def build_bowtie_index(self):
io.execute([
'bowtie2-build',
self.reference_fasta_filename(),
self/'bowtie',
],
)
def build_snpeff(self):
jar = io.find_jar('snpEff.jar')
with open(self/'snpeff.config','wb') as f:
print >> f, 'data_dir = snpeff'
print >> f, 'genomes : ' + self.name
print >> f, self.name + '.genome : ' + self.name
snpwork = io.Workspace(self/'snpeff',must_exist=False)
snpwork_genome = io.Workspace(snpwork/self.name,must_exist=False)
snpwork_genomes = io.Workspace(snpwork/'genomes',must_exist=False)
annotations = self.annotations_filename()
assert annotations
with open(snpwork_genome/'genes.gff','wb') as f:
for record in annotation.read_annotations(annotations):
if record.end <= record.start: continue
if not record.attr:
record.attr['attributes'] = 'none'
print >> f, record.as_gff()
with open(snpwork_genomes/(self.name+'.fa'),'wb') as f:
for name, seq in io.read_sequences(self.reference_fasta_filename()):
io.write_fasta(f, name, seq)
io.execute('java -jar JAR build NAME -gff3 -c CONFIG',
JAR=jar, NAME=self.name, CONFIG=self/'snpeff.config')
def get_table(self, table_name):
if table_name not in self.tables:
if self.action.download:
for filename in [table_name + '.txt.gz', table_name + '.sql']:
io.execute([
'rsync', '-P',
'rsync://hgdownload.cse.ucsc.edu/goldenPath/' +
self.action.ucsc_name + '/database/' + filename,
self.ucsc / filename
])
fields = []
with open(self.ucsc / table_name + '.sql', 'rU') as f:
for line in f:
if line.startswith(' `'):
parts = line.strip().split()
assert parts[0][0] == '`' and parts[0][-1] == '`'
fields.append(parts[0][1:-1])
tup_class = collections.namedtuple(table_name, fields)
data = []
with gzip.open(self.ucsc / table_name + '.txt.gz', 'rb') as f:
for line in f:
data.append(tup_class(*line.rstrip('\n').split('\t')))
self.tables[table_name] = data
return self.tables[table_name]
def run(self):
genome = self.genome
if os.path.isdir(genome):
genome = os.path.join(genome, os.path.split(genome)[1]+'.genome')
print genome
#pref_filename = os.path.join(os.path.expanduser('~'),'igv','prefs.properties')
#if os.path.exists(pref_filename):
# with open(pref_filename,'rb') as f:
# lines = f.readlines()
# with open(pref_filename,'wb') as f:
# for line in lines:
# if line.startswith('DEFAULT_GENOME_KEY='):
# #line = 'DEFAULT_GENOME_KEY=\n'
# continue
# f.write(line)
with workspace.tempspace() as temp:
with open(temp/'batch.txt','wb') as f:
print >> f, 'new'
print >> f, 'preference LAST_TRACK_DIRECTORY', os.getcwd()
print >> f, 'preference LAST_GENOME_IMPORT_DIRECTORY', os.getcwd()
print >> f, 'genome '+os.path.abspath(genome)
for filename in self.files:
print >> f, 'load '+os.path.abspath(filename)
io.execute(['java','-Xmx32000m',
#Flags from igb.sh script:
'-Dproduction=true','-Dapple.laf.useScreenMenuBar=true','-Djava.net.preferIPv4Stack=true',
'-jar',io.find_jar('igv.jar'),'-b',temp/'batch.txt'])
def sort_bam(in_filename, out_prefix, by_name=False, cores=8):
cores = min(cores, legion.coordinator().get_cores())
megs = max(10, 800 // cores)
io.execute(
[ 'samtools', 'sort', '-@', '%d' % cores, '-m', '%dM' % megs ] +
([ '-n' ] if by_name else [ ]) +
[ in_filename, out_prefix ], cores=cores)
def index_vcf(filename):
""" IGV index a VCF file.
Don't fail if igvtools fails (eg not installed).
"""
try:
io.execute('igvtools index FILENAME', FILENAME=filename)
except (OSError, AssertionError):
print >> sys.stderr, 'Failed to index VCF file with igvtools. Continuing.'
def sort_and_index(in_filename, out_prefix):
io.execute([
'samtools', 'sort', in_filename, out_prefix
])
io.execute([
'samtools', 'index', out_prefix + '.bam'
])
def index_vcf(filename):
""" IGV index a VCF file.
Don't fail if igvtools fails (eg not installed).
"""
try:
io.execute('igvtools index FILENAME',FILENAME=filename)
except (OSError, AssertionError):
print >> sys.stderr, 'Failed to index VCF file with igvtools. Continuing.'
def build_bowtie_index(self, log_to=sys.stdout):
io.execute([
'bowtie2-build',
self.reference_fasta_filename(),
self/'bowtie',
],
stdout = log_to,
)
def sort_bam(in_filename, out_prefix, by_name=False, cores=8):
cores = min(cores, legion.coordinator().get_cores())
megs = max(10, 800 // cores)
io.execute(
[ 'samtools', 'sort', '-@', '%d' % cores, '-m', '%dM' % megs ] +
([ '-n' ] if by_name else [ ]) +
[ in_filename, out_prefix ], cores=cores)
def sort_and_index(in_filename, out_prefix):
io.execute([
'samtools', 'sort', in_filename, out_prefix
])
io.execute([
'samtools', 'index', out_prefix + '.bam'
])
def run(self):
base = os.path.split(self.prefix)[1]
annotations = [ ]
sequences = [ ]
for filename in self.filenames:
any = False
if io.is_sequence_file(filename):
sequences.append(filename)
any = True
if annotation.is_annotation_file(filename):
annotations.append(filename)
any = True
assert any, 'File is neither a recognized sequence or annotation file'
cytoband_filename = os.path.join(self.prefix,base+'_cytoband.txt')
property_filename = os.path.join(self.prefix,'property.txt')
gff_filename = os.path.join(self.prefix,base+'.gff')
output_filenames = [ cytoband_filename, property_filename, gff_filename ]
if not os.path.exists(self.prefix):
os.mkdir(self.prefix)
f = open(property_filename,'wb')
print >> f, 'ordered=true'
print >> f, 'id=%s' % base
print >> f, 'name=%s' % (self.name or base)
print >> f, 'cytobandFile=%s_cytoband.txt' % base
print >> f, 'geneFile=%s.gff' % base
print >> f, 'sequenceLocation=%s' % base
f.close()
trivia.As_gff(output=gff_filename,
filenames=annotations,
exclude=[ 'gene', 'source' ]
).run()
f_cyt = open(cytoband_filename,'wb')
for filename in sequences:
for name, seq in io.read_sequences(filename):
assert '/' not in name
f = open(os.path.join(self.prefix, name + '.txt'), 'wb')
f.write(seq)
f.close()
print >> f_cyt, '%s\t0\t%d' % (name, len(seq))
f_cyt.close()
genome_filename = self.prefix + '.genome'
if os.path.exists(genome_filename):
os.unlink(genome_filename)
io.execute(
['zip', '-j', io.abspath(genome_filename)] +
[ io.abspath(item) for item in output_filenames ]
)
for filename in output_filenames:
if os.path.exists(filename):
os.unlink(filename)
def run(self):
with workspace.tempspace() as temp:
with open(temp/'batch.txt','wb') as f:
print >> f, 'new'
print >> f, 'genome '+os.path.abspath(self.genome)
for filename in self.files:
print >> f, 'load '+os.path.abspath(filename)
io.execute(['java','-jar',io.find_jar('igv.jar'),'-b',temp/'batch.txt'])
def build_shrimp_mmap(self, cs=False):
suffix = '-cs' if cs else '-ls'
grace.status('Building SHRiMP mmap')
io.execute([
'gmapper' + suffix,
'--save', self.object_filename('reference' + suffix),
self.reference_fasta_filename(),
])
grace.status('')
def run(self):
from nesoni import io
f_in = self.begin_input()
f_out = self.begin_output()
try:
io.execute(self.command, stdin=f_in, stdout=f_out)
finally:
self.end_output(f_out)
self.end_input(f_in)
def run(self):
with workspace.tempspace() as temp:
with open(temp/'batch.txt','wb') as f:
print >> f, 'new'
print >> f, 'preference LAST_TRACK_DIRECTORY', os.getcwd()
print >> f, 'preference LAST_GENOME_IMPORT_DIRECTORY', os.getcwd()
print >> f, 'genome '+os.path.abspath(self.genome)
for filename in self.files:
print >> f, 'load '+os.path.abspath(filename)
io.execute(['java','-jar',io.find_jar('igv.jar'),'-b',temp/'batch.txt'])
def run(self):
reference = reference_directory.Reference(self.reference, must_exist=True)
jar = io.find_jar('snpEff.jar')
with open(self.prefix + '.vcf','wb') as f:
io.execute('java -jar JAR eff GENOME VCF -c CONFIG',
JAR=jar, GENOME=reference.name, VCF=self.vcf, CONFIG=reference/'snpeff.config',
stdout=f)
index_vcf(self.prefix+'.vcf')
def build_shrimp_mmap(self, cs=False):
suffix = '-cs' if cs else '-ls'
grace.status('Building SHRiMP mmap')
io.execute([
'gmapper' + suffix,
'--save',
self.object_filename('reference' + suffix),
self.reference_fasta_filename(),
])
grace.status('')
def run(self):
assert self.release
assert self.species
assert self.assembly
assert self.dna
extractions = [ ]
for item in self.genes.split(','):
extraction = item.split('/')
assert len(extraction) == 4
extractions.append(extraction)
rename = { }
if self.rename:
for item in self.rename.split(','):
old,new = item.split('=')
rename[old] = new
work = self.get_workspace()
ensembl = workspace.Workspace(work/'ensembl')
genome_filename = self.species+"."+self.assembly+"."+self.dna+".fa.gz"
genome_url = "rsync://ftp.ensembl.org/ensembl/pub/release-"+self.release+"/fasta/"+self.species.lower()+"/dna/"+genome_filename
gff_filename = self.species+"."+self.assembly+"."+self.release+".gff3.gz"
gff_url = "rsync://ftp.ensembl.org/ensembl/pub/release-"+self.release+"/gff3/"+self.species.lower()+"/"+gff_filename
if self.download:
self.log.log("Fetching "+genome_url+"\n")
io.execute(['rsync','-aP',genome_url, ensembl/genome_filename])
self.log.log("Fetching "+gff_url+"\n")
io.execute(['rsync','-aP',gff_url, ensembl/gff_filename])
with workspace.tempspace() as temp:
items = list(annotation.read_annotations(ensembl/gff_filename))
for item in items:
item.seqid = rename.get(item.seqid, item.seqid)
annotation.write_gff3(temp/'temp.gff', get_genes(items, extractions, self.log))
del items
with open(temp/'temp.fa','wb') as f:
for name,seq in io.read_sequences(ensembl/genome_filename):
name = name.split()[0]
name = rename.get(name,name)
io.write_fasta(f, name, seq)
reference_directory.Make_tt_reference(
self.output_dir,
filenames = [ temp/'temp.fa', temp/'temp.gff' ],
index = self.index,
).run()
def run(self):
reference = reference_directory.Reference(self.reference,
must_exist=True)
jar = io.find_jar('snpEff.jar')
with open(self.prefix + '.vcf', 'wb') as f:
io.execute('java -jar JAR eff GENOME VCF -c CONFIG',
JAR=jar,
GENOME=reference.name,
VCF=self.vcf,
CONFIG=reference / 'snpeff.config',
stdout=f)
index_vcf(self.prefix + '.vcf')
def run(self):
from nesoni import io
assert self.command, 'Nothing to execute!'
