"""
Sequence classes
"""
from . import data
import gzip
import json
import logging
import os
import re
import string
from cgi import escape
from galaxy import eggs, util
from galaxy.datatypes import metadata
from galaxy.datatypes.checkers import is_gzip
from galaxy.datatypes.sniff import get_test_fname, get_headers
from galaxy.datatypes.metadata import MetadataElement
from galaxy.datatypes.util.image_util import check_image_type
try:
eggs.require( "bx-python" )
import bx.align.maf
except:
pass
log = logging.getLogger(__name__)
[docs]class SequenceSplitLocations( data.Text ):
"""
Class storing information about a sequence file composed of multiple gzip files concatenated as
one OR an uncompressed file. In the GZIP case, each sub-file's location is stored in start and end.
The format of the file is JSON::
{ "sections" : [
{ "start" : "x", "end" : "y", "sequences" : "z" },
...
]}
"""
[docs] def set_peek( self, dataset, is_multi_byte=False ):
if not dataset.dataset.purged:
try:
parsed_data = json.load(open(dataset.file_name))
# dataset.peek = json.dumps(data, sort_keys=True, indent=4)
dataset.peek = data.get_file_peek( dataset.file_name, is_multi_byte=is_multi_byte )
dataset.blurb = '%d sections' % len(parsed_data['sections'])
except Exception, e:
dataset.peek = 'Not FQTOC file'
dataset.blurb = 'Not FQTOC file'
else:
dataset.peek = 'file does not exist'
dataset.blurb = 'file purged from disk'
file_ext = "fqtoc"
[docs] def sniff( self, filename ):
if os.path.getsize(filename) < 50000:
try:
data = json.load(open(filename))
sections = data['sections']
for section in sections:
if 'start' not in section or 'end' not in section or 'sequences' not in section:
return False
return True
except:
pass
return False
[docs]class Sequence( data.Text ):
"""Class describing a sequence"""
"""Add metadata elements"""
MetadataElement( name="sequences", default=0, desc="Number of sequences", readonly=True, visible=False, optional=True, no_value=0 )
[docs] def set_peek( self, dataset, is_multi_byte=False ):
if not dataset.dataset.purged:
dataset.peek = data.get_file_peek( dataset.file_name, is_multi_byte=is_multi_byte )
if dataset.metadata.sequences:
dataset.blurb = "%s sequences" % util.commaify( str( dataset.metadata.sequences ) )
else:
dataset.blurb = data.nice_size( dataset.get_size() )
else:
dataset.peek = 'file does not exist'
dataset.blurb = 'file purged from disk'
[docs] def get_sequences_per_file(total_sequences, split_params):
if split_params['split_mode'] == 'number_of_parts':
# legacy basic mode - split into a specified number of parts
parts = int(split_params['split_size'])
sequences_per_file = [total_sequences/parts for i in range(parts)]
for i in range(total_sequences % parts):
sequences_per_file[i] += 1
elif split_params['split_mode'] == 'to_size':
# loop through the sections and calculate the number of sequences
chunk_size = long(split_params['split_size'])
rem = total_sequences % chunk_size
sequences_per_file = [chunk_size for i in range(total_sequences / chunk_size)]
# TODO: Should we invest the time in a better way to handle small remainders?
if rem > 0:
sequences_per_file.append(rem)
else:
raise Exception('Unsupported split mode %s' % split_params['split_mode'])
return sequences_per_file
get_sequences_per_file = staticmethod(get_sequences_per_file)
[docs] def do_slow_split( cls, input_datasets, subdir_generator_function, split_params):
# count the sequences so we can split
# TODO: if metadata is present, take the number of lines / 4
if input_datasets[0].metadata is not None and input_datasets[0].metadata.sequences is not None:
total_sequences = input_datasets[0].metadata.sequences
else:
input_file = input_datasets[0].file_name
compress = is_gzip(input_file)
if compress:
# gzip is really slow before python 2.7!
in_file = gzip.GzipFile(input_file, 'r')
else:
