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galaxy.datatypes package

Subpackages

• galaxy.datatypes.converters package
  • Submodules
  • galaxy.datatypes.converters.bed_to_gff_converter module
  • galaxy.datatypes.converters.bgzip module
  • galaxy.datatypes.converters.cram_to_bam module
  • galaxy.datatypes.converters.fasta_to_len module
  • galaxy.datatypes.converters.fasta_to_tabular_converter module
  • galaxy.datatypes.converters.fastq_to_fqtoc module
  • galaxy.datatypes.converters.fastqsolexa_to_fasta_converter module
  • galaxy.datatypes.converters.fastqsolexa_to_qual_converter module
  • galaxy.datatypes.converters.gff_to_bed_converter module
  • galaxy.datatypes.converters.gff_to_interval_index_converter module
  • galaxy.datatypes.converters.interval_to_bed_converter module
  • galaxy.datatypes.converters.interval_to_bedstrict_converter module
  • galaxy.datatypes.converters.interval_to_fli module
  • galaxy.datatypes.converters.interval_to_interval_index_converter module
  • galaxy.datatypes.converters.interval_to_tabix_converter module
  • galaxy.datatypes.converters.lped_to_fped_converter module
  • galaxy.datatypes.converters.lped_to_pbed_converter module
  • galaxy.datatypes.converters.maf_to_fasta_converter module
  • galaxy.datatypes.converters.maf_to_interval_converter module
  • galaxy.datatypes.converters.pbed_ldreduced_converter module
  • galaxy.datatypes.converters.pbed_to_lped_converter module
  • galaxy.datatypes.converters.picard_interval_list_to_bed6_converter module
  • galaxy.datatypes.converters.pileup_to_interval_index_converter module
  • galaxy.datatypes.converters.ref_to_seq_taxonomy_converter module
  • galaxy.datatypes.converters.tabular_csv module
  • galaxy.datatypes.converters.tabular_to_dbnsfp module
  • galaxy.datatypes.converters.vcf_to_interval_index_converter module
  • galaxy.datatypes.converters.vcf_to_vcf_bgzip module
  • galaxy.datatypes.converters.wiggle_to_simple_converter module
• galaxy.datatypes.dataproviders package
  • Submodules
  • galaxy.datatypes.dataproviders.base module
  • galaxy.datatypes.dataproviders.chunk module
  • galaxy.datatypes.dataproviders.column module
  • galaxy.datatypes.dataproviders.dataset module
  • galaxy.datatypes.dataproviders.decorators module
  • galaxy.datatypes.dataproviders.exceptions module
  • galaxy.datatypes.dataproviders.external module
  • galaxy.datatypes.dataproviders.hierarchy module
  • galaxy.datatypes.dataproviders.line module
• galaxy.datatypes.display_applications package
  • Submodules
  • galaxy.datatypes.display_applications.application module
  • galaxy.datatypes.display_applications.parameters module
  • galaxy.datatypes.display_applications.util module
• galaxy.datatypes.util package
  • Submodules
  • galaxy.datatypes.util.generic_util module
  • galaxy.datatypes.util.gff_util module
  • galaxy.datatypes.util.maf_utilities module

Submodules

galaxy.datatypes.annotation module

class galaxy.datatypes.annotation.SnapHmm(**kwd)

Bases: galaxy.datatypes.data.Text

file_ext = 'snaphmm'

edam_data = 'data_1364'

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

sniff_prefix(file_prefix)

SNAP model files start with zoeHMM

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1998d7f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

sniff(filename)

class galaxy.datatypes.annotation.Augustus(**kwd)

Bases: galaxy.datatypes.binary.CompressedArchive

Class describing an Augustus prediction model

file_ext = 'augustus'

edam_data = 'data_0950'

compressed = True

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

sniff(filename)

Augustus archives always contain the same files

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1998d320>}

galaxy.datatypes.anvio module

Datatypes for Anvi’o https://github.com/merenlab/anvio

class galaxy.datatypes.anvio.AnvioComposite(**kwd)

Bases: galaxy.datatypes.text.Html

Base class to use for Anvi’o composite datatypes. Generally consist of a sqlite database, plus optional additional files

file_ext = 'anvio_composite'

composite_type = 'auto_primary_file'

generate_primary_file(dataset=None)

This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately

get_mime()

Returns the mime type of the datatype

set_peek(dataset, is_multi_byte=False)

Set the peek and blurb text

display_peek(dataset)

Create HTML content, used for displaying peek.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f3cfa2d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.anvio.AnvioDB(*args, **kwd)

Bases: galaxy.datatypes.anvio.AnvioComposite

Class for AnvioDB database files.

file_ext = 'anvio_db'

composite_type = 'auto_primary_file'

allow_datatype_change = False

__init__(*args, **kwd)

set_meta(dataset, **kwd)

Set the anvio_basename based upon actual extra_files_path contents.

metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f3cfa2518>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f3cfa2d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.anvio.AnvioStructureDB(*args, **kwd)

Bases: galaxy.datatypes.anvio.AnvioDB

Class for Anvio Structure DB database files.

file_ext = 'anvio_structure_db'

composite_type = 'auto_primary_file'

allow_datatype_change = False

metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa366a0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f3cfa2d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.anvio.AnvioGenomesDB(*args, **kwd)

Bases: galaxy.datatypes.anvio.AnvioDB

Class for Anvio Genomes DB database files.

file_ext = 'anvio_genomes_db'

composite_type = 'auto_primary_file'

allow_datatype_change = False

metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36710>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f3cfa2d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.anvio.AnvioContigsDB(*args, **kwd)

Bases: galaxy.datatypes.anvio.AnvioDB

Class for Anvio Contigs DB database files.

file_ext = 'anvio_contigs_db'

composite_type = 'auto_primary_file'

allow_datatype_change = False

__init__(*args, **kwd)

metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1adcf2e8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f3cfa2d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.anvio.AnvioProfileDB(*args, **kwd)

Bases: galaxy.datatypes.anvio.AnvioDB

Class for Anvio Profile DB database files.

file_ext = 'anvio_profile_db'

composite_type = 'auto_primary_file'

allow_datatype_change = False

__init__(*args, **kwd)

metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1adcf438>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f3cfa2d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.anvio.AnvioPanDB(*args, **kwd)

Bases: galaxy.datatypes.anvio.AnvioDB

Class for Anvio Pan DB database files.

file_ext = 'anvio_pan_db'

composite_type = 'auto_primary_file'

allow_datatype_change = False

metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1adcf860>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f3cfa2d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.anvio.AnvioSamplesDB(*args, **kwd)

Bases: galaxy.datatypes.anvio.AnvioDB

Class for Anvio Samples DB database files.

file_ext = 'anvio_samples_db'

composite_type = 'auto_primary_file'

allow_datatype_change = False

metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1adcf8d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f3cfa2d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

galaxy.datatypes.assembly module

velvet datatypes James E Johnson - University of Minnesota for velvet assembler tool in galaxy

class galaxy.datatypes.assembly.Amos(**kwd)

Bases: galaxy.datatypes.data.Text

Class describing the AMOS assembly file

edam_data = 'data_0925'

edam_format = 'format_3582'

file_ext = 'afg'

sniff_prefix(file_prefix)

Determines whether the file is an amos assembly file format Example:

{CTG
iid:1
eid:1
seq:
CCTCTCCTGTAGAGTTCAACCGA-GCCGGTAGAGTTTTATCA
.
qlt:
DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD
.
{TLE
src:1027
off:0
clr:618,0
gap:
250 612
.
}
}

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f198ea908>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

sniff(filename)

class galaxy.datatypes.assembly.Sequences(**kwd)

Bases: galaxy.datatypes.sequence.Fasta

Class describing the Sequences file generated by velveth

edam_data = 'data_0925'

file_ext = 'sequences'

sniff_prefix(file_prefix)

Determines whether the file is a velveth produced fasta format The id line has 3 fields separated by tabs: sequence_name sequence_index category:

>SEQUENCE_0_length_35   1       1
GGATATAGGGCCAACCCAACTCAACGGCCTGTCTT
>SEQUENCE_1_length_35   2       1
CGACGAATGACAGGTCACGAATTTGGCGGGGATTA

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f198ea9b0>}

sniff(filename)

class galaxy.datatypes.assembly.Roadmaps(**kwd)

Bases: galaxy.datatypes.data.Text

Class describing the Sequences file generated by velveth

edam_format = 'format_2561'

file_ext = 'roadmaps'

sniff_prefix(file_prefix)

Determines whether the file is a velveth produced RoadMap::

142858 21 1 ROADMAP 1 ROADMAP 2 …

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f198eab00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

sniff(filename)

class galaxy.datatypes.assembly.Velvet(**kwd)

Bases: galaxy.datatypes.text.Html

composite_type = 'auto_primary_file'

allow_datatype_change = False

file_ext = 'velvet'

__init__(**kwd)

generate_primary_file(dataset=None)

regenerate_primary_file(dataset)

cannot do this until we are setting metadata

set_meta(dataset, **kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f198eac18>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'long_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19f67b38>, 'paired_end_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19f67198>, 'short2_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19f67668>}

galaxy.datatypes.binary module

Binary classes

class galaxy.datatypes.binary.Binary(**kwd)

Bases: galaxy.datatypes.data.Data

Binary data

edam_format = 'format_2333'

static register_sniffable_binary_format(data_type, ext, type_class)

Deprecated method.

static register_unsniffable_binary_ext(ext)

Deprecated method.

set_peek(dataset, is_multi_byte=False)

Set the peek and blurb text

get_mime()

Returns the mime type of the datatype

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>}

class galaxy.datatypes.binary.Ab1(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing an ab1 binary sequence file

file_ext = 'ab1'

edam_format = 'format_3000'

edam_data = 'data_0924'

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7c18>}

class galaxy.datatypes.binary.Idat(**kwd)

Bases: galaxy.datatypes.binary.Binary

Binary data in idat format

file_ext = 'idat'

edam_format = 'format_2058'

edam_data = 'data_2603'

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7c88>}

class galaxy.datatypes.binary.Cel(**kwd)

Bases: galaxy.datatypes.binary.Binary

Cel File format described at: http://media.affymetrix.com/support/developer/powertools/changelog/gcos-agcc/cel.html

file_ext = 'cel'

edam_format = 'format_1638'

edam_data = 'data_3110'

sniff(filename)

Try to guess if the file is a Cel file. >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname(‘affy_v_agcc.cel’) >>> Cel().sniff(fname) True >>> fname = get_test_fname(‘affy_v_3.cel’) >>> Cel().sniff(fname) True >>> fname = get_test_fname(‘affy_v_4.cel’) >>> Cel().sniff(fname) True >>> fname = get_test_fname(‘test.gal’) >>> Cel().sniff(fname) False

set_meta(dataset, **kwd)

Set metadata for Cel file.

set_peek(dataset, is_multi_byte=False)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7cf8>}

class galaxy.datatypes.binary.MashSketch(**kwd)

Bases: galaxy.datatypes.binary.Binary

Mash Sketch file. Sketches are used by the MinHash algorithm to allow fast distance estimations with low storage and memory requirements. To make a sketch, each k-mer in a sequence is hashed, which creates a pseudo-random identifier. By sorting these identifiers (hashes), a small subset from the top of the sorted list can represent the entire sequence (these are min-hashes). The more similar another sequence is, the more min-hashes it is likely to share.

file_ext = 'msh'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7f60>}

class galaxy.datatypes.binary.CompressedArchive(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing an compressed binary file This class can be sublass’ed to implement archive filetypes that will not be unpacked by upload.py.

file_ext = 'compressed_archive'

compressed = True

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7fd0>}

class galaxy.datatypes.binary.DynamicCompressedArchive(**kwd)

Bases: galaxy.datatypes.binary.CompressedArchive

matches_any(target_datatypes)

Treat two aspects of compressed datatypes separately.

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d080>}

class galaxy.datatypes.binary.GzDynamicCompressedArchive(**kwd)

Bases: galaxy.datatypes.binary.DynamicCompressedArchive

compressed_format = 'gzip'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d0f0>}

class galaxy.datatypes.binary.Bz2DynamicCompressedArchive(**kwd)

Bases: galaxy.datatypes.binary.DynamicCompressedArchive

compressed_format = 'bz2'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d160>}

class galaxy.datatypes.binary.CompressedZipArchive(**kwd)

Bases: galaxy.datatypes.binary.CompressedArchive

Class describing an compressed binary file This class can be sublass’ed to implement archive filetypes that will not be unpacked by upload.py.

file_ext = 'zip'

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d1d0>}

class galaxy.datatypes.binary.GenericAsn1Binary(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class for generic ASN.1 binary format

file_ext = 'asn1-binary'

edam_format = 'format_1966'

edam_data = 'data_0849'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d240>}

class galaxy.datatypes.binary.BamNative(**kwd)

Bases: galaxy.datatypes.binary.CompressedArchive

Class describing a BAM binary file that is not necessarily sorted

edam_format = 'format_2572'

edam_data = 'data_0863'

file_ext = 'unsorted.bam'

sort_flag = None

static merge(split_files, output_file)

Merges BAM files

Parameters:

• split_files – List of bam file paths to merge
• output_file – Write merged bam file to this location

init_meta(dataset, copy_from=None)

sniff(filename)

classmethod is_bam(filename)

set_meta(dataset, overwrite=True, **kwd)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

to_archive(trans, dataset, name='')

groom_dataset_content(file_name)

Ensures that the BAM file contents are coordinate-sorted. This function is called on an output dataset after the content is initially generated.

get_chunk(trans, dataset, offset=0, ck_size=None)

display_data(trans, dataset, preview=False, filename=None, to_ext=None, offset=None, ck_size=None, **kwd)

validate(dataset, **kwd)

metadata_spec = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d550>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d320>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d6a0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d630>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d5c0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7fd0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d400>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d4e0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d470>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d390>}

class galaxy.datatypes.binary.Bam(**kwd)

Bases: galaxy.datatypes.binary.BamNative

Class describing a BAM binary file

edam_format = 'format_2572'

edam_data = 'data_0863'

file_ext = 'bam'

track_type = 'ReadTrack'

data_sources = {'data': 'bai', 'index': 'bigwig'}

get_index_flag(file_name)

Return pysam flag for bai index (default) or csi index (contig size > (2**29 - 1) )

dataset_content_needs_grooming(file_name)

Check if file_name is a coordinate-sorted BAM file

set_meta(dataset, overwrite=True, **kwd)

sniff(file_name)

line_dataprovider(dataset, **settings)

regex_line_dataprovider(dataset, **settings)

column_dataprovider(dataset, **settings)

dict_dataprovider(dataset, **settings)

header_dataprovider(dataset, **settings)

id_seq_qual_dataprovider(dataset, **settings)

genomic_region_dataprovider(dataset, **settings)

genomic_region_dict_dataprovider(dataset, **settings)

samtools_dataprovider(dataset, **settings)

Generic samtools interface - all options available through settings.

dataproviders = {'base': <function Data.base_dataprovider at 0x7f2f4530bd08>, 'chunk': <function Data.chunk_dataprovider at 0x7f2f4530bea0>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f2f453080d0>, 'column': <function Bam.column_dataprovider at 0x7f2f42110ea0>, 'dict': <function Bam.dict_dataprovider at 0x7f2f421130d0>, 'genomic-region': <function Bam.genomic_region_dataprovider at 0x7f2f42113598>, 'genomic-region-dict': <function Bam.genomic_region_dict_dataprovider at 0x7f2f42113730>, 'header': <function Bam.header_dataprovider at 0x7f2f42113268>, 'id-seq-qual': <function Bam.id_seq_qual_dataprovider at 0x7f2f42113400>, 'line': <function Bam.line_dataprovider at 0x7f2f42110b70>, 'regex-line': <function Bam.regex_line_dataprovider at 0x7f2f42110d08>, 'samtools': <function Bam.samtools_dataprovider at 0x7f2f421138c8>}

metadata_spec = {'bam_csi_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210dac8>, 'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d550>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210da58>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d320>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d6a0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d630>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d5c0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7fd0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d400>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d4e0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d470>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d390>}

class galaxy.datatypes.binary.ProBam(**kwd)

Bases: galaxy.datatypes.binary.Bam

Class describing a BAM binary file - extended for proteomics data

edam_format = 'format_3826'

edam_data = 'data_0863'

file_ext = 'probam'

metadata_spec = {'bam_csi_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210dbe0>, 'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d550>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210db70>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d320>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d6a0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d630>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d5c0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7fd0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d400>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d4e0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d470>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210d390>}

class galaxy.datatypes.binary.BamInputSorted(**kwd)

Bases: galaxy.datatypes.binary.BamNative

sort_flag = '-n'

file_ext = 'qname_input_sorted.bam'

A class for BAM files that can formally be unsorted or queryname sorted. Alignments are either ordered based on the order with which the queries appear when producing the alignment, or ordered by their queryname. This notaby keeps alignments produced by paired end sequencing adjacent.

sniff(file_name)

dataset_content_needs_grooming(file_name)

Groom if the file is coordinate sorted

metadata_spec = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210deb8>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210dc88>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116048>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210df98>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210df28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7fd0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210dd68>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210de48>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210ddd8>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4210dcf8>}

class galaxy.datatypes.binary.BamQuerynameSorted(**kwd)

Bases: galaxy.datatypes.binary.BamInputSorted

A class for queryname sorted BAM files.

sort_flag = '-n'

file_ext = 'qname_sorted.bam'

sniff(file_name)

dataset_content_needs_grooming(file_name)

