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Source code for galaxy.datatypes.interval

"""
Interval datatypes
"""
import logging
import math
import os
import sys
import tempfile

from bx.intervals.io import GenomicIntervalReader, ParseError
from six.moves.urllib.parse import quote_plus

from galaxy import util
from galaxy.datatypes import metadata
from galaxy.datatypes.metadata import MetadataElement
from galaxy.datatypes.sniff import (
    get_headers,
    iter_headers
)
from galaxy.datatypes.tabular import Tabular
from galaxy.datatypes.util.gff_util import parse_gff3_attributes, parse_gff_attributes
from galaxy.web import url_for

from . import (
    data,
    dataproviders
)

log = logging.getLogger(__name__)

# Contains the meta columns and the words that map to it; list aliases on the
# right side of the : in decreasing order of priority
alias_spec = {
    'chromCol'  : ['chrom', 'CHROMOSOME', 'CHROM', 'Chromosome Name'],
    'startCol'  : ['start', 'START', 'chromStart', 'txStart', 'Start Position (bp)'],
    'endCol'    : ['end', 'END', 'STOP', 'chromEnd', 'txEnd', 'End Position (bp)'],
    'strandCol' : ['strand', 'STRAND', 'Strand'],
    'nameCol'   : ['name', 'NAME', 'Name', 'name2', 'NAME2', 'Name2', 'Ensembl Gene ID', 'Ensembl Transcript ID', 'Ensembl Peptide ID']
}

# a little faster lookup
alias_helper = {}
for key, value in alias_spec.items():
    for elem in value:
        alias_helper[elem] = key

# Constants for configuring viewport generation: If a line is greater than
# VIEWPORT_MAX_READS_PER_LINE * VIEWPORT_READLINE_BUFFER_SIZE bytes in size,
# then we will not generate a viewport for that dataset
VIEWPORT_READLINE_BUFFER_SIZE = 1048576  # 1MB
VIEWPORT_MAX_READS_PER_LINE = 10


[docs]@dataproviders.decorators.has_dataproviders class Interval(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""" MetadataElement(name="chromCol", default=1, desc="Chrom column", param=metadata.ColumnParameter) MetadataElement(name="startCol", default=2, desc="Start column", param=metadata.ColumnParameter) MetadataElement(name="endCol", default=3, desc="End column", param=metadata.ColumnParameter) MetadataElement(name="strandCol", desc="Strand column (click box & select)", param=metadata.ColumnParameter, optional=True, no_value=0) MetadataElement(name="nameCol", desc="Name/Identifier column (click box & select)", param=metadata.ColumnParameter, optional=True, no_value=0) MetadataElement(name="columns", default=3, desc="Number of columns", readonly=True, visible=False)
[docs] def __init__(self, **kwd): """Initialize interval datatype, by adding UCSC display apps""" Tabular.__init__(self, **kwd) self.add_display_app('ucsc', 'display at UCSC', 'as_ucsc_display_file', 'ucsc_links')
[docs] def init_meta(self, dataset, copy_from=None): Tabular.init_meta(self, dataset, copy_from=copy_from)
[docs] def set_meta(self, 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""" Tabular.set_meta(self, dataset, overwrite=overwrite, skip=0) if dataset.has_data(): empty_line_count = 0 num_check_lines = 100 # only check up to this many non empty lines for i, line in enumerate(open(dataset.file_name)): line = line.rstrip('\r\n') if line: if (first_line_is_header or line[0] == '#'): self.init_meta(dataset) line = line.strip('#') elems = line.split('\t') for meta_name, header_list in alias_spec.items(): for header_val in header_list: if header_val in elems: # found highest priority header to meta_name setattr(dataset.metadata, meta_name, elems.index(header_val) + 1) break # next meta_name break # Our metadata is set, so break out of the outer loop else: # Header lines in Interval files are optional. For example, BED is Interval but has no header. # We'll make a best guess at the location of the metadata columns. metadata_is_set = False elems = line.split('\t') if len(elems) > 2: for str in data.col1_startswith: if line.lower().startswith(str): if overwrite or not dataset.metadata.element_is_set('chromCol'): dataset.metadata.chromCol = 1 try: int(elems[1]) if overwrite or not dataset.metadata.element_is_set('startCol'): dataset.metadata.startCol = 2 except: pass # Metadata default will be used try: int(elems[2]) if overwrite or not dataset.metadata.element_is_set('endCol'): dataset.metadata.endCol = 3 except: pass # Metadata default will be used # we no longer want to guess that this column is the 'name', name must now be set manually for interval files # we will still guess at the strand, as we can make a more educated guess # if len( elems ) > 3: # try: # int( elems[3] ) # except: # if overwrite or not dataset.metadata.element_is_set( 'nameCol' ): # dataset.metadata.nameCol = 4 if len(elems) < 6 or elems[5] not in data.valid_strand: if overwrite or not dataset.metadata.element_is_set('strandCol'): dataset.metadata.strandCol = 0 else: if overwrite or not dataset.metadata.element_is_set('strandCol'): dataset.metadata.strandCol = 6 metadata_is_set = True break if metadata_is_set or (i - empty_line_count) > num_check_lines: break # Our metadata is set or we examined 100 non-empty lines, so break out of the outer loop else: empty_line_count += 1
[docs] def displayable(self, dataset): try: return dataset.has_data() \ and dataset.state == dataset.states.OK \ and dataset.metadata.columns > 0 \ and dataset.metadata.data_lines != 0 \ and dataset.metadata.chromCol \ and dataset.metadata.startCol \ and dataset.metadata.endCol except: return False
