def main(blastfile, p, opts):
sqlite = opts.sqlite
qbed, sbed, qorder, sorder, is_self = check_beds(blastfile, p, opts)
filtered_blast = read_blast(blastfile, qorder, sorder, \
is_self=is_self, ostrip=opts.strip_names)
all_data = [(b.qi, b.si) for b in filtered_blast]
c = None
if sqlite:
conn = sqlite3.connect(sqlite)
c = conn.cursor()
c.execute("drop table if exists synteny")
c.execute("create table synteny (query text, anchor text, "
"gray varchar(1), score integer, dr integer, "
"orientation varchar(1), qnote text, snote text)")
fw = None
else:
fw = must_open(opts.outfile, "w")
batch_query(qbed, sbed, all_data, opts, fw=fw, c=c, transpose=False)
if qbed.filename == sbed.filename:
logging.debug("Self comparisons, mirror ignored")
else:
batch_query(qbed, sbed, all_data, opts, fw=fw, c=c, transpose=True)
if sqlite:
c.execute("create index q on synteny (query)")
conn.commit()
c.close()
else:
fw.close()
def bed(args):
"""
%prog bed anchorsfile
Convert ANCHORS file to BED format.
"""
from collections import defaultdict
from jcvi.compara.synteny import AnchorFile, check_beds
from jcvi.formats.bed import Bed
from jcvi.formats.base import get_number
p = OptionParser(bed.__doc__)
p.add_option("--switch", default=False, action="store_true",
help="Switch reference and aligned map elements")
p.add_option("--scale", type="float",
help="Scale the aligned map distance by factor")
p.set_beds()
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
anchorsfile, = args
switch = opts.switch
scale = opts.scale
ac = AnchorFile(anchorsfile)
pairs = defaultdict(list)
for a, b, block_id in ac.iter_pairs():
pairs[a].append(b)
qbed, sbed, qorder, sorder, is_self = check_beds(anchorsfile, p, opts)
bd = Bed()
for q in qbed:
qseqid, qstart, qend, qaccn = q.seqid, q.start, q.end, q.accn
if qaccn not in pairs:
continue
for s in pairs[qaccn]:
si, s = sorder[s]
sseqid, sstart, send, saccn = s.seqid, s.start, s.end, s.accn
if switch:
qseqid, sseqid = sseqid, qseqid
qstart, sstart = sstart, qstart
qend, send = send, qend
qaccn, saccn = saccn, qaccn
if scale:
sstart /= scale
try:
newsseqid = get_number(sseqid)
except ValueError:
raise ValueError, "`{0}` is on `{1}` with no number to extract".\
format(saccn, sseqid)
bedline = "\t".join(str(x) for x in (qseqid, qstart - 1, qend,
"{0}:{1}".format(newsseqid, sstart)))
bd.add(bedline)
bd.print_to_file(filename=opts.outfile, sorted=True)
def movieframe(args):
"""
%prog movieframe tour test.clm contigs.ref.anchors
Draw heatmap and synteny in the same plot.
"""
p = OptionParser(movieframe.__doc__)
p.add_option("--label", help="Figure title")
p.set_beds()
p.set_outfile(outfile=None)
opts, args, iopts = p.set_image_options(args, figsize="16x8",
style="white", cmap="coolwarm",
format="png", dpi=120)
if len(args) != 3:
sys.exit(not p.print_help())
tour, clmfile, anchorsfile = args
tour = tour.split(",")
image_name = opts.outfile or ("movieframe." + iopts.format)
label = opts.label or op.basename(image_name).rsplit(".", 1)[0]
clm = CLMFile(clmfile)
totalbins, bins, breaks = make_bins(tour, clm.tig_to_size)
M = read_clm(clm, totalbins, bins)
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes([0, 0, 1, 1]) # whole canvas
ax1 = fig.add_axes([.05, .1, .4, .8]) # heatmap
ax2 = fig.add_axes([.55, .1, .4, .8]) # dot plot
ax2_root = fig.add_axes([.5, 0, .5, 1]) # dot plot canvas
# Left axis: heatmap
plot_heatmap(ax1, M, breaks, iopts)
# Right axis: synteny
qbed, sbed, qorder, sorder, is_self = check_beds(anchorsfile, p, opts,
sorted=False)
dotplot(anchorsfile, qbed, sbed, fig, ax2_root, ax2, sep=False, title="")
root.text(.5, .98, clm.name, color="g", ha="center", va="center")
root.text(.5, .95, label, color="darkslategray", ha="center", va="center")
normalize_axes(root)
savefig(image_name, dpi=iopts.dpi, iopts=iopts)
def offdiag(args):
"""
%prog offdiag diploid.napus.1x1.lifted.anchors
Find gene pairs that are off diagnoal. "Off diagonal" are the pairs that are
not on the orthologous chromosomes. For example, napus chrA01 and brapa A01.