print self.ident()
f_in = self.begin_input()
f_out = self.begin_output()
try:
io.execute(self.command[:1] + self.execution_options + self.command[1:],
stdin=f_in, stdout=f_out)
finally:
self.end_output(f_out)
self.end_input(f_in)
def run(self):
work = self.get_workspace()
acc = self.run_accession
io.execute(
'wget -c URL',
URL='http://ftp-private.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByRun/sra/%s/%s/%s/%s.sra'
% (acc[:3],acc[:6],acc,acc),
cwd=work.working_dir,
)
io.execute(
'fastq-dump --split-files --bzip2 FILENAME',
FILENAME=acc+'.sra',
cwd=work.working_dir,
)
def run(self):
from nesoni import io
assert self.command, 'Nothing to execute!'
print self.ident()
f_in = self.begin_input()
f_out = self.begin_output()
try:
io.execute(self.command[:1] + self.execution_options +
self.command[1:],
stdin=f_in,
stdout=f_out)
finally:
self.end_output(f_out)
self.end_input(f_in)
def run(self):
work = self.get_workspace()
acc = self.run_accession
io.execute(
'wget -c URL',
#URL='http://ftp-private.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByRun/sra/%s/%s/%s/%s.sra'
URL=
'http://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByRun/sra/%s/%s/%s/%s.sra'
% (acc[:3], acc[:6], acc, acc),
cwd=work.working_dir,
)
io.execute(
'fastq-dump --split-files --bzip2 FILENAME',
FILENAME='./' + acc + '.sra',
cwd=work.working_dir,
)
def run(self):
assert self.sort in ('queryname', 'coordinate')
jar = io.find_jar('MergeSamFiles.jar', 'MergeSamFiles is part of the Picard package.')
io.execute([
'java','-jar',jar,
'USE_THREADING=true',
'TMP_DIR='+tempfile.gettempdir(), #Force picard to use same temp dir as Python
'SORT_ORDER='+self.sort,
'OUTPUT='+self.prefix+'.bam'
] + [ 'INPUT='+item for item in self.bams ])
if self.sort == 'coordinate' and self.index:
jar = io.find_jar('BuildBamIndex.jar', 'BuildBamIndex is part of the Picard package.')
io.execute([
'java','-jar',jar,
'INPUT='+self.prefix+'.bam'
])
def run(self):
assert self.sort in ('queryname', 'coordinate')
jar = io.find_jar('MergeSamFiles.jar', 'MergeSamFiles is part of the Picard package.')
io.execute([
'java','-jar',jar,
'USE_THREADING=true',
'TMP_DIR='+tempfile.gettempdir(), #Force picard to use same temp dir as Python
'SORT_ORDER='+self.sort,
'OUTPUT='+self.prefix+'.bam'
] + [ 'INPUT='+item for item in self.bams ])
if self.sort == 'coordinate' and self.index:
jar = io.find_jar('BuildBamIndex.jar', 'BuildBamIndex is part of the Picard package.')
io.execute([
'java','-jar',jar,
'INPUT='+self.prefix+'.bam'
])
def href(self, filename, title=None, image=False):
relative = self.workspace.path_as_relative_path(filename)
if title is None:
title = os.path.split(filename)[1]
size = os.stat(filename).st_size
if size >= 1<<30:
title += ' (%.1fGb)' % (float(size)/(1<<30))
elif size >= 1<<20:
title += ' (%.1fMb)' % (float(size)/(1<<20))
elif size >= 1<<10:
title += ' (%.1fkb)' % (float(size)/(1<<10))
if image:
thumb_name = 'thumb-'+os.path.splitext(relative)[0]+'.png'
thumb_filename = self.workspace/thumb_name
io.execute(['convert', '-thumbnail', '50x50', filename, thumb_filename])
title = ('<span style="display: inline-block; width: 50px;"><img src="%s"/></span> ' % thumb_name) + title
return '<a href="%s">%s</a>' % (relative, title)
def set_sequences(self, filenames):
reference_genbank_filename = self / 'reference.gbk'
reference_filename = self / 'reference.fa'
reference_genbank_file = open(reference_genbank_filename,'wb')
any_genbank = [ False ]
def genbank_callback(name, record):
""" Make a copy of any genbank files passed in. """
from Bio import SeqIO
SeqIO.write([record], reference_genbank_file, 'genbank')
f = open(self / (grace.filesystem_friendly_name(name) + '.gbk'), 'wb')
SeqIO.write([record], f, 'genbank')
f.close()
any_genbank[0] = True
lengths = [ ]
seen = set()
f = open(reference_filename, 'wb')
for filename in filenames:
for name, seq in io.read_sequences(filename, genbank_callback=genbank_callback):
name = name.split()[0]
assert name not in seen, 'Duplicate chromosome name: ' + name
seen.add(name)
lengths.append( (name, len(seq)) )
io.write_fasta(f, name, seq)
f.close()
self.set_object(lengths, 'reference-lengths.pickle.gz')
reference_genbank_file.close()
if not any_genbank[0]:
os.unlink(reference_genbank_filename)
# Create an index of the reference sequences for samtools
io.execute([
'samtools', 'faidx', reference_filename
])
def get_table(self, table_name):
if table_name not in self.tables:
if self.action.download:
for filename in [ table_name+'.txt.gz', table_name+'.sql' ]:
io.execute(['rsync','-P','rsync://hgdownload.cse.ucsc.edu/goldenPath/'+self.action.ucsc_name+'/database/'+filename, self.ucsc/filename])
fields = [ ]
with open(self.ucsc/table_name+'.sql','rU') as f:
for line in f:
if line.startswith(' `'):
parts = line.strip().split()
assert parts[0][0] == '`' and parts[0][-1] == '`'
fields.append(parts[0][1:-1])
tup_class = collections.namedtuple(table_name, fields)
data = [ ]
with gzip.open(self.ucsc/table_name+'.txt.gz','rb') as f:
for line in f:
data.append(tup_class(* line.rstrip('\n').split('\t') ))
self.tables[table_name] = data
return self.tables[table_name]
def run(self):
workspace = working_directory.Working(self.output_dir)
workspace.setup_reference(self.reference)
workspace.update_param(snp_cost = self.snp_cost)
#assert os.path.exists(self.reference), 'Reference file does not exist'
#reference_filename = workspace._object_filename('reference.fa')
#if os.path.exists(reference_filename):
# os.unlink(reference_filename)
#os.symlink(os.path.relpath(self.reference, self.output_dir), reference_filename)
bam_filename = io.abspath(self.output_dir, 'alignments.bam')
bam_prefix = io.abspath(self.output_dir, 'alignments')
if sam.is_bam(self.input):
sort_input_filename = self.input
temp_filename = None
else:
temp_filename = io.abspath(self.output_dir, 'temp.bam')
sort_input_filename = temp_filename
writer = io.Pipe_writer(temp_filename, ['samtools', 'view', '-S', '-b', '-'])
f = open(self.input, 'rb')
while True:
data = f.read(1<<20)
if not data: break
writer.write(data)
writer.close()
f.close()
grace.status('Sort')
io.execute([
'samtools', 'sort', '-n', sort_input_filename, bam_prefix
])
if temp_filename is not None:
os.unlink(temp_filename)
grace.status('')
def href(self, filename, title=None, image=False):
relative = self.workspace.path_as_relative_path(filename)
if title is None:
title = os.path.split(filename)[1]
size = os.stat(filename).st_size
if size >= 1 << 30:
title += ' (%.1fGb)' % (float(size) / (1 << 30))