# TODO
# if a file is not compressed, seek locations can be calculated and stored
# ideally, this would be done in metadata
# TODO
# Add BufferedReader if python 2.7?
in_file = open(input_file, 'rt')
total_sequences = long(0)
for i, line in enumerate(in_file):
total_sequences += 1
in_file.close()
total_sequences /= 4
sequences_per_file = cls.get_sequences_per_file(total_sequences, split_params)
return cls.write_split_files(input_datasets, None, subdir_generator_function, sequences_per_file)
do_slow_split = classmethod(do_slow_split)
[docs] def do_fast_split( cls, input_datasets, toc_file_datasets, subdir_generator_function, split_params):
data = json.load(open(toc_file_datasets[0].file_name))
sections = data['sections']
total_sequences = long(0)
for section in sections:
total_sequences += long(section['sequences'])
sequences_per_file = cls.get_sequences_per_file(total_sequences, split_params)
return cls.write_split_files(input_datasets, toc_file_datasets, subdir_generator_function, sequences_per_file)
do_fast_split = classmethod(do_fast_split)
[docs] def write_split_files(cls, input_datasets, toc_file_datasets, subdir_generator_function, sequences_per_file):
directories = []
def get_subdir(idx):
if idx < len(directories):
return directories[idx]
dir = subdir_generator_function()
directories.append(dir)
return dir
# we know how many splits and how many sequences in each. What remains is to write out instructions for the
# splitting of all the input files. To decouple the format of those instructions from this code, the exact format of
# those instructions is delegated to scripts
start_sequence=0
for part_no in range(len(sequences_per_file)):
dir = get_subdir(part_no)
for ds_no in range(len(input_datasets)):
ds = input_datasets[ds_no]
base_name = os.path.basename(ds.file_name)
part_path = os.path.join(dir, base_name)
split_data = dict(class_name='%s.%s' % (cls.__module__, cls.__name__),
output_name=part_path,
input_name=ds.file_name,
args=dict(start_sequence=start_sequence, num_sequences=sequences_per_file[part_no]))
if toc_file_datasets is not None:
toc = toc_file_datasets[ds_no]
split_data['args']['toc_file'] = toc.file_name
f = open(os.path.join(dir, 'split_info_%s.json' % base_name), 'w')
json.dump(split_data, f)
f.close()
start_sequence += sequences_per_file[part_no]
return directories
write_split_files = classmethod(write_split_files)
[docs] def split( cls, input_datasets, subdir_generator_function, split_params):
"""Split a generic sequence file (not sensible or possible, see subclasses)."""
if split_params is None:
return None
raise NotImplementedError("Can't split generic sequence files")
[docs] def get_split_commands_with_toc(input_name, output_name, toc_file, start_sequence, sequence_count):
"""
Uses a Table of Contents dict, parsed from an FQTOC file, to come up with a set of
shell commands that will extract the parts necessary
>>> three_sections=[dict(start=0, end=74, sequences=10), dict(start=74, end=148, sequences=10), dict(start=148, end=148+76, sequences=10)]
>>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=0, sequence_count=10)
['dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null >> ./output.gz']
>>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=1, sequence_count=5)
['(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +5 2> /dev/null) | head -20 | gzip -c >> ./output.gz']
>>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=0, sequence_count=20)
['dd bs=1 skip=0 count=148 if=./input.gz 2> /dev/null >> ./output.gz']
>>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=5, sequence_count=10)
['(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +21 2> /dev/null) | head -20 | gzip -c >> ./output.gz', '(dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +1 2> /dev/null) | head -20 | gzip -c >> ./output.gz']
>>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=10, sequence_count=10)
['dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null >> ./output.gz']
>>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=5, sequence_count=20)
['(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +21 2> /dev/null) | head -20 | gzip -c >> ./output.gz', 'dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null >> ./output.gz', '(dd bs=1 skip=148 count=76 if=./input.gz 2> /dev/null )| zcat | ( tail -n +1 2> /dev/null) | head -20 | gzip -c >> ./output.gz']
"""
sections = toc_file['sections']
result = []
current_sequence = long(0)
i=0
# skip to the section that contains my starting sequence
while i < len(sections) and start_sequence >= current_sequence + long(sections[i]['sequences']):
current_sequence += long(sections[i]['sequences'])
i += 1
if i == len(sections): # bad input data!