[docs] def get_estimated_display_viewport(self, dataset, chrom_col=None, start_col=None, end_col=None): """Return a chrom, start, stop tuple for viewing a file.""" viewport_feature_count = 100 # viewport should check at least 100 features; excludes comment lines max_line_count = max(viewport_feature_count, 500) # maximum number of lines to check; includes comment lines if not self.displayable(dataset): return (None, None, None) try: # If column indexes were not passwed, determine from metadata if chrom_col is None: chrom_col = int(dataset.metadata.chromCol) - 1 if start_col is None: start_col = int(dataset.metadata.startCol) - 1 if end_col is None: end_col = int(dataset.metadata.endCol) - 1 # Scan lines of file to find a reasonable chromosome and range chrom = None start = sys.maxsize end = 0 max_col = max(chrom_col, start_col, end_col) fh = open(dataset.file_name) while True: line = fh.readline(VIEWPORT_READLINE_BUFFER_SIZE) # Stop if at end of file if not line: break # Skip comment lines if not line.startswith('#'): try: fields = line.rstrip().split('\t') if len(fields) > max_col: if chrom is None or chrom == fields[chrom_col]: start = min(start, int(fields[start_col])) end = max(end, int(fields[end_col])) # Set chrom last, in case start and end are not integers chrom = fields[chrom_col] viewport_feature_count -= 1 except Exception: # Most likely a non-integer field has been encountered # for start / stop. Just ignore and make sure we finish # reading the line and decrementing the counters. pass # Make sure we are at the next new line readline_count = VIEWPORT_MAX_READS_PER_LINE while line.rstrip('\n\r') == line: assert readline_count > 0, Exception('Viewport readline count exceeded for dataset %s.' % dataset.id) line = fh.readline(VIEWPORT_READLINE_BUFFER_SIZE) if not line: break # EOF readline_count -= 1 max_line_count -= 1 if not viewport_feature_count or not max_line_count: # exceeded viewport or total line count to check break if chrom is not None: return (chrom, str(start), str(end)) # Necessary to return strings? except Exception: # Unexpected error, possibly missing metadata log.exception("Exception caught attempting to generate viewport for dataset '%d'", dataset.id) return (None, None, None)
[docs] def as_ucsc_display_file(self, dataset, **kwd): """Returns file contents with only the bed data""" fd, temp_name = tempfile.mkstemp() c, s, e, t, n = dataset.metadata.chromCol, dataset.metadata.startCol, dataset.metadata.endCol, dataset.metadata.strandCol or 0, dataset.metadata.nameCol or 0 c, s, e, t, n = int(c) - 1, int(s) - 1, int(e) - 1, int(t) - 1, int(n) - 1 if t >= 0: # strand column (should) exists for i, elems in enumerate(util.file_iter(dataset.file_name)): strand = "+" name = "region_%i" % i if n >= 0 and n < len(elems): name = elems[n] if t < len(elems): strand = elems[t] tmp = [elems[c], elems[s], elems[e], name, '0', strand] os.write(fd, '%s\n' % '\t'.join(tmp)) elif n >= 0: # name column (should) exists for i, elems in enumerate(util.file_iter(dataset.file_name)): name = "region_%i" % i if n >= 0 and n < len(elems): name = elems[n] tmp = [elems[c], elems[s], elems[e], name] os.write(fd, '%s\n' % '\t'.join(tmp)) else: for elems in util.file_iter(dataset.file_name): tmp = [elems[c], elems[s], elems[e]] os.write(fd, '%s\n' % '\t'.join(tmp)) os.close(fd) return open(temp_name)
[docs] def display_peek(self, dataset): """Returns formated html of peek""" return self.make_html_table(dataset, column_parameter_alias={'chromCol': 'Chrom', 'startCol': 'Start', 'endCol': 'End', 'strandCol': 'Strand', 'nameCol': 'Name'})
[docs] def validate(self, dataset): """Validate an interval file using the bx GenomicIntervalReader""" errors = list() c, s, e, t = dataset.metadata.chromCol, dataset.metadata.startCol, dataset.metadata.endCol, dataset.metadata.strandCol c, s, e, t = int(c) - 1, int(s) - 1, int(e) - 1, int(t) - 1 infile = open(dataset.file_name, "r") reader = GenomicIntervalReader( infile, chrom_col=c, start_col=s, end_col=e, strand_col=t) while True: try: next(reader) except ParseError as e: errors.append(e) except StopIteration: infile.close() return errors
[docs] def repair_methods(self, dataset): """Return options for removing errors along with a description""" return [("lines", "Remove erroneous lines")]
[docs] def sniff(self, filename): """ 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 """ try: """ If we got here, we already know the file is_column_based and is not bed, so we'll just look for some valid data. """ headers = iter_headers(filename, '\t', comment_designator='#') for hdr in headers: if hdr: if len(hdr) < 3: return False try: # Assume chrom start and end are in column positions 1 and 2 # respectively ( for 0 based columns ) int(hdr[1]) int(hdr[2]) except: return False return True except: return False
[docs] def get_track_resolution(self, dataset, start, end): return None