elif size >= 1 << 20:
title += ' (%.1fMb)' % (float(size) / (1 << 20))
elif size >= 1 << 10:
title += ' (%.1fkb)' % (float(size) / (1 << 10))
if image:
thumb_name = 'thumb-' + relative
thumb_filename = self.workspace / thumb_name
io.execute(
['convert', '-thumbnail', '50x50', filename, thumb_filename])
title = (
'<span style="display: inline-block; width: 50px;"><img src="%s"/></span> '
% thumb_name) + title
return '<a href="%s">%s</a>' % (relative, title)
def run(self):
workspace = working_directory.Working(self.output_dir)
workspace.setup_reference(self.reference)
# assert os.path.exists(self.reference), 'Reference file does not exist'
# reference_filename = workspace._object_filename('reference.fa')
# if os.path.exists(reference_filename):
# os.unlink(reference_filename)
# os.symlink(os.path.relpath(self.reference, self.output_dir), reference_filename)
bam_filename = io.abspath(self.output_dir, "alignments.bam")
bam_prefix = io.abspath(self.output_dir, "alignments")
if sam.is_bam(self.input):
sort_input_filename = self.input
temp_filename = None
else:
temp_filename = io.abspath(self.output_dir, "temp.bam")
sort_input_filename = temp_filename
writer = io.Pipe_writer(temp_filename, ["samtools", "view", "-S", "-b", "-"])
f = open(self.input, "rb")
while True:
data = f.read(1 << 20)
if not data:
break
writer.write(data)
writer.close()
f.close()
grace.status("Sort")
io.execute(["samtools", "sort", "-n", sort_input_filename, bam_prefix])
if temp_filename is not None:
os.unlink(temp_filename)
grace.status("")
def run(self):
workspace = working_directory.Working(self.output_dir)
workspace.setup_reference(self.reference)
#assert os.path.exists(self.reference), 'Reference file does not exist'
#reference_filename = workspace._object_filename('reference.fa')
#if os.path.exists(reference_filename):
# os.unlink(reference_filename)
#os.symlink(os.path.relpath(self.reference, self.output_dir), reference_filename)
bam_filename = io.abspath(self.output_dir, 'alignments.bam')
bam_prefix = io.abspath(self.output_dir, 'alignments')
if sam.is_bam(self.input):
sort_input_filename = self.input
temp_filename = None
else:
temp_filename = io.abspath(self.output_dir, 'temp.bam')
sort_input_filename = temp_filename
writer = io.Pipe_writer(temp_filename,
['samtools', 'view', '-S', '-b', '-'])
f = open(self.input, 'rb')
while True:
data = f.read(1 << 20)
if not data: break
writer.write(data)
writer.close()
f.close()
grace.status('Sort')
io.execute(['samtools', 'sort', '-n', sort_input_filename, bam_prefix])
if temp_filename is not None:
os.unlink(temp_filename)
grace.status('')
def run(self):
assert self.method in ("limma", "fitnoise1", "fitnoise2"), "Unknown method."
assert self.method != "limma" or not self.empirical_controls
title = self.get_title()
n_alt = len(self.alt)
n_null = len(self.null)
suffix = '-dedup' if self.dedup else ''
genewise_filename = join(self.analysis,'expression','genewise'+suffix,'counts.csv')
genewise_norm_filename = join(self.analysis,'expression','genewise'+suffix,'norm.csv')
primarypeakwise_filename = join(self.analysis,'expression','primarypeakwise'+suffix,'counts.csv')
primarypeakwise_norm_filename = join(self.analysis,'expression','primarypeakwise'+suffix,'norm.csv')
peakwise_filename = join(self.analysis,'expression','peakwise'+suffix,'counts.csv')
peakwise_norm_filename = join(self.analysis,'expression','peakwise'+suffix,'norm.csv')
pairwise_filename = join(self.analysis,'peak-shift'+suffix,'individual-pairs.csv')
pairwise_norm_filename = join(self.analysis,'peak-shift'+suffix,'individual-pairs-norm.csv')
reader = io.Table_reader(genewise_filename, 'Count')
reader.close()
samples = [ item for i, item in enumerate(reader.headings) if reader.groups[i] == 'Count' ]
tags = { }
for item in samples:
tags[item] = [ item ]
for line in reader.comments:
if line.startswith('#sampleTags='):
parts = line[len('#sampleTags='):].split(',')
tags[parts[0]] = parts
model = [ ]
for term in self.alt + self.null:
spec = selection.term_specification(term)
model.append([ selection.weight(spec, tags[item]) for item in samples ])
model = zip(*model) #Transpose
select = [ any(row) for row in model ]
model = [ row for row,selected in zip(model,select) if selected ]
model_columns = [ selection.term_name(item) for item in self.alt + self.null ]
model_rows = [ item for keep, item in zip(select, samples) if keep ]
#degust complains if name starts with '-', delimits with commas
model_columns = [ ('.' if item[:1] == '-' else '') + item.replace(',',';') for item in model_columns ]
pairs_n_alt = n_alt
pairs_select = select + select
pairs_model = (
[ (0,) * n_alt + row + (0,) for row in model ] +
[ row[:n_alt] + row + (1,) for row in model ]
)
pairs_model_columns = (
[ item+'-interaction' for item in model_columns[:n_alt] ] +
model_columns +
[ 'pair2' ]
)
pairs_model_rows = [ item+'-peak1' for item in model_rows ] + [ item+'-peak2' for item in model_rows ]
design_str = '['+('-'*(8*n_alt-2))+'] test coefficients\n'
for row, name in zip(model, model_rows):
design_str += "%s %s\n" % (''.join('%7g ' % item for item in row), name)
print
print "Design matrix"
print design_str
print
print 'Pair design matrix'
print '['+('-'*(8*n_alt-2))+'] test coefficients'
for row, name in zip(pairs_model, pairs_model_rows):
print ''.join('%7g ' % item for item in row), name
print
workspace = self.get_workspace()
runr.run_script(TEST_R, self.tell,
DIR = workspace.working_dir,
METHOD = self.method,
WEIGHT = self.weight,
EMPIRICAL_CONTROLS = self.empirical_controls,
MIN_READS = self.min_reads,
BIOTYPE = self.biotype,
RELATION = self.relation,
QUANTILE_TAIL = self.quantile_tail,
DO_EXPRESSION = self.do_expression,
DO_TAIL_LENGTH = self.do_tail_length,
VERBOSE = self.verbose,
GENEWISE_FILENAME = genewise_filename,
GENEWISE_NORM_FILENAME = genewise_norm_filename,
PRIMARYPEAKWISE_FILENAME = primarypeakwise_filename,
PRIMARYPEAKWISE_NORM_FILENAME = primarypeakwise_norm_filename,
PEAKWISE_FILENAME = peakwise_filename,
PEAKWISE_NORM_FILENAME = peakwise_norm_filename,
PAIRWISE_FILENAME = pairwise_filename,
PAIRWISE_NORM_FILENAME = pairwise_norm_filename,
N_ALT = n_alt,
SELECT = select,
MODEL = model,
MODEL_COLUMNS = model_columns,
PAIRS_N_ALT = pairs_n_alt,
PAIRS_SELECT = pairs_select,
PAIRS_MODEL = pairs_model,
PAIRS_MODEL_COLUMNS = pairs_model_columns,
)
if self.tell: return
reporter = reporting.Reporter(workspace.working_dir, title, style=web.style())
if self.dedup:
reporter.p('Read deduplication was used.')
