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Source code for galaxy.datatypes.interval
"""
Interval datatypes
"""
import logging
import math
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 (
build_sniff_from_prefix,
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
@build_sniff_from_prefix
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.
elems = line.split('\t')
if len(elems) > 2:
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 Exception:
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 Exception:
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 Exception:
# 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
break
if (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 Exception:
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"""
with tempfile.NamedTemporaryFile(delete=False) as fh:
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]
fh.write('%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]
fh.write('%s\n' % '\t'.join(tmp))
else:
for elems in util.file_iter(dataset.file_name):
tmp = [elems[c], elems[s], elems[e]]
fh.write('%s\n' % '\t'.join(tmp))
return open(fh.name, 'rb')
[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 ucsc_links(self, dataset, type, app, base_url):
"""
Generate links to UCSC genome browser sites based on the dbkey
and content of dataset.
"""
# Filter UCSC sites to only those that are supported by this build and
# enabled.
valid_sites = [(name, url)
for name, url in app.datatypes_registry.get_legacy_sites_by_build('ucsc', dataset.dbkey)
if name in app.datatypes_registry.get_display_sites('ucsc')]
if not valid_sites:
return []
# If there are any valid sites, we need to generate the estimated
# viewport
chrom, start, stop = self.get_estimated_display_viewport(dataset)
if chrom is None:
return []
# Accumulate links for valid sites
ret_val = []
for site_name, site_url in valid_sites:
internal_url = url_for(controller='dataset', dataset_id=dataset.id,
action='display_at', filename='ucsc_' + site_name)
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))
redirect_url = quote_plus("%sdb=%s&position=%s:%s-%s&hgt.customText=%%s" %
(site_url, dataset.dbkey, chrom, start, stop))
link = '%s?redirect_url=%s&display_url=%s' % (internal_url, redirect_url, display_url)
ret_val.append((site_name, link))
return ret_val
[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
with open(dataset.file_name, "r") as infile:
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:
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_prefix(self, 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
"""
found_valid_lines = False
try:
headers = iter_headers(file_prefix, '\t', comment_designator='#')
# 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.
for hdr in headers:
if hdr:
if len(hdr) < 3:
return False
# Assume chrom start and end are in column positions 1 and 2
# respectively ( for 0 based columns )
int(hdr[1])
int(hdr[2])
found_valid_lines = True
except Exception:
return False
return found_valid_lines
# ------------- 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, 'rb')
[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)):
line = line.rstrip('\r\n')
if line and not line.startswith('#'):
elems = line.split('\t')
if len(elems) > 2:
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
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 Exception:
return Interval.as_ucsc_display_file(self, dataset)
# only check first line for proper form
break
try:
return open(dataset.file_name, 'rb')
except Exception:
return "This item contains no content"
[docs] def sniff_prefix(self, 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
"""
if not get_headers(file_prefix, '\t', comment_designator='#', count=1):
return False
try:
headers = iter_headers(file_prefix, '\t', comment_designator='#')
for hdr in headers:
if hdr[0] == '':
continue
if len(hdr) < 3 or len(hdr) > 12:
return False
try:
int(hdr[1])
int(hdr[2])
except Exception:
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 Exception:
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 Exception:
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 Exception:
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 Exception:
try:
hdr[8].split(',')
except Exception:
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 Exception:
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 Exception:
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 Exception:
return False
if len(block_sizes) != block_count or len(block_starts) != block_count:
return False
return True
except Exception:
return False
[docs]class ProBed(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']
[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(object):
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
@build_sniff_from_prefix
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 ValueError:
try:
# Try float.
float(value)
value_type = "float"
except ValueError:
pass
attribute_types[name] = value_type
except Exception:
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 Exception:
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 Exception:
# 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 ucsc_links(self, dataset, type, app, base_url):
ret_val = []
seqid, start, stop = self.get_estimated_display_viewport(dataset)
if seqid is not None:
for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('ucsc', dataset.dbkey):
if site_name in app.datatypes_registry.get_display_sites('ucsc'):
redirect_url = quote_plus(
"%sdb=%s&position=%s:%s-%s&hgt.customText=%%s" %
(site_url, dataset.dbkey, seqid, start, stop))
link = self._get_remote_call_url(redirect_url, site_name, dataset, type, app, base_url)
ret_val.append((site_name, link))
return ret_val
[docs] def gbrowse_links(self, dataset, type, app, base_url):
ret_val = []
seqid, start, stop = self.get_estimated_display_viewport(dataset)
if seqid is not None:
for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('gbrowse', dataset.dbkey):
if site_name in app.datatypes_registry.get_display_sites('gbrowse'):
if seqid.startswith('chr') and len(seqid) > 3:
seqid = seqid[3:]
redirect_url = quote_plus("%s/?q=%s:%s..%s&eurl=%%s" % (site_url, seqid, start, stop))
link = self._get_remote_call_url(redirect_url, site_name, dataset, type, app, base_url)
ret_val.append((site_name, link))
return ret_val
[docs] def sniff_prefix(self, 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
"""
if len(get_headers(file_prefix, '\t', count=2)) < 2:
return False
try:
headers = iter_headers(file_prefix, '\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 Exception:
return False
if hdr[5] != '.':
try:
float(hdr[5])
except Exception:
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 Exception:
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 Exception:
if elems[3] == '.':
valid_start = True
try:
end = int(elems[4])
valid_end = True
except Exception:
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_prefix(self, 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
"""
if len(get_headers(file_prefix, '\t', count=2)) < 2:
return False
try:
headers = iter_headers(file_prefix, '\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 Exception:
if hdr[3] != '.':
return False
try:
int(hdr[4])
except Exception:
if hdr[4] != '.':
return False
if hdr[5] != '.':
try:
float(hdr[5])
except Exception:
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 Exception:
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_prefix(self, 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
"""
if len(get_headers(file_prefix, '\t', count=2)) < 2:
return False
try:
headers = iter_headers(file_prefix, '\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 Exception:
return False
if hdr[5] != '.':
try:
float(hdr[5])
except Exception:
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 Exception:
return False
[docs]@dataproviders.decorators.has_dataproviders
@build_sniff_from_prefix
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 Exception:
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 gbrowse_links(self, dataset, type, app, base_url):
ret_val = []
chrom, start, stop = self.get_estimated_display_viewport(dataset)
if chrom is not None:
for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('gbrowse', dataset.dbkey):
if site_name in app.datatypes_registry.get_display_sites('gbrowse'):
if chrom.startswith('chr') and len(chrom) > 3:
chrom = chrom[3:]
redirect_url = quote_plus("%s/?q=%s:%s..%s&eurl=%%s" % (site_url, chrom, start, stop))
link = self._get_remote_call_url(redirect_url, site_name, dataset, type, app, base_url)
ret_val.append((site_name, link))
return ret_val
[docs] def ucsc_links(self, dataset, type, app, base_url):
ret_val = []
chrom, start, stop = self.get_estimated_display_viewport(dataset)
if chrom is not None:
for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('ucsc', dataset.dbkey):
if site_name in app.datatypes_registry.get_display_sites('ucsc'):
redirect_url = quote_plus("%sdb=%s&position=%s:%s-%s&hgt.customText=%%s" % (site_url, dataset.dbkey, chrom, start, stop))
link = self._get_remote_call_url(redirect_url, site_name, dataset, type, app, base_url)
ret_val.append((site_name, link))
return ret_val
[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:
# variableStep format is nucleotide position\tvalue\n,
# fixedStep is value\n
# "Wiggle track data values can be integer or real, positive or negative values"
float(elems[0])
break
except Exception:
# We are either in the track definition line or in a declaration line
pass
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_prefix(self, 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
"""
try:
headers = iter_headers(file_prefix, None)
for hdr in headers:
if len(hdr) > 1 and hdr[0] == 'track' and hdr[1].startswith('type=wiggle'):
return True
return False
except Exception:
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]@build_sniff_from_prefix
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 ucsc_links(self, dataset, type, app, base_url):
ret_val = []
chrom, start, stop = self.get_estimated_display_viewport(dataset)
if chrom is not None:
for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('ucsc', dataset.dbkey):
if site_name in app.datatypes_registry.get_display_sites('ucsc'):
internal_url = "%s" % url_for(controller='dataset', dataset_id=dataset.id, action='display_at', filename='ucsc_' + site_name)
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))
redirect_url = quote_plus("%sdb=%s&position=%s:%s-%s&hgt.customText=%%s" % (site_url, dataset.dbkey, chrom, start, stop))
link = '%s?redirect_url=%s&display_url=%s' % (internal_url, redirect_url, display_url)
ret_val.append((site_name, link))
return ret_val
[docs] def sniff_prefix(self, 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
"""
headers = iter_headers(file_prefix, None)
found_at_least_one_track = False
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 Exception:
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 Exception:
return False
found_at_least_one_track = True
except Exception:
return False
return found_at_least_one_track
[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]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]@build_sniff_from_prefix
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_prefix(self, file_prefix):
"""
Checks for 'scidx-ness.'
"""
count = 0
fh = file_prefix.string_io()
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
return False
if __name__ == '__main__':
import doctest
doctest.testmod(sys.modules[__name__])