raise Exception('No FQTOC section contains starting sequence %s' % start_sequence)
# These two variables act as an accumulator for consecutive entire blocks that
# can be copied verbatim (without decompressing)
start_chunk = long(-1)
end_chunk = long(-1)
copy_chunk_cmd = 'dd bs=1 skip=%s count=%s if=%s 2> /dev/null >> %s'
while sequence_count > 0 and i < len(sections):
# we need to extract partial data. So, find the byte offsets of the chunks that contain the data we need
# use a combination of dd (to pull just the right sections out) tail (to skip lines) and head (to get the
# right number of lines
sequences = long(sections[i]['sequences'])
skip_sequences = start_sequence-current_sequence
sequences_to_extract = min(sequence_count, sequences-skip_sequences)
start_copy = long(sections[i]['start'])
end_copy = long(sections[i]['end'])
if sequences_to_extract < sequences:
if start_chunk > -1:
result.append(copy_chunk_cmd % (start_chunk, end_chunk-start_chunk, input_name, output_name))
start_chunk = -1
# extract, unzip, trim, recompress
result.append('(dd bs=1 skip=%s count=%s if=%s 2> /dev/null )| zcat | ( tail -n +%s 2> /dev/null) | head -%s | gzip -c >> %s' %
(start_copy, end_copy-start_copy, input_name, skip_sequences*4+1, sequences_to_extract*4, output_name))
else: # whole section - add it to the start_chunk/end_chunk accumulator
if start_chunk == -1:
start_chunk = start_copy
end_chunk = end_copy
sequence_count -= sequences_to_extract
start_sequence += sequences_to_extract
current_sequence += sequences
i += 1
if start_chunk > -1:
result.append(copy_chunk_cmd % (start_chunk, end_chunk-start_chunk, input_name, output_name))
if sequence_count > 0:
raise Exception('%s sequences not found in file' % sequence_count)
return result
get_split_commands_with_toc = staticmethod(get_split_commands_with_toc)
[docs] def get_split_commands_sequential(is_compressed, input_name, output_name, start_sequence, sequence_count):
"""
Does a brain-dead sequential scan & extract of certain sequences
>>> Sequence.get_split_commands_sequential(True, './input.gz', './output.gz', start_sequence=0, sequence_count=10)
['zcat "./input.gz" | ( tail -n +1 2> /dev/null) | head -40 | gzip -c > "./output.gz"']
>>> Sequence.get_split_commands_sequential(False, './input.fastq', './output.fastq', start_sequence=10, sequence_count=10)
['tail -n +41 "./input.fastq" 2> /dev/null | head -40 > "./output.fastq"']
"""
start_line = start_sequence * 4
line_count = sequence_count * 4
# TODO: verify that tail can handle 64-bit numbers
if is_compressed:
cmd = 'zcat "%s" | ( tail -n +%s 2> /dev/null) | head -%s | gzip -c' % (input_name, start_line+1, line_count)
else:
cmd = 'tail -n +%s "%s" 2> /dev/null | head -%s' % (start_line+1, input_name, line_count)
cmd += ' > "%s"' % output_name
return [cmd]
get_split_commands_sequential = staticmethod(get_split_commands_sequential)
[docs]class Alignment( data.Text ):
"""Class describing an alignment"""
"""Add metadata elements"""
MetadataElement( name="species", desc="Species", default=[], param=metadata.SelectParameter, multiple=True, readonly=True, no_value=None )
[docs] def split( cls, input_datasets, subdir_generator_function, split_params):
"""Split a generic alignment file (not sensible or possible, see subclasses)."""
if split_params is None:
return None
raise NotImplementedError("Can't split generic alignment files")
[docs]class Fasta( Sequence ):
"""Class representing a FASTA sequence"""
file_ext = "fasta"
[docs] def sniff( self, filename ):
"""
Determines whether the file is in fasta format
A sequence in FASTA format consists of a single-line description, followed by lines of sequence data.
The first character of the description line is a greater-than (">") symbol in the first column.
All lines should be shorter than 80 characters
For complete details see http://www.ncbi.nlm.nih.gov/blast/fasta.shtml
Rules for sniffing as True:
We don't care about line length (other than empty lines).
The first non-empty line must start with '>' and the Very Next line.strip() must have sequence data and not be a header.
'sequence data' here is loosely defined as non-empty lines which do not start with '>'
This will cause Color Space FASTA (csfasta) to be detected as True (they are, after all, still FASTA files - they have a header line followed by sequence data)
Previously this method did some checking to determine if the sequence data had integers (presumably to differentiate between fasta and csfasta)
This should be done through sniff order, where csfasta (currently has a null sniff function) is detected for first (stricter definition) followed sometime after by fasta
We will only check that the first purported sequence is correctly formatted.