reporter.write('<table>\n')
for is_expression, entities, result, aveexpr, subtitle, terms in [
(True, 'genes', 'genewise-voom', 'avg.expression', 'Genewise expression level', model_columns[:n_alt]),
(False, 'genes', 'genewise-tail', 'avg.tail', 'Genewise tail length', model_columns[:n_alt]),
(True, 'primary peaks', 'primarypeakwise-voom', 'avg.expression', 'Primary-peakwise expression level', model_columns[:n_alt]),
(False, 'primary peaks', 'primarypeakwise-tail', 'avg.tail', 'Primary-peakwise tail length', model_columns[:n_alt]),
(True, 'peaks', 'peakwise-voom', 'avg.expression', 'Peakwise expression level', model_columns[:n_alt]),
(False, 'peaks', 'peakwise-tail', 'avg.tail', 'Peakwise tail length', model_columns[:n_alt]),
(True, 'peak pairs', 'pairwise-voom', 'avg.expression', 'Peak-pair expression shift', pairs_model_columns[:n_alt]),
(False, 'peak pairs', 'pairwise-tail', 'avg.tail', 'Peak-pair tail length shift', pairs_model_columns[:n_alt]),
]:
#data = io.read_grouped_table(workspace/(result+'-toptable.csv'))['All']
#n = 0
#n_01 = 0
#n_05 = 0
#for row in data.values():
# fdr = float(row['adj.P.Val'])
# if fdr <= 0.01: n_01 += 1
# if fdr <= 0.05: n_05 += 1
# n += 1
if is_expression and not self.do_expression: continue
if not is_expression and not self.do_tail_length: continue
io.execute([
'degust.py',
'--name', title + ' : ' + subtitle,
'--avg', aveexpr,
'--primary', 'baseline',
'--logFC', ','.join(terms),
'--fdr', 'adj.P.Val',
'--info', 'gene,locus_tag,product,reads,polya.reads,tail.lengths,'+aveexpr,
'--notour', '1',
'--out', workspace/(result+'.html'),
workspace/(result+'-toptable.csv'),
])
with open(workspace/(result+'.txt'),'rU') as f:
lines = f.readlines()
reporter.write('<tr><td valign="top" width="33%">')
reporter.subheading( reporter.href(workspace/(result+'.html'), subtitle) )
#reporter.p( '%d %s, %d with fdr<=0.01, %d with fdr<=0.05' % (n,entities,n_01,n_05) )
line = reporter.href(workspace/(result+'-toptable.csv'), 'Spreadsheet')
if result.endswith('voom'):
line += ', ' + reporter.href(workspace/(result+'.png'), 'voom plot')
reporter.p(line)
for line in lines[-2:]:
reporter.p(line.strip())
reporter.write('</td><td valign="top"><br/><br/>')
for line in lines[:-2]:
reporter.write(line.strip() + '<br/>\n')
reporter.write('</td></tr>')
reporter.write('</table>\n')
reporter.subheading("Design matrix")
reporter.write('<pre>' + design_str + '</pre>')
reporter.close()
def run(self):
assert self.ucsc_name, 'Need a UCSC genome name'
scratch = _ucsc_scratch(self)
# Load annotations
source = 'tt-ucsc-%s-%s' % (self.ucsc_name, self.table)
table = scratch.get_table(self.table)
get_name = scratch.getter(self.name)
get_product = scratch.getter(self.product)
mrnas = [ ]
for item in table:
ann = annotation.Annotation(
seqid = item.chrom,
source = source,
type = 'mRNA',
strand = {'+':1, '-':-1}[item.strand],
start = int(item.txStart),
end = int(item.txEnd),
attr = {
'ID' : item.name,
'Name' : get_name(item),
'Product' : get_product(item),
#'UCSC_name2' : item.name2,
}
)
ann.record = item
mrnas.append(ann)
_uniquify_ids(mrnas)
annotations = [ ]
for group in _grouped_features(mrnas):
ID = '/'.join(item.attr['ID'] for item in group)
for item in group:
item.attr['Parent'] = ID
item.attr['ID'] = item.attr['ID'] + '-mRNA'
annotations.append(annotation.Annotation(
source = source,
type = 'gene',
seqid = group[0].seqid,
strand = group[0].strand,
start = min(item.start for item in group),
end = max(item.end for item in group),
attr = {
'ID' : ID,
'Name' : annotation_tools.join_descriptions([ item.attr['Name'] for item in group ], '/'),
'Product' : annotation_tools.join_descriptions([ item.attr['Product'] for item in group ], '/'),
#'UCSC_name2' : annotation_tools.join_descriptions([ item.attr['UCSC_name2'] for item in group ], '/'),
}
))
for item in group:
annotations.append(item)
exonStarts = _parse_ints(item.record.exonStarts)
exonEnds = _parse_ints(item.record.exonEnds)
cdsStart = int(item.record.cdsStart)
cdsEnd = int(item.record.cdsEnd)
for start,end in zip(exonStarts,exonEnds):
annotations.append(annotation.Annotation(
source = source,
type = 'exon',
seqid = item.seqid,
strand = item.strand,
start = start,
end = end,
attr = {
'Parent' : item.attr['ID'],
}
))
if max(cdsStart,start) < min(cdsEnd,end):
annotations.append(annotation.Annotation(
source = source,
type = 'CDS',
seqid = item.seqid,
strand = item.strand,
start = max(cdsStart,start),
end = min(cdsEnd,end),
#TODO: phase
attr = {
'Parent' : item.attr['ID'],
}
))
# Load sequence
if self.download:
io.execute(['rsync','-P','rsync://hgdownload.cse.ucsc.edu/goldenPath/'+self.ucsc_name+'/bigZips/chromFa.tar.gz',scratch.ucsc/'chromFa.tar.gz'])
with workspace.tempspace() as temp:
io.execute(['tar','-C',temp.working_dir,'-zxf',scratch.ucsc/'chromFa.tar.gz'])
sequences = [ temp/item for item in natural_sorted(os.listdir(temp.working_dir)) ]
with open(temp/'reference.gff','wb') as f:
annotation.write_gff3_header(f)
for item in annotations:
print >> f, item.as_gff()
Make_tt_reference(
self.output_dir,
filenames = sequences + [ temp/'reference.gff' ],
index = self.index,
).run()
def run(self):
reader_f = io.open_possibly_compressed_file(self.vcf)
reader = vcf.Reader(reader_f)
tags = {}
for item in reader.metadata.get('sampleTags', []):
parts = item.split(',')
tags[parts[0]] = parts
assert 'reference' not in reader.samples, 'Can\'t have a sample called reference, sorry.'
samples = ['reference'] + reader.samples
for sample in samples:
if sample not in tags:
tags[sample] = [sample, 'all']
samples = selection.select_and_sort(self.select, self.sort, samples,
lambda sample: tags[sample])
required = [
i for i, sample in enumerate(samples)
if selection.matches(self.require, tags[sample])
]
sample_number = dict((b, a) for a, b in enumerate(reader.samples))
items = []
for record in reader:
variants = get_variants(record)
genotypes = []
counts = []
qualities = []
for sample in samples:
if sample == 'reference':
genotypes.append([0])
counts.append([1])
qualities.append(float('inf'))
else:
genotypes.append(
get_genotype(record.samples[sample_number[sample]]))
counts.append(
get_variant_counts(
record.samples[sample_number[sample]]))
qualities.append(
record.samples[sample_number[sample]].data.GQ)
# Only output when there are at least two genotypes
any_interesting = False
for i in xrange(len(genotypes)):
for j in xrange(i):
if (genotypes[i] is not None and genotypes[j] is not None
and
not genotypes_equal(genotypes[i], genotypes[j])):
any_interesting = True
break
if any_interesting: break
if not any_interesting:
continue
if any(genotypes[i] is None for i in required):
continue
if self.only_snps and any(genotype is not None and any(
len(variants[i]) != 1 for i in genotype)
for genotype in genotypes):
continue
snpeff = snpeff_describe(record.INFO.get('EFF', ''))
if not any(
selection.matches(self.snpeff_filter, item[1])
for item in (snpeff or [('', [])])):
continue
items.append(
_Nway_record(variants=variants,
genotypes=genotypes,
counts=counts,
qualities=qualities,
snpeff=snpeff,
record=record))
self.log.log('%d variants\n\n' % len(items))
if self.as_ == 'table':
self._write_table(samples, items)
elif self.as_ == 'nexus':
self._write_nexus(samples, items)
elif self.as_ == 'splitstree':
self._write_nexus(samples, items)
io.execute(
'SplitsTree +g -i INPUT -x COMMAND',
no_display=True,
INPUT=self.prefix + '.nex',
COMMAND='UPDATE; '
'SAVE FILE=\'%s.nex\' REPLACE=yes; '
'EXPORTGRAPHICS format=svg file=\'%s.svg\' REPLACE=yes TITLE=\'NeighborNet from %d variants\'; '
'QUIT' % (self.prefix, self.prefix, len(items)),
)
elif self.as_ == 'vcf':
self._write_vcf(samples, items, reader)
else:
raise grace.Error('Unknown output format: ' + self.as_)
def run(self):
assert self.reads or self.pairs or self.interleaved, 'No reads given'
io.check_name_uniqueness(self.reads, self.pairs, self.interleaved)
working = self.get_workspace()
working.setup_reference(self.references, bowtie=True)
working.update_param(snp_cost=2.0)
reference = working.get_reference()
log_file = open(self.log_filename(),'wb')
with workspace.tempspace(dir=working.working_dir) as temp:
n = [ 0 ]
def tempname():
n[0] += 1
return temp/('%d.fq'%n[0])
def convert(filename):
info = io.get_file_info(filename)
ok = selection.matches('type-fastq:[compression-none/compression-gzip/compression-bzip2]', info)
if ok:
return filename
result_name = tempname()
with open(result_name,'wb') as f:
for name, seq, qual in io.read_sequences(filename, qualities='required'):
io.write_fastq(f, name, seq, qual)
return result_name
ones = [ ]
twos = [ ]
singles = [ ]
for pair in self.pairs:
assert len(pair) == 2, 'Need two files in each "pair:" section.'
