def run( o_options ):
"""The Main function/pipeline for duplication filter.
"""
# Parse options...
options = opt_validate( o_options )
# end of parsing commandline options
info = options.info
warn = options.warn
debug = options.debug
error = options.error
#0 output arguments
options.PE_MODE = options.format in ('BAMPE','BEDPE')
#1 Read tag files
if options.PE_MODE:
info("# read input file in Paired-end mode.")
treat = load_frag_files_options ( options ) # return PETrackI object
t0 = treat.total
info("# total fragments/pairs in alignment file: %d" % (t0) )
else:
info("# read alignment files...")
treat = load_tag_files_options (options)
t0 = treat.total
info("# tag size = %d" % options.tsize)
treat.fw = options.tsize
info("# total tags in alignment file: %d", t0)
#2 Build Model
info("# Build Peak Model...")
if options.PE_MODE:
d = treat.average_template_length
info("# Average insertion length of all pairs is %d bps" % d)
return
try:
peakmodel = PeakModel(treatment = treat,
max_pairnum = MAX_PAIRNUM,
opt = options
)
info("# finished!")
debug("# Summary Model:")
debug("# min_tags: %d" % (peakmodel.min_tags))
debug("# d: %d" % (peakmodel.d))
info("# predicted fragment length is %d bps" % peakmodel.d)
info("# alternative fragment length(s) may be %s bps" % ','.join(map(str,peakmodel.alternative_d)))
info("# Generate R script for model : %s" % (options.modelR))
model2r_script(peakmodel,options.modelR, options.rfile )
options.d = peakmodel.d
except NotEnoughPairsException:
warn("# Can't find enough pairs of symmetric peaks to build model!")
def run( o_options ):
"""The Main function/pipeline for duplication filter.
"""
# Parse options...
options = opt_validate( o_options )
# end of parsing commandline options
info = options.info
warn = options.warn
debug = options.debug
error = options.error
#0 output arguments
assert options.format != 'BAMPE', "Pair-end data with BAMPE option doesn't work with predictd command. You can pretend your data to be single-end with -f BAM. Please try again!"
#1 Read tag files
info("# read alignment files...")
treat = load_tag_files_options (options)
info("# tag size = %d", options.tsize)
t0 = treat.total
info("# total tags in alignment file: %d", t0)
#2 Build Model
info("# Build Peak Model...")
try:
peakmodel = PeakModel(treatment = treat,
max_pairnum = MAX_PAIRNUM,
opt = options
)
info("# finished!")
debug("# Summary Model:")
debug("# min_tags: %d" % (peakmodel.min_tags))
debug("# d: %d" % (peakmodel.d))
info("# predicted fragment length is %d bps" % peakmodel.d)
info("# alternative fragment length(s) may be %s bps" % ','.join(map(str,peakmodel.alternative_d)))
info("# Generate R script for model : %s" % (options.modelR))
model2r_script(peakmodel,options.modelR, options.rfile )
options.d = peakmodel.d
except NotEnoughPairsException:
warn("# Can't find enough pairs of symmetric peaks to build model!")
def run(args):
"""The Main function/pipeline for MACS.
"""