"""
p = OptionParser(offdiag.__doc__)
p.set_beds()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
anchorsfile, = args
qbed, sbed, qorder, sorder, is_self = check_beds(anchorsfile, p, opts)
fp = open(anchorsfile)
pf = "-".join(anchorsfile.split(".")[:2])
header = "Block-id|Napus|Diploid|Napus-chr|Diploid-chr|RBH?".split("|")
print "\t".join(header)
i = -1
for row in fp:
if row[0] == '#':
i += 1
continue
q, s, score = row.split()
rbh = 'no' if score[-1] == 'L' else 'yes'
qi, qq = qorder[q]
si, ss = sorder[s]
oqseqid = qseqid = qq.seqid
osseqid = sseqid = ss.seqid
sseqid = sseqid.split("_")[0][-3:]
if qseqid[0] == 'A':
qseqid = qseqid[-3:] # A09 => A09
elif qseqid[0] == 'C':
qseqid = 'C0' + qseqid[-1] # C9 => C09
else:
continue
if qseqid == sseqid or sseqid[-2:] == 'nn':
continue
block_id = pf + "-block-{0}".format(i)
print "\t".join((block_id, q, s, oqseqid, osseqid, rbh))
def mergechrom(args):
"""
%prog mergechrom a.b.anchors
merge synteny blocks on the same chromosome
"""
p = OptionParser(mergechrom.__doc__)
p.set_beds()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
anchorfile, = args
qbed, sbed, qorder, sorder, is_self = check_beds(anchorfile, p, opts)
af = AnchorFile(anchorfile)
newanchorfile = anchorfile.rsplit(".", 1)[0] + ".mergechrom.anchors"
fw = open(newanchorfile, "w")
qchrom_dic = dict((b.accn,b.seqid) for b in qbed)
schrom_dic = dict((b.accn,b.seqid) for b in sbed)
block_dic = dict()
blocks = af.blocks
for (i,block) in enumerate(blocks):
q, s, score = block[0]
qchrom, schrom = qchrom_dic[q], schrom_dic[s]
k = "%s_%s" % (qchrom, schrom)
if k not in block_dic: block_dic[k] = []
block_dic[k].append(i)
for (k, idxs) in block_dic.items():
print("#" * 3, file=fw)
for i in idxs:
for q, s, score in blocks[i]:
print("\t".join((q, s, str(score))), file=fw)
fw.close()
print("%d blocks merged to %d" % (len(blocks), len(block_dic.keys())))
def collinear(args):
"""
%prog collinear a.b.anchors
Reduce synteny blocks to strictly collinear, use dynamic programming in a
procedure similar to DAGchainer.
"""
p = OptionParser(collinear.__doc__)
p.set_beds()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
anchorfile, = args
qbed, sbed, qorder, sorder, is_self = check_beds(anchorfile, p, opts)
af = AnchorFile(anchorfile)
newanchorfile = anchorfile.rsplit(".", 1)[0] + ".collinear.anchors"
fw = open(newanchorfile, "w")
blocks = af.blocks
for block in blocks:
print("#" * 3, file=fw)
iblock = []
for q, s, score in block:
qi, q = qorder[q]
si, s = sorder[s]
score = int(long(score))
iblock.append([qi, si, score])
block = get_collinear(iblock)
for q, s, score in block:
q = qbed[q].accn
s = sbed[s].accn
print("\t".join((q, s, str(score))), file=fw)
fw.close()
def dotplot_main(args):
p = OptionParser(__doc__)
p.set_beds()
p.add_option("--synteny", default=False, action="store_true",
help="Run a fast synteny scan and display blocks [default: %default]")
p.add_option("--cmaptext", help="Draw colormap box on the bottom-left corner")