>>> fname = get_test_fname( 'sequence.maf' )
>>> Fasta().sniff( fname )
False
>>> fname = get_test_fname( 'sequence.fasta' )
>>> Fasta().sniff( fname )
True
"""
try:
fh = open( filename )
while True:
line = fh.readline()
if not line:
break #EOF
line = line.strip()
if line: #first non-empty line
if line.startswith( '>' ):
#The next line.strip() must not be '', nor startwith '>'
line = fh.readline().strip()
if line == '' or line.startswith( '>' ):
break
return True
else:
break #we found a non-empty line, but it's not a fasta header
fh.close()
except:
pass
return False
[docs] def split(cls, input_datasets, subdir_generator_function, split_params):
"""Split a FASTA file sequence by sequence.
Note that even if split_mode="number_of_parts", the actual number of
sub-files produced may not match that requested by split_size.
If split_mode="to_size" then split_size is treated as the number of
FASTA records to put in each sub-file (not size in bytes).
"""
if split_params is None:
return
if len(input_datasets) > 1:
raise Exception("FASTA file splitting does not support multiple files")
input_file = input_datasets[0].file_name
#Counting chunk size as number of sequences.
if 'split_mode' not in split_params:
raise Exception('Tool does not define a split mode')
elif split_params['split_mode'] == 'number_of_parts':
split_size = int(split_params['split_size'])
log.debug("Split %s into %i parts..." % (input_file, split_size))
#if split_mode = number_of_parts, and split_size = 10, and
#we know the number of sequences (say 1234), then divide by
#by ten, giving ten files of approx 123 sequences each.
if input_datasets[0].metadata is not None \
and input_datasets[0].metadata.sequences:
#Galaxy has already counted/estimated the number
batch_size = 1 + input_datasets[0].metadata.sequences // split_size
cls._count_split(input_file, batch_size, subdir_generator_function)
else:
#OK, if Galaxy hasn't counted them, it may be a big file.
#We're not going to count the records which would be slow
#and a waste of disk IO time - instead we'll split using
#the file size.
chunk_size = os.path.getsize(input_file) // split_size
cls._size_split(input_file, chunk_size, subdir_generator_function)
elif split_params['split_mode'] == 'to_size':
#Split the input file into as many sub-files as required,
#each containing to_size many sequences
batch_size = int(split_params['split_size'])
log.debug("Split %s into batches of %i records..." % (input_file, batch_size))
cls._count_split(input_file, batch_size, subdir_generator_function)
else:
raise Exception('Unsupported split mode %s' % split_params['split_mode'])
split = classmethod(split)
def _size_split(cls, input_file, chunk_size, subdir_generator_function):
"""Split a FASTA file into chunks based on size on disk.
This does of course preserve complete records - it only splits at the
start of a new FASTQ sequence record.
"""
log.debug("Attemping to split FASTA file %s into chunks of %i bytes" \
% (input_file, chunk_size))
f = open(input_file, "rU")
part_file = None
try:
#Note if the input FASTA file has no sequences, we will
#produce just one sub-file which will be a copy of it.
part_dir = subdir_generator_function()
part_path = os.path.join(part_dir, os.path.basename(input_file))
part_file = open(part_path, 'w')
log.debug("Writing %s part to %s" % (input_file, part_path))
start_offset = 0
while True:
offset = f.tell()
line = f.readline()
if not line:
break
if line[0]==">" and offset - start_offset >= chunk_size:
#Start a new sub-file
part_file.close()
part_dir = subdir_generator_function()
part_path = os.path.join(part_dir, os.path.basename(input_file))
part_file = open(part_path, 'w')
log.debug("Writing %s part to %s" % (input_file, part_path))
start_offset = f.tell()
part_file.write(line)
except Exception, e:
log.error('Unable to size split FASTA file: %s' % str(e))
f.close()
if part_file is not None:
part_file.close()
raise
f.close()
_size_split = classmethod(_size_split)
def _count_split(cls, input_file, chunk_size, subdir_generator_function):
"""Split a FASTA file into chunks based on counting records."""
