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(
version=q2_quality_filter.__version__,
website='https://github.com/wasade/q2-quality-filter',
package='q2_quality_filter',
citation_text=('Quality-filtering vastly improves diversity estimates '
'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
},
'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)
cells = line.rstrip('\n').split('\t')
if len(cells) != 2:
return False
return True
# 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.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
)
@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)
cells = line.rstrip('\n').split('\t')
if len(cells) != 2:
return False
return True
# 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
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')
citations=qiime2.plugin.Citations.load("citations.bib",
package="q2_winnowing"),
description=
"A Qiime2 plugin used to perform a feature selection on data in order to generate a winnowed community."
" Diversity and Environmental measures can then be performed on this output to measure the connectivity"
" 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]
website='https://github.com/biocore/q2-fragment-insertion',
short_description='Plugin for extending phylogenies.',
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 '
@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)
int(line.rstrip('\n'))
except (TypeError, ValueError):
return False
return True
# 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
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')
'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')
MetaStormsSearchResultsFmt, MetaStormsSearchResultsDirFmt,
MetaStormsMetaResultsFmt, MetaStormsMetaResultsDirFmt,
MetaStormsMNSResultsFmt, MetaStormsMNSResultsDirFmt)
plugin = qiime2.plugin.Plugin(
name='metastorms',
version='std 2.2.1',
website='https://github.com/qibebt-bioinfo/q2-metastorms',
package='q2_metastorms',
citations=qiime2.plugin.Citations.load('citations.bib',
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)
'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')
name='pepsirf',
version=q2_pepsirf.__version__,
website='https://github.com/LadnerLab/q2-pepsirf',
package='q2_pepsirf',
user_support_text=None,
short_description='Plugin for pepsirf.',
description=('This QIIME2 plugin wraps pep_sirf, '
'where each module in pep_sirf has at least '
'command. To get specific information about each '
'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],
from ._format import (EMPMultiplexedDirFmt, EMPSingleEndDirFmt,
EMPSingleEndCasavaDirFmt, EMPPairedEndDirFmt,
EMPPairedEndCasavaDirFmt)
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
from q2_abundance_filtering._format import AbundanceFilteringStatsFmt, AbundanceFilteringStatsDirFmt
plugin = qiime2.plugin.Plugin(
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)],
from q2_deblur._type import DeblurStats
from q2_deblur._format import DeblurStatsFmt, DeblurStatsDirFmt
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 "
'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')
'Shiffer A, McDonald D, Knight R, Caporaso JG, Kelley ST. '
'2016. Ghost-tree: creating hybrid-gene phylogenetic trees '
'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],