p.add_option("--vmin", dest="vmin", type="float", default=0,
help="Minimum value in the colormap [default: %default]")
p.add_option("--vmax", dest="vmax", type="float", default=2,
help="Maximum value in the colormap [default: %default]")
p.add_option("--genomenames", type="string", default=None,
help="genome names for labeling axes in the form of qname_sname, " \
"eg. \"Vitis vinifera_Oryza sativa\"")
p.add_option("--nmax", dest="sample_number", type="int", default=10000,
help="Maximum number of data points to plot [default: %default]")
p.add_option("--minfont", type="int", default=4,
help="Do not render labels with size smaller than")
p.add_option("--colormap",
help="Two column file, block id to color mapping [default: %default]")
p.add_option("--nosort", default=False, action="store_true",
help="Do not sort the seqids along the axes")
p.add_option("--nosep", default=False, action="store_true",
help="Do not add contig lines")
p.add_option("--nostdpf", default=False, action="store_true",
help="Do not standardize contig names")
p.add_option("--skipempty", default=False, action="store_true",
help="Skip seqids that do not have matches")
p.add_option("--title", help="Title of the dot plot")
p.set_outfile(outfile=None)
opts, args, iopts = p.set_image_options(args, figsize="8x8",
style="dark", dpi=90, cmap="copper")
if len(args) != 1:
sys.exit(not p.print_help())
palette = opts.colormap
if palette:
palette = Palette(palette)
anchorfile, = args
cmaptext = opts.cmaptext
if anchorfile.endswith(".ks"):
from jcvi.apps.ks import KsFile
logging.debug("Anchors contain Ks values")
cmaptext = cmaptext or "*Ks* values"
anchorksfile = anchorfile + ".anchors"
if need_update(anchorfile, anchorksfile):
ksfile = KsFile(anchorfile)
ksfile.print_to_anchors(anchorksfile)
anchorfile = anchorksfile
qbed, sbed, qorder, sorder, is_self = check_beds(anchorfile, p, opts,
sorted=(not opts.nosort))
if opts.skipempty:
ac = AnchorFile(anchorfile)
if is_self:
qseqids = sseqids = set()
else:
qseqids, sseqids = set(), set()
for pair in ac.iter_pairs():
q, s = pair[:2]
qi, q = qorder[q]
si, s = sorder[s]
qseqids.add(q.seqid)
sseqids.add(s.seqid)
if is_self:
qbed = sbed = subset_bed(qbed, qseqids)
else:
qbed = subset_bed(qbed, qseqids)
sbed = subset_bed(sbed, sseqids)
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes([0, 0, 1, 1]) # the whole canvas
ax = fig.add_axes([.1, .1, .8, .8]) # the dot plot
dotplot(anchorfile, qbed, sbed, fig, root, ax,
vmin=opts.vmin, vmax=opts.vmax, is_self=is_self,
synteny=opts.synteny, cmap_text=opts.cmaptext, cmap=iopts.cmap,
genomenames=opts.genomenames, sample_number=opts.sample_number,
minfont=opts.minfont, palette=palette, sep=(not opts.nosep),
title=opts.title, stdpf=(not opts.nostdpf))
image_name = opts.outfile or \
(op.splitext(anchorfile)[0] + "." + opts.format)
savefig(image_name, dpi=iopts.dpi, iopts=iopts)
fig.clear()
def loss(args):
"""
%prog loss a.b.i1.blocks [a.b-genomic.blast]
Extract likely gene loss candidates between genome a and b.