log.debug("Attemping to split FASTA file %s into chunks of %i sequences" \
% (input_file, chunk_size))
f = open(input_file, "rU")
part_file = None
try:
#Note if the input FASTA file has no sequences, we will
#produce just one sub-file which will be a copy of it.
part_dir = subdir_generator_function()
part_path = os.path.join(part_dir, os.path.basename(input_file))
part_file = open(part_path, 'w')
log.debug("Writing %s part to %s" % (input_file, part_path))
rec_count = 0
while True:
line = f.readline()
if not line:
break
if line[0]==">":
rec_count += 1
if rec_count > chunk_size:
#Start a new sub-file
part_file.close()
part_dir = subdir_generator_function()
part_path = os.path.join(part_dir, os.path.basename(input_file))
part_file = open(part_path, 'w')
log.debug("Writing %s part to %s" % (input_file, part_path))
rec_count = 1
part_file.write(line)
part_file.close()
except Exception, e:
log.error('Unable to count split FASTA file: %s' % str(e))
f.close()
if part_file is not None:
part_file.close()
raise
f.close()
_count_split = classmethod(_count_split)
[docs]class csFasta( Sequence ):
""" Class representing the SOLID Color-Space sequence ( csfasta ) """
file_ext = "csfasta"
[docs] def sniff( self, filename ):
"""
Color-space sequence:
>2_15_85_F3
T213021013012303002332212012112221222112212222
>>> fname = get_test_fname( 'sequence.fasta' )
>>> csFasta().sniff( fname )
False
>>> fname = get_test_fname( 'sequence.csfasta' )
>>> csFasta().sniff( fname )
True
"""
try:
fh = open( filename )
while True:
line = fh.readline()
if not line:
break #EOF
line = line.strip()
if line and not line.startswith( '#' ): #first non-empty non-comment line
if line.startswith( '>' ):
line = fh.readline().strip()
if line == '' or line.startswith( '>' ):
break
elif line[0] not in string.ascii_uppercase:
return False
elif len( line ) > 1 and not re.search( '^[\d.]+$', line[1:] ):
return False
return True
else:
break #we found a non-empty line, but it's not a header
fh.close()
except:
pass
return False
[docs]class Fastq ( Sequence ):
"""Class representing a generic FASTQ sequence"""
file_ext = "fastq"
[docs] def sniff ( self, filename ):
"""
Determines whether the file is in generic fastq format
For details, see http://maq.sourceforge.net/fastq.shtml
Note: There are three kinds of FASTQ files, known as "Sanger" (sometimes called "Standard"), Solexa, and Illumina
These differ in the representation of the quality scores
>>> fname = get_test_fname( '1.fastqsanger' )
>>> Fastq().sniff( fname )
True
>>> fname = get_test_fname( '2.fastqsanger' )
>>> Fastq().sniff( fname )
True
"""
headers = get_headers( filename, None )
bases_regexp = re.compile( "^[NGTAC]*" )
# check that first block looks like a fastq block
try:
if len( headers ) >= 4 and headers[0][0] and headers[0][0][0] == "@" and headers[2][0] and headers[2][0][0] == "+" and headers[1][0]:
# Check the sequence line, make sure it contains only G/C/A/T/N
if not bases_regexp.match( headers[1][0] ):
return False
return True
return False
except:
return False
[docs] def split( cls, input_datasets, subdir_generator_function, split_params):
"""
FASTQ files are split on cluster boundaries, in increments of 4 lines
"""
if split_params is None:
return None
# first, see if there are any associated FQTOC files that will give us the split locations
# if so, we don't need to read the files to do the splitting
toc_file_datasets = []
for ds in input_datasets:
tmp_ds = ds
fqtoc_file = None
while fqtoc_file is None and tmp_ds is not None:
fqtoc_file = tmp_ds.get_converted_files_by_type('fqtoc')
tmp_ds = tmp_ds.copied_from_library_dataset_dataset_association
if fqtoc_file is not None:
toc_file_datasets.append(fqtoc_file)
if len(toc_file_datasets) == len(input_datasets):
return cls.do_fast_split(input_datasets, toc_file_datasets, subdir_generator_function, split_params)
return cls.do_slow_split(input_datasets, subdir_generator_function, split_params)
split = classmethod(split)
[docs] def process_split_file(data):
"""