# ------------- Dataproviders
[docs] @dataproviders.decorators.dataprovider_factory('genomic-region', dataproviders.dataset.GenomicRegionDataProvider.settings) def genomic_region_dataprovider(self, dataset, **settings): return dataproviders.dataset.GenomicRegionDataProvider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory('genomic-region-dict', dataproviders.dataset.GenomicRegionDataProvider.settings) def genomic_region_dict_dataprovider(self, dataset, **settings): settings['named_columns'] = True return self.genomic_region_dataprovider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory('interval', dataproviders.dataset.IntervalDataProvider.settings) def interval_dataprovider(self, dataset, **settings): return dataproviders.dataset.IntervalDataProvider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory('interval-dict', dataproviders.dataset.IntervalDataProvider.settings) def interval_dict_dataprovider(self, dataset, **settings): settings['named_columns'] = True return self.interval_dataprovider(dataset, **settings)
[docs]class BedGraph(Interval): """Tab delimited chrom/start/end/datavalue dataset""" edam_format = "format_3583" file_ext = "bedgraph" track_type = "LineTrack" data_sources = {"data": "bigwig", "index": "bigwig"}
[docs] def as_ucsc_display_file(self, 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. """ return open(dataset.file_name)
[docs] def get_estimated_display_viewport(self, dataset, chrom_col=0, start_col=1, end_col=2): """ Set viewport based on dataset's first 100 lines. """ return Interval.get_estimated_display_viewport(self, dataset, chrom_col=chrom_col, start_col=start_col, end_col=end_col)
[docs]class Bed(Interval): """Tab delimited data in BED format""" edam_format = "format_3003" file_ext = "bed" data_sources = {"data": "tabix", "index": "bigwig", "feature_search": "fli"} track_type = Interval.track_type column_names = ['Chrom', 'Start', 'End', 'Name', 'Score', 'Strand', 'ThickStart', 'ThickEnd', 'ItemRGB', 'BlockCount', 'BlockSizes', 'BlockStarts'] """Add metadata elements""" MetadataElement(name="chromCol", default=1, desc="Chrom column", param=metadata.ColumnParameter) MetadataElement(name="startCol", default=2, desc="Start column", param=metadata.ColumnParameter) MetadataElement(name="endCol", default=3, desc="End column", param=metadata.ColumnParameter) MetadataElement(name="strandCol", desc="Strand column (click box & select)", param=metadata.ColumnParameter, optional=True, no_value=0) MetadataElement(name="columns", default=3, desc="Number of columns", readonly=True, visible=False) MetadataElement(name="viz_filter_cols", desc="Score column for visualization", default=[4], param=metadata.ColumnParameter, optional=True, multiple=True) # do we need to repeat these? they are the same as should be inherited from interval type
[docs] def set_meta(self, dataset, overwrite=True, **kwd): """Sets the metadata information for datasets previously determined to be in bed format.""" i = 0 if dataset.has_data(): for i, line in enumerate(open(dataset.file_name)): metadata_set = False line = line.rstrip('\r\n') if line and not line.startswith('#'): elems = line.split('\t') if len(elems) > 2: for startswith in data.col1_startswith: if line.lower().startswith(startswith): if len(elems) > 3: if overwrite or not dataset.metadata.element_is_set('nameCol'): dataset.metadata.nameCol = 4 if len(elems) < 6: if overwrite or not dataset.metadata.element_is_set('strandCol'): dataset.metadata.strandCol = 0 else: if overwrite or not dataset.metadata.element_is_set('strandCol'): dataset.metadata.strandCol = 6 metadata_set = True break if metadata_set: break Tabular.set_meta(self, dataset, overwrite=overwrite, skip=i)
[docs] def as_ucsc_display_file(self, dataset, **kwd): """Returns file contents with only the bed data. If bed 6+, treat as interval.""" for line in open(dataset.file_name): line = line.strip() if line == "" or line.startswith("#"): continue fields = line.split('\t') """check to see if this file doesn't conform to strict genome browser accepted bed""" try: if len(fields) > 12: return Interval.as_ucsc_display_file(self, dataset) # too many fields if len(fields) > 6: int(fields[6]) if len(fields) > 7: int(fields[7]) if len(fields) > 8: if int(fields[8]) != 0: return Interval.as_ucsc_display_file(self, dataset) if len(fields) > 9: int(fields[9]) if len(fields) > 10: fields2 = fields[10].rstrip(",").split(",") # remove trailing comma and split on comma for field in fields2: int(field) if len(fields) > 11: fields2 = fields[11].rstrip(",").split(",") # remove trailing comma and split on comma for field in fields2: int(field) except: return Interval.as_ucsc_display_file(self, dataset) # only check first line for proper form break try: return open(dataset.file_name) except: return "This item contains no content"
