'from Illumina amplicon sequencing. Nicholas A Bokulich, ' 'Sathish Subramanian, Jeremiah J Faith, Dirk Gevers, ' 'Jeffrey I Gordon, Rob Knight, David A Mills & J Gregory ' 'Caporaso. Nature Methods 10, 57–59 (2013) ' 'doi:10.1038/nmeth.2276'), description=('This QIIME 2 plugin supports filtering and trimming of ' 'sequence reads based on PHRED scores and ambiguous ' 'nucleotide characters.'), short_description='Plugin for PHRED-based filtering and trimming.' ) plugin.register_formats(QualityFilterStatsFmt, QualityFilterStatsDirFmt) plugin.register_semantic_types(QualityFilterStats) plugin.register_semantic_type_to_format( QualityFilterStats, artifact_format=QualityFilterStatsDirFmt) plugin.methods.register_function( function=q2_quality_filter.q_score, inputs={'demux': SampleData[SequencesWithQuality]}, parameters={ 'min_quality': qiime2.plugin.Int, 'quality_window': qiime2.plugin.Int, 'min_length_fraction': qiime2.plugin.Float, 'max_ambiguous': qiime2.plugin.Int }, outputs=[ ('filtered_sequences', SampleData[SequencesWithQuality]), ('filter_stats', QualityFilterStats) ],
# Define a directory format. A directory format is a directory structure # composed of one or more files (nested directories are also supported). Each # file has a specific file format associated with it. This directory format # only has a single file, ints.txt, with file format `IntSequenceFormat`. MappingDirectoryFormat = model.SingleFileDirectoryFormat( 'MappingDirectoryFormat', 'mapping.tsv', MappingFormat) # Register the formats defined above. Formats must be unique across all # plugins installed on a users system. plugin.register_formats(MappingFormat, MappingDirectoryFormat) # Register the directory format with the semantic types defined above. A # directory format can be registered to multiple semantic types. Currently, a # semantic type can only have a single directory format associated with it. plugin.register_semantic_type_to_format( Mapping, artifact_format=MappingDirectoryFormat) # Define a transformer for converting a file format (`MappingFormat`) into a # view type (`dict` in this case). To indicate that only the QIIME 2 Framework # should interact with a transformer, a non-meaningful name is used. The # convention is `_<int counter>`, but anything is acceptable. The aim is to # draw the reader to the function annotations, which convey precisely what the # transformer is responsible for. @plugin.register_transformer def _1(ff: MappingFormat) -> dict: with ff.open() as fh: data = {} for line in fh: key, value = line.rstrip('\n').split('\t')
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]),
@plugin.register_transformer def _3(dirfmt: TaxonomicClassifierDirFmt) -> Pipeline: raise ValueError('The scikit-learn version could not be determined for' ' this artifact, please retrain your classifier for your' ' current deployment to prevent data-corruption errors.') @plugin.register_transformer def _4(fmt: JSONFormat) -> dict: with fmt.open() as fh: return json.load(fh) @plugin.register_transformer def _5(data: dict) -> JSONFormat: result = JSONFormat() with result.open() as fh: json.dump(data, fh) return result # Registrations plugin.register_semantic_types(TaxonomicClassifier) plugin.register_formats(TaxonomicClassifierDirFmt, TaxonomicClassiferTemporaryPickleDirFmt) plugin.register_semantic_type_to_format( TaxonomicClassifier, artifact_format=TaxonomicClassiferTemporaryPickleDirFmt)
