# ---------------------------------------------------------------------------- # 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 import q2_vsearch._cluster_features import q2_vsearch._cluster_sequences import q2_vsearch._merge_pairs import q2_vsearch._chimera import q2_vsearch._stats from q2_vsearch._type import UchimeStats from q2_vsearch._format import UchimeStatsFmt, UchimeStatsDirFmt from q2_types.feature_data import FeatureData, Sequence from q2_types.feature_table import FeatureTable, Frequency from q2_types.sample_data import SampleData from q2_types.per_sample_sequences import ( Sequences, SequencesWithQuality, PairedEndSequencesWithQuality, JoinedSequencesWithQuality) citations = qiime2.plugin.Citations.load('citations.bib', package='q2_vsearch') plugin = qiime2.plugin.Plugin( name='vsearch', version=q2_vsearch.__version__, website='https://github.com/qiime2/q2-vsearch', package='q2_vsearch', user_support_text=None, short_description='Plugin for clustering and dereplicating with vsearch.', description=('This plugin wraps the vsearch application, and provides ' 'methods for clustering and dereplicating features and ' 'sequences.'), citations=[citations['rognes2016vsearch']] ) plugin.register_formats(UchimeStatsFmt, UchimeStatsDirFmt) plugin.register_semantic_types(UchimeStats) plugin.register_semantic_type_to_format( UchimeStats, artifact_format=UchimeStatsDirFmt) plugin.methods.register_function( function=q2_vsearch._cluster_features.cluster_features_de_novo, inputs={ 'table': FeatureTable[Frequency], 'sequences': FeatureData[Sequence]}, parameters={ 'perc_identity': qiime2.plugin.Float % qiime2.plugin.Range( 0, 1, inclusive_start=False, inclusive_end=True), 'threads': qiime2.plugin.Int % qiime2.plugin.Range( 0, 256, inclusive_start=True, inclusive_end=True) }, outputs=[ ('clustered_table', FeatureTable[Frequency]), ('clustered_sequences', FeatureData[Sequence]), ], input_descriptions={ 'table': 'The feature table to be clustered.', 'sequences': 'The sequences corresponding to the features in table.', }, parameter_descriptions={ 'perc_identity': ('The percent identity at which clustering should be ' 'performed. This parameter maps to vsearch\'s --id ' 'parameter.'), 'threads': ('The number of threads to use for computation. Passing 0 ' 'will launch one thread per CPU core.') }, output_descriptions={ 'clustered_table': 'The table following clustering of features.', 'clustered_sequences': 'Sequences representing clustered features.', }, name='De novo clustering of features.', description=('Given a feature table and the associated feature ' 'sequences, cluster the features based on user-specified ' 'percent identity threshold of their sequences. This is not ' 'a general-purpose de novo clustering method, but rather is ' 'intended to be used for clustering the results of ' 'quality-filtering/dereplication methods, such as DADA2, or ' 'for re-clustering a FeatureTable at a lower percent ' 'identity than it was originally clustered at. When a group ' 'of features in the input table are clustered into a single ' 'feature, the frequency of that single feature in a given ' 'sample is the sum of the frequencies of the features that ' 'were clustered in that sample. Feature identifiers and ' 'sequences will be inherited from the centroid feature ' 'of each cluster. See the vsearch documentation for details ' 'on how sequence clustering is performed.') ) plugin.methods.register_function( function=q2_vsearch._cluster_features.cluster_features_closed_reference, inputs={ 'table': FeatureTable[Frequency], 'sequences': FeatureData[Sequence], 'reference_sequences': FeatureData[Sequence] }, parameters={ 'perc_identity': qiime2.plugin.Float % qiime2.plugin.Range( 0, 1, inclusive_start=False, inclusive_end=True), 'strand': qiime2.plugin.Str % qiime2.plugin.Choices(['plus', 'both']), 'threads': qiime2.plugin.Int % qiime2.plugin.Range( 0, 256, inclusive_start=True, inclusive_end=True) }, outputs=[ ('clustered_table', FeatureTable[Frequency]), ('clustered_sequences', FeatureData[Sequence]), ('unmatched_sequences', FeatureData[Sequence]), ], input_descriptions={ 'table': 'The feature table to be clustered.', 'sequences': 'The sequences corresponding to the features in table.', 'reference_sequences': 'The sequences to use as cluster centroids.', }, parameter_descriptions={ 'perc_identity': ('The percent identity at which clustering should be ' 'performed. This