[docs] def sniff(self, filename): """ 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( 'interval1.bed' ) >>> Bed().sniff( fname ) True >>> fname = get_test_fname( 'complete.bed' ) >>> Bed().sniff( fname ) True """ if not get_headers(filename, '\t', comment_designator='#', count=1): return False try: headers = iter_headers(filename, '\t', comment_designator='#') for hdr in headers: if hdr[0] == '': continue valid_col1 = False if len(hdr) < 3 or len(hdr) > 12: return False for str in data.col1_startswith: if hdr[0].lower().startswith(str): valid_col1 = True break if valid_col1: try: int(hdr[1]) int(hdr[2]) except: return False if len(hdr) > 4: # hdr[3] is a string, 'name', which defines the name of the BED line - difficult to test for this. # hdr[4] is an int, 'score', a score between 0 and 1000. try: if int(hdr[4]) < 0 or int(hdr[4]) > 1000: return False except: return False if len(hdr) > 5: # hdr[5] is strand if hdr[5] not in data.valid_strand: return False if len(hdr) > 6: # hdr[6] is thickStart, the starting position at which the feature is drawn thickly. try: int(hdr[6]) except: return False if len(hdr) > 7: # hdr[7] is thickEnd, the ending position at which the feature is drawn thickly try: int(hdr[7]) except: return False if len(hdr) > 8: # hdr[8] is itemRgb, an RGB value of the form R,G,B (e.g. 255,0,0). However, this could also be an int (e.g., 0) try: int(hdr[8]) except: try: hdr[8].split(',') except: return False if len(hdr) > 9: # hdr[9] is blockCount, the number of blocks (exons) in the BED line. try: block_count = int(hdr[9]) except: return False if len(hdr) > 10: # hdr[10] is blockSizes - A comma-separated list of the block sizes. # Sometimes the blosck_sizes and block_starts lists end in extra commas try: block_sizes = hdr[10].rstrip(',').split(',') except: return False if len(hdr) > 11: # hdr[11] is blockStarts - A comma-separated list of block starts. try: block_starts = hdr[11].rstrip(',').split(',') except: return False if len(block_sizes) != block_count or len(block_starts) != block_count: return False else: return False return True except: return False
[docs]class BedStrict(Bed): """Tab delimited data in strict BED format - no non-standard columns allowed""" edam_format = "format_3584" file_ext = "bedstrict" # no user change of datatype allowed allow_datatype_change = False # Read only metadata elements MetadataElement(name="chromCol", default=1, desc="Chrom column", readonly=True, param=metadata.MetadataParameter) MetadataElement(name="startCol", default=2, desc="Start column", readonly=True, param=metadata.MetadataParameter) # TODO: start and end should be able to be set to these or the proper thick[start/end]? MetadataElement(name="endCol", default=3, desc="End column", readonly=True, param=metadata.MetadataParameter) MetadataElement(name="strandCol", desc="Strand column (click box & select)", readonly=True, param=metadata.MetadataParameter, no_value=0, optional=True) MetadataElement(name="nameCol", desc="Name/Identifier column (click box & select)", readonly=True, param=metadata.MetadataParameter, no_value=0, optional=True) MetadataElement(name="columns", default=3, desc="Number of columns", readonly=True, visible=False)
[docs] def __init__(self, **kwd): Tabular.__init__(self, **kwd) self.clear_display_apps() # only new style display applications for this datatype
[docs] def set_meta(self, dataset, overwrite=True, **kwd): Tabular.set_meta(self, dataset, overwrite=overwrite, **kwd) # need column count first if dataset.metadata.columns >= 4: dataset.metadata.nameCol = 4 if dataset.metadata.columns >= 6: dataset.metadata.strandCol = 6
[docs] def sniff(self, filename): return False # NOTE: This would require aggressively validating the entire file
[docs]class Bed6(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"
[docs]class Bed12(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"
class _RemoteCallMixin: def _get_remote_call_url(self, redirect_url, site_name, dataset, type, app, base_url): """Retrieve the URL to call out to an external site and retrieve data. This routes our external URL through a local galaxy instance which makes the data available, followed by redirecting to the remote site with a link back to the available information. """ internal_url = "%s" % url_for(controller='dataset', dataset_id=dataset.id, action='display_at', filename='%s_%s' % (type, site_name)) base_url = app.config.get("display_at_callback", base_url) display_url = quote_plus("%s%s/display_as?id=%i&display_app=%s&authz_method=display_at" % (base_url, url_for(controller='root'), dataset.id, type)) link = '%s?redirect_url=%s&display_url=%s' % (internal_url, redirect_url, display_url) return link
[docs]@dataproviders.decorators.has_dataproviders class Gff(Tabular, _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", "index": "bigwig", "feature_search": "fli"} track_type = Interval.track_type """Add metadata elements""" MetadataElement(name="columns", default=9, desc="Number of columns", readonly=True, visible=False) MetadataElement(name="column_types", default=['str', 'str', 'str', 'int', 'int', 'int', 'str', 'str', 'str'], param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False) MetadataElement(name="attributes", default=0, desc="Number of attributes", readonly=True, visible=False, no_value=0) MetadataElement(name="attribute_types", default={}, desc="Attribute types", param=metadata.DictParameter, readonly=True, visible=False, no_value=[])
[docs] def __init__(self, **kwd): """Initialize datatype, by adding GBrowse display app""" Tabular.__init__(self, **kwd) self.add_display_app('ucsc', 'display at UCSC', 'as_ucsc_display_file', 'ucsc_links') self.add_display_app('gbrowse', 'display in Gbrowse', 'as_gbrowse_display_file', 'gbrowse_links')