'the input table. You can ignore this table for ' 'downstream analyses.', }, name='Filter fragments in tree from table.', description='Filters fragments not inserted into a phylogenetic tree from ' 'a feature-table. Some fragments computed by e.g. Deblur or ' 'DADA2 are too remote to get inserted by SEPP into a ' 'reference phylogeny. To be able to use the feature-table for ' 'downstream analyses like computing Faith\'s PD or UniFrac, ' 'the feature-table must be cleared of fragments that are not ' 'part of the phylogenetic tree, because their path length can ' 'otherwise not be determined. Typically, the number of ' 'rejected fragments is low (<= 10), but it might be worth to ' 'inspect the ratio of rea' 'ds assigned to those rejected ' 'fragments.', ) # TODO: rough in method to merge database components # TODO: rough in method to destructure database components importlib.import_module('q2_fragment_insertion._transformer') plugin.register_formats(PlacementsFormat, PlacementsDirFmt, RAxMLinfoFormat, SeppReferenceDirFmt) plugin.register_semantic_types(Placements, SeppReferenceDatabase) plugin.register_semantic_type_to_format(Placements, artifact_format=PlacementsDirFmt) plugin.register_semantic_type_to_format(SeppReferenceDatabase, artifact_format=SeppReferenceDirFmt)
outputs=[('subject_biplot', PCoAResults % Properties("biplot")), ('state_biplot', PCoAResults % Properties("biplot")), ('distance_matrix', DistanceMatrix), ('state_subject_ordination', SampleData[SampleTrajectory]), ('state_feature_ordination', FeatureData[FeatureTrajectory])], input_descriptions={'table': DESC_BIN}, parameter_descriptions=PARAMETERDESC, output_descriptions={ 'subject_biplot': QLOAD, 'state_biplot': QSOAD, 'distance_matrix': QDIST, 'state_subject_ordination': QORD, 'state_feature_ordination': QORD }, name='Compositional Tensor Factorization - Mode 3', description=("Gemelli resolves spatiotemporal subject variation and the" " biological features that separate them. In this case, a " "subject may have several paired samples, where each sample" " may be a time point. The output is akin to conventional " "beta-diversity analyses but with the paired component " "integrated in the dimensionality reduction."), ) plugin.register_semantic_types(SampleTrajectory, FeatureTrajectory) plugin.register_semantic_type_to_format( SampleData[SampleTrajectory], artifact_format=TrajectoryDirectoryFormat) plugin.register_semantic_type_to_format( FeatureData[FeatureTrajectory], artifact_format=TrajectoryDirectoryFormat) plugin.register_formats(TrajectoryDirectoryFormat) importlib.import_module('gemelli.q2._transformer')
plugin.methods.register_function( function=qarcoal, inputs={ "table": FeatureTable[Frequency], "taxonomy": FeatureData[Taxonomy], }, parameters=qarcoal_params, parameter_descriptions=qarcoal_param_descs, input_descriptions={ "table": QPD.QARCOAL_TBL, "taxonomy": QPD.QARCOAL_TAXONOMY, }, outputs=[("qarcoal_log_ratios", SampleData[LogRatios])], description=QPD.QARCOAL_DESC, name="Compute feature log-ratios based on textual taxonomy searching.", ) # this line may be necessary to register transformers # found in songbird's plugin_setup file as well as Q2 forum post # https://github.com/biocore/songbird/blob/master/songbird/q2/plugin_setup.py # https://forum.qiime2.org/t/question-about-error-no-transformation-class-for-dataframe-to-dir/4576 importlib.import_module("qurro.q2._transformer") # Register types plugin.register_formats(LogRatiosFormat, LogRatiosDirFmt) plugin.register_semantic_types(LogRatios) plugin.register_semantic_type_to_format( SampleData[LogRatios], artifact_format=LogRatiosDirFmt )
package='q2_fragment_insertion', user_support_text=('https://github.com/biocore/' 'q2-fragment-insertion/issues'), citation_text=('Stefan Janssen, Daniel McDonald, Antonio Gonzalez, ' 'Jose A. Navas-Molina, Lingjing Jiang, ' 'Zhenjiang Zech Xu, Kevin Winker, Deborah M. Kado, ' 'Eric Orwoll, Mark Manary, Siavash Mirarab, Rob Knight.' ' "Phylogenetic Placement of Exact Amplicon Sequences ' 'Improves Associations with Clinical Information." ' 'mSystems. 2018.') ) plugin.register_formats(PlacementsFormat, PlacementsDirFmt) plugin.register_semantic_types(Placements) plugin.register_semantic_type_to_format(Placements, artifact_format=PlacementsDirFmt) _parameter_descriptions = { 'threads': 'The number of threads to use', 'alignment_subset_size': ('Each placement subset is further broken into subsets of at most these ' 'many sequences and a separate HMM is trained on each subset. The ' 'default alignment subset size is set to balance the exhaustiveness of ' 'the alignment step with the running time.'), 'placement_subset_size': ('The tree is divided into subsets such that each subset includes at most ' 'these many subsets. The placement step places the fragment on only one ' 'subset, determined based on alignment scores. The default placement ' 'subset is set to make sure the memory requirement of the pplacer step ' 'does not become prohibitively large.\nFurther reading: ' 'https://github.com/smirarab/sepp/blob/master/tutorial/sepp-tutorial.md'