parameter maps to vsearch\'s --id ' 'parameter.'), 'strand': ('Search plus (i.e., forward) or both (i.e., forward and ' 'reverse complement) strands.'), 'threads': ('The number of threads to use for computation. Passing 0 ' 'will launch one thread per CPU core.') }, output_descriptions={ 'clustered_table': 'The table following clustering of features.', 'clustered_sequences': 'The sequences representing clustered ' 'features, relabeled by the reference IDs.', 'unmatched_sequences': 'The sequences which failed to match any ' 'reference sequences. This output maps to ' 'vsearch\'s --notmatched parameter.' }, name='Closed-reference clustering of features.', description=('Given a feature table and the associated feature ' 'sequences, cluster the features against a reference ' 'database based on user-specified ' 'percent identity threshold of their sequences. This is not ' 'a general-purpose closed-reference clustering method, but ' 'rather is intended to be used for clustering the results of ' 'quality-filtering/dereplication methods, such as DADA2, or ' 'for re-clustering a FeatureTable at a lower percent ' 'identity than it was originally clustered at. When a group ' 'of features in the input table are clustered into a single ' 'feature, the frequency of that single feature in a given ' 'sample is the sum of the frequencies of the features that ' 'were clustered in that sample. Feature identifiers ' 'will be inherited from the centroid feature ' 'of each cluster. See the vsearch documentation for details ' 'on how sequence clustering is performed.') ) plugin.pipelines.register_function( function=q2_vsearch._cluster_features.cluster_features_open_reference, inputs={ 'table': FeatureTable[Frequency], 'sequences': FeatureData[Sequence], 'reference_sequences': FeatureData[Sequence] }, parameters={ 'perc_identity': qiime2.plugin.Float % qiime2.plugin.Range( 0, 1, inclusive_start=False, inclusive_end=True), 'strand': qiime2.plugin.Str % qiime2.plugin.Choices(['plus', 'both']), 'threads': qiime2.plugin.Int % qiime2.plugin.Range( 0, 256, inclusive_start=True, inclusive_end=True) }, outputs=[ ('clustered_table', FeatureTable[Frequency]), ('clustered_sequences', FeatureData[Sequence]), ('new_reference_sequences', FeatureData[Sequence]), ], input_descriptions={ 'table': 'The feature table to be clustered.', 'sequences': 'The sequences corresponding to the features in table.', 'reference_sequences': 'The sequences to use as cluster centroids.', }, parameter_descriptions={ 'perc_identity': ('The percent identity at which clustering should be ' 'performed. This parameter maps to vsearch\'s --id ' 'parameter.'), 'strand': ('Search plus (i.e., forward) or both (i.e., forward and ' 'reverse complement) strands.'), 'threads': ('The number of threads to use for computation. Passing 0 ' 'will launch one thread per CPU core.') }, output_descriptions={ 'clustered_table': 'The table following clustering of features.', 'clustered_sequences': 'Sequences representing clustered features.', 'new_reference_sequences': 'The new reference sequences. This can be ' 'used for subsequent runs of ' 'open-reference clustering for consistent ' 'definitions of features across ' 'open-reference feature tables.', }, name='Open-reference clustering of features.', description='Given a feature table and the associated feature sequences, ' 'cluster the features against a reference database based on ' 'user-specified percent identity threshold of their sequences.' ' Any sequences that don\'t match are then clustered de novo. ' 'This is not a general-purpose clustering method, but rather ' 'is intended to be used for clustering the results of ' 'quality-filtering/dereplication methods, such as DADA2, or ' 'for re-clustering a FeatureTable at a lower percent identity ' 'than it was originally clustered at. When a group of ' 'features in the input table are clustered into a single ' 'feature, the frequency of that single feature in a given ' 'sample is the sum of the frequencies of the features that ' 'were clustered in that sample. Feature identifiers will be ' 'inherited from the centroid feature of each cluster. For ' 'features that match a reference sequence, the centroid ' 'feature is that reference sequence, so its identifier will ' 'become the feature identifier. The clustered_sequences ' 'result will contain feature representative sequences that ' 'are derived from the sequences input for all features