[docs] def set_attribute_metadata(self, dataset): """ Sets metadata elements for dataset's attributes. """ # Use first N lines to set metadata for dataset attributes. Attributes # not found in the first N lines will not have metadata. num_lines = 200 attribute_types = {} for i, line in enumerate(open(dataset.file_name)): if line and not line.startswith('#'): elems = line.split('\t') if len(elems) == 9: try: # Loop through attributes to set types. for name, value in parse_gff_attributes(elems[8]).items(): # Default type is string. value_type = "str" try: # Try int. int(value) value_type = "int" except: try: # Try float. float(value) value_type = "float" except: pass attribute_types[name] = value_type except: pass if i + 1 == num_lines: break # Set attribute metadata and then set additional metadata. dataset.metadata.attribute_types = attribute_types dataset.metadata.attributes = len(attribute_types)
[docs] def set_meta(self, dataset, overwrite=True, **kwd): self.set_attribute_metadata(dataset) i = 0 for i, line in enumerate(open(dataset.file_name)): line = line.rstrip('\r\n') if line and not line.startswith('#'): elems = line.split('\t') if len(elems) == 9: try: int(elems[3]) int(elems[4]) break except: pass Tabular.set_meta(self, dataset, overwrite=overwrite, skip=i)
[docs] def display_peek(self, dataset): """Returns formated html of peek""" return self.make_html_table(dataset, column_names=self.column_names)
[docs] def get_estimated_display_viewport(self, 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... """ viewport_feature_count = 100 # viewport should check at least 100 features; excludes comment lines max_line_count = max(viewport_feature_count, 500) # maximum number of lines to check; includes comment lines if self.displayable(dataset): try: seqid = None start = sys.maxsize stop = 0 fh = open(dataset.file_name) while True: line = fh.readline(VIEWPORT_READLINE_BUFFER_SIZE) if not line: break # EOF try: if line.startswith('##sequence-region'): # ##sequence-region IV 6000000 6030000 elems = line.rstrip('\n\r').split() if len(elems) > 3: # line looks like: # sequence-region ctg123 1 1497228 seqid = elems[1] # IV start = int(elems[2]) # 6000000 stop = int(elems[3]) # 6030000 break # use location declared in file elif len(elems) == 2 and elems[1].find('..') > 0: # line looks like this: # sequence-region X:120000..140000 elems = elems[1].split(':') seqid = elems[0] start = int(elems[1].split('..')[0]) stop = int(elems[1].split('..')[1]) break # use location declared in file else: log.exception("line (%s) uses an unsupported ##sequence-region definition." % str(line)) # break #no break, if bad definition, we try another method elif line.startswith("browser position"): # Allow UCSC style browser and track info in the GFF file pos_info = line.split()[-1] seqid, startend = pos_info.split(":") start, stop = map(int, startend.split("-")) break # use location declared in file elif True not in map(line.startswith, ('#', 'track', 'browser')): # line.startswith() does not accept iterator in python2.4 viewport_feature_count -= 1 elems = line.rstrip('\n\r').split('\t') if len(elems) > 3: if not seqid: # We can only set the viewport for a single chromosome seqid = elems[0] if seqid == elems[0]: # Make sure we have not spanned chromosomes start = min(start, int(elems[3])) stop = max(stop, int(elems[4])) except: # most likely start/stop is not an int or not enough fields pass # make sure we are at the next new line readline_count = VIEWPORT_MAX_READS_PER_LINE while line.rstrip('\n\r') == line: assert readline_count > 0, Exception('Viewport readline count exceeded for dataset %s.' % dataset.id) line = fh.readline(VIEWPORT_READLINE_BUFFER_SIZE) if not line: break # EOF readline_count -= 1 max_line_count -= 1 if not viewport_feature_count or not max_line_count: # exceeded viewport or total line count to check break if seqid is not None: return (seqid, str(start), str(stop)) # Necessary to return strings? except Exception as e: # unexpected error log.exception(str(e)) return (None, None, None) # could not determine viewport
[docs] def sniff(self, filename): """ 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_version_3.gff' ) >>> Gff().sniff( fname ) False >>> fname = get_test_fname( 'test.gff' ) >>> Gff().sniff( fname ) True """ if len(get_headers(filename, '\t', count=2)) < 2: return False try: headers = iter_headers(filename, '\t') for hdr in headers: if hdr and hdr[0].startswith('##gff-version') and hdr[0].find('2') < 0: return False if hdr and hdr[0] and not hdr[0].startswith('#'): if len(hdr) != 9: return False try: int(hdr[3]) int(hdr[4]) except: return False if hdr[5] != '.': try: float(hdr[5]) except: return False if hdr[6] not in data.valid_strand: return False if hdr[7] not in self.valid_gff_frame: return False return True except: return False
# ------------- Dataproviders # redefine bc super is Tabular
[docs] @dataproviders.decorators.dataprovider_factory('genomic-region', dataproviders.dataset.GenomicRegionDataProvider.settings) def genomic_region_dataprovider(self, dataset, **settings): return dataproviders.dataset.GenomicRegionDataProvider(dataset, 0, 3, 4, **settings)
[docs] @dataproviders.decorators.dataprovider_factory('genomic-region-dict', dataproviders.dataset.GenomicRegionDataProvider.settings) def genomic_region_dict_dataprovider(self, dataset, **settings): settings['named_columns'] = True return self.genomic_region_dataprovider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory('interval', dataproviders.dataset.IntervalDataProvider.settings) def interval_dataprovider(self, dataset, **settings): return dataproviders.dataset.IntervalDataProvider(dataset, 0, 3, 4, 6, 2, **settings)