Check if file_name is a queryname-sorted BAM file

metadata_spec = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116320>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f421160f0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116470>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116400>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116390>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7fd0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f421161d0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f421162b0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116240>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116160>}

class galaxy.datatypes.binary.CRAM(**kwd)

Bases: galaxy.datatypes.binary.Binary

file_ext = 'cram'

edam_format = 'format_3462'

edam_data = 'format_0863'

set_meta(dataset, overwrite=True, **kwd)

get_cram_version(filename)

set_index_file(dataset, index_file)

set_peek(dataset, is_multi_byte=False)

sniff(filename)

metadata_spec = {'cram_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116588>, 'cram_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116518>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>}

class galaxy.datatypes.binary.BaseBcf(**kwd)

Bases: galaxy.datatypes.binary.CompressedArchive

edam_format = 'format_3020'

edam_data = 'data_3498'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116630>}

class galaxy.datatypes.binary.Bcf(**kwd)

Bases: galaxy.datatypes.binary.BaseBcf

Class describing a (BGZF-compressed) BCF file

file_ext = 'bcf'

sniff(filename)

set_meta(dataset, overwrite=True, **kwd)

Creates the index for the BCF file.

metadata_spec = {'bcf_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f421166d8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116630>}

class galaxy.datatypes.binary.BcfUncompressed(**kwd)

Bases: galaxy.datatypes.binary.BaseBcf

Class describing an uncompressed BCF file

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('1.bcf_uncompressed')
>>> BcfUncompressed().sniff(fname)
True
>>> fname = get_test_fname('1.bcf')
>>> BcfUncompressed().sniff(fname)
False

file_ext = 'bcf_uncompressed'

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116780>}

class galaxy.datatypes.binary.H5(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing an HDF5 file

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.mz5')
>>> H5().sniff(fname)
True
>>> fname = get_test_fname('interval.interval')
>>> H5().sniff(fname)
False

file_ext = 'h5'

edam_format = 'format_3590'

__init__(**kwd)

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116828>}

class galaxy.datatypes.binary.Loom(**kwd)

Bases: galaxy.datatypes.binary.H5

Class describing a Loom file: http://loompy.org/

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.loom')
>>> Loom().sniff(fname)
True
>>> fname = get_test_fname('test.mz5')
>>> Loom().sniff(fname)
False

file_ext = 'loom'

edam_format = 'format_3590'

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

set_meta(dataset, overwrite=True, **kwd)

metadata_spec = {'col_attrs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116e48>, 'col_attrs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116eb8>, 'col_graphs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116f28>, 'col_graphs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116f98>, 'creation_date': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116b70>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116828>, 'description': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f421169b0>, 'doi': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116a90>, 'layers_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116c50>, 'layers_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116cc0>, 'loom_spec_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116b00>, 'row_attrs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116d68>, 'row_attrs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116dd8>, 'row_graphs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f048>, 'row_graphs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f0b8>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116be0>, 'title': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116940>, 'url': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116a20>}

class galaxy.datatypes.binary.Anndata(**kwd)

Bases: galaxy.datatypes.binary.H5

Class describing an HDF5 anndata files: http://anndata.rtfd.io >>> from galaxy.datatypes.sniff import get_test_fname >>> Anndata().sniff(get_test_fname(‘pbmc3k_tiny.h5ad’)) True >>> Anndata().sniff(get_test_fname(‘test.mz5’)) False

file_ext = 'h5ad'

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f160>}

class galaxy.datatypes.binary.GmxBinary(**kwd)

Bases: galaxy.datatypes.binary.Binary

Base class for GROMACS binary files - xtc, trr, cpt

magic_number = None

file_ext = ''

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f208>}

class galaxy.datatypes.binary.Trr(**kwd)

Bases: galaxy.datatypes.binary.GmxBinary

Class describing an trr file from the GROMACS suite

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('md.trr')
>>> Trr().sniff(fname)
True
>>> fname = get_test_fname('interval.interval')
>>> Trr().sniff(fname)
False

file_ext = 'trr'

magic_number = 1993

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f2b0>}

class galaxy.datatypes.binary.Cpt(**kwd)

Bases: galaxy.datatypes.binary.GmxBinary

Class describing a checkpoint (.cpt) file from the GROMACS suite

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('md.cpt')
>>> Cpt().sniff(fname)
True
>>> fname = get_test_fname('md.trr')
>>> Cpt().sniff(fname)
False

file_ext = 'cpt'

magic_number = 171817

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f358>}

class galaxy.datatypes.binary.Xtc(**kwd)

Bases: galaxy.datatypes.binary.GmxBinary

Class describing an xtc file from the GROMACS suite

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('md.xtc')
>>> Xtc().sniff(fname)
True
>>> fname = get_test_fname('md.trr')
>>> Xtc().sniff(fname)
False

file_ext = 'xtc'

magic_number = 1995

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f400>}

class galaxy.datatypes.binary.Edr(**kwd)

Bases: galaxy.datatypes.binary.GmxBinary

Class describing an edr file from the GROMACS suite

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('md.edr')
>>> Edr().sniff(fname)
True
>>> fname = get_test_fname('md.trr')
>>> Edr().sniff(fname)
False

file_ext = 'edr'

magic_number = -55555

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f4a8>}

class galaxy.datatypes.binary.Biom2(**kwd)

Bases: galaxy.datatypes.binary.H5

Class describing a biom2 file (http://biom-format.org/documentation/biom_format.html)

file_ext = 'biom2'

edam_format = 'format_3746'

sniff(filename)

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('biom2_sparse_otu_table_hdf5.biom2')
>>> Biom2().sniff(fname)
True
>>> fname = get_test_fname('test.mz5')
>>> Biom2().sniff(fname)
False
>>> fname = get_test_fname('wiggle.wig')
>>> Biom2().sniff(fname)
False

set_meta(dataset, overwrite=True, **kwd)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'creation_date': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f860>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f42116828>, 'format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f710>, 'format_url': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f630>, 'format_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f6a0>, 'generated_by': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f7f0>, 'id': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f5c0>, 'nnz': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f8d0>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f940>, 'type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211f780>}

class galaxy.datatypes.binary.Cool(**kwd)

Bases: galaxy.datatypes.binary.H5

Class describing the cool format (https://github.com/mirnylab/cooler)

file_ext = 'cool'

sniff(filename)

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('matrix.cool')
>>> Cool().sniff(fname)
True
>>> fname = get_test_fname('test.mz5')
>>> Cool().sniff(fname)
False
>>> fname = get_test_fname('wiggle.wig')
>>> Cool().sniff(fname)
False
>>> fname = get_test_fname('biom2_sparse_otu_table_hdf5.biom2')
>>> Cool().sniff(fname)
False

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211fa20>}

class galaxy.datatypes.binary.MCool(**kwd)

Bases: galaxy.datatypes.binary.H5

Class describing the multi-resolution cool format (https://github.com/mirnylab/cooler)

file_ext = 'mcool'

sniff(filename)

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('matrix.mcool')
>>> MCool().sniff(fname)
True
>>> fname = get_test_fname('matrix.cool')
>>> MCool().sniff(fname)
False
>>> fname = get_test_fname('test.mz5')
>>> MCool().sniff(fname)
False
>>> fname = get_test_fname('wiggle.wig')
>>> MCool().sniff(fname)
False
>>> fname = get_test_fname('biom2_sparse_otu_table_hdf5.biom2')
>>> MCool().sniff(fname)
False

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211fb00>}

class galaxy.datatypes.binary.Scf(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing an scf binary sequence file

edam_format = 'format_1632'

edam_data = 'data_0924'

file_ext = 'scf'

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211fba8>}

class galaxy.datatypes.binary.Sff(**kwd)

Bases: galaxy.datatypes.binary.Binary

Standard Flowgram Format (SFF)

edam_format = 'format_3284'

edam_data = 'data_0924'

file_ext = 'sff'

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211fc50>}

class galaxy.datatypes.binary.BigWig(**kwd)

Bases: galaxy.datatypes.binary.Binary

Accessing binary BigWig files from UCSC. The supplemental info in the paper has the binary details: http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btq351v1

edam_format = 'format_3006'

edam_data = 'data_3002'

file_ext = 'bigwig'

track_type = 'LineTrack'

data_sources = {'data_standalone': 'bigwig'}

__init__(**kwd)

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211fcf8>}

class galaxy.datatypes.binary.BigBed(**kwd)

Bases: galaxy.datatypes.binary.BigWig

BigBed support from UCSC.

edam_format = 'format_3004'

edam_data = 'data_3002'

file_ext = 'bigbed'

data_sources = {'data_standalone': 'bigbed'}

__init__(**kwd)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211fda0>}

class galaxy.datatypes.binary.TwoBit(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing a TwoBit format nucleotide file

edam_format = 'format_3009'

edam_data = 'data_0848'

file_ext = 'twobit'

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4211feb8>}

class galaxy.datatypes.binary.SQlite(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing a Sqlite database

file_ext = 'sqlite'

edam_format = 'format_3621'

init_meta(dataset, copy_from=None)

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

sniff_table_names(filename, table_names)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

sqlite_dataprovider(dataset, **settings)

sqlite_datatableprovider(dataset, **settings)

sqlite_datadictprovider(dataset, **settings)

dataproviders = {'base': <function Data.base_dataprovider at 0x7f2f4530bd08>, 'chunk': <function Data.chunk_dataprovider at 0x7f2f4530bea0>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f2f453080d0>, 'sqlite': <function SQlite.sqlite_dataprovider at 0x7f2f42069598>, 'sqlite-dict': <function SQlite.sqlite_datadictprovider at 0x7f2f420698c8>, 'sqlite-table': <function SQlite.sqlite_datatableprovider at 0x7f2f42069730>}

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a160>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f44869898>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a0f0>}

class galaxy.datatypes.binary.GeminiSQLite(**kwd)

Bases: galaxy.datatypes.binary.SQlite

Class describing a Gemini Sqlite database

file_ext = 'gemini.sqlite'

edam_format = 'format_3622'

edam_data = 'data_3498'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'gemini_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a2b0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a160>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f44869898>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a0f0>}

class galaxy.datatypes.binary.ChiraSQLite(**kwd)

Bases: galaxy.datatypes.binary.SQlite

Class describing a ChiRAViz Sqlite database

file_ext = 'chira.sqlite'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a438>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a4a8>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a3c8>}

class galaxy.datatypes.binary.CuffDiffSQlite(**kwd)

Bases: galaxy.datatypes.binary.SQlite

Class describing a CuffDiff SQLite database

file_ext = 'cuffdiff.sqlite'

edam_format = 'format_3621'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'cuffdiff_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a5c0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'genes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a630>, 'samples': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a6a0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a160>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f44869898>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a0f0>}

class galaxy.datatypes.binary.MzSQlite(**kwd)

Bases: galaxy.datatypes.binary.SQlite

Class describing a Proteomics Sqlite database

file_ext = 'mz.sqlite'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a828>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a898>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a7b8>}

class galaxy.datatypes.binary.PQP(**kwd)

Bases: galaxy.datatypes.binary.SQlite

Class describing a Peptide query parameters file

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.pqp')
>>> PQP().sniff(fname)
True
>>> fname = get_test_fname('test.osw')
>>> PQP().sniff(fname)
False

file_ext = 'pqp'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

table definition according to https://github.com/grosenberger/OpenMS/blob/develop/src/openms/source/ANALYSIS/OPENSWATH/TransitionPQPFile.cpp#L264 for now VERSION GENE PEPTIDE_GENE_MAPPING are excluded, since there is test data wo these tables, see also here https://github.com/OpenMS/OpenMS/issues/4365

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206aa20>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206aa90>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a9b0>}

class galaxy.datatypes.binary.OSW(**kwd)

Bases: galaxy.datatypes.binary.SQlite

Class describing OpenSwath output

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.osw')
>>> OSW().sniff(fname)
True
>>> fname = get_test_fname('test.sqmass')
>>> OSW().sniff(fname)
False

file_ext = 'osw'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206ac18>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206ac88>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206aba8>}

class galaxy.datatypes.binary.SQmass(**kwd)

Bases: galaxy.datatypes.binary.SQlite

Class describing a Sqmass database

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.sqmass')
>>> SQmass().sniff(fname)
True
>>> fname = get_test_fname('test.pqp')
>>> SQmass().sniff(fname)
False

file_ext = 'sqmass'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206ae10>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206ae80>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206ada0>}

class galaxy.datatypes.binary.BlibSQlite(**kwd)

Bases: galaxy.datatypes.binary.SQlite

Class describing a Proteomics Spectral Library Sqlite database

file_ext = 'blib'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

metadata_spec = {'blib_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206af98>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a160>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f44869898>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a0f0>}

class galaxy.datatypes.binary.DlibSQlite(**kwd)

Bases: galaxy.datatypes.binary.SQlite

Class describing a Proteomics Spectral Library Sqlite database DLIBs only have the “entries”, “metadata”, and “peptidetoprotein” tables populated. ELIBs have the rest of the tables populated too, such as “peptidequants” or “peptidescores”.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.dlib')
>>> DlibSQlite().sniff(fname)
True
>>> fname = get_test_fname('interval.interval')
>>> DlibSQlite().sniff(fname)
False

file_ext = 'dlib'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'dlib_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f0f0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a160>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f44869898>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a0f0>}

class galaxy.datatypes.binary.ElibSQlite(**kwd)

Bases: galaxy.datatypes.binary.SQlite

Class describing a Proteomics Chromatagram Library Sqlite database DLIBs only have the “entries”, “metadata”, and “peptidetoprotein” tables populated. ELIBs have the rest of the tables populated too, such as “peptidequants” or “peptidescores”.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.elib')
>>> ElibSQlite().sniff(fname)
True
>>> fname = get_test_fname('test.dlib')
>>> ElibSQlite().sniff(fname)
False

file_ext = 'elib'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a160>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f44869898>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a0f0>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f208>}

class galaxy.datatypes.binary.IdpDB(**kwd)

Bases: galaxy.datatypes.binary.SQlite

Class describing an IDPicker 3 idpDB (sqlite) database

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.idpdb')
>>> IdpDB().sniff(fname)
True
>>> fname = get_test_fname('interval.interval')
>>> IdpDB().sniff(fname)
False

file_ext = 'idpdb'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f390>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f400>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f320>}

class galaxy.datatypes.binary.GAFASQLite(**kwd)

Bases: galaxy.datatypes.binary.SQlite

Class describing a GAFA SQLite database

file_ext = 'gafa.sqlite'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'gafa_schema_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f518>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a160>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f44869898>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206a0f0>}

class galaxy.datatypes.binary.Xlsx(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class for Excel 2007 (xlsx) files

file_ext = 'xlsx'

compressed = True

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f5c0>}

class galaxy.datatypes.binary.ExcelXls(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing an Excel (xls) file

file_ext = 'excel.xls'

edam_format = 'format_3468'

sniff(filename)

get_mime()

Returns the mime type of the datatype

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f668>}

class galaxy.datatypes.binary.Sra(**kwd)

Bases: galaxy.datatypes.binary.Binary

Sequence Read Archive (SRA) datatype originally from mdshw5/sra-tools-galaxy

file_ext = 'sra'

sniff(filename)

The first 8 bytes of any NCBI sra file is ‘NCBI.sra’, and the file is binary. For details about the format, see http://www.ncbi.nlm.nih.gov/books/n/helpsra/SRA_Overview_BK/#SRA_Overview_BK.4_SRA_Data_Structure

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f710>}

class galaxy.datatypes.binary.RData(**kwd)

Bases: galaxy.datatypes.binary.Binary

Generic R Data file datatype implementation

file_ext = 'rdata'

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f7b8>}

class galaxy.datatypes.binary.OxliBinary(**kwd)

Bases: galaxy.datatypes.binary.Binary

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f898>}

class galaxy.datatypes.binary.OxliCountGraph(**kwd)

Bases: galaxy.datatypes.binary.OxliBinary

OxliCountGraph starts with “OXLI” + one byte version number + 8-bit binary ‘1’ Test file generated via:

load-into-counting.py --n_tables 1 --max-tablesize 1 \
    oxli_countgraph.oxlicg khmer/tests/test-data/100-reads.fq.bz2

using khmer 2.0

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('sequence.csfasta')
>>> OxliCountGraph().sniff(fname)
False
>>> fname = get_test_fname("oxli_countgraph.oxlicg")
>>> OxliCountGraph().sniff(fname)
True

file_ext = 'oxlicg'

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f940>}

class galaxy.datatypes.binary.OxliNodeGraph(**kwd)

Bases: galaxy.datatypes.binary.OxliBinary

OxliNodeGraph starts with “OXLI” + one byte version number + 8-bit binary ‘2’ Test file generated via:

load-graph.py --n_tables 1 --max-tablesize 1 oxli_nodegraph.oxling \
    khmer/tests/test-data/100-reads.fq.bz2

using khmer 2.0

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('sequence.csfasta')
>>> OxliNodeGraph().sniff(fname)
False
>>> fname = get_test_fname("oxli_nodegraph.oxling")
>>> OxliNodeGraph().sniff(fname)
True

file_ext = 'oxling'

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206f9e8>}

class galaxy.datatypes.binary.OxliTagSet(**kwd)

Bases: galaxy.datatypes.binary.OxliBinary

OxliTagSet starts with “OXLI” + one byte version number + 8-bit binary ‘3’ Test file generated via:

load-graph.py --n_tables 1 --max-tablesize 1 oxli_nodegraph.oxling \
    khmer/tests/test-data/100-reads.fq.bz2;
mv oxli_nodegraph.oxling.tagset oxli_tagset.oxlits

using khmer 2.0

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('sequence.csfasta')
>>> OxliTagSet().sniff(fname)
False
>>> fname = get_test_fname("oxli_tagset.oxlits")
>>> OxliTagSet().sniff(fname)
True

file_ext = 'oxlits'