# Define a directory format. A directory format is a directory structure # composed of one or more files (nested directories are also supported). Each # file has a specific file format associated with it. This directory format # only has a single file, ints.txt, with file format `IntSequenceFormat`. MappingDirectoryFormat = model.SingleFileDirectoryFormat( 'MappingDirectoryFormat', 'mapping.tsv', MappingFormat) # Register the formats defined above. Formats must be unique across all # plugins installed on a users system. plugin.register_formats(MappingFormat, MappingDirectoryFormat) # Register the directory format with the semantic types defined above. A # directory format can be registered to multiple semantic types. Currently, a # semantic type can only have a single directory format associated with it. plugin.register_semantic_type_to_format(Mapping, artifact_format=MappingDirectoryFormat) # Define a transformer for converting a file format (`MappingFormat`) into a # view type (`dict` in this case). To indicate that only the QIIME 2 Framework # should interact with a transformer, a non-meaningful name is used. The # convention is `_<int counter>`, but anything is acceptable. The aim is to # draw the reader to the function annotations, which convey precisely what the # transformer is responsible for. @plugin.register_transformer def _1(ff: MappingFormat) -> dict: with ff.open() as fh: data = {} for line in fh: key, value = line.rstrip('\n').split('\t') if key in data:
" of the generated winnowed community.", short_description= "Plugin for performing feature orderings in order to generate winnowed communities." ) # <><><> Register semantic types and formats <><><> plugin.register_semantic_types(Winnowed) # Register the formats defined plugin.register_formats(WinnowedFeatureOrderingFormat, WinnowedAucOrderingFormat, WinnowedPermanovaOrderingFormat, WinnowedDirectoryFormat) # Register directory format with semantic type plugin.register_semantic_type_to_format( Winnowed, artifact_format=WinnowedDirectoryFormat) # <><><> Register functions <><><> plugin.methods.register_function( name='Winnowing Processing Function', description=("Perform a feature selection to get the winnowed community."), function=process, inputs={ "infile1": FeatureTable[Frequency], "infile2": FeatureTable[Frequency] }, outputs=[("result", Winnowed)], input_descriptions={ "infile1": ("This is the biom file which will have OTU info extracted from and analyzed to generate "
# Define a directory format. A directory format is a directory structure # composed of one or more files (nested directories are also supported). Each # file has a specific file format associated with it. This directory format # only has a single file, ints.txt, with file format `IntSequenceFormat`. IntSequenceDirectoryFormat = model.SingleFileDirectoryFormat( 'IntSequenceDirectoryFormat', 'ints.txt', IntSequenceFormat) # Register the formats defined above. Formats must be unique across all # plugins installed on a users system. plugin.register_formats(IntSequenceFormat, IntSequenceDirectoryFormat) # Register the directory format with the semantic types defined above. A # directory format can be registered to multiple semantic types. Currently, a # semantic type can only have a single directory format associated with it. plugin.register_semantic_type_to_format( IntSequence1, artifact_format=IntSequenceDirectoryFormat) plugin.register_semantic_type_to_format( IntSequence2, artifact_format=IntSequenceDirectoryFormat) # Define