from q2_dada2 import DADA2Stats, DADA2StatsFormat, DADA2StatsDirFmt import q2_dada2._examples as ex _POOL_OPT = {'pseudo', 'independent'} _CHIM_OPT = {'pooled', 'consensus', 'none'} plugin = qiime2.plugin.Plugin( name='dada2', version=q2_dada2.__version__, website='http://benjjneb.github.io/dada2/', package='q2_dada2', description=('This QIIME 2 plugin wraps DADA2 and supports ' 'sequence quality control for single-end and paired-end ' 'reads using the DADA2 R library.'), short_description='Plugin for sequence quality control with DADA2.', citations=qiime2.plugin.Citations.load('citations.bib', package='q2_dada2') ) plugin.methods.register_function( function=q2_dada2.denoise_single, inputs={'demultiplexed_seqs': SampleData[SequencesWithQuality | PairedEndSequencesWithQuality]}, parameters={'trunc_len': qiime2.plugin.Int, 'trim_left': qiime2.plugin.Int, 'max_ee': qiime2.plugin.Float, 'trunc_q': qiime2.plugin.Int, 'pooling_method': qiime2.plugin.Str % qiime2.plugin.Choices(_POOL_OPT), 'chimera_method': qiime2.plugin.Str % qiime2.plugin.Choices(_CHIM_OPT), 'min_fold_parent_over_abundance': qiime2.plugin.Float, 'allow_one_off': qiime2.plugin.Bool, 'n_threads': qiime2.plugin.Int, 'n_reads_learn': qiime2.plugin.Int, 'hashed_feature_ids': qiime2.plugin.Bool}, outputs=[('table', FeatureTable[Frequency]), ('representative_sequences', FeatureData[Sequence]), ('denoising_stats', SampleData[DADA2Stats])], input_descriptions={ 'demultiplexed_seqs': ('The single-end demultiplexed sequences to be ' 'denoised.') }, parameter_descriptions={ 'trunc_len': ('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': ('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': ('Reads with number of expected errors higher than this ' 'value will be discarded.'), 'trunc_q': ('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.'), 'pooling_method': ( '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': ('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': ( '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': ( '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': ('The number of threads to use for multithreaded ' 'processing. If 0 is provided, all available cores will ' 'be used.'), 'n_reads_learn': ('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': ('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.') }, output_descriptions={ 'table': 'The resulting feature table.', 'representative_sequences': ('The resulting feature sequences. Each ' 'feature in the feature table will be ' 'represented by exactly one sequence.') }, name='Denoise and dereplicate single-end sequences', description=('This method denoises single-end sequences, dereplicates ' 'them, and filters chimeras.'), examples={ 'denoise_single': ex.denoise_single } ) plugin.methods.register_function( function=q2_dada2.denoise_paired, inputs={'demultiplexed_seqs': SampleData[PairedEndSequencesWithQuality]}, parameters={'trunc_len_f': qiime2.plugin.Int, 'trunc_len_r': qiime2.plugin.Int, 'trim_left_f': qiime2.plugin.Int, 'trim_left_r': qiime2.plugin.Int, 'max_ee_f': qiime2.plugin.Float, 'max_ee_r': qiime2.plugin.Float, 'trunc_q': qiime2.plugin.Int, 'min_overlap': qiime2.plugin.Int % qiime2.plugin.Range(4, None), 'pooling_method': qiime2.plugin.Str % qiime2.plugin.Choices(_POOL_OPT), 'chimera_method': qiime2.plugin.Str % qiime2.plugin.Choices(_CHIM_OPT), 'min_fold_parent_over_abundance': qiime2.plugin.Float, 'allow_one_off': qiime2.plugin.Bool, 'n_threads': qiime2.plugin.Int, 'n_reads_learn': qiime2.plugin.Int, 'hashed_feature_ids': qiime2.plugin.Bool}, outputs=[('table', FeatureTable[Frequency]), ('representative_sequences', FeatureData[Sequence]), ('denoising_stats', SampleData[DADA2Stats])], input_descriptions={ 'demultiplexed_seqs': ('The paired-end demultiplexed sequences to be ' 'denoised.') }, parameter_descriptions={ 'trunc_len_f': ('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'), 'trim_left_f': ('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.'), 'trunc_len_r': ('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_r': ('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': ('Forward reads with number of expected errors higher ' 'than this value will be discarded.'), 'max_ee_r': ('Reverse reads with number of expected errors higher ' 'than this value will be discarded.'), 'trunc_q': ('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': ('The minimum length of the overlap required for ' 'merging the forward and reverse reads.'), 'pooling_method': ('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': ('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': ( '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': ( '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': ('The number of threads to use for multithreaded ' 'processing. If 0 is provided, all available cores will ' 'be used.'), 'n_reads_learn': ('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': ('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.') }, output_descriptions={ 'table': 'The resulting feature table.', 'representative_sequences': ('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.') }, name='Denoise and dereplicate paired-end sequences', description=('This method denoises paired-end sequences, dereplicates ' 'them, and filters chimeras.'), examples={ 'denoise_paired': ex.denoise_paired } ) plugin.methods.register_function( function=q2_dada2.denoise_pyro, inputs={'demultiplexed_seqs': SampleData[SequencesWithQuality]}, parameters={'trunc_len': qiime2.plugin.Int, 'trim_left': qiime2.plugin.Int, 'max_ee': qiime2.plugin.Float, 'trunc_q': qiime2.plugin.Int, 'max_len': qiime2.plugin.Int, 'pooling_method': qiime2.plugin.Str % qiime2.plugin.Choices(_POOL_OPT), 'chimera_method': qiime2.plugin.Str % qiime2.plugin.Choices(_CHIM_OPT), 'min_fold_parent_over_abundance': qiime2.plugin.Float, 'allow_one_off': qiime2.plugin.Bool, 'n_threads': qiime2.plugin.Int, 'n_reads_learn': qiime2.plugin.Int, 'hashed_feature_ids': qiime2.plugin.Bool}, outputs=[('table', FeatureTable[Frequency]), ('representative_sequences', FeatureData[Sequence]), ('denoising_stats', SampleData[DADA2Stats])], input_descriptions={ 'demultiplexed_seqs': 'The single-end demultiplexed pyrosequencing ' 'sequences (e.g. 454, IonTorrent) to be ' 'denoised.' }, parameter_descriptions={ 'trunc_len': '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': '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': 'Reads with number of expected errors higher than this ' 'value will be discarded.', 'trunc_q': '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': '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': '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': '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': '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': ( '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': 'The number of threads to use for multithreaded ' 'processing. If 0 is provided, all available cores will ' 'be used.', 'n_reads_learn': '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': '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.' }, output_descriptions={ 'table': 'The resulting feature table.', 'representative_sequences': 'The resulting feature sequences. Each ' 'feature in the feature table will be ' 'represented by exactly one sequence.' }, name='Denoise and dereplicate single-end pyrosequences', description='This method denoises single-end pyrosequencing sequences, ' 'dereplicates them, and filters chimeras.' ) plugin.methods.register_function( function=q2_dada2.denoise_ccs, inputs={'demultiplexed_seqs': SampleData[SequencesWithQuality]}, parameters={'front': qiime2.plugin.Str, 'adapter': qiime2.plugin.Str, 'max_mismatch': qiime2.plugin.Int, 'indels': qiime2.plugin.Bool, 'trunc_len': qiime2.plugin.Int, 'trim_left': qiime2.plugin.Int, 'max_ee': qiime2.plugin.Float, 'trunc_q': qiime2.plugin.Int, 'min_len': qiime2.plugin.Int, 'max_len': qiime2.plugin.Int, 'pooling_method': qiime2.plugin.Str % qiime2.plugin.Choices(_POOL_OPT), 'chimera_method': qiime2.plugin.Str % qiime2.plugin.Choices(_CHIM_OPT), 'min_fold_parent_over_abundance': qiime2.plugin.Float, 'allow_one_off': qiime2.plugin.Bool, 'n_threads': qiime2.plugin.Int, 'n_reads_learn': qiime2.plugin.Int, 'hashed_feature_ids': qiime2.plugin.Bool}, outputs=[('table', FeatureTable[Frequency]), ('representative_sequences', FeatureData[Sequence]), ('denoising_stats', SampleData[DADA2Stats])], input_descriptions={ 'demultiplexed_seqs': 'The single-end demultiplexed PacBio CCS ' 'sequences to be denoised.' }, parameter_descriptions={ 'front': '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': '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': 'The number of mismatches to tolerate when matching ' 'reads to primer sequences ' '- see http://benjjneb.github.io/dada2/ ' 'for complete details.', 'indels': 'Allow insertions or deletions of bases when ' 'matching adapters. Note that primer matching can ' 'be significantly slower, currently about 4x slower', 'trunc_len': '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': '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': 'Reads with number of expected errors higher than this ' 'value will be discarded.', 'trunc_q': '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': 'Remove reads with length less than minLen. ' 'minLen is enforced after trimming and truncation.' ' For 16S Pacbio CCS, suggest 1000.', 'max_len': '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': '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': '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': '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".', '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.'), 'n_threads': 'The number of threads to use for multithreaded ' 'processing. If 0 is provided, all available cores will ' 'be used.', 'n_reads_learn': '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': '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.' }, output_descriptions={ 'table': 'The resulting feature table.', 'representative_sequences': 'The resulting feature sequences. Each ' 'feature in the feature table will be ' 'represented by exactly one sequence.' }, name='Denoise and dereplicate single-end Pacbio CCS', description='This method denoises single-end Pacbio CCS sequences, ' 'dereplicates them, and filters chimeras. ' 'Tutorial and workflow: ' 'https://github.com/benjjneb/LRASManuscript' ) plugin.register_formats(DADA2StatsFormat, DADA2StatsDirFmt) plugin.register_semantic_types(DADA2Stats) plugin.register_semantic_type_to_format( SampleData[DADA2Stats], DADA2StatsDirFmt) importlib.import_module('q2_dada2._transformer')