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206fa90>}

class galaxy.datatypes.binary.OxliStopTags(**kwd)

Bases: galaxy.datatypes.binary.OxliBinary

OxliStopTags starts with “OXLI” + one byte version number + 8-bit binary ‘4’ Test file adapted from khmer 2.0’s “khmer/tests/test-data/goodversion-k32.stoptags”

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('sequence.csfasta')
>>> OxliStopTags().sniff(fname)
False
>>> fname = get_test_fname("oxli_stoptags.oxlist")
>>> OxliStopTags().sniff(fname)
True

file_ext = 'oxlist'

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206fb38>}

class galaxy.datatypes.binary.OxliSubset(**kwd)

Bases: galaxy.datatypes.binary.OxliBinary

OxliSubset starts with “OXLI” + one byte version number + 8-bit binary ‘5’ Test file generated via:

load-graph.py -k 20 example tests/test-data/random-20-a.fa;
partition-graph.py example;
mv example.subset.0.pmap oxli_subset.oxliss

using khmer 2.0

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('sequence.csfasta')
>>> OxliSubset().sniff(fname)
False
>>> fname = get_test_fname("oxli_subset.oxliss")
>>> OxliSubset().sniff(fname)
True

file_ext = 'oxliss'

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206fbe0>}

class galaxy.datatypes.binary.OxliGraphLabels(**kwd)

Bases: galaxy.datatypes.binary.OxliBinary

OxliGraphLabels starts with “OXLI” + one byte version number + 8-bit binary ‘6’ Test file generated via:

python -c "from khmer import GraphLabels; \
    gl = GraphLabels(20, 1e7, 4); \
    gl.consume_fasta_and_tag_with_labels('tests/test-data/test-labels.fa'); \
    gl.save_labels_and_tags('oxli_graphlabels.oxligl')"

using khmer 2.0

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('sequence.csfasta')
>>> OxliGraphLabels().sniff(fname)
False
>>> fname = get_test_fname("oxli_graphlabels.oxligl")
>>> OxliGraphLabels().sniff(fname)
True

file_ext = 'oxligl'

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206fc88>}

class galaxy.datatypes.binary.PostgresqlArchive(**kwd)

Bases: galaxy.datatypes.binary.CompressedArchive

Class describing a Postgresql database packed into a tar archive

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('postgresql_fake.tar.bz2')
>>> PostgresqlArchive().sniff(fname)
True
>>> fname = get_test_fname('test.fast5.tar')
>>> PostgresqlArchive().sniff(fname)
False

file_ext = 'postgresql'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7fd0>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206fd68>}

class galaxy.datatypes.binary.Fast5Archive(**kwd)

Bases: galaxy.datatypes.binary.CompressedArchive

Class describing a FAST5 archive

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.fast5.tar')
>>> Fast5Archive().sniff(fname)
True

file_ext = 'fast5.tar'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7fd0>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206fe48>}

class galaxy.datatypes.binary.Fast5ArchiveGz(**kwd)

Bases: galaxy.datatypes.binary.Fast5Archive

Class describing a gzip-compressed FAST5 archive

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.fast5.tar.gz')
>>> Fast5ArchiveGz().sniff(fname)
True
>>> fname = get_test_fname('test.fast5.tar.bz2')
>>> Fast5ArchiveGz().sniff(fname)
False
>>> fname = get_test_fname('test.fast5.tar')
>>> Fast5ArchiveGz().sniff(fname)
False

file_ext = 'fast5.tar.gz'

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7fd0>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206fef0>}

class galaxy.datatypes.binary.Fast5ArchiveBz2(**kwd)

Bases: galaxy.datatypes.binary.Fast5Archive

Class describing a bzip2-compressed FAST5 archive

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.fast5.tar.bz2')
>>> Fast5ArchiveBz2().sniff(fname)
True
>>> fname = get_test_fname('test.fast5.tar.gz')
>>> Fast5ArchiveBz2().sniff(fname)
False
>>> fname = get_test_fname('test.fast5.tar')
>>> Fast5ArchiveBz2().sniff(fname)
False

file_ext = 'fast5.tar.bz2'

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7fd0>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4206ff98>}

class galaxy.datatypes.binary.SearchGuiArchive(**kwd)

Bases: galaxy.datatypes.binary.CompressedArchive

Class describing a SearchGUI archive

file_ext = 'searchgui_archive'

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7fd0>, 'searchgui_major_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c128>, 'searchgui_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c0b8>}

class galaxy.datatypes.binary.NetCDF(**kwd)

Bases: galaxy.datatypes.binary.Binary

Binary data in netCDF format

file_ext = 'netcdf'

edam_format = 'format_3650'

edam_data = 'data_0943'

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c1d0>}

class galaxy.datatypes.binary.Dcd(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing a dcd file from the CHARMM molecular simulation program

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test_glucose_vacuum.dcd')
>>> Dcd().sniff(fname)
True
>>> fname = get_test_fname('interval.interval')
>>> Dcd().sniff(fname)
False

file_ext = 'dcd'

edam_data = 'data_3842'

__init__(**kwd)

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c278>}

class galaxy.datatypes.binary.Vel(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing a velocity file from the CHARMM molecular simulation program

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test_charmm.vel')
>>> Vel().sniff(fname)
True
>>> fname = get_test_fname('interval.interval')
>>> Vel().sniff(fname)
False

file_ext = 'vel'

__init__(**kwd)

sniff(filename)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c320>}

class galaxy.datatypes.binary.DAA(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing an DAA (diamond alignment archive) file >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname(‘diamond.daa’) >>> DAA().sniff(fname) True >>> fname = get_test_fname(‘interval.interval’) >>> DAA().sniff(fname) False

file_ext = 'daa'

__init__(**kwd)

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c3c8>}

class galaxy.datatypes.binary.RMA6(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing an RMA6 (MEGAN6 read-match archive) file >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname(‘diamond.rma6’) >>> RMA6().sniff(fname) True >>> fname = get_test_fname(‘interval.interval’) >>> RMA6().sniff(fname) False

file_ext = 'rma6'

__init__(**kwd)

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c470>}

class galaxy.datatypes.binary.DMND(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing an DMND file >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname(‘diamond_db.dmnd’) >>> DMND().sniff(fname) True >>> fname = get_test_fname(‘interval.interval’) >>> DMND().sniff(fname) False

file_ext = 'dmnd'

__init__(**kwd)

sniff(filename)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c518>}

class galaxy.datatypes.binary.ICM(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class describing an ICM (interpolated context model) file, used by Glimmer

file_ext = 'icm'

edam_data = 'data_0950'

set_peek(dataset, is_multi_byte=False)

sniff(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c5c0>}

class galaxy.datatypes.binary.BafTar(**kwd)

Bases: galaxy.datatypes.binary.CompressedArchive

Base class for common behavior of tar files of directory-based raw file formats >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname(‘brukerbaf.d.tar’) >>> BafTar().sniff(fname) True >>> fname = get_test_fname(‘test.fast5.tar’) >>> BafTar().sniff(fname) False

edam_data = 'data_2536'

edam_format = 'format_3712'

file_ext = 'brukerbaf.d.tar'

get_signature_file()

sniff(filename)

get_type()

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c668>}

class galaxy.datatypes.binary.YepTar(**kwd)

Bases: galaxy.datatypes.binary.BafTar

A tar’d up .d directory containing Agilent/Bruker YEP format data

file_ext = 'agilentbrukeryep.d.tar'

get_signature_file()

get_type()

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c710>}

class galaxy.datatypes.binary.TdfTar(**kwd)

Bases: galaxy.datatypes.binary.BafTar

A tar’d up .d directory containing Bruker TDF format data

file_ext = 'brukertdf.d.tar'

get_signature_file()

get_type()

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c7b8>}

class galaxy.datatypes.binary.MassHunterTar(**kwd)

Bases: galaxy.datatypes.binary.BafTar

A tar’d up .d directory containing Agilent MassHunter format data

file_ext = 'agilentmasshunter.d.tar'

get_signature_file()

get_type()

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c860>}

class galaxy.datatypes.binary.MassLynxTar(**kwd)

Bases: galaxy.datatypes.binary.BafTar

A tar’d up .d directory containing Waters MassLynx format data

file_ext = 'watersmasslynx.raw.tar'

get_signature_file()

get_type()

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c908>}

class galaxy.datatypes.binary.WiffTar(**kwd)

Bases: galaxy.datatypes.binary.BafTar

A tar’d up .wiff/.scan pair containing Sciex WIFF format data >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname(‘some.wiff.tar’) >>> WiffTar().sniff(fname) True >>> fname = get_test_fname(‘brukerbaf.d.tar’) >>> WiffTar().sniff(fname) False >>> fname = get_test_fname(‘test.fast5.tar’) >>> WiffTar().sniff(fname) False

file_ext = 'wiff.tar'

sniff(filename)

get_type()

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4207c9b0>}

galaxy.datatypes.blast module

NCBI BLAST datatypes.

Covers the blastxml format and the BLAST databases.

class galaxy.datatypes.blast.BlastXml(**kwd)

Bases: galaxy.datatypes.xml.GenericXml

NCBI Blast XML Output data

file_ext = 'blastxml'

edam_format = 'format_3331'

edam_data = 'data_0857'

set_peek(dataset, is_multi_byte=False)

Set the peek and blurb text

sniff_prefix(file_prefix)

Determines whether the file is blastxml

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('megablast_xml_parser_test1.blastxml')
>>> BlastXml().sniff(fname)
True
>>> fname = get_test_fname('tblastn_four_human_vs_rhodopsin.blastxml')
>>> BlastXml().sniff(fname)
True
>>> fname = get_test_fname('interval.interval')
>>> BlastXml().sniff(fname)
False

static merge(split_files, output_file)

Merging multiple XML files is non-trivial and must be done in subclasses.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1b31d128>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

sniff(filename)

class galaxy.datatypes.blast.BlastNucDb(**kwd)

Bases: galaxy.datatypes.blast._BlastDb, galaxy.datatypes.data.Data

Class for nucleotide BLAST database files.

file_ext = 'blastdbn'

allow_datatype_change = False

composite_type = 'basic'

__init__(**kwd)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19a44da0>}

class galaxy.datatypes.blast.BlastProtDb(**kwd)

Bases: galaxy.datatypes.blast._BlastDb, galaxy.datatypes.data.Data

Class for protein BLAST database files.

file_ext = 'blastdbp'

allow_datatype_change = False

composite_type = 'basic'

__init__(**kwd)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19a440b8>}

class galaxy.datatypes.blast.BlastDomainDb(**kwd)

Bases: galaxy.datatypes.blast._BlastDb, galaxy.datatypes.data.Data

Class for domain BLAST database files.

file_ext = 'blastdbd'

allow_datatype_change = False

composite_type = 'basic'

__init__(**kwd)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a6b7358>}

galaxy.datatypes.checkers module

Module proxies galaxy.util.checkers for backward compatibility.

External datatypes may make use of these functions.

galaxy.datatypes.checkers.check_binary(name, file_path=True)

galaxy.datatypes.checkers.check_bz2(file_path, check_content=True)

galaxy.datatypes.checkers.check_gzip(file_path, check_content=True)

galaxy.datatypes.checkers.check_html(name, file_path=True)

Returns True if the file/string contains HTML code.

galaxy.datatypes.checkers.check_image(file_path)

Simple wrapper around image_type to yield a True/False verdict

galaxy.datatypes.checkers.check_zip(file_path, check_content=True, files=1)

galaxy.datatypes.checkers.is_gzip(file_path)

galaxy.datatypes.checkers.is_bz2(file_path)

galaxy.datatypes.chrominfo module

class galaxy.datatypes.chrominfo.ChromInfo(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'len'

metadata_spec = {'chrom': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1988b160>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e0b8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'length': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a3fe1d0>}

galaxy.datatypes.constructive_solid_geometry module

Constructive Solid Geometry file formats.

class galaxy.datatypes.constructive_solid_geometry.Ply(**kwd)

Bases: object

The PLY format describes an object as a collection of vertices, faces and other elements, along with properties such as color and normal direction that can be attached to these elements. A PLY file contains the description of exactly one object.

subtype = ''

__init__(**kwd)

sniff_prefix(file_prefix)

The structure of a typical PLY file: Header, Vertex List, Face List, (lists of other elements)

set_meta(dataset, **kwd)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

sniff(filename)

class galaxy.datatypes.constructive_solid_geometry.PlyAscii(**kwd)

Bases: galaxy.datatypes.constructive_solid_geometry.Ply, galaxy.datatypes.data.Text

file_ext = 'plyascii'

subtype = 'ascii'

__init__(**kwd)

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'face': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a4de198>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a4decf8>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a4dee48>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a4deda0>}

class galaxy.datatypes.constructive_solid_geometry.PlyBinary(**kwd)

Bases: galaxy.datatypes.constructive_solid_geometry.Ply, galaxy.datatypes.binary.Binary

file_ext = 'plybinary'

subtype = 'binary'

__init__(**kwd)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'face': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a4e4438>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f199f16a0>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a4e4780>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a4e4a90>}

class galaxy.datatypes.constructive_solid_geometry.Vtk(**kwd)

Bases: object

The Visualization Toolkit provides a number of source and writer objects to read and write popular data file formats. The Visualization Toolkit also provides some of its own file formats.

There are two different styles of file formats available in VTK. The simplest are the legacy, serial formats that are easy to read and write either by hand or programmatically. However, these formats are less flexible than the XML based file formats which support random access, parallel I/O, and portable data compression and are preferred to the serial VTK file formats whenever possible.

All keyword phrases are written in ASCII form whether the file is binary or ASCII. The binary section of the file (if in binary form) is the data proper; i.e., the numbers that define points coordinates, scalars, cell indices, and so forth.

Binary data must be placed into the file immediately after the newline (‘\n’) character from the previous ASCII keyword and parameter sequence.

TODO: only legacy formats are currently supported and support for XML formats should be added.

subtype = ''

__init__(**kwd)

sniff_prefix(file_prefix)

VTK files can be either ASCII or binary, with two different styles of file formats: legacy or XML. We’ll assume if the file contains a valid VTK header, then it is a valid VTK file.

set_meta(dataset, **kwd)

set_initial_metadata(i, line, dataset)

set_structure_metadata(line, dataset, dataset_type)

The fourth part of legacy VTK files is the dataset structure. The geometry part describes the geometry and topology of the dataset. This part begins with a line containing the keyword DATASET followed by a keyword describing the type of dataset. Then, depending upon the type of dataset, other keyword/ data combinations define the actual data.

get_blurb(dataset)

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

sniff(filename)

class galaxy.datatypes.constructive_solid_geometry.VtkAscii(**kwd)

Bases: galaxy.datatypes.constructive_solid_geometry.Vtk, galaxy.datatypes.data.Text

file_ext = 'vtkascii'

subtype = 'ASCII'

__init__(**kwd)

metadata_spec = {'cells': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471438>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a48d198>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a48dac8>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471b00>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471f60>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a48d2e8>, 'lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471898>, 'origin': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a48dcc0>, 'points': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a48d0f0>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471320>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a48ddd8>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471358>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471748>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a48dcf8>}

class galaxy.datatypes.constructive_solid_geometry.VtkBinary(**kwd)

Bases: galaxy.datatypes.constructive_solid_geometry.Vtk, galaxy.datatypes.binary.Binary

file_ext = 'vtkbinary'

subtype = 'BINARY'

__init__(**kwd)

metadata_spec = {'cells': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471400>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471160>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471ac8>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a4716a0>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471908>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471240>, 'lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a4715c0>, 'origin': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a4712e8>, 'points': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471c50>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471a90>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471f98>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a4713c8>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471ba8>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471cc0>}

class galaxy.datatypes.constructive_solid_geometry.STL(**kwd)

Bases: galaxy.datatypes.data.Data

file_ext = 'stl'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a471be0>}

galaxy.datatypes.constructive_solid_geometry.get_next_line(fh)

galaxy.datatypes.coverage module

Coverage datatypes

class galaxy.datatypes.coverage.LastzCoverage(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'coverage'

get_track_resolution(dataset, start, end)

metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c5c3470>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c5c3668>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'forwardCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c5c3588>, 'positionCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c5c3518>, 'reverseCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c5c35f8>}

galaxy.datatypes.data module

class galaxy.datatypes.data.DatatypeValidation(state, message)

Bases: object

__init__(state, message)

static validated()

static invalid(message)

static unvalidated()

galaxy.datatypes.data.validate(dataset_instance)

class galaxy.datatypes.data.DataMeta(name, bases, dict_)

Bases: abc.ABCMeta

Metaclass for Data class. Sets up metadata spec.

__init__(name, bases, dict_)

class galaxy.datatypes.data.Data(**kwd)

Bases: object

Base class for all datatypes. Implements basic interfaces as well as class methods for metadata.