a transformer for converting a file format (`IntSequenceFormat`) into # a view type (`list` in this case). To indicate that only the QIIME 2 # Framework should interact with a transformer, a non-meaningful name is used. # The convention is `_<int counter>`, but anything is acceptable. The aim is to # draw the reader to the function annotations, which convey precisely what the # transformer is responsible for. @plugin.register_transformer def _1(ff: IntSequenceFormat) -> list: with ff.open() as fh: data = [] for line in fh:
name='Visualize feature assignments with an interactive bar plot', description='This visualizer produces an interactive barplot visualization' ' of the feature assignments. ' 'Interactive features include multi-level ' 'sorting, plot recoloring, sample relabeling, and SVG ' 'figure export.' ) plugin.visualizers.register_function( function=barplot, inputs={'proportions': FeatureTable[RelativeFrequency]}, parameters={'sample_metadata': Metadata, 'category_column': Str}, input_descriptions={'proportions': OUT_MEAN}, parameter_descriptions={'sample_metadata': DESC_MAP, 'category_column': DESC_CAT}, name='Visualize feature assignments with an interactive bar plot', description='This visualizer produces an interactive barplot visualization' ' of the feature assignments. ' 'Interactive features include multi-level ' 'sorting, plot recoloring, sample relabeling, and SVG ' 'figure export.' ) plugin.register_semantic_types(SinkSourceMap) plugin.register_semantic_type_to_format( SampleData[SinkSourceMap], artifact_format=SinkSourceMapDirectoryFormat) plugin.register_formats(SinkSourceMapDirectoryFormat) importlib.import_module('sourcetracker._q2._transformer')
from q2_types.per_sample_sequences import SequencesWithQuality from ._format import MinHashSigJsonDirFormat, MinHashSigJson from ._types import MinHashSig plugin = Plugin( name='sourmash', version='0.0.0', website='http://sourmash.readthedocs.io/en/latest/', package='q2_sourmash', citations=Citations.load('citations.bib', package='q2_sourmash'), description=('This QIIME 2 plugin wraps sourmash and ' 'supports the calculation and comparison of ' 'minhash signatures.'), short_description='Plugin for generation of minhash signatures.') plugin.register_semantic_type_to_format( MinHashSig, artifact_format=MinHashSigJsonDirFormat) plugin.register_views(MinHashSigJson, MinHashSigJsonDirFormat) plugin.register_semantic_types(MinHashSig) plugin.methods.register_function( function=compute, inputs={'sequence_file': SampleData[SequencesWithQuality]}, parameters={ 'ksizes': qiime2.plugin.Int, 'scaled': qiime2.plugin.Int, 'track_abundance': qiime2.plugin.Bool }, outputs=[('min_hash_signature', MinHashSig)], name='compute sourmash signature', description='Computes a sourmash MinHash signature from fasta/q files.')
'regression_stats': SampleData[SongbirdStats], 'baseline_stats': SampleData[SongbirdStats] }, parameters={}, input_descriptions={ 'feature_table': ('Input biom table that was used for the ' 'regression analysis.'), 'regression_stats': ('results from multinomial regression ' 'for reference model'), 'baseline_stats': ('results from multinomial regression ' 'for baseline model') }, parameter_descriptions={}, name='Paired regression summary statistics', description=("Visualize the convergence statistics of regression fit " "including cross validation accuracy, loglikehood over the " "iterations and the R2.")) # Register types plugin.register_formats(SongbirdStatsFormat, SongbirdStatsDirFmt) plugin.register_semantic_types(SongbirdStats) plugin.register_semantic_type_to_format(SampleData[SongbirdStats], SongbirdStatsDirFmt) plugin.register_formats(DifferentialFormat, DifferentialDirFmt) plugin.register_semantic_types(Differential) plugin.register_semantic_type_to_format(FeatureData[Differential], DifferentialDirFmt) importlib.import_module('songbird.q2._transformer')