ones.append(convert(pair[0]))
twos.append(convert(pair[1]))
for item in self.interleaved:
left_name = tempname()
right_name = tempname()
ones.append(left_name)
twos.append(right_name)
with open(left_name,'wb') as left, \
open(right_name,'wb') as right:
reader = io.read_sequences(item, qualities='required')
while True:
try:
name, seq, qual = reader.next()
except StopIteration:
break
io.write_fastq(left, name,seq,qual)
try:
name, seq, qual = reader.next()
except StopIteration:
raise grace.Error('Interleaved file contains odd number of sequences')
io.write_fastq(right, name,seq,qual)
for item in self.reads:
singles.append(convert(item))
cores = min(self.cores, legion.coordinator().get_cores())
command = (
[ 'bowtie2',
'--threads', str(cores),
'--rg-id', '1',
'--rg', 'SM:'+working.name,
] +
self.bowtie_options +
[ '-x', reference.get_bowtie_index_prefix() ]
)
commands = [ ]
if ones:
commands.append(command + [ '-1', ','.join(ones), '-2', ','.join(twos) ])
if singles:
commands.append(command + [ '-U', ','.join(singles) ])
temp_bam_name = temp/'temp.bam'
with io.pipe_to(
['samtools', 'view', '-S', '-b', '-'],
stdout=open(temp_bam_name,'wb'),
stderr=log_file
) as f:
header_sent = False
for command in commands:
self.log.log('Running:\n' + ' '.join(command) + '\n')
with io.pipe_from(
command,
stderr=log_file,
cores=cores
) as f_out:
for line in f_out:
if not header_sent or not line.startswith('@'):
f.write(line)
header_sent = True
io.execute([
'samtools', 'sort', '-n', temp_bam_name, working/'alignments'
])
log_file.close()
def sort_and_index_bam(in_filename, out_prefix, cores=8):
sort_bam(in_filename, out_prefix, cores=cores)
io.execute([
'samtools', 'index', out_prefix + '.bam'
])
def run(self):
work = self.get_workspace()
work.update_param(remove=['tail_tools_reference_version'])
nesoni.Make_reference(
self.output_dir,
filenames = self.filenames,
snpeff = False,
cs = 'ifavailable' if self.index and self.shrimp else False,
ls = False,
bowtie = 'ifavailable' if self.index and self.bowtie else False,
).run()
annotations = list(annotation.read_annotations(work/'reference.gff'))
annotation.link_up_annotations(annotations)
exon_index = span_index.index_annotations([
item for item in annotations if item.type == "exon"
])
mrna_end_index = span_index.index_annotations([
item.three_prime() for item in annotations if item.type == "mRNA"
])
mrna_utrs = [ ]
gene_utrs = [ ]
for gene in annotations:
if gene.type != 'gene': continue
mrnas = [ item for item in gene.children if item.type == 'mRNA' ]
assert mrnas, "Gene without any mRNAs: "+gene.get_id()
gene.attr['color'] = '#880088'
gene.start = min(item.start for item in mrnas)
gene.end = max(item.end for item in mrnas)
gene.attr["max_extension"] = str(_max_extension(gene, exon_index, mrna_end_index))
gene_utr_5primes = [ ]
for mrna in mrnas:
assert mrna.strand == gene.strand, mrna
assert mrna.seqid == gene.seqid, mrna
mrna.attr["max_extension"] = str(_max_extension(mrna, exon_index, mrna_end_index))
cdss = [ item for item in mrna.children if item.type == 'CDS' ]
exons = [ item for item in mrna.children if item.type == 'exon' ]
if not exons: continue
#link up annotations sorts children, so final is really final
for item in exons[:-1]:
item.attr["max_extension"] = "0"
exons[-1].attr["max_extension"] = mrna.attr["max_extension"]
if not cdss: continue
mrna_utr_5primes = [ ]
if gene.strand >= 0:
cds_3prime = max(item.end for item in cdss)
for item in exons:
if item.end >= cds_3prime:
mrna_utr_5primes.append(max(item.start,cds_3prime))
else:
cds_3prime = min(item.start for item in cdss)
for item in exons:
if item.start <= cds_3prime:
mrna_utr_5primes.append(min(item.end,cds_3prime))
if mrna.strand >= 0:
utr_start = min(mrna_utr_5primes) if mrna_utr_5primes else mrna.end
utr_end = max(utr_start+1,mrna.end)
gene_utr_5primes.append(utr_start)
else:
utr_end = max(mrna_utr_5primes) if mrna_utr_5primes else mrna.start
utr_start = min(mrna.start,utr_end-1)
gene_utr_5primes.append(utr_end)
attr = mrna.attr.copy()
attr['Parent'] = attr['ID']
attr['ID'] = attr['ID']+'-3UTR'
attr['color'] = '#008888'
utr = annotation.Annotation(
source = 'tt',
type = 'three_prime_utr',
seqid = mrna.seqid,
strand = mrna.strand,
start = utr_start,
end = utr_end,
attr = attr,
)
max_ext = _max_extension(utr, exon_index, mrna_end_index)
utr.attr["max_extension"] = str(max_ext)
#Only include if there is an annotated 3' UTR or end is not in the middle of some other isoform's exon
if utr_end-utr_start+max_ext > 1:
mrna_utrs.append(utr)
if gene.strand >= 0:
utr_start = max(gene_utr_5primes) if gene_utr_5primes else gene.end
utr_end = max(utr_start+1,gene.end)
else:
utr_end = min(gene_utr_5primes) if gene_utr_5primes else gene.start
utr_start = min(gene.start,utr_end-1)
attr = gene.attr.copy()
attr['Parent'] = attr['ID']
attr['ID'] = attr['ID']+'-3UTR'
attr['color'] = '#008888'
utr = annotation.Annotation(
source = 'tt',
type = 'three_prime_utr',
seqid = gene.seqid,
strand = gene.strand,
start = utr_start,
end = utr_end,
attr = attr,
)
utr.attr["max_extension"] = str(_max_extension(utr, exon_index, mrna_end_index))
gene_utrs.append(utr)
annotation.write_gff3(work/'reference.gff', annotations + mrna_utrs)
annotation.write_gff3(work/'utr.gff', gene_utrs)
if self.index and self.star and grace.can_execute("STAR"):
star_work = workspace.Workspace(work/'star')
io.execute([
'STAR','--runMode','genomeGenerate',
'--outFileNamePrefix',star_work.working_dir+'/',
'--genomeDir',star_work.working_dir,
'--genomeFastaFiles',work/'reference.fa',
'--sjdbGTFfile',work/'reference.gff',
'--sjdbGTFtagExonParentTranscript','Parent',
'--sjdbOverhang','100',
])
work.update_param(tail_tools_reference_version=work.VERSION)
def run(self):
reader_f = io.open_possibly_compressed_file(self.vcf)
reader = vcf.Reader(reader_f)
tags = { }
for item in reader.metadata.get('sampleTags',[]):
parts = item.split(',')
tags[parts[0]] = parts
assert 'reference' not in reader.samples, 'Can\'t have a sample called reference, sorry.'
samples = [ 'reference'] + reader.samples
for sample in samples:
if sample not in tags:
tags[sample] = [ sample, 'all' ]
samples = selection.select_and_sort(
self.select, self.sort, samples, lambda sample: tags[sample])
required = [ i for i, sample in enumerate(samples)
if selection.matches(self.require, tags[sample]) ]
sample_number = dict((b,a) for a,b in enumerate(reader.samples))
items = [ ]
for record in reader:
variants = get_variants(record)
genotypes = [ ]
counts = [ ]
qualities = [ ]
for sample in samples:
if sample == 'reference':
genotypes.append([0])
counts.append([1])
qualities.append(float('inf'))
else:
genotypes.append(get_genotype(record.samples[sample_number[sample]]))
counts.append(get_variant_counts(record.samples[sample_number[sample]]))
qualities.append(record.samples[sample_number[sample]].data.GQ)
# Only output when there are at least two genotypes
any_interesting = False
for i in xrange(len(genotypes)):
for j in xrange(i):
if (genotypes[i] is not None and genotypes[j] is not None and
not genotypes_equal(genotypes[i], genotypes[j])):
any_interesting = True
break
if any_interesting: break
if not any_interesting:
continue
if any(genotypes[i] is None for i in required):
continue
if self.only_snps and any(
genotype is not None and any(len(variants[i]) != 1 for i in genotype)
for genotype in genotypes):
continue
snpeff = snpeff_describe(record.INFO.get('EFF',''))
if not any( selection.matches(self.snpeff_filter, item[1]) for item in (snpeff or [('',[])]) ):
continue
items.append(_Nway_record(variants=variants, genotypes=genotypes, counts=counts, qualities=qualities, snpeff=snpeff, record=record))
self.log.log('%d variants\n\n' % len(items))
if self.as_ == 'table':
self._write_table(samples, items)
elif self.as_ == 'nexus':
self._write_nexus(samples, items)
elif self.as_ == 'splitstree':
self._write_nexus(samples, items)
io.execute(
'SplitsTree +g -i INPUT -x COMMAND',
no_display=True,
INPUT=self.prefix + '.nex',
COMMAND='UPDATE; '
'SAVE FILE=\'%s.nex\' REPLACE=yes; '
'EXPORTGRAPHICS format=svg file=\'%s.svg\' REPLACE=yes TITLE=\'NeighborNet from %d variants\'; '
'QUIT'
% (self.prefix, self.prefix, len(items)),
)
elif self.as_ == 'vcf':
self._write_vcf(samples, items, reader)
else:
raise grace.Error('Unknown output format: '+self.as_)
def run(self):
assert self.method in ("limma", "fitnoise1",
"fitnoise2"), "Unknown method."