>>> class DataTest( Data ):
...     MetadataElement( name="test" )
...
>>> DataTest.metadata_spec.test.name
'test'
>>> DataTest.metadata_spec.test.desc
'test'
>>> type( DataTest.metadata_spec.test.param )
<class 'galaxy.model.metadata.MetadataParameter'>

edam_data = 'data_0006'

edam_format = 'format_1915'

file_ext = 'data'

CHUNKABLE = False

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

Dictionary of metadata fields for this datatype

copy_safe_peek = True

is_binary = True

allow_datatype_change = True

composite_type = None

primary_file_name = 'index'

track_type = None

data_sources = {}

__init__(**kwd)

Initialize the datatype

supported_display_apps = {}

composite_files = {}

get_raw_data(dataset)

Returns the full data. To stream it open the file_name and read/write as needed

dataset_content_needs_grooming(file_name)

This function is called on an output dataset file after the content is initially generated.

groom_dataset_content(file_name)

This function is called on an output dataset file if dataset_content_needs_grooming returns True.

init_meta(dataset, copy_from=None)

set_meta(dataset, overwrite=True, **kwd)

Unimplemented method, allows guessing of metadata from contents of file

missing_meta(dataset, check=None, skip=None)

Checks for empty metadata values, Returns True if non-optional metadata is missing Specifying a list of ‘check’ values will only check those names provided; when used, optionality is ignored Specifying a list of ‘skip’ items will return True even when a named metadata value is missing

set_max_optional_metadata_filesize(max_value)

get_max_optional_metadata_filesize()

max_optional_metadata_filesize

set_peek(dataset, is_multi_byte=False)

Set the peek and blurb text

Parameters:is_multi_byte (bool) – deprecated

display_peek(dataset)

Create HTML table, used for displaying peek

to_archive(trans, dataset, name='')

Collect archive paths and file handles that need to be exported when archiving dataset.

Parameters:

• dataset – HistoryDatasetAssociation
• name – archive name, in collection context corresponds to collection name(s) and element_identifier, joined by ‘/’, e.g ‘fastq_collection/sample1/forward’

display_data(trans, data, preview=False, filename=None, to_ext=None, **kwd)

Displays data in central pane if preview is True, else handles download.

Datatypes should be very careful if overridding this method and this interface between datatypes and Galaxy will likely change.

TOOD: Document alternatives to overridding this method (data providers?).

display_as_markdown(dataset_instance, markdown_format_helpers)

Prepare for embedding dataset into a basic Markdown document.

This is a somewhat experimental interface and should not be implemented on datatypes not tightly tied to a Galaxy version (e.g. datatypes in the Tool Shed).

Speaking very losely - the datatype should should load a bounded amount of data from the supplied dataset instance and prepare for embedding it into Markdown. This should be relatively vanilla Markdown - the result of this is bleached and it should not contain nested Galaxy Markdown directives.

If the data cannot reasonably be displayed, just indicate this and do not throw an exception.

display_name(dataset)

Returns formatted html of dataset name

display_info(dataset)

Returns formatted html of dataset info

repair_methods(dataset)

Unimplemented method, returns dict with method/option for repairing errors

get_mime()

Returns the mime type of the datatype

add_display_app(app_id, label, file_function, links_function)

Adds a display app to the datatype. app_id is a unique id label is the primary display label, e.g., display at ‘UCSC’ file_function is a string containing the name of the function that returns a properly formatted display links_function is a string containing the name of the function that returns a list of (link_name,link)

remove_display_app(app_id)

Removes a display app from the datatype

clear_display_apps()

add_display_application(display_application)

New style display applications

get_display_application(key, default=None)

get_display_applications_by_dataset(dataset, trans)

get_display_types()

Returns display types available

get_display_label(type)

Returns primary label for display app

as_display_type(dataset, type, **kwd)

Returns modified file contents for a particular display type

get_display_links(dataset, type, app, base_url, target_frame='_blank', **kwd)

Returns a list of tuples of (name, link) for a particular display type. No check on ‘access’ permissions is done here - if you can view the dataset, you can also save it or send it to a destination outside of Galaxy, so Galaxy security restrictions do not apply anyway.

get_converter_types(original_dataset, datatypes_registry)

Returns available converters by type for this dataset

find_conversion_destination(dataset, accepted_formats, datatypes_registry, **kwd)

Returns ( target_ext, existing converted dataset )

convert_dataset(trans, original_dataset, target_type, return_output=False, visible=True, deps=None, target_context=None, history=None)

This function adds a job to the queue to convert a dataset to another type. Returns a message about success/failure.

after_setting_metadata(dataset)

This function is called on the dataset after metadata is set.

before_setting_metadata(dataset)

This function is called on the dataset before metadata is set.

add_composite_file(name, **kwds)

writable_files

get_composite_files(dataset=None)

generate_primary_file(dataset=None)

has_resolution

matches_any(target_datatypes)

Check if this datatype is of any of the target_datatypes or is a subtype thereof.

static merge(split_files, output_file)

Merge files with copy.copyfileobj() will not hit the max argument limitation of cat. gz and bz2 files are also working.

get_visualizations(dataset)

Returns a list of visualizations for datatype.

has_dataprovider(data_format)

Returns True if data_format is available in dataproviders.

dataprovider(dataset, data_format, **settings)

Base dataprovider factory for all datatypes that returns the proper provider for the given data_format or raises a NoProviderAvailable.

validate(dataset, **kwd)

base_dataprovider(dataset, **settings)

chunk_dataprovider(dataset, **settings)

chunk64_dataprovider(dataset, **settings)

dataproviders = {'base': <function Data.base_dataprovider at 0x7f2f4530bd08>, 'chunk': <function Data.chunk_dataprovider at 0x7f2f4530bea0>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f2f453080d0>}

class galaxy.datatypes.data.Text(**kwd)

Bases: galaxy.datatypes.data.Data

edam_format = 'format_2330'

file_ext = 'txt'

line_class = 'line'

is_binary = False

get_mime()

Returns the mime type of the datatype

set_meta(dataset, **kwd)

Set the number of lines of data in dataset.

estimate_file_lines(dataset)

Perform a rough estimate by extrapolating number of lines from a small read.

count_data_lines(dataset)

Count the number of lines of data in dataset, skipping all blank lines and comments.

set_peek(dataset, line_count=None, is_multi_byte=False, WIDTH=256, skipchars=None, line_wrap=True)

Set the peek. This method is used by various subclasses of Text.

classmethod split(input_datasets, subdir_generator_function, split_params)

Split the input files by line.

line_dataprovider(dataset, **settings)

Returns an iterator over the dataset’s lines (that have been stripped) optionally excluding blank lines and lines that start with a comment character.

regex_line_dataprovider(dataset, **settings)

Returns an iterator over the dataset’s lines optionally including/excluding lines that match one or more regex filters.

dataproviders = {'base': <function Data.base_dataprovider at 0x7f2f4530bd08>, 'chunk': <function Data.chunk_dataprovider at 0x7f2f4530bea0>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f2f453080d0>, 'line': <function Text.line_dataprovider at 0x7f2f45308620>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f2f453087b8>}

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.data.Directory(**kwd)

Bases: galaxy.datatypes.data.Data

Class representing a directory of files.

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a588>}

class galaxy.datatypes.data.GenericAsn1(**kwd)

Bases: galaxy.datatypes.data.Text

Class for generic ASN.1 text format

edam_data = 'data_0849'

edam_format = 'format_1966'

file_ext = 'asn1'

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a630>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.data.LineCount(**kwd)

Bases: galaxy.datatypes.data.Text

Dataset contains a single line with a single integer that denotes the line count for a related dataset. Used for custom builds.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a6d8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.data.Newick(**kwd)

Bases: galaxy.datatypes.data.Text

New Hampshire/Newick Format

edam_data = 'data_0872'

edam_format = 'format_1910'

file_ext = 'newick'

__init__(**kwd)

Initialize foobar datatype

init_meta(dataset, copy_from=None)

sniff(filename)

Returning false as the newick format is too general and cannot be sniffed.

get_visualizations(dataset)

Returns a list of visualizations for datatype.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.data.Nexus(**kwd)

Bases: galaxy.datatypes.data.Text

Nexus format as used By Paup, Mr Bayes, etc

edam_data = 'data_0872'

edam_format = 'format_1912'

file_ext = 'nex'

__init__(**kwd)

Initialize foobar datatype

init_meta(dataset, copy_from=None)

sniff_prefix(file_prefix)

All Nexus Files Simply puts a ‘#NEXUS’ in its first line

get_visualizations(dataset)

Returns a list of visualizations for datatype.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a828>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

sniff(filename)

galaxy.datatypes.data.get_test_fname(fname)

Returns test data filename

galaxy.datatypes.data.get_file_peek(file_name, is_multi_byte=False, WIDTH=256, LINE_COUNT=5, skipchars=None, line_wrap=True)

Returns the first LINE_COUNT lines wrapped to WIDTH.

Parameters:is_multi_byte (bool) – deprecated

>>> def assert_peek_is(file_name, expected, *args, **kwd):
...     path = get_test_fname(file_name)
...     peek = get_file_peek(path, *args, **kwd)
...     assert peek == expected, "%s != %s" % (peek, expected)
>>> assert_peek_is('0_nonewline', u'0')
>>> assert_peek_is('0.txt', u'0\n')
>>> assert_peek_is('4.bed', u'chr22\t30128507\t31828507\tuc003bnx.1_cds_2_0_chr22_29227_f\t0\t+\n', LINE_COUNT=1)
>>> assert_peek_is('1.bed', u'chr1\t147962192\t147962580\tCCDS989.1_cds_0_0_chr1_147962193_r\t0\t-\nchr1\t147984545\t147984630\tCCDS990.1_cds_0_0_chr1_147984546_f\t0\t+\n', LINE_COUNT=2)

galaxy.datatypes.genetics module

rgenetics datatypes Use at your peril Ross Lazarus for the rgenetics and galaxy projects

genome graphs datatypes derived from Interval datatypes genome graphs datasets have a header row with appropriate columnames The first column is always the marker - eg columname = rs, first row= rs12345 if the rows are snps subsequent row values are all numeric ! Will fail if any non numeric (eg ‘+’ or ‘NA’) values ross lazarus for rgenetics august 20 2007

class galaxy.datatypes.genetics.GenomeGraphs(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

Tab delimited data containing a marker id and any number of numeric values

file_ext = 'gg'

__init__(**kwd)

Initialize gg datatype, by adding UCSC display apps

set_meta(dataset, **kwd)

as_ucsc_display_file(dataset, **kwd)

Returns file

ucsc_links(dataset, type, app, base_url)

from the ever-helpful angie hinrichs angie@soe.ucsc.edu a genome graphs call looks like this

http://genome.ucsc.edu/cgi-bin/hgGenome?clade=mammal&org=Human&db=hg18&hgGenome_dataSetName=dname &hgGenome_dataSetDescription=test&hgGenome_formatType=best%20guess&hgGenome_markerType=best%20guess &hgGenome_columnLabels=best%20guess&hgGenome_maxVal=&hgGenome_labelVals= &hgGenome_maxGapToFill=25000000&hgGenome_uploadFile=http://galaxy.esphealth.org/datasets/333/display/index &hgGenome_doSubmitUpload=submit

Galaxy gives this for an interval file

http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg18&position=chr1:1-1000&hgt.customText= http%3A%2F%2Fgalaxy.esphealth.org%2Fdisplay_as%3Fid%3D339%26display_app%3Ducsc

make_html_table(dataset, skipchars=[])

Create HTML table, used for displaying peek

validate(dataset, **kwd)

Validate a gg file - all numeric after header row

sniff_prefix(file_prefix)

Determines whether the file is in gg format

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'test_space.txt' )
>>> GenomeGraphs().sniff( fname )
False
>>> fname = get_test_fname( '1.gg' )
>>> GenomeGraphs().sniff( fname )
True

get_mime()

Returns the mime type of the datatype

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191d17f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191d12e8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'markerCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191d1320>}

sniff(filename)

class galaxy.datatypes.genetics.rgTabList(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

for sampleid and for featureid lists of exclusions or inclusions in the clean tool featureid subsets on statistical criteria -> specialized display such as gg

file_ext = 'rgTList'

__init__(**kwd)

Initialize featurelistt datatype

display_peek(dataset)

Returns formated html of peek

get_mime()

Returns the mime type of the datatype

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191ff160>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191ff198>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19627748>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191d17b8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191d1748>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191ff208>}

class galaxy.datatypes.genetics.rgSampleList(**kwd)

Bases: galaxy.datatypes.genetics.rgTabList

for sampleid exclusions or inclusions in the clean tool output from QC eg excess het, gender error, ibd pair member,eigen outlier,excess mendel errors,… since they can be uploaded, should be flexible but they are persistent at least same infrastructure for expression?

file_ext = 'rgSList'

__init__(**kwd)

Initialize samplelist datatype

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191ffe48>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191ff940>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191ff4a8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191ff278>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191ff358>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191ff080>}

class galaxy.datatypes.genetics.rgFeatureList(**kwd)

Bases: galaxy.datatypes.genetics.rgTabList

for featureid lists of exclusions or inclusions in the clean tool output from QC eg low maf, high missingness, bad hwe in controls, excess mendel errors,… featureid subsets on statistical criteria -> specialized display such as gg same infrastructure for expression?

file_ext = 'rgFList'

__init__(**kwd)

Initialize featurelist datatype

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19395da0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19395e10>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19395ef0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19395fd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19395f60>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9128>}

class galaxy.datatypes.genetics.Rgenetics(**kwd)

Bases: galaxy.datatypes.text.Html

base class to use for rgenetics datatypes derived from html - composite datatype elements stored in extra files path

composite_type = 'auto_primary_file'

allow_datatype_change = False

file_ext = 'rgenetics'

generate_primary_file(dataset=None)

regenerate_primary_file(dataset)

cannot do this until we are setting metadata

get_mime()

Returns the mime type of the datatype

set_meta(dataset, **kwd)

for lped/pbed eg

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c91d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.SNPMatrix(**kwd)

Bases: galaxy.datatypes.genetics.Rgenetics

BioC SNPMatrix Rgenetics data collections

file_ext = 'snpmatrix'

set_peek(dataset, **kwd)

sniff(filename)

need to check the file header hex code

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c92b0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.Lped(**kwd)

Bases: galaxy.datatypes.genetics.Rgenetics

linkage pedigree (ped,map) Rgenetics data collections

file_ext = 'lped'

__init__(**kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9358>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.Pphe(**kwd)

Bases: galaxy.datatypes.genetics.Rgenetics

Plink phenotype file - header must have FID IID… Rgenetics data collections

file_ext = 'pphe'

__init__(**kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.Fphe(**kwd)

Bases: galaxy.datatypes.genetics.Rgenetics

fbat pedigree file - mad format with ! as first char on header row Rgenetics data collections

file_ext = 'fphe'

__init__(**kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c94a8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.Phe(**kwd)

Bases: galaxy.datatypes.genetics.Rgenetics

Phenotype file

file_ext = 'phe'

__init__(**kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9518>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.Fped(**kwd)

Bases: galaxy.datatypes.genetics.Rgenetics

FBAT pedigree format - single file, map is header row of rs numbers. Strange. Rgenetics data collections

file_ext = 'fped'

__init__(**kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c95c0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.Pbed(**kwd)

Bases: galaxy.datatypes.genetics.Rgenetics

Plink Binary compressed 2bit/geno Rgenetics data collections

file_ext = 'pbed'

__init__(**kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9668>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.ldIndep(**kwd)

Bases: galaxy.datatypes.genetics.Rgenetics

LD (a good measure of redundancy of information) depleted Plink Binary compressed 2bit/geno This is really a plink binary, but some tools work better with less redundancy so are constrained to these files

file_ext = 'ldreduced'

__init__(**kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9710>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.Eigenstratgeno(**kwd)

Bases: galaxy.datatypes.genetics.Rgenetics

Eigenstrat format - may be able to get rid of this if we move to shellfish Rgenetics data collections

file_ext = 'eigenstratgeno'

__init__(**kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c97b8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.Eigenstratpca(**kwd)

Bases: galaxy.datatypes.genetics.Rgenetics

Eigenstrat PCA file for case control adjustment Rgenetics data collections

file_ext = 'eigenstratpca'

__init__(**kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9860>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.Snptest(**kwd)

Bases: galaxy.datatypes.genetics.Rgenetics

BioC snptest Rgenetics data collections

file_ext = 'snptest'

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9940>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.IdeasPre(**kwd)

Bases: galaxy.datatypes.text.Html

This datatype defines the input format required by IDEAS: https://academic.oup.com/nar/article/44/14/6721/2468150 The IDEAS preprocessor tool produces an output using this format. The extra_files_path of the primary input dataset contains the following files and directories. - chromosome_windows.txt (optional) - chromosomes.bed (optional) - IDEAS_input_config.txt - compressed archived tmp directory containing a number of compressed bed files.

composite_type = 'auto_primary_file'

allow_datatype_change = False

file_ext = 'ideaspre'

__init__(**kwd)

set_meta(dataset, **kwd)

generate_primary_file(dataset=None)

regenerate_primary_file(dataset)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c99e8>, 'chrom_bed': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9a58>, 'chrom_windows': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9ac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'input_config': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9b38>, 'tmp_archive': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9ba8>}

class galaxy.datatypes.genetics.Pheno(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

base class for pheno files

file_ext = 'pheno'

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9e10>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9da0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9d30>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9c50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9cc0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9e80>}

class galaxy.datatypes.genetics.RexpBase(**kwd)

Bases: galaxy.datatypes.text.Html

base class for BioC data structures in Galaxy must be constructed with the pheno data in place since that goes into the metadata for each instance

file_ext = 'rexpbase'

html_table = None

composite_type = 'auto_primary_file'

allow_datatype_change = False

__init__(**kwd)

generate_primary_file(dataset=None)

This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately

get_mime()