# Parse options...
options = opt_validate(args)
# end of parsing commandline options
info = options.info
warn = options.warn
debug = options.debug
error = options.error
#0 output arguments
info("\n" + options.argtxt)
options.PE_MODE = options.format in ('BAMPE', 'BEDPE')
if options.PE_MODE: tag = 'fragment' # call things fragments not tags
else: tag = 'tag'
tempfile.tempdir = options.tempdir
#1 Read tag files
info("#1 read %s files...", tag)
if options.PE_MODE: (treat, control) = load_frag_files_options(options)
else: (treat, control) = load_tag_files_options(options)
if control is not None: check_names(treat, control, error)
info("#1 %s size = %.1f", tag, options.tsize)
tagsinfo = "# %s size is determined as %d bps\n" % (tag, options.tsize)
t0 = treat.total
tagsinfo += "# total %ss in treatment: %d\n" % (tag, t0)
info("#1 total %ss in treatment: %d", tag, t0)
# not ready yet
# options.filteringmodel = True
# if options.filteringmodel:
# treat.separate_dups()
# t0 = treat.total + treat.dups.total
# t1 = treat.total
# info("#1 Redundant rate of treatment: %.2f", float(t0 - t1) / t0)
# tagsinfo += "# Redundant rate in treatment: %.2f\n" % (float(t0-t1)/t0)
# elif options.keepduplicates != "all":
if options.keepduplicates != "all":
if options.keepduplicates == "auto":
info(
"#1 calculate max duplicate %ss in single position based on binomial distribution...",
tag)
treatment_max_dup_tags = cal_max_dup_tags(options.gsize, t0)
info("#1 max_dup_tags based on binomial = %d" %
(treatment_max_dup_tags))
else:
info("#1 user defined the maximum %ss...", tag)
treatment_max_dup_tags = int(options.keepduplicates)
if options.PE_MODE:
info(
"#1 filter out redundant fragments by allowing at most %d identical fragment(s)",
treatment_max_dup_tags)
else:
info(
"#1 filter out redundant tags at the same location and the same strand by allowing at most %d tag(s)",
treatment_max_dup_tags)
#treat.separate_dups(treatment_max_dup_tags) # changed 5-29; changed back since we don't need to addbackdup+refinepeak anymore
treat.filter_dup(treatment_max_dup_tags)
t1 = treat.total
info("#1 %ss after filtering in treatment: %d", tag, t1)
tagsinfo += "# %ss after filtering in treatment: %d\n" % (tag, t1)
if options.PE_MODE:
tagsinfo += "# maximum duplicate fragments in treatment = %d\n" % (
treatment_max_dup_tags)
else:
tagsinfo += "# maximum duplicate tags at the same position in treatment = %d\n" % (
treatment_max_dup_tags)
info("#1 Redundant rate of treatment: %.2f", float(t0 - t1) / t0)
tagsinfo += "# Redundant rate in treatment: %.2f\n" % (float(t0 - t1) /
t0)
else:
t1 = t0
if control is not None:
c0 = control.total
tagsinfo += "# total %ss in control: %d\n" % (tag, c0)
info("#1 total %ss in control: %d", tag, c0)
# not ready yet
#if options.filteringmodel:
# control.separate_dups()
# c0 = treat.total + treat.dups.total
# c1 = treat.total
# info("#1 Redundant rate of treatment: %.2f", float(c0 - c1) / c0)
# tagsinfo += "# Redundant rate in treatment: %.2f\n" % (float(c0-c1)/c0)
#elif options.keepduplicates != "all":
if options.keepduplicates != "all":
if options.keepduplicates == "auto":
info(
"#1 for control, calculate max duplicate %ss in single position based on binomial distribution...",
tag)
control_max_dup_tags = cal_max_dup_tags(options.gsize, c0)
info("#1 max_dup_tags based on binomial = %d" %
(control_max_dup_tags))
else:
info("#1 user defined the maximum %ss...", tag)
control_max_dup_tags = int(options.keepduplicates)
if options.PE_MODE:
info(
"#1 filter out redundant fragments by allowing at most %d identical fragment(s)",
treatment_max_dup_tags)
else:
info(
"#1 filter out redundant tags at the same location and the same strand by allowing at most %d tag(s)",
treatment_max_dup_tags)
control.filter_dup(treatment_max_dup_tags)
#control.separate_dups(treatment_max_dup_tags) # changed 5-29; changed back since we don't need to call addbackdup+refinepeak anymore
c1 = control.total
info("#1 %ss after filtering in control: %d", tag, c1)
tagsinfo += "# %ss after filtering in control: %d\n" % (tag, c1)
if options.PE_MODE:
tagsinfo += "# maximum duplicate fragments in control = %d\n" % (
treatment_max_dup_tags)
else:
tagsinfo += "# maximum duplicate tags at the same position in control = %d\n" % (
treatment_max_dup_tags)
info("#1 Redundant rate of control: %.2f" % (float(c0 - c1) / c0))
tagsinfo += "# Redundant rate in control: %.2f\n" % (
float(c0 - c1) / c0)
else:
c1 = c0
info("#1 finished!")