output_descriptions={'curated_metadata': 'The curated sample metadata.'}, name='Normalize metadata', description='Normalize metadata according to a series of rules.' ) plugin.visualizers.register_function( function=tabulate, inputs={}, parameters={ 'input': qiime2.plugin.Metadata, 'page_size': qiime2.plugin.Int, }, parameter_descriptions={ 'input': 'The metadata to tabulate.', 'page_size': 'The maximum number of Metadata records to display ' 'per page', }, name='Interactively explore Metadata in an HTML table', description='Generate a tabular view of Metadata. The output ' 'visualization supports interactive filtering, sorting, and ' 'exporting to common file formats.', ) plugin.register_semantic_types(MetadataX) plugin.register_semantic_type_to_format( MetadataX, artifact_format=MetadataDirectoryFormat ) plugin.register_formats(MetadataFormat, MetadataDirectoryFormat) importlib.import_module('q2_metadata._transformer')
'normalize': 'Optionally normalize heatmap values by columns or rows.', 'top_k_metabolites': 'Select top k metabolites associated with each ' 'of the chosen features to display on heatmap.', 'keep_top_samples': 'Display only samples in which at least one of ' 'the selected microbes is the most abundant ' 'feature.', 'level': 'taxonomic level for annotating clustermap. Set to -1 if not ' 'parsing semicolon-delimited taxonomies or wish to print ' 'entire annotation.', 'row_center': 'Center conditional probability table ' 'around average row.' }, name='Paired feature abundance heatmaps', description="Generate paired heatmaps that depict microbial and " "metabolite feature abundances. The left panel displays the " "abundance of each selected microbial feature in each sample. " "The right panel displays the abundances of the top k " "metabolites most highly correlated with these microbes in " "each sample. The y-axis (sample axis) is shared between each " "panel.", citations=[] ) plugin.register_formats(ConditionalFormat, ConditionalDirFmt) plugin.register_semantic_types(Conditional) plugin.register_semantic_type_to_format( FeatureData[Conditional], ConditionalDirFmt) importlib.import_module('mmvec.q2._transformer')
package='q2_metastorms'), description='This QIIME 2 plugin supports standalone implementation of ' 'Microbiome Search Engine (MSE; http://mse.single-cell.cn).', short_description='Plugin for search in a micriobiome database.') plugin.register_formats(MetaStormsOTUDatabaseFmt, MetaStormsOTUDatabaseDirFmt) plugin.register_formats(MetaStormsSPDatabaseFmt, MetaStormsSPDatabaseDirFmt) plugin.register_formats(MetaStormsFUNCDatabaseFmt, MetaStormsFUNCDatabaseDirFmt) plugin.register_formats(MetaStormsSearchResultsFmt, MetaStormsSearchResultsDirFmt) plugin.register_formats(MetaStormsMetaResultsFmt, MetaStormsMetaResultsDirFmt) plugin.register_formats(MetaStormsMNSResultsFmt, MetaStormsMNSResultsDirFmt) plugin.register_semantic_types(MetaStormsOTUDatabase) plugin.register_semantic_type_to_format( MetaStormsOTUDatabase, artifact_format=MetaStormsOTUDatabaseDirFmt) plugin.register_semantic_types(MetaStormsSPDatabase) plugin.register_semantic_type_to_format( MetaStormsSPDatabase, artifact_format=MetaStormsSPDatabaseDirFmt) plugin.register_semantic_types(MetaStormsFUNCDatabase) plugin.register_semantic_type_to_format( MetaStormsFUNCDatabase, artifact_format=MetaStormsFUNCDatabaseDirFmt) plugin.register_semantic_types(MetaStormsSearchResults) plugin.register_semantic_type_to_format( MetaStormsSearchResults, artifact_format=MetaStormsSearchResultsDirFmt) plugin.register_semantic_types(MetaStormsMetaResults) plugin.register_semantic_type_to_format(
'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.', 'retain_all_samples': 'If True all samples input to dada2 will be ' 'retained in the output of dada2, if false ' 'samples with zero total frequency are removed ' 'from the table.' }, 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.register_formats(DADA2StatsFormat, DADA2StatsDirFmt) plugin.register_semantic_types(DADA2Stats) plugin.register_semantic_type_to_format(SampleData[DADA2Stats], DADA2StatsDirFmt) importlib.import_module('q2_dada2._transformer')