Returns the mime type of the datatype

get_phecols(phenolist=[], maxConc=20)

sept 2009: cannot use whitespace to split - make a more complex structure here and adjust the methods that rely on this structure return interesting phenotype column names for an rexpression eset or affybatch to use in array subsetting and so on. Returns a data structure for a dynamic Galaxy select parameter. A column with only 1 value doesn’t change, so is not interesting for analysis. A column with a different value in every row is equivalent to a unique identifier so is also not interesting for anova or limma analysis - both these are removed after the concordance (count of unique terms) is constructed for each column. Then a complication - each remaining pair of columns is tested for redundancy - if two columns are always paired, then only one is needed :)

get_pheno(dataset)

expects a .pheno file in the extra_files_dir - ugh note that R is wierd and adds the row.name in the header so the columns are all wrong - unless you tell it not to. A file can be written as write.table(file=’foo.pheno’,pData(foo),sep=’ ‘,quote=F,row.names=F)

set_peek(dataset, **kwd)

expects a .pheno file in the extra_files_dir - ugh note that R is weird and does not include the row.name in the header. why?

get_peek(dataset)

expects a .pheno file in the extra_files_dir - ugh

get_file_peek(filename)

can’t really peek at a filename - need the extra_files_path and such?

regenerate_primary_file(dataset)

cannot do this until we are setting metadata

init_meta(dataset, copy_from=None)

set_meta(dataset, **kwd)

NOTE we apply the tabular machinary to the phenodata extracted from a BioC eSet or affybatch.

make_html_table(pp='nothing supplied from peek\n')

Create HTML table, used for displaying peek

display_peek(dataset)

Returns formatted html of peek

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a60b8>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9f98>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191c9f28>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6048>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6128>}

class galaxy.datatypes.genetics.Affybatch(**kwd)

Bases: galaxy.datatypes.genetics.RexpBase

derived class for BioC data structures in Galaxy

file_ext = 'affybatch'

__init__(**kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6320>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6240>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a61d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a62b0>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6390>}

class galaxy.datatypes.genetics.Eset(**kwd)

Bases: galaxy.datatypes.genetics.RexpBase

derived class for BioC data structures in Galaxy

file_ext = 'eset'

__init__(**kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6588>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a64a8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6438>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6518>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a65f8>}

class galaxy.datatypes.genetics.MAlist(**kwd)

Bases: galaxy.datatypes.genetics.RexpBase

derived class for BioC data structures in Galaxy

file_ext = 'malist'

__init__(**kwd)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a67f0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a66a0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6780>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6860>}

class galaxy.datatypes.genetics.LinkageStudies(**kwd)

Bases: galaxy.datatypes.data.Text

superclass for classical linkage analysis suites

test_files = ['linkstudies.allegro_fparam', 'linkstudies.alohomora_gts', 'linkstudies.linkage_datain', 'linkstudies.linkage_map']

__init__(**kwd)

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6908>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.genetics.GenotypeMatrix(**kwd)

Bases: galaxy.datatypes.genetics.LinkageStudies

Sample matrix of genotypes - GTs as columns

file_ext = 'alohomora_gts'

__init__(**kwd)

header_check(fio)

sniff_prefix(file_prefix)

>>> classname = GenotypeMatrix
>>> from galaxy.datatypes.sniff import get_test_fname
>>> extn_true = classname().file_ext
>>> file_true = get_test_fname("linkstudies." + extn_true)
>>> classname().sniff(file_true)
True
>>> false_files = list(LinkageStudies.test_files)
>>> false_files.remove("linkstudies." + extn_true)
>>> result_true = []
>>> for fname in false_files:
...     file_false = get_test_fname(fname)
...     res = classname().sniff(file_false)
...     if res:
...         result_true.append(fname)
>>>
>>> result_true
[]

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a69e8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

sniff(filename)

class galaxy.datatypes.genetics.MarkerMap(**kwd)

Bases: galaxy.datatypes.genetics.LinkageStudies

Map of genetic markers including physical and genetic distance Common input format for linkage programs

chrom, genetic pos, markername, physical pos, Nr

file_ext = 'linkage_map'

header_check(fio)

sniff_prefix(file_prefix)

>>> classname = MarkerMap
>>> from galaxy.datatypes.sniff import get_test_fname
>>> extn_true = classname().file_ext
>>> file_true = get_test_fname("linkstudies." + extn_true)
>>> classname().sniff(file_true)
True
>>> false_files = list(LinkageStudies.test_files)
>>> false_files.remove("linkstudies." + extn_true)
>>> result_true = []
>>> for fname in false_files:
...     file_false = get_test_fname(fname)
...     res = classname().sniff(file_false)
...     if res:
...         result_true.append(fname)
>>>
>>> result_true
[]

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6a90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

sniff(filename)

class galaxy.datatypes.genetics.DataIn(**kwd)

Bases: galaxy.datatypes.genetics.LinkageStudies

Common linkage input file for intermarker distances and recombination rates

file_ext = 'linkage_datain'

__init__(**kwd)

sniff_prefix(file_prefix)

>>> classname = DataIn
>>> from galaxy.datatypes.sniff import get_test_fname
>>> extn_true = classname().file_ext
>>> file_true = get_test_fname("linkstudies." + extn_true)
>>> classname().sniff(file_true)
True
>>> false_files = list(LinkageStudies.test_files)
>>> false_files.remove("linkstudies." + extn_true)
>>> result_true = []
>>> for fname in false_files:
...     file_false = get_test_fname(fname)
...     res = classname().sniff(file_false)
...     if res:
...         result_true.append(fname)
>>>
>>> result_true
[]

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6b70>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

sniff(filename)

class galaxy.datatypes.genetics.AllegroLOD(**kwd)

Bases: galaxy.datatypes.genetics.LinkageStudies

Allegro output format for LOD scores

file_ext = 'allegro_fparam'

header_check(fio)

sniff_prefix(file_prefix)

>>> classname = AllegroLOD
>>> from galaxy.datatypes.sniff import get_test_fname
>>> extn_true = classname().file_ext
>>> file_true = get_test_fname("linkstudies." + extn_true)
>>> classname().sniff(file_true)
True
>>> false_files = list(LinkageStudies.test_files)
>>> false_files.remove("linkstudies." + extn_true)
>>> result_true = []
>>> for fname in false_files:
...     file_false = get_test_fname(fname)
...     res = classname().sniff(file_false)
...     if res:
...         result_true.append(fname)
>>>
>>> result_true
[]

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f191a6c18>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

sniff(filename)

galaxy.datatypes.gis module

GIS classes

class galaxy.datatypes.gis.Shapefile(**kwd)

Bases: galaxy.datatypes.binary.Binary

The Shapefile data format: For more information please see http://en.wikipedia.org/wiki/Shapefile

composite_type = 'auto_primary_file'

file_ext = 'shp'

allow_datatype_change = False

__init__(**kwd)

generate_primary_file(dataset=None)

set_peek(dataset, is_multi_byte=False)

Set the peek and blurb text.

display_peek(dataset)

Create HTML content, used for displaying peek.

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1950e198>}

galaxy.datatypes.graph module

Graph content classes.

class galaxy.datatypes.graph.Xgmml(**kwd)

Bases: galaxy.datatypes.xml.GenericXml

XGMML graph format (http://wiki.cytoscape.org/Cytoscape_User_Manual/Network_Formats).

file_ext = 'xgmml'

set_peek(dataset, is_multi_byte=False)

Set the peek and blurb text

sniff(filename)

Returns false and the user must manually set.

static merge(split_files, output_file)

Merging multiple XML files is non-trivial and must be done in subclasses.

node_edge_dataprovider(dataset, **settings)

dataproviders = {'base': <function Data.base_dataprovider at 0x7f2f4530bd08>, 'chunk': <function Data.chunk_dataprovider at 0x7f2f4530bea0>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f2f453080d0>, 'line': <function Text.line_dataprovider at 0x7f2f45308620>, 'node-edge': <function Xgmml.node_edge_dataprovider at 0x7f2f191dbea0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f2f453087b8>, 'xml': <function GenericXml.xml_dataprovider at 0x7f2f45441ea0>}

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f193ada58>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.graph.Sif(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

SIF graph format (http://wiki.cytoscape.org/Cytoscape_User_Manual/Network_Formats).

First column: node id Second column: relationship type Third to Nth column: target ids for link

file_ext = 'sif'

set_peek(dataset, is_multi_byte=False)

Set the peek and blurb text

sniff(filename)

Returns false and the user must manually set.

static merge(split_files, output_file)

node_edge_dataprovider(dataset, **settings)

dataproviders = {'base': <function Data.base_dataprovider at 0x7f2f4530bd08>, 'chunk': <function Data.chunk_dataprovider at 0x7f2f4530bea0>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f2f453080d0>, 'column': <function TabularData.column_dataprovider at 0x7f2f420829d8>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f2f42082b70>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f2f42082ea0>, 'dict': <function TabularData.dict_dataprovider at 0x7f2f42082d08>, 'line': <function Text.line_dataprovider at 0x7f2f45308620>, 'node-edge': <function Sif.node_edge_dataprovider at 0x7f2f195072f0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f2f453087b8>}

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f193a6470>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f193a6eb8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f193a6da0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f193a65c0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f193a6a58>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f193a6198>}

class galaxy.datatypes.graph.XGMMLGraphDataProvider(source, selector=None, max_depth=None, **kwargs)

Bases: galaxy.datatypes.dataproviders.hierarchy.XMLDataProvider

Provide two lists: nodes, edges:

'nodes': contains objects of the form:
    { 'id' : <some string id>, 'data': <any extra data> }
'edges': contains objects of the form:
    { 'source' : <an index into nodes>, 'target': <an index into nodes>, 'data': <any extra data> }

settings = {'limit': 'int', 'max_depth': 'int', 'offset': 'int', 'selector': 'str'}

class galaxy.datatypes.graph.SIFGraphDataProvider(source, indeces=None, column_count=None, column_types=None, parsers=None, parse_columns=True, deliminator='t', filters=None, **kwargs)

Bases: galaxy.datatypes.dataproviders.column.ColumnarDataProvider

Provide two lists: nodes, edges:

'nodes': contains objects of the form:
    { 'id' : <some string id>, 'data': <any extra data> }
'edges': contains objects of the form:
    { 'source' : <an index into nodes>, 'target': <an index into nodes>, 'data': <any extra data> }

settings = {'column_count': 'int', 'column_types': 'list:str', 'comment_char': 'str', 'deliminator': 'str', 'filters': 'list:str', 'indeces': 'list:int', 'invert': 'bool', 'limit': 'int', 'offset': 'int', 'parse_columns': 'bool', 'provide_blank': 'bool', 'regex_list': 'list:escaped', 'strip_lines': 'bool', 'strip_newlines': 'bool'}

galaxy.datatypes.images module

Image classes

class galaxy.datatypes.images.Image(**kwd)

Bases: galaxy.datatypes.data.Data

Class describing an image

edam_data = 'data_2968'

edam_format = 'format_3547'

file_ext = ''

__init__(**kwd)

set_peek(dataset, is_multi_byte=False)

sniff(filename)

Determine if the file is in this format

handle_dataset_as_image(hda)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c0f54e0>}

class galaxy.datatypes.images.Jpg(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3579'

file_ext = 'jpg'

__init__(**kwd)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c0f56a0>}

class galaxy.datatypes.images.Png(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3603'

file_ext = 'png'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c0f5908>}

class galaxy.datatypes.images.Tiff(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3591'

file_ext = 'tiff'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c0f5c18>}

class galaxy.datatypes.images.Hamamatsu(**kwd)

Bases: galaxy.datatypes.images.Image

file_ext = 'vms'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c0f5470>}

class galaxy.datatypes.images.Mirax(**kwd)

Bases: galaxy.datatypes.images.Image

file_ext = 'mrxs'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c0f5e48>}

class galaxy.datatypes.images.Sakura(**kwd)

Bases: galaxy.datatypes.images.Image

file_ext = 'svslide'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c0f5f28>}

class galaxy.datatypes.images.Nrrd(**kwd)

Bases: galaxy.datatypes.images.Image

file_ext = 'nrrd'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c0f5fd0>}

class galaxy.datatypes.images.Bmp(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3592'

file_ext = 'bmp'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c12af98>}

class galaxy.datatypes.images.Gif(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3467'

file_ext = 'gif'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a560c50>}

class galaxy.datatypes.images.Im(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3593'

file_ext = 'im'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c0f3080>}

class galaxy.datatypes.images.Pcd(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3594'

file_ext = 'pcd'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c0f3588>}

class galaxy.datatypes.images.Pcx(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3595'

file_ext = 'pcx'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f0f0>}

class galaxy.datatypes.images.Ppm(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3596'

file_ext = 'ppm'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f198>}

class galaxy.datatypes.images.Psd(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3597'

file_ext = 'psd'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f320>}

class galaxy.datatypes.images.Xbm(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3598'

file_ext = 'xbm'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f3c8>}

class galaxy.datatypes.images.Xpm(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3599'

file_ext = 'xpm'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f4a8>}

class galaxy.datatypes.images.Rgb(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3600'

file_ext = 'rgb'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f550>}

class galaxy.datatypes.images.Pbm(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3601'

file_ext = 'pbm'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f5f8>}

class galaxy.datatypes.images.Pgm(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3602'

file_ext = 'pgm'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f6a0>}

class galaxy.datatypes.images.Eps(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3466'

file_ext = 'eps'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f748>}

class galaxy.datatypes.images.Rast(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3605'

file_ext = 'rast'

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f7f0>}

class galaxy.datatypes.images.Pdf(**kwd)

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3508'

file_ext = 'pdf'

sniff(filename)

Determine if the file is in pdf format.

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f898>}

galaxy.datatypes.images.create_applet_tag_peek(class_name, archive, params)

class galaxy.datatypes.images.Gmaj(**kwd)

Bases: galaxy.datatypes.data.Data

Class describing a GMAJ Applet

edam_format = 'format_3547'

file_ext = 'gmaj.zip'

copy_safe_peek = False

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

get_mime()

Returns the mime type of the datatype

sniff(filename)

NOTE: the sniff.convert_newlines() call in the upload utility will keep Gmaj data types from being correctly sniffed, but the files can be uploaded (they’ll be sniffed as ‘txt’). This sniff function is here to provide an example of a sniffer for a zip file.

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f940>}

class galaxy.datatypes.images.Html(**kwd)

Bases: galaxy.datatypes.text.Html

Deprecated class. This class should not be used anymore, but the galaxy.datatypes.text:Html one. This is for backwards compatibilities only.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3f9e8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.images.Laj(**kwd)

Bases: galaxy.datatypes.data.Text

Class describing a LAJ Applet

file_ext = 'laj'

copy_safe_peek = False

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab3fa90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

galaxy.datatypes.interval module

Interval datatypes

class galaxy.datatypes.interval.Interval(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

Tab delimited data containing interval information

edam_data = 'data_3002'

edam_format = 'format_3475'

file_ext = 'interval'

line_class = 'region'

track_type = 'FeatureTrack'

data_sources = {'data': 'tabix', 'index': 'bigwig'}

Add metadata elements

__init__(**kwd)

Initialize interval datatype, by adding UCSC display apps

init_meta(dataset, copy_from=None)

set_meta(dataset, overwrite=True, first_line_is_header=False, **kwd)

Tries to guess from the line the location number of the column for the chromosome, region start-end and strand

displayable(dataset)

get_estimated_display_viewport(dataset, chrom_col=None, start_col=None, end_col=None)

Return a chrom, start, stop tuple for viewing a file.

as_ucsc_display_file(dataset, **kwd)

Returns file contents with only the bed data

display_peek(dataset)

Returns formated html of peek

ucsc_links(dataset, type, app, base_url)

Generate links to UCSC genome browser sites based on the dbkey and content of dataset.

validate(dataset, **kwd)

Validate an interval file using the bx GenomicIntervalReader

repair_methods(dataset)

Return options for removing errors along with a description

sniff_prefix(file_prefix)

Checks for ‘intervalness’

This format is mostly used by galaxy itself. Valid interval files should include a valid header comment, but this seems to be loosely regulated.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'test_space.txt' )
>>> Interval().sniff( fname )
False
>>> fname = get_test_fname( 'interval.interval' )
>>> Interval().sniff( fname )
True

get_track_resolution(dataset, start, end)

genomic_region_dataprovider(dataset, **settings)

genomic_region_dict_dataprovider(dataset, **settings)

interval_dataprovider(dataset, **settings)

interval_dict_dataprovider(dataset, **settings)

dataproviders = {'base': <function Data.base_dataprovider at 0x7f2f4530bd08>, 'chunk': <function Data.chunk_dataprovider at 0x7f2f4530bea0>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f2f453080d0>, 'column': <function TabularData.column_dataprovider at 0x7f2f420829d8>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f2f42082b70>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f2f42082ea0>, 'dict': <function TabularData.dict_dataprovider at 0x7f2f42082d08>, 'genomic-region': <function Interval.genomic_region_dataprovider at 0x7f2f420a0ae8>, 'genomic-region-dict': <function Interval.genomic_region_dict_dataprovider at 0x7f2f420a0c80>, 'interval': <function Interval.interval_dataprovider at 0x7f2f420a0e18>, 'interval-dict': <function Interval.interval_dict_dataprovider at 0x7f2f4209d048>, 'line': <function Text.line_dataprovider at 0x7f2f45308620>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f2f453087b8>}

metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209a2e8>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209ac88>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209a5f8>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209ac18>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209a588>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209a668>}

sniff(filename)

class galaxy.datatypes.interval.BedGraph(**kwd)

Bases: galaxy.datatypes.interval.Interval

Tab delimited chrom/start/end/datavalue dataset

edam_format = 'format_3583'

file_ext = 'bedgraph'

track_type = 'LineTrack'

data_sources = {'data': 'bigwig', 'index': 'bigwig'}

as_ucsc_display_file(dataset, **kwd)

Returns file contents as is with no modifications. TODO: this is a functional stub and will need to be enhanced moving forward to provide additional support for bedgraph.