#2 Build Model
info("#2 Build Peak Model...")
if options.nomodel:
info("#2 Skipped...")
if options.PE_MODE:
#options.shiftsize = 0
options.d = options.tsize
else:
options.d = options.extsize
info("#2 Use %d as fragment length" % (options.d))
if options.shift > 0:
info("#2 Sequencing ends will be shifted towards 3' by %d bp(s)" %
(options.shift))
elif options.shift < 0:
info("#2 Sequencing ends will be shifted towards 5' by %d bp(s)" %
(options.shift * -1))
options.scanwindow = 2 * options.d # remove the effect of --bw
else:
try:
peakmodel = PeakModel(treatment=treat,
max_pairnum=MAX_PAIRNUM,
opt=options)
info("#2 finished!")
debug("#2 Summary Model:")
debug("#2 min_tags: %d" % (peakmodel.min_tags))
debug("#2 d: %d" % (peakmodel.d))
debug("#2 scan_window: %d" % (peakmodel.scan_window))
info("#2 predicted fragment length is %d bps" % peakmodel.d)
info("#2 alternative fragment length(s) may be %s bps" %
','.join(map(str, peakmodel.alternative_d)))
info("#2.2 Generate R script for model : %s" % (options.modelR))
model2r_script(peakmodel, options.modelR, options.name)
options.d = peakmodel.d
options.scanwindow = 2 * options.d
if options.d <= 2 * options.tsize:
warn(
"#2 Since the d (%.0f) calculated from paired-peaks are smaller than 2*tag length, it may be influenced by unknown sequencing problem!"
% (options.d))
if options.onauto:
options.d = options.extsize
options.scanwindow = 2 * options.d
warn(
"#2 MACS will use %d as EXTSIZE/fragment length d. NOTE: if the d calculated is still acceptable, please do not use --fix-bimodal option!"
% (options.d))
else:
warn(
"#2 You may need to consider one of the other alternative d(s): %s"
% ','.join(map(str, peakmodel.alternative_d)))
warn(
"#2 You can restart the process with --nomodel --extsize XXX with your choice or an arbitrary number. Nontheless, MACS will continute computing."
)
except NotEnoughPairsException:
if not options.onauto:
sys.exit(1)
warn("#2 Skipped...")
options.d = options.extsize
options.scanwindow = 2 * options.d
warn(
"#2 Since --fix-bimodal is set, MACS will use %d as fragment length"
% (options.d))
#3 Call Peaks
info("#3 Call peaks...")
if options.nolambda:
info("# local lambda is disabled!")
# decide options.tocontrol according to options.tolarge
if control and options.PE_MODE:
c1 = c1 * 2
if control:
if options.downsample:
# use random sampling to balance treatment and control
info(
"#3 User prefers to use random sampling instead of linear scaling."
)
if t1 > c1:
info("#3 MACS is random sampling treatment %ss...", tag)
if options.seed < 0:
warn(
"#3 Your results may not be reproducible due to the random sampling!"
)
else:
info("#3 Random seed (%d) is used." % options.seed)
treat.sample_num(c1, options.seed)
info("#3 %d Tags from treatment are kept", treat.total)
elif c1 > t1:
info("#3 MACS is random sampling control %ss...", tag)
if options.seed < 0:
warn(
"#3 Your results may not be reproducible due to the random sampling!"
)
else:
info("#3 Random seed (%d) is used." % options.seed)
control.sample_num(t1, options.seed)
info("#3 %d %ss from control are kept", control.total, tag)
# set options.tocontrol although it would;t matter now
options.tocontrol = False
else:
if options.scaleto == "large":
if t1 > c1:
# treatment has more tags than control, since tolarge is
# true, we will scale control to treatment.
options.tocontrol = False
else:
# treatment has less tags than control, since tolarge is
# true, we will scale treatment to control.
options.tocontrol = True
else:
if t1 > c1:
# treatment has more tags than control, since tolarge is
# false, we will scale treatment to control.
options.tocontrol = True
else:
# treatment has less tags than control, since tolarge is
# false, we will scale control to treatment.
options.tocontrol = False
peakdetect = PeakDetect(treat=treat, control=control, opt=options)
peakdetect.call_peaks()
# filter out low FE peaks
peakdetect.peaks.filter_fc(fc_low=options.fecutoff)
#call refinepeak if needed.