This is called in the context of an external process launched by a Task (possibly not on the Galaxy machine)
to create the input files for the Task. The parameters:
data - a dict containing the contents of the split file
"""
args = data['args']
input_name = data['input_name']
output_name = data['output_name']
start_sequence = long(args['start_sequence'])
sequence_count = long(args['num_sequences'])
if 'toc_file' in args:
toc_file = json.load(open(args['toc_file'], 'r'))
commands = Sequence.get_split_commands_with_toc(input_name, output_name, toc_file, start_sequence, sequence_count)
else:
commands = Sequence.get_split_commands_sequential(is_gzip(input_name), input_name, output_name, start_sequence, sequence_count)
for cmd in commands:
if 0 != os.system(cmd):
raise Exception("Executing '%s' failed" % cmd)
return True
process_split_file = staticmethod(process_split_file)
[docs]class FastqSanger( Fastq ):
"""Class representing a FASTQ sequence ( the Sanger variant )"""
file_ext = "fastqsanger"
[docs]class FastqSolexa( Fastq ):
"""Class representing a FASTQ sequence ( the Solexa variant )"""
file_ext = "fastqsolexa"
[docs]class FastqIllumina( Fastq ):
"""Class representing a FASTQ sequence ( the Illumina 1.3+ variant )"""
file_ext = "fastqillumina"
[docs]class FastqCSSanger( Fastq ):
"""Class representing a Color Space FASTQ sequence ( e.g a SOLiD variant )"""
file_ext = "fastqcssanger"
[docs]class Maf( Alignment ):
"""Class describing a Maf alignment"""
file_ext = "maf"
#Readonly and optional, users can't unset it, but if it is not set, we are generally ok; if required use a metadata validator in the tool definition
MetadataElement( name="blocks", default=0, desc="Number of blocks", readonly=True, optional=True, visible=False, no_value=0 )
MetadataElement( name="species_chromosomes", desc="Species Chromosomes", param=metadata.FileParameter, readonly=True, no_value=None, visible=False, optional=True )
MetadataElement( name="maf_index", desc="MAF Index File", param=metadata.FileParameter, readonly=True, no_value=None, visible=False, optional=True )
[docs] def set_peek( self, dataset, is_multi_byte=False ):
if not dataset.dataset.purged:
# The file must exist on disk for the get_file_peek() method
dataset.peek = data.get_file_peek( dataset.file_name, is_multi_byte=is_multi_byte )
if dataset.metadata.blocks:
dataset.blurb = "%s blocks" % util.commaify( str( dataset.metadata.blocks ) )
else:
# Number of blocks is not known ( this should not happen ), and auto-detect is
# needed to set metadata
dataset.blurb = "? blocks"
else:
dataset.peek = 'file does not exist'
dataset.blurb = 'file purged from disk'
[docs] def display_peek( self, dataset ):
"""Returns formated html of peek"""
return self.make_html_table( dataset )
[docs] def make_html_table( self, dataset, skipchars=[] ):
"""Create HTML table, used for displaying peek"""
out = ['<table cellspacing="0" cellpadding="3">']
try:
out.append('<tr><th>Species: ')
for species in dataset.metadata.species:
out.append( '%s ' % species )
out.append( '</th></tr>' )
if not dataset.peek:
dataset.set_peek()
data = dataset.peek
lines = data.splitlines()
for line in lines:
line = line.strip()
if not line:
continue
out.append( '<tr><td>%s</td></tr>' % escape( line ) )
out.append( '</table>' )
out = "".join( out )
except Exception, exc:
out = "Can't create peek %s" % exc
return out
[docs] def sniff( self, filename ):
"""
Determines wether the file is in maf format
The .maf format is line-oriented. Each multiple alignment ends with a blank line.
Each sequence in an alignment is on a single line, which can get quite long, but
there is no length limit. Words in a line are delimited by any white space.
Lines starting with # are considered to be comments. Lines starting with ## can
be ignored by most programs, but contain meta-data of one form or another.
The first line of a .maf file begins with ##maf. This word is followed by white-space-separated
variable=value pairs. There should be no white space surrounding the "=".