get_estimated_display_viewport(dataset, chrom_col=0, start_col=1, end_col=2)

Set viewport based on dataset’s first 100 lines.

metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209acc0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209af60>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209ae10>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209aef0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209ada0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209ae80>}

class galaxy.datatypes.interval.Bed(**kwd)

Bases: galaxy.datatypes.interval.Interval

Tab delimited data in BED format

edam_format = 'format_3003'

file_ext = 'bed'

data_sources = {'data': 'tabix', 'feature_search': 'fli', 'index': 'bigwig'}

track_type = 'FeatureTrack'

column_names = ['Chrom', 'Start', 'End', 'Name', 'Score', 'Strand', 'ThickStart', 'ThickEnd', 'ItemRGB', 'BlockCount', 'BlockSizes', 'BlockStarts']

Add metadata elements

set_meta(dataset, overwrite=True, **kwd)

Sets the metadata information for datasets previously determined to be in bed format.

as_ucsc_display_file(dataset, **kwd)

Returns file contents with only the bed data. If bed 6+, treat as interval.

sniff_prefix(file_prefix)

Checks for ‘bedness’

BED lines have three required fields and nine additional optional fields. The number of fields per line must be consistent throughout any single set of data in an annotation track. The order of the optional fields is binding: lower-numbered fields must always be populated if higher-numbered fields are used. The data type of all 12 columns is: 1-str, 2-int, 3-int, 4-str, 5-int, 6-str, 7-int, 8-int, 9-int or list, 10-int, 11-list, 12-list

For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format1

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'test_tab.bed' )
>>> Bed().sniff( fname )
True
>>> fname = get_test_fname( 'interv1.bed' )
>>> Bed().sniff( fname )
True
>>> fname = get_test_fname( 'complete.bed' )
>>> Bed().sniff( fname )
True

metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209af98>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2208>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2128>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209ac18>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a20b8>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2198>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2240>}

class galaxy.datatypes.interval.ProBed(**kwd)

Bases: galaxy.datatypes.interval.Bed

Tab delimited data in proBED format - adaptation of BED for proteomics data.

edam_format = 'format_3827'

file_ext = 'probed'

column_names = ['Chrom', 'Start', 'End', 'Name', 'Score', 'Strand', 'ThickStart', 'ThickEnd', 'ItemRGB', 'BlockCount', 'BlockSizes', 'BlockStarts', 'ProteinAccession', 'PeptideSequence', 'Uniqueness', 'GenomeReferenceVersion', 'PsmScore', 'Fdr', 'Modifications', 'Charge', 'ExpMassToCharge', 'CalcMassToCharge', 'PsmRank', 'DatasetID', 'Uri']

metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a22b0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a24e0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2400>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209ac18>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2390>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2470>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2518>}

class galaxy.datatypes.interval.BedStrict(**kwd)

Bases: galaxy.datatypes.interval.Bed

Tab delimited data in strict BED format - no non-standard columns allowed

edam_format = 'format_3584'

file_ext = 'bedstrict'

allow_datatype_change = False

__init__(**kwd)

set_meta(dataset, overwrite=True, **kwd)

sniff(filename)

metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2588>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a27b8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2668>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2748>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a25f8>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a26d8>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2240>}

class galaxy.datatypes.interval.Bed6(**kwd)

Bases: galaxy.datatypes.interval.BedStrict

Tab delimited data in strict BED format - no non-standard columns allowed; column count forced to 6

edam_format = 'format_3585'

file_ext = 'bed6'

metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2860>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2a90>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2940>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2a20>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a28d0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a29b0>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2240>}

class galaxy.datatypes.interval.Bed12(**kwd)

Bases: galaxy.datatypes.interval.BedStrict

Tab delimited data in strict BED format - no non-standard columns allowed; column count forced to 12

edam_format = 'format_3586'

file_ext = 'bed12'

metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2b38>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2d68>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2c18>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2cf8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2ba8>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2c88>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2240>}

class galaxy.datatypes.interval.Gff(**kwd)

Bases: galaxy.datatypes.tabular.Tabular, galaxy.datatypes.interval._RemoteCallMixin

Tab delimited data in Gff format

edam_data = 'data_1255'

edam_format = 'format_2305'

file_ext = 'gff'

valid_gff_frame = ['.', '0', '1', '2']

column_names = ['Seqname', 'Source', 'Feature', 'Start', 'End', 'Score', 'Strand', 'Frame', 'Group']

data_sources = {'data': 'interval_index', 'feature_search': 'fli', 'index': 'bigwig'}

track_type = 'FeatureTrack'

Add metadata elements

__init__(**kwd)

Initialize datatype, by adding GBrowse display app

set_attribute_metadata(dataset)

Sets metadata elements for dataset’s attributes.

set_meta(dataset, overwrite=True, **kwd)

display_peek(dataset)

Returns formated html of peek

get_estimated_display_viewport(dataset)

Return a chrom, start, stop tuple for viewing a file. There are slight differences between gff 2 and gff 3 formats. This function should correctly handle both…

ucsc_links(dataset, type, app, base_url)

gbrowse_links(dataset, type, app, base_url)

sniff_prefix(file_prefix)

Determines whether the file is in gff format

GFF lines have nine required fields that must be tab-separated.

For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format3

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('gff.gff3')
>>> Gff().sniff( fname )
False
>>> fname = get_test_fname('test.gff')
>>> Gff().sniff( fname )
True

genomic_region_dataprovider(dataset, **settings)

genomic_region_dict_dataprovider(dataset, **settings)

interval_dataprovider(dataset, **settings)

interval_dict_dataprovider(dataset, **settings)

dataproviders = {'base': <function Data.base_dataprovider at 0x7f2f4530bd08>, 'chunk': <function Data.chunk_dataprovider at 0x7f2f4530bea0>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f2f453080d0>, 'column': <function TabularData.column_dataprovider at 0x7f2f420829d8>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f2f42082b70>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f2f42082ea0>, 'dict': <function TabularData.dict_dataprovider at 0x7f2f42082d08>, 'genomic-region': <function Gff.genomic_region_dataprovider at 0x7f2f4209df28>, 'genomic-region-dict': <function Gff.genomic_region_dict_dataprovider at 0x7f2f295c6158>, 'interval': <function Gff.interval_dataprovider at 0x7f2f295c62f0>, 'interval-dict': <function Gff.interval_dict_dataprovider at 0x7f2f295c6488>, 'line': <function Text.line_dataprovider at 0x7f2f45308620>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f2f453087b8>}

metadata_spec = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9160>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c90f0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9080>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2fd0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>}

sniff(filename)

class galaxy.datatypes.interval.Gff3(**kwd)

Bases: galaxy.datatypes.interval.Gff

Tab delimited data in Gff3 format

edam_format = 'format_1975'

file_ext = 'gff3'

valid_gff3_strand = ['+', '-', '.', '?']

valid_gff3_phase = ['.', '0', '1', '2']

column_names = ['Seqid', 'Source', 'Type', 'Start', 'End', 'Score', 'Strand', 'Phase', 'Attributes']

track_type = 'FeatureTrack'

Add metadata elements

__init__(**kwd)

Initialize datatype, by adding GBrowse display app

set_meta(dataset, overwrite=True, **kwd)

sniff_prefix(file_prefix)

Determines whether the file is in GFF version 3 format

GFF 3 format:

1. adds a mechanism for representing more than one level of hierarchical grouping of features and subfeatures.
2. separates the ideas of group membership and feature name/id
3. constrains the feature type field to be taken from a controlled vocabulary.
4. allows a single feature, such as an exon, to belong to more than one group at a time.
5. provides an explicit convention for pairwise alignments
6. provides an explicit convention for features that occupy disjunct regions

The format consists of 9 columns, separated by tabs (NOT spaces).

Undefined fields are replaced with the “.” character, as described in the original GFF spec.

For complete details see http://song.sourceforge.net/gff3.shtml

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'test.gff' )
>>> Gff3().sniff( fname )
False
>>> fname = get_test_fname( 'test.gtf' )
>>> Gff3().sniff( fname )
False
>>> fname = get_test_fname('gff.gff3')
>>> Gff3().sniff( fname )
True

metadata_spec = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9160>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c90f0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9208>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420a2fd0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>}

class galaxy.datatypes.interval.Gtf(**kwd)

Bases: galaxy.datatypes.interval.Gff

Tab delimited data in Gtf format

edam_format = 'format_2306'

file_ext = 'gtf'

column_names = ['Seqname', 'Source', 'Feature', 'Start', 'End', 'Score', 'Strand', 'Frame', 'Attributes']

track_type = 'FeatureTrack'

Add metadata elements

sniff_prefix(file_prefix)

Determines whether the file is in gtf format

GTF lines have nine required fields that must be tab-separated. The first eight GTF fields are the same as GFF. The group field has been expanded into a list of attributes. Each attribute consists of a type/value pair. Attributes must end in a semi-colon, and be separated from any following attribute by exactly one space. The attribute list must begin with the two mandatory attributes:

gene_id value - A globally unique identifier for the genomic source of the sequence. transcript_id value - A globally unique identifier for the predicted transcript.

For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format4

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( '1.bed' )
>>> Gtf().sniff( fname )
False
>>> fname = get_test_fname( 'test.gff' )
>>> Gtf().sniff( fname )
False
>>> fname = get_test_fname( 'test.gtf' )
>>> Gtf().sniff( fname )
True

metadata_spec = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9160>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c90f0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9320>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c92b0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>}

class galaxy.datatypes.interval.Wiggle(**kwd)

Bases: galaxy.datatypes.tabular.Tabular, galaxy.datatypes.interval._RemoteCallMixin

Tab delimited data in wiggle format

edam_format = 'format_3005'

file_ext = 'wig'

track_type = 'LineTrack'

data_sources = {'data': 'bigwig', 'index': 'bigwig'}

__init__(**kwd)

get_estimated_display_viewport(dataset)

Return a chrom, start, stop tuple for viewing a file.

gbrowse_links(dataset, type, app, base_url)

ucsc_links(dataset, type, app, base_url)

display_peek(dataset)

Returns formated html of peek

set_meta(dataset, overwrite=True, **kwd)

sniff_prefix(file_prefix)

Determines wether the file is in wiggle format

The .wig format is line-oriented. Wiggle data is preceeded by a track definition line, which adds a number of options for controlling the default display of this track. Following the track definition line is the track data, which can be entered in several different formats.

The track definition line begins with the word ‘track’ followed by the track type. The track type with version is REQUIRED, and it currently must be wiggle_0. For example, track type=wiggle_0…

For complete details see http://genome.ucsc.edu/goldenPath/help/wiggle.html

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'interv1.bed' )
>>> Wiggle().sniff( fname )
False
>>> fname = get_test_fname( 'wiggle.wig' )
>>> Wiggle().sniff( fname )
True

get_track_resolution(dataset, start, end)

wiggle_dataprovider(dataset, **settings)

wiggle_dict_dataprovider(dataset, **settings)

dataproviders = {'base': <function Data.base_dataprovider at 0x7f2f4530bd08>, 'chunk': <function Data.chunk_dataprovider at 0x7f2f4530bea0>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f2f453080d0>, 'column': <function TabularData.column_dataprovider at 0x7f2f420829d8>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f2f42082b70>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f2f42082ea0>, 'dict': <function TabularData.dict_dataprovider at 0x7f2f42082d08>, 'line': <function Text.line_dataprovider at 0x7f2f45308620>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f2f453087b8>, 'wiggle': <function Wiggle.wiggle_dataprovider at 0x7f2f295c6ea0>, 'wiggle-dict': <function Wiggle.wiggle_dict_dataprovider at 0x7f2f295cc0d0>}

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9470>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>}

sniff(filename)

class galaxy.datatypes.interval.CustomTrack(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

UCSC CustomTrack

edam_format = 'format_3588'

file_ext = 'customtrack'

__init__(**kwd)

Initialize interval datatype, by adding UCSC display app

set_meta(dataset, overwrite=True, **kwd)

display_peek(dataset)

Returns formated html of peek

get_estimated_display_viewport(dataset, chrom_col=None, start_col=None, end_col=None)

Return a chrom, start, stop tuple for viewing a file.

ucsc_links(dataset, type, app, base_url)

sniff_prefix(file_prefix)

Determines whether the file is in customtrack format.

CustomTrack files are built within Galaxy and are basically bed or interval files with the first line looking something like this.

track name=”User Track” description=”User Supplied Track (from Galaxy)” color=0,0,0 visibility=1

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'complete.bed' )
>>> CustomTrack().sniff( fname )
False
>>> fname = get_test_fname( 'ucsc.customtrack' )
>>> CustomTrack().sniff( fname )
True

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c96d8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9668>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c95f8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9518>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9588>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9748>}

sniff(filename)

class galaxy.datatypes.interval.ENCODEPeak(**kwd)

Bases: galaxy.datatypes.interval.Interval

Human ENCODE peak format. There are both broad and narrow peak formats. Formats are very similar; narrow peak has an additional column, though.

Broad peak ( http://genome.ucsc.edu/FAQ/FAQformat#format13 ): This format is used to provide called regions of signal enrichment based on pooled, normalized (interpreted) data. It is a BED 6+3 format.

Narrow peak http://genome.ucsc.edu/FAQ/FAQformat#format12 and : This format is used to provide called peaks of signal enrichment based on pooled, normalized (interpreted) data. It is a BED6+4 format.

edam_format = 'format_3612'

file_ext = 'encodepeak'

column_names = ['Chrom', 'Start', 'End', 'Name', 'Score', 'Strand', 'SignalValue', 'pValue', 'qValue', 'Peak']

data_sources = {'data': 'tabix', 'index': 'bigwig'}

Add metadata elements

sniff(filename)

metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c97f0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9a20>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9940>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209ac18>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c98d0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c99b0>}

class galaxy.datatypes.interval.ChromatinInteractions(**kwd)

Bases: galaxy.datatypes.interval.Interval

Chromatin interactions obtained from 3C/5C/Hi-C experiments.

file_ext = 'chrint'

track_type = 'DiagonalHeatmapTrack'

data_sources = {'data': 'tabix', 'index': 'bigwig'}

column_names = ['Chrom1', 'Start1', 'End1', 'Chrom2', 'Start2', 'End2', 'Value']

Add metadata elements

metadata_spec = {'chrom1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9a90>, 'chrom2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9c50>, 'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209a2e8>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9e10>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'end1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9be0>, 'end2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9d30>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209a5f8>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209ac18>, 'start1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9b70>, 'start2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9cc0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209a588>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4209a668>, 'valueCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9da0>}

sniff(filename)

class galaxy.datatypes.interval.ScIdx(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

ScIdx files are 1-based and consist of strand-specific coordinate counts. They always have 5 columns, and the first row is the column labels: ‘chrom’, ‘index’, ‘forward’, ‘reverse’, ‘value’. Each line following the first consists of data: chromosome name (type str), peak index (type int), Forward strand peak count (type int), Reverse strand peak count (type int) and value (type int). The value of the 5th ‘value’ column is the sum of the forward and reverse peak count values.

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9eb8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f295c9e48>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>}

sniff(filename)

file_ext = 'scidx'

__init__(**kwd)

Initialize scidx datatype.

sniff_prefix(file_prefix)

Checks for ‘scidx-ness.’

galaxy.datatypes.isa module

ISA datatype

See https://github.com/ISA-tools

galaxy.datatypes.isa.utf8_text_file_open(path)

class galaxy.datatypes.isa.IsaTab(**kwd)

Bases: galaxy.datatypes.isa._Isa

file_ext = 'isa-tab'

__init__(**kwd)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f185bdd68>}

class galaxy.datatypes.isa.IsaJson(**kwd)

Bases: galaxy.datatypes.isa._Isa

file_ext = 'isa-json'

__init__(**kwd)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f185c4278>}

galaxy.datatypes.metadata module

Expose the model metadata module as a datatype module also, allowing it to live in galaxy.model means the model module doesn’t have any dependencies on th datatypes module. This module will need to remain here for datatypes living in the tool shed so we might as well keep and use this interface from the datatypes module.

class galaxy.datatypes.metadata.Statement(target)

Bases: object

This class inserts its target into a list in the surrounding class. the data.Data class has a metaclass which executes these statements. This is how we shove the metadata element spec into the class.

__init__(target)

classmethod process(element)

class galaxy.datatypes.metadata.MetadataCollection(parent)

Bases: object

MetadataCollection is not a collection at all, but rather a proxy to the real metadata which is stored as a Dictionary. This class handles processing the metadata elements when they are set and retrieved, returning default values in cases when metadata is not set.

__init__(parent)

get_parent()

set_parent(parent)

parent

spec

get(key, default=None)

items()

remove_key(name)

element_is_set(name)

get_metadata_parameter(name, **kwd)

make_dict_copy(to_copy)

Makes a deep copy of input iterable to_copy according to self.spec

requires_dataset_id

from_JSON_dict(filename=None, path_rewriter=None, json_dict=None)

to_JSON_dict(filename=None)

class galaxy.datatypes.metadata.MetadataSpecCollection(*args, **kwds)

Bases: collections.OrderedDict

A simple extension of OrderedDict which allows cleaner access to items and allows the values to be iterated over directly as if it were a list. append() is also implemented for simplicity and does not “append”.