# if options.refine_peaks:
# info("#3 now put back duplicate reads...")
# treat.addback_dups()
# info("#3 calculate reads balance to refine peak summits...")
# refined_peaks = treat.refine_peak_from_tags_distribution ( peakdetect.peaks, options.d, 0 )
# info("#3 reassign scores for newly refined peak summits...")
# peakdetect.peaks = peakdetect.scoretrack.reassign_peaks( refined_peaks ) # replace
# #info("#3 write to file: %s ..." % options.name+"_refined_peaks.encodePeak" )
# #refinedpeakfile = open(options.name+"_refined_peaks.encodePeak", "w")
# #refined_peaks.write_to_narrowPeak (refinedpeakfile, name_prefix="%s_refined_peak_", name=options.name, score_column=score_column, trackline=options.trackline )
#diag_result = peakdetect.diag_result()
#4 output
#4.1 peaks in XLS
info("#4 Write output xls file... %s" % (options.peakxls))
ofhd_xls = open(options.peakxls, "w")
ofhd_xls.write("# This file is generated by MACS version %s\n" %
(MACS_VERSION))
ofhd_xls.write(options.argtxt + "\n")
ofhd_xls.write(tagsinfo)
if options.shift > 0:
ofhd_xls.write(
"# Sequencing ends will be shifted towards 3' by %d bp(s)\n" %
(options.shift))
elif options.shift < 0:
ofhd_xls.write(
"# Sequencing ends will be shifted towards 5' by %d bp(s)\n" %
(options.shift * -1))
ofhd_xls.write("# d = %d\n" % (options.d))
try:
ofhd_xls.write("# alternative fragment length(s) may be %s bps\n" %
','.join(map(str, peakmodel.alternative_d)))
except:
# when --nomodel is used, there is no peakmodel object. Simply skip this line.
pass
if options.nolambda:
ofhd_xls.write("# local lambda is disabled!\n")
# pass write method so we can print too, and include name
peakdetect.peaks.write_to_xls(ofhd_xls, name=options.name.encode())
ofhd_xls.close()
#4.2 peaks in BED
if options.log_pvalue != None:
score_column = "pscore"
elif options.log_qvalue != None:
score_column = "qscore"
#4.2 peaks in narrowPeak
if not options.broad:
#info("#4 Write peak bed file... %s" % (options.peakbed))
#ofhd_bed = open(options.peakbed,"w")
#peakdetect.peaks.write_to_bed (ofhd_bed, name_prefix="%s_peak_", name = options.name, description="Peaks for %s (Made with MACS v2, " + strftime("%x") + ")", score_column=score_column, trackline=options.trackline)
#ofhd_bed.close()
info("#4 Write peak in narrowPeak format file... %s" %
(options.peakNarrowPeak))
ofhd_bed = open(options.peakNarrowPeak, "w")
peakdetect.peaks.write_to_narrowPeak(ofhd_bed,
name_prefix=b"%s_peak_",
name=options.name.encode(),
score_column=score_column,
trackline=options.trackline)
ofhd_bed.close()
#4.2-2 summits in BED
info("#4 Write summits bed file... %s" % (options.summitbed))
ofhd_summits = open(options.summitbed, "w")
peakdetect.peaks.write_to_summit_bed(
ofhd_summits,
name_prefix="%s_peak_".encode(),
name=options.name.encode(),
description=("Summits for %s (Made with MACS v2, " +
strftime("%x") + ")").encode(),
score_column=score_column,
trackline=options.trackline)
ofhd_summits.close()
#4.2 broad peaks in bed12 or gappedPeak
else:
info("#4 Write broad peak in broadPeak format file... %s" %
(options.peakBroadPeak))
ofhd_bed = open(options.peakBroadPeak, "w")
peakdetect.peaks.write_to_broadPeak(ofhd_bed,
name_prefix=b"%s_peak_",
name=options.name.encode(),
description=options.name.encode(),
score_column=score_column,
trackline=options.trackline)
ofhd_bed.close()
info("#4 Write broad peak in bed12/gappedPeak format file... %s" %
(options.peakGappedPeak))
ofhd_bed = open(options.peakGappedPeak, "w")
peakdetect.peaks.write_to_gappedPeak(ofhd_bed,
name_prefix=b"%s_peak_",
name=options.name.encode(),
description=options.name.encode(),
score_column=score_column,
trackline=options.trackline)
ofhd_bed.close()
info("Done!")