in ' 'clustered_table. This will always be the most abundant ' 'sequence in the cluster. The new_reference_sequences result ' 'will contain the entire reference database, plus feature ' 'representative sequences for any de novo features. This is ' 'intended to be used as a reference database in subsequent ' 'iterations of cluster_features_open_reference, if ' 'applicable. See the vsearch documentation for details on how ' 'sequence clustering is performed.', citations=[citations['rideout2014subsampled']] ) plugin.methods.register_function( function=q2_vsearch._cluster_sequences.dereplicate_sequences, inputs={ 'sequences': (SampleData[Sequences] | SampleData[SequencesWithQuality] | SampleData[JoinedSequencesWithQuality]) }, parameters={ 'derep_prefix': qiime2.plugin.Bool, 'min_seq_length': qiime2.plugin.Int % qiime2.plugin.Range(1, None), 'min_unique_size': qiime2.plugin.Int % qiime2.plugin.Range(1, None), }, outputs=[ ('dereplicated_table', FeatureTable[Frequency]), ('dereplicated_sequences', FeatureData[Sequence]), ], input_descriptions={ 'sequences': 'The sequences to be dereplicated.', }, parameter_descriptions={ 'derep_prefix': ('Merge sequences with identical prefixes. If a ' 'sequence is identical to the prefix of two or more ' 'longer sequences, it is clustered with the shortest ' 'of them. If they are equally long, it is clustered ' 'with the most abundant.'), 'min_seq_length': ('Discard sequences shorter than this integer.'), 'min_unique_size': ('Discard sequences with a post-dereplication ' 'abundance value smaller than integer.'), }, output_descriptions={ 'dereplicated_table': 'The table of dereplicated sequences.', 'dereplicated_sequences': 'The dereplicated sequences.', }, name='Dereplicate sequences.', description=('Dereplicate sequence data and create a feature table and ' 'feature representative sequences. Feature identifiers ' 'in the resulting artifacts will be the sha1 hash ' 'of the sequence defining each feature. If clustering of ' 'features into OTUs is desired, the resulting artifacts ' 'can be passed to the cluster_features_* methods in this ' 'plugin.') ) plugin.methods.register_function( function=q2_vsearch._merge_pairs.merge_pairs, inputs={ 'demultiplexed_seqs': SampleData[PairedEndSequencesWithQuality] }, parameters={ 'truncqual': qiime2.plugin.Int % qiime2.plugin.Range(0, None), 'minlen': qiime2.plugin.Int % qiime2.plugin.Range(0, None), 'maxns': qiime2.plugin.Int % qiime2.plugin.Range(0, None), 'allowmergestagger': qiime2.plugin.Bool, 'minovlen': qiime2.plugin.Int % qiime2.plugin.Range(0, None), 'maxdiffs': qiime2.plugin.Int % qiime2.plugin.Range(0, None), 'minmergelen': qiime2.plugin.Int % qiime2.plugin.Range(0, None), 'maxmergelen': qiime2.plugin.Int % qiime2.plugin.Range(0, None), 'maxee': qiime2.plugin.Float % qiime2.plugin.Range(0., None), 'threads': qiime2.plugin.Int % qiime2.plugin.Range( 0, 8, inclusive_start=True, inclusive_end=True) }, outputs=[ ('merged_sequences', SampleData[JoinedSequencesWithQuality]) ], input_descriptions={ 'demultiplexed_seqs': ('The demultiplexed paired-end sequences to ' 'be merged.'), }, parameter_descriptions={ 'truncqual': ('Truncate sequences at the first base with the ' 'specified quality score value or lower.'), 'minlen': ('Sequences shorter than minlen after truncation are ' 'discarded.'), 'maxns': ('Sequences with more than maxns N characters are ' 'discarded.'), 'allowmergestagger': ('Allow merging of staggered read pairs.'), 'minovlen': ('Minimum length of the area of overlap between reads ' 'during merging.'), 'maxdiffs': ('Maximum number of mismatches in the area of overlap ' 'during merging.'), 'minmergelen': ('Minimum length of the merged read to be retained.'), 'maxmergelen': ('Maximum length of the merged read to be retained.'), 'maxee': ('Maximum number of expected errors in the merged read ' 'to be retained.'), 'threads': ('The number of threads to use for computation. Does ' 'not scale much past 4 threads.') }, output_descriptions={ 'merged_sequences': ('The merged sequences.'), }, name='Merge paired-end reads.', description=('Merge paired-end sequence reads using vsearch\'s ' 'merge_pairs function. See the vsearch documentation for ' 'details on how paired-end merging is performed, and for ' 'more information on the parameters to this method.') ) plugin.methods.register_function( function=q2_vsearch._chimera.uchime_ref, inputs={ 'sequences': FeatureData[Sequence], 