__init__(*args, **kwds)

append(item)

class galaxy.datatypes.metadata.MetadataParameter(spec)

Bases: object

__init__(spec)

get_field(value=None, context=None, other_values=None, **kwd)

to_string(value)

to_safe_string(value)

make_copy(value, target_context=None, source_context=None)

classmethod marshal(value)

This method should/can be overridden to convert the incoming value to whatever type it is supposed to be.

validate(value)

Throw an exception if the value is invalid.

unwrap(form_value)

Turns a value into its storable form.

wrap(value, session)

Turns a value into its usable form.

from_external_value(value, parent)

Turns a value read from an external dict into its value to be pushed directly into the metadata dict.

to_external_value(value)

Turns a value read from a metadata into its value to be pushed directly into the external dict.

class galaxy.datatypes.metadata.MetadataElementSpec(datatype, name=None, desc=None, param=<class 'galaxy.model.metadata.MetadataParameter'>, default=None, no_value=None, visible=True, set_in_upload=False, **kwargs)

Bases: object

Defines a metadata element and adds it to the metadata_spec (which is a MetadataSpecCollection) of datatype.

__init__(datatype, name=None, desc=None, param=<class 'galaxy.model.metadata.MetadataParameter'>, default=None, no_value=None, visible=True, set_in_upload=False, **kwargs)

get(name, default=None)

wrap(value, session)

Turns a stored value into its usable form.

unwrap(value)

Turns an incoming value into its storable form.

class galaxy.datatypes.metadata.SelectParameter(spec)

Bases: galaxy.model.metadata.MetadataParameter

__init__(spec)

to_string(value)

get_field(value=None, context=None, other_values=None, values=None, **kwd)

wrap(value, session)

classmethod marshal(value)

class galaxy.datatypes.metadata.DBKeyParameter(spec)

Bases: galaxy.model.metadata.SelectParameter

get_field(value=None, context=None, other_values=None, values=None, **kwd)

class galaxy.datatypes.metadata.RangeParameter(spec)

Bases: galaxy.model.metadata.SelectParameter

__init__(spec)

get_field(value=None, context=None, other_values=None, values=None, **kwd)

classmethod marshal(value)

class galaxy.datatypes.metadata.ColumnParameter(spec)

Bases: galaxy.model.metadata.RangeParameter

get_field(value=None, context=None, other_values=None, values=None, **kwd)

class galaxy.datatypes.metadata.ColumnTypesParameter(spec)

Bases: galaxy.model.metadata.MetadataParameter

to_string(value)

class galaxy.datatypes.metadata.ListParameter(spec)

Bases: galaxy.model.metadata.MetadataParameter

to_string(value)

class galaxy.datatypes.metadata.DictParameter(spec)

Bases: galaxy.model.metadata.MetadataParameter

to_string(value)

to_safe_string(value)

class galaxy.datatypes.metadata.PythonObjectParameter(spec)

Bases: galaxy.model.metadata.MetadataParameter

to_string(value)

get_field(value=None, context=None, other_values=None, **kwd)

classmethod marshal(value)

class galaxy.datatypes.metadata.FileParameter(spec)

Bases: galaxy.model.metadata.MetadataParameter

to_string(value)

to_safe_string(value)

get_field(value=None, context=None, other_values=None, **kwd)

wrap(value, session)

make_copy(value, target_context, source_context)

classmethod marshal(value)

from_external_value(value, parent, path_rewriter=None)

Turns a value read from a external dict into its value to be pushed directly into the metadata dict.

to_external_value(value)

Turns a value read from a metadata into its value to be pushed directly into the external dict.

new_file(dataset=None, **kwds)

class galaxy.datatypes.metadata.MetadataTempFile(**kwds)

Bases: object

tmp_dir = 'database/tmp'

__init__(**kwds)

file_name

to_JSON()

classmethod from_JSON(json_dict)

classmethod is_JSONified_value(value)

classmethod cleanup_from_JSON_dict_filename(filename)

galaxy.datatypes.microarrays module

class galaxy.datatypes.microarrays.GenericMicroarrayFile(**kwd)

Bases: galaxy.datatypes.data.Text

Abstract class for most of the microarray files.

set_peek(dataset, is_multi_byte=False)

get_mime()

metadata_spec = {'block_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f181d57f0>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18229470>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18145c50>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1813a6d8>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f181a57f0>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18145e10>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18145be0>}

class galaxy.datatypes.microarrays.Gal(**kwd)

Bases: galaxy.datatypes.microarrays.GenericMicroarrayFile

Gal File format described at: http://mdc.custhelp.com/app/answers/detail/a_id/18883/#gal

edam_format = 'format_3829'

edam_data = 'data_3110'

file_ext = 'gal'

sniff_prefix(file_prefix)

Try to guess if the file is a Gal file. >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname(‘test.gal’) >>> Gal().sniff(fname) True >>> fname = get_test_fname(‘test.gpr’) >>> Gal().sniff(fname) False

set_meta(dataset, **kwd)

Set metadata for Gal file.

metadata_spec = {'block_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1815a048>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1815aba8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18452a58>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1815a080>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18292ba8>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18292b38>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1827e898>}

sniff(filename)

class galaxy.datatypes.microarrays.Gpr(**kwd)

Bases: galaxy.datatypes.microarrays.GenericMicroarrayFile

Gpr File format described at: http://mdc.custhelp.com/app/answers/detail/a_id/18883/#gpr

edam_format = 'format_3829'

edam_data = 'data_3110'

file_ext = 'gpr'

sniff_prefix(file_prefix)

Try to guess if the file is a Gpr file. >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname(‘test.gpr’) >>> Gpr().sniff(fname) True >>> fname = get_test_fname(‘test.gal’) >>> Gpr().sniff(fname) False

set_meta(dataset, **kwd)

Set metadata for Gpr file.

metadata_spec = {'block_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1815ad68>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1815add8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1815a9b0>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1815acf8>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1815ac88>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1815ac18>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1815a8d0>}

sniff(filename)

galaxy.datatypes.molecules module

galaxy.datatypes.molecules.count_lines(filename, non_empty=False)

counting the number of lines from the ‘filename’ file

class galaxy.datatypes.molecules.GenericMolFile(**kwd)

Bases: galaxy.datatypes.data.Text

Abstract class for most of the molecule files.

set_peek(dataset, is_multi_byte=False)

get_mime()

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1805d860>}

class galaxy.datatypes.molecules.MOL(**kwd)

Bases: galaxy.datatypes.molecules.GenericMolFile

file_ext = 'mol'

set_meta(dataset, **kwd)

Set the number molecules, in the case of MOL its always one.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f17f510b8>}

class galaxy.datatypes.molecules.SDF(**kwd)

Bases: galaxy.datatypes.molecules.GenericMolFile

file_ext = 'sdf'

sniff_prefix(file_prefix)

Try to guess if the file is a SDF2 file.

An SDfile (structure-data file) can contain multiple compounds.

Each compound starts with a block in V2000 or V3000 molfile format, which ends with a line equal to ‘M END’. This is followed by a non-structural data block, which ends with a line equal to ‘$$$$’.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('drugbank_drugs.sdf')
>>> SDF().sniff(fname)
True
>>> fname = get_test_fname('github88.v3k.sdf')
>>> SDF().sniff(fname)
True
>>> fname = get_test_fname('chebi_57262.v3k.mol')
>>> SDF().sniff(fname)
False

set_meta(dataset, **kwd)

Set the number of molecules in dataset.

classmethod split(input_datasets, subdir_generator_function, split_params)

Split the input files by molecule records.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f17f51320>}

sniff(filename)

class galaxy.datatypes.molecules.MOL2(**kwd)

Bases: galaxy.datatypes.molecules.GenericMolFile

file_ext = 'mol2'

sniff_prefix(file_prefix)

Try to guess if the file is a MOL2 file.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('drugbank_drugs.mol2')
>>> MOL2().sniff(fname)
True
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> MOL2().sniff(fname)
False

set_meta(dataset, **kwd)

Set the number of lines of data in dataset.

classmethod split(input_datasets, subdir_generator_function, split_params)

Split the input files by molecule records.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f17f51518>}

sniff(filename)

class galaxy.datatypes.molecules.FPS(**kwd)

Bases: galaxy.datatypes.molecules.GenericMolFile

chemfp fingerprint file: http://code.google.com/p/chem-fingerprints/wiki/FPS

file_ext = 'fps'

sniff_prefix(file_prefix)

Try to guess if the file is a FPS file.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('q.fps')
>>> FPS().sniff(fname)
True
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> FPS().sniff(fname)
False

set_meta(dataset, **kwd)

Set the number of lines of data in dataset.

classmethod split(input_datasets, subdir_generator_function, split_params)

Split the input files by fingerprint records.

static merge(split_files, output_file)

Merging fps files requires merging the header manually. We take the header from the first file.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f17f51748>}

sniff(filename)

class galaxy.datatypes.molecules.OBFS(**kwd)

Bases: galaxy.datatypes.binary.Binary

OpenBabel Fastsearch format (fs).

file_ext = 'obfs'

composite_type = 'basic'

allow_datatype_change = False

__init__(**kwd)

A Fastsearch Index consists of a binary file with the fingerprints and a pointer the actual molecule file.

set_peek(dataset, is_multi_byte=False)

Set the peek and blurb text.

display_peek(dataset)

Create HTML content, used for displaying peek.

get_mime()

Returns the mime type of the datatype (pretend it is text for peek)

merge(split_files, output_file, extra_merge_args)

Merging Fastsearch indices is not supported.

split(input_datasets, subdir_generator_function, split_params)

Splitting Fastsearch indices is not supported.

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f17f518d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f420f7be0>}

class galaxy.datatypes.molecules.DRF(**kwd)

Bases: galaxy.datatypes.molecules.GenericMolFile

file_ext = 'drf'

set_meta(dataset, **kwd)

Set the number of lines of data in dataset.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f17f51978>}

class galaxy.datatypes.molecules.PHAR(**kwd)

Bases: galaxy.datatypes.molecules.GenericMolFile

Pharmacophore database format from silicos-it.

file_ext = 'phar'

set_peek(dataset, is_multi_byte=False)

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18052f98>}

class galaxy.datatypes.molecules.PDB(**kwd)

Bases: galaxy.datatypes.molecules.GenericMolFile

Protein Databank format. http://www.wwpdb.org/documentation/format33/v3.3.html

file_ext = 'pdb'

sniff_prefix(file_prefix)

Try to guess if the file is a PDB file.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('5e5z.pdb')
>>> PDB().sniff(fname)
True
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> PDB().sniff(fname)
False

set_meta(dataset, **kwd)

Find Chain_IDs for metadata.

set_peek(dataset, is_multi_byte=False)

metadata_spec = {'chain_ids': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1808a6a0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1805d860>}

sniff(filename)

class galaxy.datatypes.molecules.PDBQT(**kwd)

Bases: galaxy.datatypes.molecules.GenericMolFile

PDBQT Autodock and Autodock Vina format http://autodock.scripps.edu/faqs-help/faq/what-is-the-format-of-a-pdbqt-file

file_ext = 'pdbqt'

sniff_prefix(file_prefix)

Try to guess if the file is a PDBQT file.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('NuBBE_1_obabel_3D.pdbqt')
>>> PDBQT().sniff(fname)
True
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> PDBQT().sniff(fname)
False

set_peek(dataset, is_multi_byte=False)

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1808a748>}

sniff(filename)

class galaxy.datatypes.molecules.PQR(**kwd)

Bases: galaxy.datatypes.molecules.GenericMolFile

Protein Databank format. https://apbs-pdb2pqr.readthedocs.io/en/latest/formats/pqr.html

file_ext = 'pqr'

get_matcher()

Atom and HETATM line fields are space separated, match group:

0: Field_name

A string which specifies the type of PQR entry: ATOM or HETATM.

1: Atom_number

An integer which provides the atom index.

2: Atom_name

A string which provides the atom name.

3: Residue_name

A string which provides the residue name.

5: Chain_ID (Optional, group 4 is whole field)

An optional string which provides the chain ID of the atom. Note that chain ID support is a new feature of APBS 0.5.0 and later versions.

6: Residue_number

An integer which provides the residue index.

7: X 8: Y 9: Z

3 floats which provide the atomic coordinates (in angstroms)

10: Charge

A float which provides the atomic charge (in electrons).

11: Radius

A float which provides the atomic radius (in angstroms).

sniff_prefix(file_prefix)

Try to guess if the file is a PQR file. >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname(‘5e5z.pqr’) >>> PQR().sniff(fname) True >>> fname = get_test_fname(‘drugbank_drugs.cml’) >>> PQR().sniff(fname) False

set_meta(dataset, **kwd)

Find Optional Chain_IDs for metadata.

set_peek(dataset, is_multi_byte=False)

metadata_spec = {'chain_ids': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1808a7f0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1805d860>}

sniff(filename)

class galaxy.datatypes.molecules.grd(**kwd)

Bases: galaxy.datatypes.data.Text

file_ext = 'grd'

set_peek(dataset, is_multi_byte=False)

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1808a898>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.molecules.grdtgz(**kwd)

Bases: galaxy.datatypes.binary.Binary

file_ext = 'grd.tgz'

set_peek(dataset, is_multi_byte=False)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1808a940>}

class galaxy.datatypes.molecules.InChI(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'inchi'

column_names = ['InChI']

set_meta(dataset, **kwd)

Set the number of lines of data in dataset.

set_peek(dataset, is_multi_byte=False)

sniff_prefix(file_prefix)

Try to guess if the file is a InChI file.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('drugbank_drugs.inchi')
>>> InChI().sniff(fname)
True
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> InChI().sniff(fname)
False

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1808aa58>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1808a9e8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1808aac8>}

sniff(filename)

class galaxy.datatypes.molecules.SMILES(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'smi'

column_names = ['SMILES', 'TITLE']

set_meta(dataset, **kwd)

Set the number of lines of data in dataset.

set_peek(dataset, is_multi_byte=False)

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1808abe0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1808ab70>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1808ac50>}

class galaxy.datatypes.molecules.CML(**kwd)

Bases: galaxy.datatypes.xml.GenericXml

Chemical Markup Language http://cml.sourceforge.net/

file_ext = 'cml'

set_meta(dataset, **kwd)

Set the number of lines of data in dataset.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f45467ac8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1808ad68>}

set_peek(dataset, is_multi_byte=False)

sniff(filename)

sniff_prefix(file_prefix)

Try to guess if the file is a CML file.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('interval.interval')
>>> CML().sniff(fname)
False
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> CML().sniff(fname)
True

classmethod split(input_datasets, subdir_generator_function, split_params)

Split the input files by molecule records.

static merge(split_files, output_file)

Merging CML files.

galaxy.datatypes.mothur module

Mothur Metagenomics Datatypes

class galaxy.datatypes.mothur.Otu(**kwd)

Bases: galaxy.datatypes.data.Text

file_ext = 'mothur.otu'

__init__(**kwd)

set_meta(dataset, overwrite=True, **kwd)

Set metadata for Otu files.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> from galaxy.util.bunch import Bunch
>>> dataset = Bunch()
>>> dataset.metadata = Bunch
>>> otu = Otu()
>>> dataset.file_name = get_test_fname( 'mothur_datatypetest_true.mothur.otu' )
>>> dataset.has_data = lambda: True
>>> otu.set_meta(dataset)
>>> dataset.metadata.columns
100
>>> len(dataset.metadata.labels) == 37
True
>>> len(dataset.metadata.otulabels) == 98
True

sniff_prefix(file_prefix)

Determines whether the file is otu (operational taxonomic unit) format

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.otu' )
>>> Otu().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.otu' )
>>> Otu().sniff( fname )
False

metadata_spec = {'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f182d47b8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f182d40b8>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f182d46d8>}

sniff(filename)

class galaxy.datatypes.mothur.Sabund(**kwd)

Bases: galaxy.datatypes.mothur.Otu

file_ext = 'mothur.sabund'

__init__(**kwd)

http://www.mothur.org/wiki/Sabund_file

init_meta(dataset, copy_from=None)

sniff_prefix(file_prefix)

Determines whether the file is otu (operational taxonomic unit) format label<TAB>count[<TAB>value(1..n)]

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.sabund' )
>>> Sabund().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.sabund' )
>>> Sabund().sniff( fname )
False

metadata_spec = {'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f183cc1d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f183ccd30>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18891f98>}

class galaxy.datatypes.mothur.GroupAbund(**kwd)

Bases: galaxy.datatypes.mothur.Otu

file_ext = 'mothur.shared'

__init__(**kwd)

init_meta(dataset, copy_from=None)

set_meta(dataset, overwrite=True, skip=1, **kwd)

sniff_prefix(file_prefix, vals_are_int=False)

Determines whether the file is a otu (operational taxonomic unit) Shared format label<TAB>group<TAB>count[<TAB>value(1..n)] The first line is column headings as of Mothur v 1.2

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.shared' )
>>> GroupAbund().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.shared' )
>>> GroupAbund().sniff( fname )
False

metadata_spec = {'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f182d47b8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18981940>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f182d40b8>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f182d46d8>}

class galaxy.datatypes.mothur.SecondaryStructureMap(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.map'

__init__(**kwd)

Initialize secondary structure map datatype

sniff_prefix(file_prefix)

Determines whether the file is a secondary structure map format A single column with an integer value which indicates the row that this row maps to. Check to make sure if structMap[10] = 380 then structMap[380] = 10 and vice versa.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.map' )
>>> SecondaryStructureMap().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.map' )
>>> SecondaryStructureMap().sniff( fname )
False

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18981470>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18981cf8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18981860>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f189815f8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f189814a8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18981550>}

sniff(filename)

class galaxy.datatypes.mothur.AlignCheck(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.align.check'

__init__(**kwd)

Initialize AlignCheck datatype

set_meta(dataset, overwrite=True, **kwd)

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18ba5dd8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18ba5eb8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18981d68>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18981eb8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18981f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18ba5f98>}

class galaxy.datatypes.mothur.AlignReport(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

QueryName QueryLength TemplateName TemplateLength SearchMethod SearchScore AlignmentMethod QueryStart QueryEnd TemplateStart TemplateEnd PairwiseAlignmentLength GapsInQuery GapsInTemplate LongestInsert SimBtwnQuery&Template AY457915 501 82283 1525 kmer 89.07 needleman 5 501 1 499 499 2 0 0 97.6

file_ext = 'mothur.align.report'

__init__(**kwd)

Initialize AlignCheck datatype

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8d1ef0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8d1f60>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8d1fd0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18ba5240>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8d11d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8d1e80>}

class galaxy.datatypes.mothur.DistanceMatrix(**kwd)

Bases: galaxy.datatypes.data.Text

file_ext = 'mothur.dist'

Add metadata elements

init_meta(dataset, copy_from=None)

set_meta(dataset, overwrite=True, skip=0, **kwd)

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8d1d68>}

class galaxy.datatypes.mothur.LowerTriangleDistanceMatrix(**kwd)

Bases: galaxy.datatypes.mothur.DistanceMatrix

file_ext = 'mothur.lower.dist'

__init__(**kwd)

Initialize secondary structure map datatype

init_meta(dataset, copy_from=None)

sniff_prefix(file_prefix)

Determines whether the file is a lower-triangle distance matrix (phylip) format The first line has the number of sequences in the matrix. The remaining lines have the sequence name followed by a list of distances from all preceeding sequences

5 # possibly but not always preceded by a tab :/ U68589 U68590 0.3371 U68591 0.3609 0.3782 U68592 0.4155 0.3197 0.4148 U68593 0.2872 0.1690 0.3361 0.2842

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.lower.dist' )
>>> LowerTriangleDistanceMatrix().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.lower.dist' )
>>> LowerTriangleDistanceMatrix().sniff( fname )
False

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8b59b0>}

sniff(filename)

class galaxy.datatypes.mothur.SquareDistanceMatrix(**kwd)

Bases: galaxy.datatypes.mothur.DistanceMatrix

file_ext = 'mothur.square.dist'

__init__(**kwd)

init_meta(dataset, copy_from=None)

sniff_prefix(file_prefix)

Determines whether the file is a square distance matrix (Column-formatted distance matrix) format The first line has the number of sequences in the matrix. The following lines have the sequence name in the first column plus a column for the distance to each sequence in the row order in which they appear in the matrix.