assert self.method != "limma" or not self.empirical_controls
title = self.get_title()
n_alt = len(self.alt)
n_null = len(self.null)
suffix = '-dedup' if self.dedup else ''
genewise_filename = join(self.analysis, 'expression',
'genewise' + suffix, 'counts.csv')
genewise_norm_filename = join(self.analysis, 'expression',
'genewise' + suffix, 'norm.csv')
primarypeakwise_filename = join(self.analysis, 'expression',
'primarypeakwise' + suffix,
'counts.csv')
primarypeakwise_norm_filename = join(self.analysis, 'expression',
'primarypeakwise' + suffix,
'norm.csv')
peakwise_filename = join(self.analysis, 'expression',
'peakwise' + suffix, 'counts.csv')
peakwise_norm_filename = join(self.analysis, 'expression',
'peakwise' + suffix, 'norm.csv')
pairwise_filename = join(self.analysis, 'peak-shift' + suffix,
'individual-pairs.csv')
pairwise_norm_filename = join(self.analysis, 'peak-shift' + suffix,
'individual-pairs-norm.csv')
reader = io.Table_reader(genewise_filename, 'Count')
reader.close()
samples = [
item for i, item in enumerate(reader.headings)
if reader.groups[i] == 'Count'
]
tags = {}
for item in samples:
tags[item] = [item]
for line in reader.comments:
if line.startswith('#sampleTags='):
parts = line[len('#sampleTags='):].split(',')
tags[parts[0]] = parts
model = []
for term in self.alt + self.null:
spec = selection.term_specification(term)
model.append(
[selection.weight(spec, tags[item]) for item in samples])
model = zip(*model) #Transpose
select = [any(row) for row in model]
model = [row for row, selected in zip(model, select) if selected]
model_columns = [
selection.term_name(item) for item in self.alt + self.null
]
model_rows = [item for keep, item in zip(select, samples) if keep]
#degust complains if name starts with '-', delimits with commas
model_columns = [
('.' if item[:1] == '-' else '') + item.replace(',', ';')
for item in model_columns
]
pairs_n_alt = n_alt
pairs_select = select + select
pairs_model = ([(0, ) * n_alt + row + (0, ) for row in model] +
[row[:n_alt] + row + (1, ) for row in model])
pairs_model_columns = (
[item + '-interaction'
for item in model_columns[:n_alt]] + model_columns + ['pair2'])
pairs_model_rows = [item + '-peak1' for item in model_rows
] + [item + '-peak2' for item in model_rows]
design_str = '[' + ('-' * (8 * n_alt - 2)) + '] test coefficients\n'
for row, name in zip(model, model_rows):
design_str += "%s %s\n" % (''.join('%7g ' % item
for item in row), name)
print
print "Design matrix"
print design_str
print
print 'Pair design matrix'
print '[' + ('-' * (8 * n_alt - 2)) + '] test coefficients'
for row, name in zip(pairs_model, pairs_model_rows):
print ''.join('%7g ' % item for item in row), name
print
workspace = self.get_workspace()
runr.run_script(
TEST_R,
self.tell,
DIR=workspace.working_dir,
METHOD=self.method,
WEIGHT=self.weight,
EMPIRICAL_CONTROLS=self.empirical_controls,
MIN_READS=self.min_reads,
BIOTYPE=self.biotype,
RELATION=self.relation,
QUANTILE_TAIL=self.quantile_tail,
DO_EXPRESSION=self.do_expression,
DO_TAIL_LENGTH=self.do_tail_length,
VERBOSE=self.verbose,
GENEWISE_FILENAME=genewise_filename,
GENEWISE_NORM_FILENAME=genewise_norm_filename,
PRIMARYPEAKWISE_FILENAME=primarypeakwise_filename,
PRIMARYPEAKWISE_NORM_FILENAME=primarypeakwise_norm_filename,
PEAKWISE_FILENAME=peakwise_filename,
PEAKWISE_NORM_FILENAME=peakwise_norm_filename,
PAIRWISE_FILENAME=pairwise_filename,
PAIRWISE_NORM_FILENAME=pairwise_norm_filename,
N_ALT=n_alt,
SELECT=select,
MODEL=model,
MODEL_COLUMNS=model_columns,
PAIRS_N_ALT=pairs_n_alt,
PAIRS_SELECT=pairs_select,
PAIRS_MODEL=pairs_model,
PAIRS_MODEL_COLUMNS=pairs_model_columns,
)
if self.tell: return
reporter = reporting.Reporter(workspace.working_dir,
title,
style=web.style())
if self.dedup:
reporter.p('Read deduplication was used.')
reporter.write('<table>\n')
for is_expression, entities, result, aveexpr, subtitle, terms in [
(True, 'genes', 'genewise-voom', 'avg.expression',
'Genewise expression level', model_columns[:n_alt]),
(False, 'genes', 'genewise-tail', 'avg.tail',
'Genewise tail length', model_columns[:n_alt]),
(True, 'primary peaks', 'primarypeakwise-voom', 'avg.expression',
'Primary-peakwise expression level', model_columns[:n_alt]),
(False, 'primary peaks', 'primarypeakwise-tail', 'avg.tail',
'Primary-peakwise tail length', model_columns[:n_alt]),
(True, 'peaks', 'peakwise-voom', 'avg.expression',
'Peakwise expression level', model_columns[:n_alt]),
(False, 'peaks', 'peakwise-tail', 'avg.tail',
'Peakwise tail length', model_columns[:n_alt]),
(True, 'peak pairs', 'pairwise-voom', 'avg.expression',
'Peak-pair expression shift', pairs_model_columns[:n_alt]),
(False, 'peak pairs', 'pairwise-tail', 'avg.tail',
'Peak-pair tail length shift', pairs_model_columns[:n_alt]),
]:
#data = io.read_grouped_table(workspace/(result+'-toptable.csv'))['All']
#n = 0
#n_01 = 0
#n_05 = 0
#for row in data.values():
# fdr = float(row['adj.P.Val'])
# if fdr <= 0.01: n_01 += 1
# if fdr <= 0.05: n_05 += 1
# n += 1
if is_expression and not self.do_expression: continue
if not is_expression and not self.do_tail_length: continue
io.execute([
'degust.py',
'--name',
title + ' : ' + subtitle,
'--avg',
aveexpr,
'--primary',
'baseline',
'--logFC',
','.join(terms),
'--fdr',
'adj.P.Val',
'--info',
'gene,locus_tag,product,reads,polya.reads,tail.lengths,' +
aveexpr,
'--notour',
'1',
'--out',
workspace / (result + '.html'),
workspace / (result + '-toptable.csv'),
])
with open(workspace / (result + '.txt'), 'rU') as f:
lines = f.readlines()
reporter.write('<tr><td valign="top" width="33%">')
reporter.subheading(
reporter.href(workspace / (result + '.html'), subtitle))
#reporter.p( '%d %s, %d with fdr<=0.01, %d with fdr<=0.05' % (n,entities,n_01,n_05) )
line = reporter.href(workspace / (result + '-toptable.csv'),
'Spreadsheet')
if result.endswith('voom'):
line += ', ' + reporter.href(workspace /
(result + '.png'), 'voom plot')
reporter.p(line)
for line in lines[-2:]:
reporter.p(line.strip())
reporter.write('</td><td valign="top"><br/><br/>')
for line in lines[:-2]:
reporter.write(line.strip() + '<br/>\n')
reporter.write('</td></tr>')
reporter.write('</table>\n')
reporter.subheading("Design matrix")
reporter.write('<pre>' + design_str + '</pre>')
reporter.close()
def run(self):
assert self.ucsc_name, 'Need a UCSC genome name'
scratch = _ucsc_scratch(self)
# Load annotations
source = 'tt-ucsc-%s-%s' % (self.ucsc_name, self.table)
table = scratch.get_table(self.table)
get_name = scratch.getter(self.name)
get_product = scratch.getter(self.product)
mrnas = []
for item in table:
ann = annotation.Annotation(
seqid=item.chrom,
source=source,
type='mRNA',
strand={
'+': 1,
'-': -1
}[item.strand],
start=int(item.txStart),
end=int(item.txEnd),
attr={
'ID': item.name,
'Name': get_name(item),
'Product': get_product(item),
#'UCSC_name2' : item.name2,
})
ann.record = item
mrnas.append(ann)
_uniquify_ids(mrnas)
annotations = []
for group in _grouped_features(mrnas):
ID = '/'.join(item.attr['ID'] for item in group)
for item in group:
item.attr['Parent'] = ID
item.attr['ID'] = item.attr['ID'] + '-mRNA'
annotations.append(
annotation.Annotation(
source=source,
type='gene',
seqid=group[0].seqid,
strand=group[0].strand,
start=min(item.start for item in group),
end=max(item.end for item in group),
attr={
'ID':
ID,
'Name':
annotation_tools.join_descriptions(
[item.attr['Name'] for item in group], '/'),
'Product':
annotation_tools.join_descriptions(
[item.attr['Product'] for item in group], '/'),