'table': FeatureTable[Frequency], 'reference_sequences': FeatureData[Sequence]}, parameters={ 'dn': qiime2.plugin.Float % qiime2.plugin.Range(0., None), 'mindiffs': qiime2.plugin.Int % qiime2.plugin.Range(1, None), 'mindiv': qiime2.plugin.Float % qiime2.plugin.Range(0., None), 'minh': qiime2.plugin.Float % qiime2.plugin.Range( 0., 1.0, inclusive_end=True), 'xn': qiime2.plugin.Float % qiime2.plugin.Range( 1., None, inclusive_start=False), 'threads': qiime2.plugin.Int % qiime2.plugin.Range( 0, 256, inclusive_start=True, inclusive_end=True) }, outputs=[ ('chimeras', FeatureData[Sequence]), ('nonchimeras', FeatureData[Sequence]), ('stats', UchimeStats) ], input_descriptions={ 'sequences': 'The feature sequences to be chimera-checked.', 'table': ('Feature table (used for computing total feature ' 'abundances).'), 'reference_sequences': 'The non-chimeric reference sequences.' }, parameter_descriptions={ 'dn': ('No vote pseudo-count, corresponding to the parameter n in ' 'the chimera scoring function.'), 'mindiffs': 'Minimum number of differences per segment.', 'mindiv': 'Minimum divergence from closest parent.', 'minh': ('Minimum score (h). Increasing this value tends to reduce ' 'the number of false positives and to decrease sensitivity.'), 'xn': ('No vote weight, corresponding to the parameter beta in the ' 'scoring function.'), 'threads': ('The number of threads to use for computation. Passing 0 ' 'will launch one thread per CPU core.') }, output_descriptions={ 'chimeras': 'The chimeric sequences.', 'nonchimeras': 'The non-chimeric sequences.', 'stats': 'Summary statistics from chimera checking.' }, name='Reference-based chimera filtering with vsearch.', description=('Apply the vsearch uchime_ref method to identify chimeric ' 'feature sequences. The results of this method can be used ' 'to filter chimeric features from the corresponding feature ' 'table. For additional details, please refer to the vsearch ' 'documentation.') ) plugin.methods.register_function( function=q2_vsearch._chimera.uchime_denovo, inputs={ 'sequences': FeatureData[Sequence], 'table': FeatureTable[Frequency]}, parameters={ 'dn': qiime2.plugin.Float % qiime2.plugin.Range(0., None), 'mindiffs': qiime2.plugin.Int % qiime2.plugin.Range(1, None), 'mindiv': qiime2.plugin.Float % qiime2.plugin.Range(0., None), 'minh': qiime2.plugin.Float % qiime2.plugin.Range( 0., 1.0, inclusive_end=True), 'xn': qiime2.plugin.Float % qiime2.plugin.Range( 1., None, inclusive_start=False) }, outputs=[ ('chimeras', FeatureData[Sequence]), ('nonchimeras', FeatureData[Sequence]), ('stats', UchimeStats) ], input_descriptions={ 'sequences': 'The feature sequences to be chimera-checked.', 'table': ('Feature table (used for computing total feature ' 'abundances).'), }, parameter_descriptions={ 'dn': ('No vote pseudo-count, corresponding to the parameter n in ' 'the chimera scoring function.'), 'mindiffs': 'Minimum number of differences per segment.', 'mindiv': 'Minimum divergence from closest parent.', 'minh': ('Minimum score (h). Increasing this value tends to reduce ' 'the number of false positives and to decrease sensitivity.'), 'xn': ('No vote weight, corresponding to the parameter beta in the ' 'scoring function.'), }, output_descriptions={ 'chimeras': 'The chimeric sequences.', 'nonchimeras': 'The non-chimeric sequences.', 'stats': 'Summary statistics from chimera checking.' }, name='De novo chimera filtering with vsearch.', description=('Apply the vsearch uchime_denovo method to identify chimeric ' 'feature sequences. The results of this method can be used ' 'to filter chimeric features from the corresponding feature ' 'table. For more details, please refer to the vsearch ' 'documentation.') ) plugin.visualizers.register_function( function=q2_vsearch._stats.fastq_stats, inputs={ 'sequences': SampleData[ SequencesWithQuality | PairedEndSequencesWithQuality], }, parameters={ 'threads': qiime2.plugin.Int % qiime2.plugin.Range( 1, None) | qiime2.plugin.Str % qiime2.plugin.Choices(['auto']) }, input_descriptions={ 'sequences': 'Fastq sequences' }, parameter_descriptions={ 'threads': 'The number of threads used for computation.', }, name='Fastq stats with vsearch.', description='A fastq overview via vsearch\'s fastq_stats, fastq_eestats ' 'and fastq_eestats2 utilities. Please see ' 'https://github.com/torognes/vsearch for detailed ' 'documentation of these tools.', ) importlib.import_module('q2_vsearch._transformer')