For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format5
>>> fname = get_test_fname( 'sequence.maf' )
>>> Maf().sniff( fname )
True
>>> fname = get_test_fname( 'sequence.fasta' )
>>> Maf().sniff( fname )
False
"""
headers = get_headers( filename, None )
try:
if len(headers) > 1 and headers[0][0] and headers[0][0] == "##maf":
return True
else:
return False
except:
return False
[docs]class MafCustomTrack( data.Text ):
file_ext = "mafcustomtrack"
MetadataElement( name="vp_chromosome", default='chr1', desc="Viewport Chromosome", readonly=True, optional=True, visible=False, no_value='' )
MetadataElement( name="vp_start", default='1', desc="Viewport Start", readonly=True, optional=True, visible=False, no_value='' )
MetadataElement( name="vp_end", default='100', desc="Viewport End", readonly=True, optional=True, visible=False, no_value='' )
[docs]class Axt( data.Text ):
"""Class describing an axt alignment"""
# gvk- 11/19/09 - This is really an alignment, but we no longer have tools that use this data type, and it is
# here simply for backward compatibility ( although it is still in the datatypes registry ). Subclassing
# from data.Text eliminates managing metadata elements inherited from the Alignemnt class.
file_ext = "axt"
[docs] def sniff( self, filename ):
"""
Determines whether the file is in axt format
axt alignment files are produced from Blastz, an alignment tool available from Webb Miller's lab
at Penn State University.
Each alignment block in an axt file contains three lines: a summary line and 2 sequence lines.
Blocks are separated from one another by blank lines.
The summary line contains chromosomal position and size information about the alignment. It
consists of 9 required fields.
The sequence lines contain the sequence of the primary assembly (line 2) and aligning assembly
(line 3) with inserts. Repeats are indicated by lower-case letters.
For complete details see http://genome.ucsc.edu/goldenPath/help/axt.html
>>> fname = get_test_fname( 'alignment.axt' )
>>> Axt().sniff( fname )
True
>>> fname = get_test_fname( 'alignment.lav' )
>>> Axt().sniff( fname )
False
"""
headers = get_headers( filename, None )
if len(headers) < 4:
return False
for hdr in headers:
if len(hdr) > 0 and hdr[0].startswith("##matrix=axt"):
return True
if len(hdr) > 0 and not hdr[0].startswith("#"):
if len(hdr) != 9:
return False
try:
map ( int, [hdr[0], hdr[2], hdr[3], hdr[5], hdr[6], hdr[8]] )
except:
return False
if hdr[7] not in data.valid_strand:
return False
else:
return True
[docs]class Lav( data.Text ):
"""Class describing a LAV alignment"""
file_ext = "lav"
# gvk- 11/19/09 - This is really an alignment, but we no longer have tools that use this data type, and it is
# here simply for backward compatibility ( although it is still in the datatypes registry ). Subclassing
# from data.Text eliminates managing metadata elements inherited from the Alignemnt class.
[docs] def sniff( self, filename ):
"""
Determines whether the file is in lav format
LAV is an alignment format developed by Webb Miller's group. It is the primary output format for BLASTZ.
The first line of a .lav file begins with #:lav.
For complete details see http://www.bioperl.org/wiki/LAV_alignment_format
>>> fname = get_test_fname( 'alignment.lav' )
>>> Lav().sniff( fname )
True
>>> fname = get_test_fname( 'alignment.axt' )
>>> Lav().sniff( fname )
False
"""
headers = get_headers( filename, None )
try:
if len(headers) > 1 and headers[0][0] and headers[0][0].startswith('#:lav'):
return True
else:
return False
except:
return False
[docs]class RNADotPlotMatrix( data.Data ):
file_ext = "rna_eps"
[docs] def set_peek( self, dataset, is_multi_byte=False ):
if not dataset.dataset.purged:
dataset.peek = 'RNA Dot Plot format (Postscript derivative)'
dataset.blurb = data.nice_size( dataset.get_size() )
else:
dataset.peek = 'file does not exist'
dataset.blurb = 'file purged from disk'
[docs] def sniff(self, filename):
"""Determine if the file is in RNA dot plot format."""
if check_image_type( filename, ['EPS'] ):
seq = False
coor = False
pairs = False
with open( filename ) as handle:
for line in handle:
line = line.strip()
if line:
if line.startswith('/sequence'):
seq = True
elif line.startswith('/coor'):
coor = True
elif line.startswith('/pairs'):
pairs = True
if seq and coor and pairs:
return True
return False