"""
p = OptionParser(loss.__doc__)
p.add_option("--bed", default=False, action="store_true",
help="Genomic BLAST is in bed format [default: %default]")
p.add_option("--gdist", default=20, type="int",
help="Gene distance [default: %default]")
p.add_option("--bdist", default=20000, type="int",
help="Base pair distance [default: %default]")
p.set_beds()
opts, args = p.parse_args(args)
if len(args) not in (1, 2):
sys.exit(not p.print_help())
blocksfile = args[0]
emptyblast = (len(args) == 1)
if emptyblast:
genomicblast = "empty.blast"
sh("touch {0}".format(genomicblast))
else:
genomicblast = args[1]
gdist, bdist = opts.gdist, opts.bdist
qbed, sbed, qorder, sorder, is_self = check_beds(blocksfile, p, opts)
blocks = []
fp = open(blocksfile)
genetrack = {}
proxytrack = {}
for row in fp:
a, b = row.split()
genetrack[a] = b
blocks.append((a, b))
data = []
for key, rows in groupby(blocks, key=lambda x: x[-1]):
rows = list(rows)
data.append((key, rows))
imax = len(data) - 1
for i, (key, rows) in enumerate(data):
if i == 0 or i == imax:
continue
if key != '.':
continue
before, br = data[i - 1]
after, ar = data[i + 1]
bi, bx = sorder[before]
ai, ax = sorder[after]
dist = abs(bi - ai)
if bx.seqid != ax.seqid or dist > gdist:
continue
start, end = range_minmax(((bx.start, bx.end), (ax.start, ax.end)))
start, end = max(start - bdist, 1), end + bdist
proxy = (bx.seqid, start, end)
for a, b in rows:
proxytrack[a] = proxy
tags = {}
if opts.bed:
bed = Bed(genomicblast, sorted=False)
key = lambda x: gene_name(x.accn.rsplit(".", 1)[0])
for query, bb in groupby(bed, key=key):
bb = list(bb)
if query not in proxytrack:
continue
proxy = proxytrack[query]
tag = "NS"
best_b = bb[0]
for b in bb:
hsp = (b.seqid, b.start, b.end)
if range_overlap(proxy, hsp):
tag = "S"
best_b = b
break
hsp = (best_b.seqid, best_b.start, best_b.end)
proxytrack[query] = hsp
tags[query] = tag
else:
blast = Blast(genomicblast)
for query, bb in blast.iter_hits():
bb = list(bb)
query = gene_name(query)
if query not in proxytrack:
continue
proxy = proxytrack[query]
tag = "NS"
best_b = bb[0]
for b in bb:
hsp = (b.subject, b.sstart, b.sstop)
if range_overlap(proxy, hsp):
tag = "S"
best_b = b
break
hsp = (best_b.subject, best_b.sstart, best_b.sstop)
proxytrack[query] = hsp
tags[query] = tag
for b in qbed:
accn = b.accn
target_region = genetrack[accn]
if accn in proxytrack:
target_region = region_str(proxytrack[accn])
if accn in tags:
ptag = "[{0}]".format(tags[accn])
else:
ptag = "[NF]"
target_region = ptag + target_region
print "\t".join((b.seqid, accn, target_region))
if emptyblast:
sh("rm -f {0}".format(genomicblast))
def pad(args):
"""
%prog pad blastfile cdtfile --qbed q.pad.bed --sbed s.pad.bed
Test and reconstruct candidate PADs.
"""
from jcvi.formats.cdt import CDT
p = OptionParser(pad.__doc__)
p.set_beds()
p.add_option(
"--cutoff",
default=.3,
type="float",
help="The clustering cutoff to call similar [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
cutoff = opts.cutoff
blastfile, cdtfile = args
qbed, sbed, qorder, sorder, is_self = check_beds(blastfile, p, opts)
cdt = CDT(cdtfile)
qparts = list(cdt.iter_partitions(cutoff=cutoff))
sparts = list(cdt.iter_partitions(cutoff=cutoff, gtr=False))
qid, sid = {}, {}
for i, part in enumerate(qparts):
qid.update(dict((x, i) for x in part))
for i, part in enumerate(sparts):
sid.update(dict((x, i) for x in part))
# Without writing files, conversion from PAD to merged PAD is done in memory
for q in qbed:
q.seqid = qid[q.seqid]
for s in sbed:
s.seqid = sid[s.seqid]
qnames = range(len(qparts))
snames = range(len(sparts))
logmp = make_arrays(blastfile, qbed, sbed, qnames, snames)
m, n = logmp.shape
pvalue_cutoff = 1e-30
cutoff = -log(pvalue_cutoff)
significant = []
for i in xrange(m):
for j in xrange(n):
score = logmp[i, j]
if score < cutoff:
continue
significant.append((qparts[i], sparts[j], score))
for a, b, score in significant:
print("|".join(a), "|".join(b), score)