[docs] @dataproviders.decorators.dataprovider_factory('interval-dict', dataproviders.dataset.IntervalDataProvider.settings) def interval_dict_dataprovider(self, dataset, **settings): settings['named_columns'] = True return self.interval_dataprovider(dataset, **settings)
[docs]class Gff3(Gff): """Tab delimited data in Gff3 format""" edam_format = "format_1975" file_ext = "gff3" valid_gff3_strand = ['+', '-', '.', '?'] valid_gff3_phase = Gff.valid_gff_frame column_names = ['Seqid', 'Source', 'Type', 'Start', 'End', 'Score', 'Strand', 'Phase', 'Attributes'] track_type = Interval.track_type """Add metadata elements""" MetadataElement(name="column_types", default=['str', 'str', 'str', 'int', 'int', 'float', 'str', 'int', 'list'], param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False)
[docs] def __init__(self, **kwd): """Initialize datatype, by adding GBrowse display app""" Gff.__init__(self, **kwd)
[docs] def set_meta(self, dataset, overwrite=True, **kwd): self.set_attribute_metadata(dataset) i = 0 for i, line in enumerate(open(dataset.file_name)): line = line.rstrip('\r\n') if line and not line.startswith('#'): elems = line.split('\t') valid_start = False valid_end = False if len(elems) == 9: try: start = int(elems[3]) valid_start = True except: if elems[3] == '.': valid_start = True try: end = int(elems[4]) valid_end = True except: if elems[4] == '.': valid_end = True strand = elems[6] phase = elems[7] if valid_start and valid_end and start < end and strand in self.valid_gff3_strand and phase in self.valid_gff3_phase: break Tabular.set_meta(self, dataset, overwrite=overwrite, skip=i)
[docs] def sniff(self, filename): """ 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_version_3.gff') >>> Gff3().sniff( fname ) True """ if len(get_headers(filename, '\t', count=2)) < 2: return False try: headers = iter_headers(filename, '\t') for hdr in headers: if hdr and hdr[0].startswith('##gff-version') and hdr[0].find('3') >= 0: return True elif hdr and hdr[0].startswith('##gff-version') and hdr[0].find('3') < 0: return False # Header comments may have been stripped, so inspect the data if hdr and hdr[0] and not hdr[0].startswith('#'): if len(hdr) != 9: return False try: int(hdr[3]) except: if hdr[3] != '.': return False try: int(hdr[4]) except: if hdr[4] != '.': return False if hdr[5] != '.': try: float(hdr[5]) except: return False if hdr[6] not in self.valid_gff3_strand: return False if hdr[7] not in self.valid_gff3_phase: return False parse_gff3_attributes(hdr[8]) return True except: return False
[docs]class Gtf(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 = Interval.track_type """Add metadata elements""" MetadataElement(name="columns", default=9, desc="Number of columns", readonly=True, visible=False) MetadataElement(name="column_types", default=['str', 'str', 'str', 'int', 'int', 'float', 'str', 'int', 'list'], param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False)
[docs] def sniff(self, filename): """ 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 """ if len(get_headers(filename, '\t', count=2)) < 2: return False try: headers = iter_headers(filename, '\t') for hdr in headers: if hdr and hdr[0].startswith('##gff-version') and hdr[0].find('2') < 0: return False if hdr and hdr[0] and not hdr[0].startswith('#'): if len(hdr) != 9: return False try: int(hdr[3]) int(hdr[4]) except: return False if hdr[5] != '.': try: float(hdr[5]) except: return False if hdr[6] not in data.valid_strand: return False if hdr[7] not in self.valid_gff_frame: return False # Check attributes for gene_id, transcript_id attributes = parse_gff_attributes(hdr[8]) if len(attributes) >= 2: if 'gene_id' not in attributes: return False if 'transcript_id' not in attributes: return False else: return False return True except: return False
[docs]@dataproviders.decorators.has_dataproviders class Wiggle(Tabular, _RemoteCallMixin): """Tab delimited data in wiggle format""" edam_format = "format_3005" file_ext = "wig" track_type = "LineTrack" data_sources = {"data": "bigwig", "index": "bigwig"} MetadataElement(name="columns", default=3, desc="Number of columns", readonly=True, visible=False)
[docs] def __init__(self, **kwd): Tabular.__init__(self, **kwd) self.add_display_app('ucsc', 'display at UCSC', 'as_ucsc_display_file', 'ucsc_links') self.add_display_app('gbrowse', 'display in Gbrowse', 'as_gbrowse_display_file', 'gbrowse_links')