3 U68589 0.0000 0.3371 0.3610 U68590 0.3371 0.0000 0.3783 U68590 0.3371 0.0000 0.3783

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.square.dist' )
>>> SquareDistanceMatrix().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.square.dist' )
>>> SquareDistanceMatrix().sniff( fname )
False

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8e1ac8>}

sniff(filename)

class galaxy.datatypes.mothur.PairwiseDistanceMatrix(**kwd)

Bases: galaxy.datatypes.mothur.DistanceMatrix, galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.pair.dist'

__init__(**kwd)

Initialize secondary structure map datatype

set_meta(dataset, overwrite=True, skip=None, **kwd)

sniff_prefix(file_prefix)

Determines whether the file is a pairwise distance matrix (Column-formatted distance matrix) format The first and second columns have the sequence names and the third column is the distance between those sequences.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.pair.dist' )
>>> PairwiseDistanceMatrix().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.pair.dist' )
>>> PairwiseDistanceMatrix().sniff( fname )
False

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e0b8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8e92b0>}

sniff(filename)

class galaxy.datatypes.mothur.Names(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.names'

__init__(**kwd)

http://www.mothur.org/wiki/Name_file Name file shows the relationship between a representative sequence(col 1) and the sequences(comma-separated) it represents(col 2)

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8df400>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8e97b8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8e9828>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8e9a90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8e9b70>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8df518>}

class galaxy.datatypes.mothur.Summary(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.summary'

__init__(**kwd)

summarizes the quality of sequences in an unaligned or aligned fasta-formatted sequence file

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8df710>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8df780>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8df6d8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8df320>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a8df470>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1be0aac8>}

class galaxy.datatypes.mothur.Group(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.groups'

__init__(**kwd)

http://www.mothur.org/wiki/Groups_file Group file assigns sequence (col 1) to a group (col 2)

set_meta(dataset, overwrite=True, skip=None, max_data_lines=None, **kwd)

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e0b8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1be0abe0>}

class galaxy.datatypes.mothur.AccNos(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.accnos'

__init__(**kwd)

A list of names

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1be0a7f0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1be0a908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1be0a390>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1be0a780>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1be0ae80>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1be0a668>}

class galaxy.datatypes.mothur.Oligos(**kwd)

Bases: galaxy.datatypes.data.Text

file_ext = 'mothur.oligos'

sniff_prefix(file_prefix)

http://www.mothur.org/wiki/Oligos_File Determines whether the file is a otu (operational taxonomic unit) format

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.oligos' )
>>> Oligos().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.oligos' )
>>> Oligos().sniff( fname )
False

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a5b3240>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

sniff(filename)

class galaxy.datatypes.mothur.Frequency(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.freq'

__init__(**kwd)

A list of names

sniff_prefix(file_prefix)

Determines whether the file is a frequency tabular format for chimera analysis #1.14.0 0 0.000 1 0.000 … 155 0.975

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.freq' )
>>> Frequency().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.freq' )
>>> Frequency().sniff( fname )
False
>>> # Expression count matrix (EdgeR wrapper)
>>> fname = get_test_fname( 'mothur_datatypetest_false_2.mothur.freq' )
>>> Frequency().sniff( fname )
False

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1af1b828>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1af1b710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1af1b7b8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1af1b860>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1af1b940>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1af1ba20>}

sniff(filename)

class galaxy.datatypes.mothur.Quantile(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.quan'

__init__(**kwd)

Quantiles for chimera analysis

sniff_prefix(file_prefix)

Determines whether the file is a quantiles tabular format for chimera analysis 1 0 0 0 0 0 0 2 0.309198 0.309198 0.37161 0.37161 0.37161 0.37161 3 0.510982 0.563213 0.693529 0.858939 1.07442 1.20608 …

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.quan' )
>>> Quantile().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.quan' )
>>> Quantile().sniff( fname )
False

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e0b8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'filtered': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1af18a20>, 'masked': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1af18898>}

sniff(filename)

class galaxy.datatypes.mothur.LaneMask(**kwd)

Bases: galaxy.datatypes.data.Text

file_ext = 'mothur.filter'

sniff_prefix(file_prefix)

Determines whether the file is a lane mask filter: 1 line consisting of zeros and ones.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.filter' )
>>> LaneMask().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.filter' )
>>> LaneMask().sniff( fname )
False

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1af18eb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

sniff(filename)

class galaxy.datatypes.mothur.CountTable(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.count_table'

__init__(**kwd)

http://www.mothur.org/wiki/Count_File A table with first column names and following columns integer counts # Example 1: Representative_Sequence total U68630 1 U68595 1 U68600 1 # Example 2 (with group columns): Representative_Sequence total forest pasture U68630 1 1 0 U68595 1 1 0 U68600 1 1 0 U68591 1 1 0 U68647 1 0 1

set_meta(dataset, overwrite=True, skip=1, max_data_lines=None, **kwd)

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e0b8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a5ba240>}

class galaxy.datatypes.mothur.RefTaxonomy(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.ref.taxonomy'

__init__(**kwd)

sniff_prefix(file_prefix)

Determines whether the file is a Reference Taxonomy

http://www.mothur.org/wiki/Taxonomy_outline A table with 2 or 3 columns: - SequenceName - Taxonomy (semicolon-separated taxonomy in descending order) - integer ? Example: 2-column (http://www.mothur.org/wiki/Taxonomy_outline)

X56533.1 Eukaryota;Alveolata;Ciliophora;Intramacronucleata;Oligohymenophorea;Hymenostomatida;Tetrahymenina;Glaucomidae;Glaucoma; X97975.1 Eukaryota;Parabasalidea;Trichomonada;Trichomonadida;unclassified_Trichomonadida; AF052717.1 Eukaryota;Parabasalidea;

Example: 3-column (http://vamps.mbl.edu/resources/databases.php)

v3_AA008 Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus 5 v3_AA016 Bacteria 120 v3_AA019 Archaea;Crenarchaeota;Marine_Group_I 1

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.ref.taxonomy' )
>>> RefTaxonomy().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.ref.taxonomy' )
>>> RefTaxonomy().sniff( fname )
False

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a5ba588>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a5ba5f8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a5ba668>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a5ba780>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a5ba710>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a5ba518>}

sniff(filename)

class galaxy.datatypes.mothur.ConsensusTaxonomy(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.cons.taxonomy'

__init__(**kwd)

A list of names

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a1f9f28>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a1f9ef0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a1f9470>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a5ba438>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a5ba3c8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a1f9390>}

class galaxy.datatypes.mothur.TaxonomySummary(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.tax.summary'

__init__(**kwd)

A Summary of taxon classification

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab5fb70>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a2ef080>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a2efe10>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a1f9400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a300588>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab5fba8>}

class galaxy.datatypes.mothur.Axes(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.axes'

__init__(**kwd)

Initialize axes datatype

sniff_prefix(file_prefix)

Determines whether the file is an axes format The first line may have column headings. The following lines have the name in the first column plus float columns for each axis. ==> 98_sq_phylip_amazon.fn.unique.pca.axes <==

group axis1 axis2 forest 0.000000 0.145743 pasture 0.145743 0.000000

==> 98_sq_phylip_amazon.nmds.axes <==

axis1 axis2

U68589 0.262608 -0.077498 U68590 0.027118 0.195197 U68591 0.329854 0.014395

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.axes' )
>>> Axes().sniff( fname )
True
>>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.axes' )
>>> Axes().sniff( fname )
False

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a317160>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a317240>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a317f98>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1ab5ff28>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a317e80>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1a3170f0>}

sniff(filename)

class galaxy.datatypes.mothur.SffFlow(**kwd)

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.sff.flow'

https://mothur.org/wiki/flow_file/ The first line is the total number of flow values - 800 for Titanium data. For GS FLX it would be 400. Following lines contain: - SequenceName - the number of useable flows as defined by 454’s software - the flow intensity for each base going in the order of TACG. Example:

800 GQY1XT001CQL4K 85 1.04 0.00 1.00 0.02 0.03 1.02 0.05 … GQY1XT001CQIRF 84 1.02 0.06 0.98 0.06 0.09 1.05 0.07 … GQY1XT001CF5YW 88 1.02 0.02 1.01 0.04 0.06 1.02 0.03 …

__init__(**kwd)

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8eba8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e8d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8e0b8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7fc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f7feb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f41f8edd8>, 'flow_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c0f5198>, 'flow_values': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1c10ccf8>}

set_meta(dataset, overwrite=True, skip=1, max_data_lines=None, **kwd)

make_html_table(dataset, skipchars=None)

Create HTML table, used for displaying peek

galaxy.datatypes.msa module

class galaxy.datatypes.msa.InfernalCM(**kwd)

Bases: galaxy.datatypes.data.Text

file_ext = 'cm'

set_peek(dataset, is_multi_byte=False)

sniff_prefix(file_prefix)

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'infernal_model.cm' )
>>> InfernalCM().sniff( fname )
True
>>> fname = get_test_fname( '2.txt' )
>>> InfernalCM().sniff( fname )
False

set_meta(dataset, **kwd)

Set the number of models and the version of CM file in dataset.

metadata_spec = {'cm_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aeb8a20>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aeb89e8>}

sniff(filename)

class galaxy.datatypes.msa.Hmmer(**kwd)

Bases: galaxy.datatypes.data.Text

edam_data = 'data_1364'

edam_format = 'format_1370'

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

sniff_prefix(filename)

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f183cc160>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

sniff(filename)

class galaxy.datatypes.msa.Hmmer2(**kwd)

Bases: galaxy.datatypes.msa.Hmmer

edam_format = 'format_3328'

file_ext = 'hmm2'

sniff_prefix(file_prefix)

HMMER2 files start with HMMER2.0

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aeb8ba8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.msa.Hmmer3(**kwd)

Bases: galaxy.datatypes.msa.Hmmer

edam_format = 'format_3329'

file_ext = 'hmm3'

sniff_prefix(file_prefix)

HMMER3 files start with HMMER3/f

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aeb8ac8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.msa.HmmerPress(**kwd)

Bases: galaxy.datatypes.binary.Binary

Class for hmmpress database files.

file_ext = 'hmmpress'

allow_datatype_change = False

composite_type = 'basic'

set_peek(dataset, is_multi_byte=False)

Set the peek and blurb text.

display_peek(dataset)

Create HTML content, used for displaying peek.

__init__(**kwd)

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aeb8710>}

class galaxy.datatypes.msa.Stockholm_1_0(**kwd)

Bases: galaxy.datatypes.data.Text

edam_data = 'data_0863'

edam_format = 'format_1961'

file_ext = 'stockholm'

set_peek(dataset, is_multi_byte=False)

sniff_prefix(file_prefix)

set_meta(dataset, **kwd)

Set the number of models in dataset.

classmethod split(input_datasets, subdir_generator_function, split_params)

Split the input files by model records.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aeebda0>}

sniff(filename)

class galaxy.datatypes.msa.MauveXmfa(**kwd)

Bases: galaxy.datatypes.data.Text

file_ext = 'xmfa'

set_peek(dataset, is_multi_byte=False)

sniff_prefix(file_prefix)

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aeebbe0>}

set_meta(dataset, **kwd)

sniff(filename)

galaxy.datatypes.neo4j module

Neo4j Composite Dataset

class galaxy.datatypes.neo4j.Neo4j(**kwd)

Bases: galaxy.datatypes.images.Html

base class to use for neostore datatypes derived from html - composite datatype elements stored in extra files path

generate_primary_file(dataset=None)

This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately

get_mime()

Returns the mime type of the datatype

set_peek(dataset, is_multi_byte=False)

Set the peek and blurb text

display_peek(dataset)

Create HTML content, used for displaying peek.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18c544a8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.neo4j.Neo4jDB(**kwd)

Bases: galaxy.datatypes.neo4j.Neo4j, galaxy.datatypes.data.Data

Class for neo4jDB database files.

file_ext = 'neostore'

composite_type = 'auto_primary_file'

allow_datatype_change = False

__init__(**kwd)

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18c54550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}

class galaxy.datatypes.neo4j.Neo4jDBzip(**kwd)

Bases: galaxy.datatypes.neo4j.Neo4j, galaxy.datatypes.data.Data

Class for neo4jDB database files.

file_ext = 'neostore.zip'

composite_type = 'auto_primary_file'

allow_datatype_change = False

__init__(**kwd)

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18c544a8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'neostore_zip': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18c544e0>, 'reference_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18c54630>}

galaxy.datatypes.ngsindex module

NGS indexes

class galaxy.datatypes.ngsindex.BowtieIndex(**kwd)

Bases: galaxy.datatypes.text.Html

base class for BowtieIndex is subclassed by BowtieColorIndex and BowtieBaseIndex

composite_type = 'auto_primary_file'

allow_datatype_change = False

generate_primary_file(dataset=None)

This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately

regenerate_primary_file(dataset)

cannot do this until we are setting metadata

set_peek(dataset, is_multi_byte=False)

display_peek(dataset)

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19ed7c50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f190ff080>}

class galaxy.datatypes.ngsindex.BowtieColorIndex(**kwd)

Bases: galaxy.datatypes.ngsindex.BowtieIndex

Bowtie color space index

file_ext = 'bowtie_color_index'

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19ed7c50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f190ff4a8>}

class galaxy.datatypes.ngsindex.BowtieBaseIndex(**kwd)

Bases: galaxy.datatypes.ngsindex.BowtieIndex

Bowtie base space index

file_ext = 'bowtie_base_index'

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19ed7c50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f1aa36a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f190ff518>}

galaxy.datatypes.phylip module

Created on January. 05, 2018

@authors: Kenzo-Hugo Hillion and Fabien Mareuil, Institut Pasteur, Paris @contacts: kehillio@pasteur.fr and fabien.mareuil@pasteur.fr @project: galaxy @githuborganization: C3BI Phylip datatype sniffer

class galaxy.datatypes.phylip.Phylip(**kwd)

Bases: galaxy.datatypes.data.Text

Phylip format stores a multiple sequence alignment

edam_data = 'data_0863'

edam_format = 'format_1997'

file_ext = 'phylip'

Add metadata elements

set_meta(dataset, **kwd)

Set the number of sequences and the number of data lines in dataset.

set_peek(dataset, is_multi_byte=False)

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f19edaac8>}

sniff(filename)

sniff_prefix(file_prefix)

All Phylip files starts with the number of sequences so we can use this to count the following number of sequences in the first ‘stack’

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.phylip')
>>> Phylip().sniff(fname)
True

galaxy.datatypes.plant_tribes module

class galaxy.datatypes.plant_tribes.Smat(**kwd)

Bases: galaxy.datatypes.data.Text

file_ext = 'smat'

display_peek(dataset)

set_peek(dataset, is_multi_byte=False)

sniff_prefix(file_prefix)

The use of ESTScan implies the creation of scores matrices which reflect the codons preferences in the studied organisms. The ESTScan package includes scripts for generating these files. The output of these scripts consists of the matrices, one for each isochor, and which look like this:

FORMAT: hse_4is.conf CODING REGION 6 3 1 s C+G: 0 44 -1 0 2 -2 2 1 -8 0

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test_space.txt')
>>> Smat().sniff(fname)
False
>>> fname = get_test_fname('test_tab.bed')
>>> Smat().sniff(fname)
False
>>> fname = get_test_fname('1.smat')
>>> Smat().sniff(fname)
True

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f18f99f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f2f4530a390>}