logging.debug("Collected {0} PAR comparisons significant at (P < {1}).".\
format(len(significant), pvalue_cutoff))
return significant
def blastfilter_main(blast_file, p, opts):
qbed, sbed, qorder, sorder, is_self = check_beds(blast_file, p, opts)
tandem_Nmax = opts.tandem_Nmax
cscore = opts.cscore
fp = open(blast_file)
total_lines = sum(1 for line in fp if line[0] != '#')
logging.debug("Load BLAST file `%s` (total %d lines)" % \
(blast_file, total_lines))
bl = Blast(blast_file)
blasts = sorted(list(bl), key=lambda b: b.score, reverse=True)
filtered_blasts = []
seen = set()
ostrip = opts.strip_names
nwarnings = 0
for b in blasts:
query, subject = b.query, b.subject
if query == subject:
continue
if ostrip:
query, subject = gene_name(query), gene_name(subject)
if query not in qorder:
if nwarnings < 100:
logging.warning("{0} not in {1}".format(query,
qbed.filename))
elif nwarnings == 100:
logging.warning("too many warnings.. suppressed")
nwarnings += 1
continue
if subject not in sorder:
if nwarnings < 100:
logging.warning("{0} not in {1}".format(subject,
sbed.filename))
elif nwarnings == 100:
logging.warning("too many warnings.. suppressed")
nwarnings += 1
continue
qi, q = qorder[query]
si, s = sorder[subject]
if is_self and qi > si:
# move all hits to same side when doing self-self BLAST
query, subject = subject, query
qi, si = si, qi
q, s = s, q
key = query, subject
if key in seen:
continue
seen.add(key)
b.query, b.subject = key
b.qi, b.si = qi, si
b.qseqid, b.sseqid = q.seqid, s.seqid
filtered_blasts.append(b)
if cscore:
before_filter = len(filtered_blasts)
logging.debug("running the cscore filter (cscore>=%.2f) .." % cscore)
filtered_blasts = list(filter_cscore(filtered_blasts, cscore=cscore))
logging.debug("after filter (%d->%d) .." % (before_filter,
len(filtered_blasts)))
if tandem_Nmax:
logging.debug("running the local dups filter (tandem_Nmax=%d) .." % \
tandem_Nmax)
qtandems = tandem_grouper(qbed, filtered_blasts,
flip=True, tandem_Nmax=tandem_Nmax)
standems = tandem_grouper(sbed, filtered_blasts,
flip=False, tandem_Nmax=tandem_Nmax)
qdups_fh = open(op.splitext(opts.qbed)[0] + ".localdups", "w") \
if opts.tandems_only else None
if is_self:
for s in standems:
qtandems.join(*s)
qdups_to_mother = write_localdups(qtandems, qbed, qdups_fh)
sdups_to_mother = qdups_to_mother
else:
qdups_to_mother = write_localdups(qtandems, qbed, qdups_fh)
sdups_fh = open(op.splitext(opts.sbed)[0] + ".localdups", "w") \
if opts.tandems_only else None
sdups_to_mother = write_localdups(standems, sbed, sdups_fh)
if opts.tandems_only:
# write out new .bed after tandem removal
write_new_bed(qbed, qdups_to_mother)
if not is_self:
write_new_bed(sbed, sdups_to_mother)
# just want to use this script as a tandem finder.
#sys.exit()
before_filter = len(filtered_blasts)
filtered_blasts = list(filter_tandem(filtered_blasts, \
qdups_to_mother, sdups_to_mother))
logging.debug("after filter (%d->%d) .." % \
(before_filter, len(filtered_blasts)))
blastfilteredfile = blast_file + ".filtered"
fw = open(blastfilteredfile, "w")
write_new_blast(filtered_blasts, fh=fw)
fw.close()
help="genome names for labeling axes in the form of qname_sname, " \
"eg. \"Vitis vinifera_Oryza sativa\"")
p.add_option("--nmax", dest="sample_number", type="int", default=10000,
help="Maximum number of data points to plot [default: %default]")
p.add_option("--ignore", type="float", default=.005,
help="Do not render labels for chr less than portion of genome [default: %default]")
p.add_option("--palette",
help="Two column file, block id to color mapping [default: %default]")
opts, args, iopts = p.set_image_options(sys.argv[1:], figsize="8x8", dpi=90)
if len(args) != 1:
sys.exit(not p.print_help())
synteny = opts.synteny
vmin, vmax = opts.vmin, opts.vmax
cmap_text = opts.cmap
genomenames = opts.genomenames
sample_number = opts.sample_number
palette = opts.palette
if palette:
palette = Palette(palette)
anchorfile, = args