#'UCSC_name2' : annotation_tools.join_descriptions([ item.attr['UCSC_name2'] for item in group ], '/'),
}))
for item in group:
annotations.append(item)
exonStarts = _parse_ints(item.record.exonStarts)
exonEnds = _parse_ints(item.record.exonEnds)
cdsStart = int(item.record.cdsStart)
cdsEnd = int(item.record.cdsEnd)
for start, end in zip(exonStarts, exonEnds):
annotations.append(
annotation.Annotation(source=source,
type='exon',
seqid=item.seqid,
strand=item.strand,
start=start,
end=end,
attr={
'Parent': item.attr['ID'],
}))
if max(cdsStart, start) < min(cdsEnd, end):
annotations.append(
annotation.Annotation(
source=source,
type='CDS',
seqid=item.seqid,
strand=item.strand,
start=max(cdsStart, start),
end=min(cdsEnd, end),
#TODO: phase
attr={
'Parent': item.attr['ID'],
}))
# Load sequence
if self.download:
io.execute([
'rsync', '-P', 'rsync://hgdownload.cse.ucsc.edu/goldenPath/' +
self.ucsc_name + '/bigZips/chromFa.tar.gz',
scratch.ucsc / 'chromFa.tar.gz'
])
with workspace.tempspace() as temp:
io.execute([
'tar', '-C', temp.working_dir, '-zxf',
scratch.ucsc / 'chromFa.tar.gz'
])
sequences = [
temp / item
for item in natural_sorted(os.listdir(temp.working_dir))
]
with open(temp / 'reference.gff', 'wb') as f:
annotation.write_gff3_header(f)
for item in annotations:
print >> f, item.as_gff()
Make_tt_reference(
self.output_dir,
filenames=sequences + [temp / 'reference.gff'],
index=self.index,
).run()
def sort_and_index_bam(in_filename, out_prefix, cores=8):
sort_bam(in_filename, out_prefix, cores=cores)
io.execute([
'samtools', 'index', out_prefix + '.bam'
])
def run(self):
title = self.get_title()
n_alt = len(self.alt)
n_null = len(self.null)
suffix = '-dedup' if self.dedup else ''
genewise_filename = join(self.analysis,'expression','genewise'+suffix,'counts.csv')
genewise_norm_filename = join(self.analysis,'expression','genewise'+suffix,'norm.csv')
peakwise_filename = join(self.analysis,'expression','peakwise'+suffix,'counts.csv')
peakwise_norm_filename = join(self.analysis,'expression','peakwise'+suffix,'norm.csv')
pairwise_filename = join(self.analysis,'peak-shift'+suffix,'individual-pairs.csv')
pairwise_norm_filename = join(self.analysis,'peak-shift'+suffix,'individual-pairs-norm.csv')
reader = io.Table_reader(genewise_filename, 'Count')
reader.close()
samples = [ item for i, item in enumerate(reader.headings) if reader.groups[i] == 'Count' ]
tags = { }
for item in samples:
tags[item] = [ item ]
for line in reader.comments:
if line.startswith('#sampleTags='):
parts = line[len('#sampleTags='):].split(',')
tags[parts[0]] = parts
model = [ ]
for term in self.alt + self.null:
spec = term_specification(term)
model.append([ 1 if selection.matches(spec, tags[item]) else 0 for item in samples ])
model = zip(*model) #Transpose
select = [ any(row) for row in model ]
model = [ row for row,selected in zip(model,select) if selected ]
model_columns = [ term_name(item) for item in self.alt + self.null ]
pairs_n_alt = n_alt
pairs_select = select + select
pairs_model = (
[ (0,) * n_alt + row + (0,) for row in model ] +
[ row[:n_alt] + row + (1,) for row in model ]
)
pairs_model_columns = (
[ item+'-interaction' for item in model_columns[:n_alt] ] +
model_columns +
[ 'pair2' ]
)
workspace = self.get_workspace()
runr.run_script(TEST_R, self.tell,
SOURCE = os.path.join(os.path.dirname(__file__),'tail_tools.R'),
DIR = workspace.working_dir,
MIN_READS = self.min_reads,
GENEWISE_FILENAME = genewise_filename,
GENEWISE_NORM_FILENAME = genewise_norm_filename,
PEAKWISE_FILENAME = peakwise_filename,
PEAKWISE_NORM_FILENAME = peakwise_norm_filename,
PAIRWISE_FILENAME = pairwise_filename,
PAIRWISE_NORM_FILENAME = pairwise_norm_filename,
N_ALT = n_alt,
SELECT = select,
MODEL = model,
MODEL_COLUMNS = model_columns,
PAIRS_N_ALT = pairs_n_alt,
PAIRS_SELECT = pairs_select,
PAIRS_MODEL = pairs_model,
PAIRS_MODEL_COLUMNS = pairs_model_columns,
)
if self.tell: return
reporter = reporting.Reporter(workspace.working_dir, title)
if self.dedup:
reporter.p('Read deduplication was used.')
for entities, result, aveexpr, subtitle, terms in [
('genes', 'genewise-voom', 'avg.expression', 'Genewise expression level', model_columns[:n_alt]),
('genes', 'genewise-tail', 'avg.tail', 'Genewise tail length', model_columns[:n_alt]),
('peaks', 'peakwise-voom', 'avg.expression', 'Peakwise expression level', model_columns[:n_alt]),
('peaks', 'peakwise-tail', 'avg.tail', 'Peakwise tail length', model_columns[:n_alt]),
('peak pairs', 'pairwise-voom', 'avg.expression', 'Peak-pair expression shift', pairs_model_columns[:n_alt]),
('peak pairs', 'pairwise-tail', 'avg.tail', 'Peak-pair tail length shift', pairs_model_columns[:n_alt]),
]:
#data = io.read_grouped_table(workspace/(result+'-toptable.csv'))['All']
#n = 0
#n_01 = 0
#n_05 = 0
#for row in data.values():
# fdr = float(row['adj.P.Val'])
# if fdr <= 0.01: n_01 += 1
# if fdr <= 0.05: n_05 += 1
# n += 1
io.execute([
'degust.py',
'--name', title + ' : ' + subtitle,
'--avg', aveexpr,
'--primary', 'baseline',
'--logFC', ','.join(terms),
'--fdr', 'adj.P.Val',
'--info', 'gene,locus_tag,product,reads,polya.reads,tail.lengths,'+aveexpr,
'--notour', '1',
'--out', workspace/(result+'.html'),
workspace/(result+'-toptable.csv'),
])
reporter.subheading( reporter.href(workspace/(result+'.html'), subtitle) )
#reporter.p( '%d %s, %d with fdr<=0.01, %d with fdr<=0.05' % (n,entities,n_01,n_05) )
with open(workspace/(result+'.txt'),'rU') as f:
for line in f:
reporter.write(line.strip() + '<br/>\n')
reporter.close()
def run(self):
grace.require_shrimp_2()
grace.require_samtools()
assert self.references, 'No reference sequences given'
assert self.reads or self.pairs or self.interleaved, 'No reads given'
for pair in self.pairs:
assert len(pair) == 2, 'Two files required in each pair: section'
read_sets = []
for item in self.reads:
read_sets.append(([item], False))
for item in self.pairs:
read_sets.append((item, True))
for item in self.interleaved:
read_sets.append(([item], True))
default_options = {
'-E': None,
'-T': None,
'-N': str(grace.how_many_cpus()),
'-n': '2',
'-w': '200%',
'-p': 'opp-in',
'-I': '0,500',
'-X': None
}
if self.sam_unaligned:
default_options['--sam-unaligned'] = None
if self.half_paired:
default_options['--half-paired'] = None
else:
default_options['--no-half-paired'] = None
cutoff = '55%' #Default changed in SHRiMP 2.0.2
if '-h' in self.shrimp_options:
cutoff = self.shrimp_options[self.shrimp_options.index('-h') + 1]
#Create working directory
workspace = self.get_workspace(
) #working_directory.Working(self.output_dir, must_exist=False)
workspace.setup_reference(self.references)
reference = workspace.get_reference()
reference_filename = reference.reference_fasta_filename()
#workspace = io.Workspace(self.output_dir)
#
#workspace.update_param(
# shrimp_cutoff = cutoff
#)
#
##Make copy of reference sequences
#
#reference_filename = io.abspath(self.output_dir,'reference.fa')
#reference_file = open(reference_filename,'wb')
#
#reference_genbank_filename = io.abspath(self.output_dir,'reference.gbk')
#reference_genbank_file = open(reference_genbank_filename,'wb')
#any_genbank = [ False ]
#
#def genbank_callback(name, record):
# """ Make a copy of any genbank files passed in. """
# from Bio import SeqIO
#
# SeqIO.write([record], reference_genbank_file, 'genbank')
#
# f = open(os.path.join(
# self.output_dir,
# grace.filesystem_friendly_name(name) + '.gbk'
# ), 'wb')
# SeqIO.write([record], f, 'genbank')
# f.close()
#
# any_genbank[0] = True
#
#for filename in self.references:
# for name, sequence in io.read_sequences(filename, genbank_callback=genbank_callback):
# #Don't retain any comment
# name = name.split()[0]
# io.write_fasta(reference_file, name, sequence.upper())
#
# f = open(os.path.join(
# self.output_dir,
# grace.filesystem_friendly_name(name) + '.fa'
# ), 'wb')
# io.write_fasta(f, name, sequence.upper())
# f.close()
#
#
#reference_file.close()
#reference_genbank_file.close()
#if not any_genbank[0]:
# os.unlink(reference_genbank_filename)
#
## Create an index of the reference sequences
#io.execute([
# 'samtools', 'faidx', reference_filename
#])
#Run shrimp
bam_filename = io.abspath(self.output_dir, 'alignments.bam')
bam_prefix = io.abspath(self.output_dir, 'alignments')
bam_sorted_prefix = io.abspath(self.output_dir, 'alignments_sorted')
temp_filename = io.abspath(self.output_dir, 'temp.bam')
log_filename = io.abspath(self.output_dir, 'shrimp_log.txt')
log_file = open(log_filename, 'wb')
sam_eater = sam.Bam_writer(temp_filename)
#if self.cs:
# program = 'gmapper-cs'
#else:
# program = 'gmapper-ls'
sam_header_sent = [False]
n_seen = [0]
def eat(process):
for line in process.stdout:
if line.startswith('@'):
if sam_header_sent[0]: continue
else:
n_seen[0] += 1
if n_seen[0] % 100000 == 0:
grace.status('%s alignments produced' %
grace.pretty_number(n_seen[0]))
sam_eater.write_raw(line)
assert process.wait() == 0, 'shrimp failed'
sam_header_sent[0] = True
def remove_pair_options(options):
for flag in ['-p', '-I']:
while flag in options:
pos = options.index(flag)
options = options[:pos] + options[pos + 2:]
for flag in ['--half-paired']:
while flag in options:
pos = options.index(flag)
options = options[:pos] + options[pos + 1:]
return options
if '--qv-offset' not in self.shrimp_options:
guesses = []
for filenames, is_paired in read_sets:
for filename in filenames:
guesses.append(io.guess_quality_offset(filename))
assert len(
set(guesses)
) == 1, 'Conflicting quality offset guesses, please specify --qv-offset manually.'
default_options['--qv-offset'] = str(guesses[0])
for filenames, is_paired in read_sets:
options = self.shrimp_options[:]
has_qualities = all(
len(io.read_sequences(filename, qualities=True).next()) ==
3 #A little ugly
for filename in filenames)
if has_qualities:
options.append('--fastq')
# temp_read_filename = io.abspath(working_dir, 'temp.fa')
#else:
# temp_read_filename = io.abspath(working_dir, 'temp.fq')
#try:
#if len(filenames) == 1: # gmapper can cope with gzipped and filenames[0].endswith('.fa') or filenames[0].endswith('.fq'):
# actual_read_filename = filenames[0]
#else:
# actual_read_filename = temp_read_filename
# grace.status('Copying reads')
# f = open(temp_read_filename, 'wb')
# if has_qualities:
# for reads in itertools.izip(*[ io.read_sequences(filename, qualities=True) for filename in filenames ]):
# for name, seq, qual in reads:
# io.write_fastq(f, name, seq, qual)
# else:
# for reads in itertools.izip(*[ io.read_sequences(filename) for filename in filenames ]):
# for name, seq in reads:
# io.write_fasta(f, name, seq)
# f.close()
# grace.status('')
if len(filenames) == 1:
reads_parameters = [filenames[0]]
else:
reads_parameters = ['-1', filenames[0], '-2', filenames[1]]
for flag in default_options:
if flag not in options:
options.append(flag)
if default_options[flag] is not None:
options.append(default_options[flag])
if not is_paired:
options = remove_pair_options(options)
grace.status('')
full_param = reference.shrimp_command(self.cs,
options + reads_parameters)
print >> sys.stderr, 'Running', ' '.join(full_param)
p = io.run(full_param, stdout=subprocess.PIPE, stderr=log_file)
eat(p)
#finally:
# if os.path.exists(temp_read_filename):
# os.unlink(temp_read_filename)
log_file.close()
sam_eater.close()
grace.status('Sort')
io.execute(['samtools', 'sort', '-n', temp_filename, bam_prefix])
os.unlink(temp_filename)
grace.status('')
def run(self):
grace.require_shrimp_2()
grace.require_samtools()
assert self.references, "No reference sequences given"
assert self.reads or self.pairs or self.interleaved, "No reads given"
for pair in self.pairs:
assert len(pair) == 2, "Two files required in each pair: section"
io.check_name_uniqueness(self.reads, self.pairs, self.interleaved)
read_sets = []
for item in self.reads:
read_sets.append(([item], False))
for item in self.pairs:
read_sets.append((item, True))
for item in self.interleaved:
read_sets.append(([item], True))
# Create working directory
workspace = self.get_workspace()
workspace.setup_reference(self.references)
workspace.update_param(snp_cost=25)
reference = workspace.get_reference()
reference_filename = reference.reference_fasta_filename()
cores = min(self.cores, legion.coordinator().get_cores())
default_options = {
"-E": None,
"-T": None,
"-N": str(cores),
"-n": "2",
"-w": "200%",
"-p": "opp-in",
"-I": "0,500",
"-X": None,
}
if self.sam_unaligned:
default_options["--sam-unaligned"] = None
if self.half_paired:
default_options["--half-paired"] = None
else:
default_options["--no-half-paired"] = None
cutoff = "55%" # Default changed in SHRiMP 2.0.2
if "-h" in self.shrimp_options:
cutoff = self.shrimp_options[self.shrimp_options.index("-h") + 1]
# Run shrimp
bam_filename = io.abspath(self.output_dir, "alignments.bam")
bam_prefix = io.abspath(self.output_dir, "alignments")
bam_sorted_prefix = io.abspath(self.output_dir, "alignments_sorted")
temp_filename = io.abspath(self.output_dir, "temp.bam")
log_filename = io.abspath(self.output_dir, "shrimp_log.txt")
log_file = open(log_filename, "wb")
sam_eater = sam.Bam_writer(temp_filename)
sam_header_sent = [False]
n_seen = [0]
def eat(f):
for line in f:
if line.startswith("@"):
if sam_header_sent[0]:
continue
else:
n_seen[0] += 1
if n_seen[0] % 100000 == 0:
grace.status("%s alignments produced" % grace.pretty_number(n_seen[0]))
sam_eater.write_raw(line)
sam_header_sent[0] = True
def remove_pair_options(options):
for flag in ["-p", "-I"]:
while flag in options:
pos = options.index(flag)
options = options[:pos] + options[pos + 2 :]
for flag in ["--half-paired"]:
while flag in options:
pos = options.index(flag)
options = options[:pos] + options[pos + 1 :]
return options
for i, (filenames, is_paired) in enumerate(read_sets):
options = self.shrimp_options[:]
has_qualities = all(
len(io.read_sequences(filename, qualities=True).next()) == 3 for filename in filenames # A little ugly
)
if has_qualities:
options.append("--fastq")
if len(filenames) == 1:
reads_parameters = [filenames[0]]
else:
reads_parameters = ["-1", filenames[0], "-2", filenames[1]]
if "--qv-offset" not in self.shrimp_options:
guesses = []
for filename in filenames:
guesses.append(io.guess_quality_offset(filename))
assert (
len(set(guesses)) == 1
), "Conflicting quality offset guesses, please specify --qv-offset manually."
default_options["--qv-offset"] = str(guesses[0])
default_options["--read-group"] = "%s,%s" % (
workspace.name.replace(",", "_"),
workspace.name.replace(",", "_"),
)
for flag in default_options:
if flag not in options:
options.append(flag)
if default_options[flag] is not None:
options.append(default_options[flag])
if not is_paired:
options = remove_pair_options(options)
grace.status("")
full_param = reference.shrimp_command(self.cs, options + reads_parameters)
print >>sys.stderr, "Running", " ".join(full_param)
with io.pipe_from(full_param, stderr=log_file, cores=cores) as f:
eat(f)
log_file.close()
sam_eater.close()
grace.status("Sort")
io.execute(["samtools", "sort", "-n", temp_filename, bam_prefix])
os.unlink(temp_filename)
grace.status("")