'module and its commands.'), citations=None) plugin.register_formats(LinkedSpeciesPeptideFmt, LinkedSpeciesPeptideDirFmt, SequenceNamesFmt, SequenceNamesDirFmt, ProteinSequenceFmt, ProteinSequenceDirFmt, TaxIdLineageFmt, TaxIdLineageDirFmt, EnrichedPeptideFmt, EnrichedPeptideDirFmt, DeconvolutedSpeciesFmt, DeconvolutedSpeciesDirFmt, SpeciesAssignMapFmt, SpeciesAssignMapDirFmt) plugin.register_semantic_types(LinkedSpeciesPeptide, SequenceNames, ProteinSequence, TaxIdLineage, EnrichedPeptide, DeconvolutedSpecies, SpeciesAssignMap) plugin.register_semantic_type_to_format( FeatureTable[DeconvolutedSpecies], artifact_format=DeconvolutedSpeciesDirFmt) plugin.register_semantic_type_to_format( LinkedSpeciesPeptide, artifact_format=LinkedSpeciesPeptideDirFmt) plugin.register_semantic_type_to_format(SequenceNames, artifact_format=SequenceNamesDirFmt) plugin.register_semantic_type_to_format(FeatureData[ProteinSequence], artifact_format=ProteinSequenceDirFmt) plugin.register_semantic_type_to_format(TaxIdLineage, artifact_format=TaxIdLineageDirFmt) plugin.register_semantic_type_to_format(EnrichedPeptide, artifact_format=EnrichedPeptideDirFmt) plugin.register_semantic_type_to_format(SpeciesAssignMap, artifact_format=SpeciesAssignMapDirFmt)
plugin = qiime2.plugin.Plugin(name='demux', version=q2_demux.__version__, website='https://github.com/qiime2/q2-demux', package='q2_demux', user_support_text=None, citation_text=None) plugin.register_semantic_types(RawSequences, EMPSingleEndSequences, EMPPairedEndSequences) plugin.register_formats(EMPMultiplexedDirFmt, EMPSingleEndDirFmt, EMPSingleEndCasavaDirFmt, EMPPairedEndDirFmt, EMPPairedEndCasavaDirFmt) # TODO: remove when aliasing exists plugin.register_semantic_type_to_format(RawSequences, artifact_format=EMPSingleEndDirFmt) plugin.register_semantic_type_to_format(EMPSingleEndSequences, artifact_format=EMPSingleEndDirFmt) plugin.register_semantic_type_to_format(EMPPairedEndSequences, artifact_format=EMPPairedEndDirFmt) plugin.methods.register_function( function=q2_demux.emp_single, # TODO: remove RawSequences by creating an alias to EMPSequences inputs={'seqs': RawSequences | EMPSingleEndSequences}, parameters={ 'barcodes': qiime2.plugin.MetadataCategory, 'rev_comp_barcodes': qiime2.plugin.Bool, 'rev_comp_mapping_barcodes': qiime2.plugin.Bool
name='abundance-filtering', version=q2_abundance_filtering.__version__, website='https://github.com/bassio/q2-abundance-filtering', package='q2_abundance_filtering', user_support_text=None, short_description= 'Plugin for abundance filtration of sequences according to the method of Wang et al.', description= ('This plugin filters out sequences according to the method of Wang et al.' ), citations=[citations['Wang2018']]) plugin.register_formats(AbundanceFilteringStatsFmt, AbundanceFilteringStatsDirFmt) plugin.register_semantic_types(AbundanceFilteringStats) plugin.register_semantic_type_to_format( AbundanceFilteringStats, artifact_format=AbundanceFilteringStatsDirFmt) plugin.methods.register_function( function=q2_abundance_filtering.abundance_filter, inputs={ 'sequences': SampleData[JoinedSequencesWithQuality | SequencesWithQuality] }, parameters={ 'threads': qiime2.plugin.Int % qiime2.plugin.Range(1, None), }, outputs=[('filtered_sequences', SampleData[SequencesWithQuality]), ('stats', AbundanceFilteringStats)], input_descriptions={'sequences': "The input sequences."}, parameter_descriptions={ 'threads':