[docs] def get_estimated_display_viewport(self, dataset): """Return a chrom, start, stop tuple for viewing a file.""" viewport_feature_count = 100 # viewport should check at least 100 features; excludes comment lines max_line_count = max(viewport_feature_count, 500) # maximum number of lines to check; includes comment lines if self.displayable(dataset): try: chrom = None start = sys.maxsize end = 0 span = 1 step = None fh = open(dataset.file_name) while True: line = fh.readline(VIEWPORT_READLINE_BUFFER_SIZE) if not line: break # EOF try: if line.startswith("browser"): chr_info = line.rstrip('\n\r').split()[-1] chrom, coords = chr_info.split(":") start, end = map(int, coords.split("-")) break # use the browser line # variableStep chrom=chr20 if line and (line.lower().startswith("variablestep") or line.lower().startswith("fixedstep")): if chrom is not None: break # different chrom or different section of the chrom chrom = line.rstrip('\n\r').split("chrom=")[1].split()[0] if 'span=' in line: span = int(line.rstrip('\n\r').split("span=")[1].split()[0]) if 'step=' in line: step = int(line.rstrip('\n\r').split("step=")[1].split()[0]) start = int(line.rstrip('\n\r').split("start=")[1].split()[0]) else: fields = line.rstrip('\n\r').split() if fields: if step is not None: if not end: end = start + span else: end += step else: start = min(int(fields[0]), start) end = max(end, int(fields[0]) + span) viewport_feature_count -= 1 except: pass # make sure we are at the next new line readline_count = VIEWPORT_MAX_READS_PER_LINE while line.rstrip('\n\r') == line: assert readline_count > 0, Exception('Viewport readline count exceeded for dataset %s.' % dataset.id) line = fh.readline(VIEWPORT_READLINE_BUFFER_SIZE) if not line: break # EOF readline_count -= 1 max_line_count -= 1 if not viewport_feature_count or not max_line_count: # exceeded viewport or total line count to check break if chrom is not None: return (chrom, str(start), str(end)) # Necessary to return strings? except Exception as e: # unexpected error log.exception(str(e)) return (None, None, None) # could not determine viewport
[docs] def display_peek(self, dataset): """Returns formated html of peek""" return self.make_html_table(dataset, skipchars=['track', '#'])
[docs] def set_meta(self, dataset, overwrite=True, **kwd): max_data_lines = None i = 0 for i, line in enumerate(open(dataset.file_name)): line = line.rstrip('\r\n') if line and not line.startswith('#'): elems = line.split('\t') try: float(elems[0]) # "Wiggle track data values can be integer or real, positive or negative values" break except: do_break = False for col_startswith in data.col1_startswith: if elems[0].lower().startswith(col_startswith): do_break = True break if do_break: break if self.max_optional_metadata_filesize >= 0 and dataset.get_size() > self.max_optional_metadata_filesize: # we'll arbitrarily only use the first 100 data lines in this wig file to calculate tabular attributes (column types) # this should be sufficient, except when we have mixed wig track types (bed, variable, fixed), # but those cases are not a single table that would have consistant column definitions # optional metadata values set in Tabular class will be 'None' max_data_lines = 100 Tabular.set_meta(self, dataset, overwrite=overwrite, skip=i, max_data_lines=max_data_lines)
[docs] def sniff(self, filename): """ 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( 'interval1.bed' ) >>> Wiggle().sniff( fname ) False >>> fname = get_test_fname( 'wiggle.wig' ) >>> Wiggle().sniff( fname ) True """ try: headers = iter_headers(filename, None) for hdr in headers: if len(hdr) > 1 and hdr[0] == 'track' and hdr[1].startswith('type=wiggle'): return True return False except: return False
[docs] def get_track_resolution(self, dataset, start, end): range = end - start # Determine appropriate resolution to plot ~1000 points resolution = math.ceil(10 ** math.ceil(math.log10(range / 1000))) # Restrict to valid range resolution = min(resolution, 100000) resolution = max(resolution, 1) return resolution
# ------------- Dataproviders
[docs] @dataproviders.decorators.dataprovider_factory('wiggle', dataproviders.dataset.WiggleDataProvider.settings) def wiggle_dataprovider(self, dataset, **settings): dataset_source = dataproviders.dataset.DatasetDataProvider(dataset) return dataproviders.dataset.WiggleDataProvider(dataset_source, **settings)
[docs] @dataproviders.decorators.dataprovider_factory('wiggle-dict', dataproviders.dataset.WiggleDataProvider.settings) def wiggle_dict_dataprovider(self, dataset, **settings): dataset_source = dataproviders.dataset.DatasetDataProvider(dataset) settings['named_columns'] = True return dataproviders.dataset.WiggleDataProvider(dataset_source, **settings)
[docs]class CustomTrack (Tabular): """UCSC CustomTrack""" edam_format = "format_3588" file_ext = "customtrack"
[docs] def __init__(self, **kwd): """Initialize interval datatype, by adding UCSC display app""" Tabular.__init__(self, **kwd) self.add_display_app('ucsc', 'display at UCSC', 'as_ucsc_display_file', 'ucsc_links')
[docs] def set_meta(self, dataset, overwrite=True, **kwd): Tabular.set_meta(self, dataset, overwrite=overwrite, skip=1)
[docs] def display_peek(self, dataset): """Returns formated html of peek""" return self.make_html_table(dataset, skipchars=['track', '#'])
[docs] def get_estimated_display_viewport(self, dataset, chrom_col=None, start_col=None, end_col=None): """Return a chrom, start, stop tuple for viewing a file.""" # FIXME: only BED and WIG custom tracks are currently supported # As per previously existing behavior, viewport will only be over the first intervals max_line_count = 100 # maximum number of lines to check; includes comment lines variable_step_wig = False chrom = None span = 1 if self.displayable(dataset): try: fh = open(dataset.file_name) while True: line = fh.readline(VIEWPORT_READLINE_BUFFER_SIZE) if not line: break # EOF if not line.startswith('#'): try: if variable_step_wig: fields = line.rstrip().split() if len(fields) == 2: start = int(fields[0]) return (chrom, str(start), str(start + span)) elif line and (line.lower().startswith("variablestep") or line.lower().startswith("fixedstep")): chrom = line.rstrip('\n\r').split("chrom=")[1].split()[0] if 'span=' in line: span = int(line.rstrip('\n\r').split("span=")[1].split()[0]) if 'start=' in line: start = int(line.rstrip('\n\r').split("start=")[1].split()[0]) return (chrom, str(start), str(start + span)) else: variable_step_wig = True else: fields = line.rstrip().split('\t') if len(fields) >= 3: chrom = fields[0] start = int(fields[1]) end = int(fields[2]) return (chrom, str(start), str(end)) except Exception: # most likely a non-integer field has been encountered for start / stop continue # make sure we are at the next new line readline_count = VIEWPORT_MAX_READS_PER_LINE while line.rstrip('\n\r') == line: assert readline_count > 0, Exception('Viewport readline count exceeded for dataset %s.' % dataset.id) line = fh.readline(VIEWPORT_READLINE_BUFFER_SIZE) if not line: break # EOF readline_count -= 1 max_line_count -= 1 if not max_line_count: # exceeded viewport or total line count to check break except Exception as e: # unexpected error log.exception(str(e)) return (None, None, None) # could not determine viewport
[docs] def sniff(self, filename): """ 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 """ headers = iter_headers(filename, None) first_line = True for hdr in headers: if first_line: first_line = False try: if hdr[0].startswith('track'): color_found = False visibility_found = False for elem in hdr[1:]: if elem.startswith('color'): color_found = True if elem.startswith('visibility'): visibility_found = True if color_found and visibility_found: break if not color_found or not visibility_found: return False else: return False except: return False else: try: if hdr[0] and not hdr[0].startswith('#'): if len(hdr) < 3: return False try: int(hdr[1]) int(hdr[2]) except: return False except: return False return True
[docs]class ENCODEPeak(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""" MetadataElement(name="chromCol", default=1, desc="Chrom column", param=metadata.ColumnParameter) MetadataElement(name="startCol", default=2, desc="Start column", param=metadata.ColumnParameter) MetadataElement(name="endCol", default=3, desc="End column", param=metadata.ColumnParameter) MetadataElement(name="strandCol", desc="Strand column (click box & select)", param=metadata.ColumnParameter, optional=True, no_value=0) MetadataElement(name="columns", default=3, desc="Number of columns", readonly=True, visible=False)
[docs] def sniff(self, filename): return False
[docs]class ChromatinInteractions(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""" MetadataElement(name="chrom1Col", default=1, desc="Chrom1 column", param=metadata.ColumnParameter) MetadataElement(name="start1Col", default=2, desc="Start1 column", param=metadata.ColumnParameter) MetadataElement(name="end1Col", default=3, desc="End1 column", param=metadata.ColumnParameter) MetadataElement(name="chrom2Col", default=4, desc="Chrom2 column", param=metadata.ColumnParameter) MetadataElement(name="start2Col", default=5, desc="Start2 column", param=metadata.ColumnParameter) MetadataElement(name="end2Col", default=6, desc="End2 column", param=metadata.ColumnParameter) MetadataElement(name="valueCol", default=7, desc="Value column", param=metadata.ColumnParameter) MetadataElement(name="columns", default=7, desc="Number of columns", readonly=True, visible=False)
[docs] def sniff(self, filename): return False
[docs]class ScIdx(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. """ file_ext = "scidx" MetadataElement(name="columns", default=0, desc="Number of columns", readonly=True, visible=False) MetadataElement(name="column_types", default=[], param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False, no_value=[])
[docs] def __init__(self, **kwd): """ Initialize scidx datatype. """ Tabular.__init__(self, **kwd) # Don't set column names since the first # line of the dataset displays them. self.column_names = ['chrom', 'index', 'forward', 'reverse', 'value']
[docs] def sniff(self, filename): """ Checks for 'scidx-ness.' """ try: count = 0 fh = open(filename, "r") while True: line = fh.readline() if not line: # EOF if count > 1: # The second line is always the labels: # chrom index forward reverse value # We need at least the column labels and a data line. return True return False line = line.strip() # The first line is always a comment like this: # 2015-11-23 20:18:56.51;input.bam;READ1 if count == 0: if line.startswith('#'): count += 1 continue else: return False # Skip first line. if count > 1: items = line.split('\t') if len(items) != 5: return False index = items[1] if not index.isdigit(): return False forward = items[2] if not forward.isdigit(): return False reverse = items[3] if not reverse.isdigit(): return False value = items[4] if not value.isdigit(): return False if int(forward) + int(reverse) != int(value): return False if count == 100: return True count += 1 if count < 100 and count > 0: return True except: return False finally: fh.close() return False
if __name__ == '__main__': import doctest doctest.testmod(sys.modules[__name__])