citations = qiime2.plugin.Citations.load('citations.bib', package='q2_deblur') plugin = qiime2.plugin.Plugin( name='deblur', version=q2_deblur.__version__, website='https://github.com/biocore/deblur', package='q2_deblur', citations=[citations['amir2017deblur']], description=('This QIIME 2 plugin wraps the Deblur software for ' 'performing sequence quality control.'), short_description='Plugin for sequence quality control with Deblur.') plugin.register_formats(DeblurStatsFmt, DeblurStatsDirFmt) plugin.register_semantic_types(DeblurStats) plugin.register_semantic_type_to_format(DeblurStats, artifact_format=DeblurStatsDirFmt) _parameter_descriptions = { 'mean_error': ("The mean per nucleotide error, used for original " "sequence estimate."), 'indel_prob': ('Insertion/deletion (indel) probability (same for N ' 'indels).'), 'indel_max': "Maximum number of insertion/deletions.", 'trim_length': "Sequence trim length, specify -1 to disable trimming.", 'left_trim_len': "Sequence trimming from the 5' end. A value of 0 will " "disable this trim.", 'min_reads': ("Retain only features appearing at least min_reads " "times across all samples in the resulting feature "
'baseline_stats': SampleData[MMvecStats] }, parameters={}, input_descriptions={ 'model_stats': ("Summary information for the reference model, produced by running " "`qiime mmvec paired-omics`."), 'baseline_stats': ("Summary information for the baseline model, produced by running " "`qiime mmvec paired-omics`.") }, parameter_descriptions={}, name='Paired MMvec summary statistics', description=( "Visualize the convergence statistics from two MMvec models, " "giving insight into how the models fit to your data. " "The produced visualization includes a 'pseudo-Q-squared' value.")) # Register types plugin.register_formats(MMvecStatsFormat, MMvecStatsDirFmt) plugin.register_semantic_types(MMvecStats) plugin.register_semantic_type_to_format(SampleData[MMvecStats], MMvecStatsDirFmt) plugin.register_formats(ConditionalFormat, ConditionalDirFmt) plugin.register_semantic_types(Conditional) plugin.register_semantic_type_to_format(FeatureData[Conditional], ConditionalDirFmt) importlib.import_module('mmvec.q2._transformer')
plugin.visualizers.register_function( function=summarize_paired, inputs={ 'regression_stats': SampleData[SongbirdStats], 'baseline_stats': SampleData[SongbirdStats] }, parameters={}, input_descriptions={ 'regression_stats': ("Summary information for the reference model, produced by running " "`qiime songbird multinomial`."), 'baseline_stats': ("Summary information for the baseline model, produced by running " "`qiime songbird multinomial`.") }, parameter_descriptions={}, name='Paired regression summary statistics', description=( "Visualize the convergence statistics from two runs of multinomial " "regression, giving insight into how the models fit to your data. " "The produced visualization includes a 'pseudo-Q-squared' value.")) # Register types plugin.register_formats(SongbirdStatsFormat, SongbirdStatsDirFmt) plugin.register_semantic_types(SongbirdStats) plugin.register_semantic_type_to_format(SampleData[SongbirdStats], SongbirdStatsDirFmt) importlib.import_module('songbird.q2._transformer')
'for diversity analyses. Microbiome', version=q2_ghost_tree.__version__, website='https://github.com/JTFouquier/ghost-tree', package='q2_ghost_tree', user_support_text=None, citation_text='Fouquier J, Rideout JR, Bolyen E, Chase J, Shiffer A, ' 'McDonald D, Knight R, Caporaso JG, Kelley ST. 2016. ' 'Ghost-tree: creating hybrid-gene phylogenetic trees for ' 'diversity analyses. Microbiome', short_description='Plugin for creating hybrid-gene phylogenetic trees.', ) OtuMap = qiime2.plugin.SemanticType('OtuMap') plugin.register_formats(OtuMapFormat, OtuMapDirectoryFormat) plugin.register_semantic_types(OtuMap) plugin.register_semantic_type_to_format(OtuMap, artifact_format=OtuMapDirectoryFormat) graft_level = qiime2.plugin graft_level = graft_level.Str % \ graft_level.Choices({'p', 'c', 'o', 'f', 'g'}) plugin.methods.register_function( function=scaffold_hybrid_tree_foundation_alignment, inputs={ 'otu_map': OtuMap, # ghost-tree semantic type 'extension_taxonomy': FeatureData[Taxonomy], 'extension_sequences': FeatureData[Sequence], 'foundation_alignment': FeatureData[AlignedSequence], }, parameters={ 'graft_level': graft_level,