qbed, sbed, qorder, sorder, is_self = check_beds(anchorfile, p, opts)
image_name = op.splitext(anchorfile)[0] + "." + opts.format
dotplot_main(anchorfile, qbed, sbed, image_name, iopts, vmin=0, vmax=1,
is_self=is_self, synteny=synteny, cmap_text=cmap_text, \
genomenames=genomenames, sample_number=sample_number,
ignore=opts.ignore, palette=palette)
def main(args):
p = OptionParser(__doc__)
p.set_beds()
p.add_option("--quota", default="1:1",
help="`quota mapping` procedure -- screen blocks to constrain mapping"\
" (useful for orthology), "\
"put in the format like (#subgenomes expected for genome X):"\
"(#subgenomes expected for genome Y) "\
"[default: %default]")
p.add_option("--Nm", dest="Nmax", type="int", default=10,
help="distance cutoff to tolerate two blocks that are "\
"slightly overlapping (cutoff for `quota mapping`) "\
"[default: %default units (gene or bp dist)]")
supported_solvers = ("SCIP", "GLPK")
p.add_option("--self", dest="self_match",
action="store_true", default=False,
help="you might turn this on when screening paralogous blocks, "\
"esp. if you have reduced mirrored blocks into non-redundant set")
p.add_option("--solver", default="SCIP", choices=supported_solvers,
help="use MIP solver [default: %default]")
p.add_option("--verbose", action="store_true",
default=False, help="show verbose solver output")
p.add_option("--screen", default=False, action="store_true",
help="generate new anchors file [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
qa_file, = args
qbed, sbed, qorder, sorder, is_self = check_beds(qa_file, p, opts)
# sanity check for the quota
if opts.quota:
try:
qa, qb = opts.quota.split(":")
qa, qb = int(qa), int(qb)
except:
print >> sys.stderr, "quota string should be the form x:x (2:4, 1:3, etc.)"
sys.exit(1)
if opts.self_match and qa != qb:
raise Exception, "when comparing genome to itself, " \
"quota must be the same number " \
"(like 1:1, 2:2) you have %s" % opts.quota
quota = (qa, qb)
self_match = opts.self_match
clusters = read_clusters(qa_file, qorder, sorder)
for cluster in clusters:
assert len(cluster) > 0
# below runs `quota mapping`
work_dir = op.join(op.dirname(op.abspath(qa_file)), "work")
selected_ids = solve_lp(clusters, quota, work_dir=work_dir, \
Nmax=opts.Nmax, self_match=self_match, \
solver=opts.solver, verbose=opts.verbose)
logging.debug("Selected {0} blocks.".format(len(selected_ids)))
prefix = qa_file.rsplit(".", 1)[0]
suffix = "{0}x{1}".format(qa, qb)
outfile = ".".join((prefix, suffix))
fw = must_open(outfile, "w")
print >> fw, ",".join(str(x) for x in selected_ids)
fw.close()
logging.debug("Screened blocks ids written to `{0}`.".format(outfile))
if opts.screen:
from jcvi.compara.synteny import screen
new_qa_file = ".".join((prefix, suffix, "anchors"))
largs = [qa_file, new_qa_file, "--ids", outfile]
if opts.qbed and opts.sbed:
largs += ["--qbed={0}".format(opts.qbed)]
largs += ["--sbed={0}".format(opts.sbed)]
screen(largs)
def blastfilter_main(blast_file, p, opts):
qbed, sbed, qorder, sorder, is_self = check_beds(blast_file, p, opts)
tandem_Nmax = opts.tandem_Nmax
cscore = opts.cscore
exclude = opts.exclude
fp = open(blast_file)
total_lines = sum(1 for line in fp if line[0] != "#")
logging.debug(
"Load BLAST file `{}` (total {} lines)".format(blast_file, total_lines)
)
bl = Blast(blast_file)
blasts = sorted(list(bl), key=lambda b: b.score, reverse=True)
filtered_blasts = []
seen = set()
ostrip = opts.strip_names
nwarnings = 0
for b in blasts:
query, subject = b.query, b.subject
if query == subject:
continue
if ostrip:
query, subject = gene_name(query), gene_name(subject)
if query not in qorder:
if nwarnings < 100:
logging.warning("{} not in {}".format(query, qbed.filename))
elif nwarnings == 100:
logging.warning("too many warnings.. suppressed")
nwarnings += 1
continue
if subject not in sorder:
if nwarnings < 100:
logging.warning("{} not in {}".format(subject, sbed.filename))
elif nwarnings == 100:
logging.warning("too many warnings.. suppressed")
nwarnings += 1
continue
qi, q = qorder[query]
si, s = sorder[subject]
if is_self and qi > si:
# move all hits to same side when doing self-self BLAST
query, subject = subject, query
qi, si = si, qi
q, s = s, q
key = query, subject
if key in seen:
continue
seen.add(key)
b.query, b.subject = [str(k) for k in key]
b.qi, b.si = qi, si
b.qseqid, b.sseqid = q.seqid, s.seqid
filtered_blasts.append(b)
if exclude:
before_filter = len(filtered_blasts)
logging.debug("running excluded pairs (--exclude `{}`) ..".format(exclude))
filtered_blasts = list(filter_exclude(filtered_blasts, exclude=exclude))
logging.debug(
"after filter ({}->{}) ..".format(before_filter, len(filtered_blasts))
)
if cscore:
before_filter = len(filtered_blasts)
logging.debug("running the cscore filter (cscore>=%.2f) .." % cscore)
filtered_blasts = list(filter_cscore(filtered_blasts, cscore=cscore))
logging.debug(
"after filter ({}->{}) ..".format(before_filter, len(filtered_blasts))
)
if tandem_Nmax:
logging.debug(
"running the local dups filter (tandem_Nmax={}) ..".format(tandem_Nmax)
)
qtandems = tandem_grouper(filtered_blasts, flip=True, tandem_Nmax=tandem_Nmax)
standems = tandem_grouper(filtered_blasts, flip=False, tandem_Nmax=tandem_Nmax)
qdups_fh = (
open(op.splitext(opts.qbed)[0] + ".localdups", "w")
if opts.tandems_only
else None
)
if is_self:
for s in standems:
qtandems.join(*s)
qdups_to_mother = write_localdups(qtandems, qbed, qdups_fh)
sdups_to_mother = qdups_to_mother
else:
qdups_to_mother = write_localdups(qtandems, qbed, qdups_fh)
sdups_fh = (
open(op.splitext(opts.sbed)[0] + ".localdups", "w")
if opts.tandems_only
else None
)
sdups_to_mother = write_localdups(standems, sbed, sdups_fh)
if opts.tandems_only:
# write out new .bed after tandem removal
write_new_bed(qbed, qdups_to_mother)
if not is_self:
write_new_bed(sbed, sdups_to_mother)
# just want to use this script as a tandem finder.
# sys.exit()
before_filter = len(filtered_blasts)
filtered_blasts = list(
filter_tandem(filtered_blasts, qdups_to_mother, sdups_to_mother)
)
logging.debug(
"after filter ({}->{}) ..".format(before_filter, len(filtered_blasts))
)
blastfilteredfile = blast_file + ".filtered"
fw = open(blastfilteredfile, "w")
write_new_blast(filtered_blasts, fh=fw)
fw.close()
def pad(args):
"""
%prog pad blastfile cdtfile --qbed q.pad.bed --sbed s.pad.bed
Test and reconstruct candidate PADs.
"""
from jcvi.formats.cdt import CDT
p = OptionParser(pad.__doc__)
p.set_beds()
p.add_option("--cutoff", default=.3, type="float",
help="The clustering cutoff to call similar [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
cutoff = opts.cutoff
blastfile, cdtfile = args
qbed, sbed, qorder, sorder, is_self = check_beds(blastfile, p, opts)
cdt = CDT(cdtfile)
qparts = list(cdt.iter_partitions(cutoff=cutoff))
sparts = list(cdt.iter_partitions(cutoff=cutoff, gtr=False))
qid, sid = {}, {}
for i, part in enumerate(qparts):
qid.update(dict((x, i) for x in part))
for i, part in enumerate(sparts):
sid.update(dict((x, i) for x in part))
# Without writing files, conversion from PAD to merged PAD is done in memory
for q in qbed:
q.seqid = qid[q.seqid]
for s in sbed:
s.seqid = sid[s.seqid]
qnames = range(len(qparts))
snames = range(len(sparts))
logmp = make_arrays(blastfile, qbed, sbed, qnames, snames)
m, n = logmp.shape
pvalue_cutoff = 1e-30
cutoff = - log(pvalue_cutoff)
significant = []
for i in xrange(m):
for j in xrange(n):
score = logmp[i, j]
if score < cutoff:
continue
significant.append((qparts[i], sparts[j], score))
for a, b, score in significant:
print("|".join(a), "|".join(b), score)
logging.debug("Collected {0} PAR comparisons significant at (P < {1}).".\
format(len(significant), pvalue_cutoff))
return significant
def cluster(args):
"""
%prog cluster blastfile anchorfile --qbed qbedfile --sbed sbedfile
Cluster the segments and form PAD. This is the method described in Tang et
al. (2010) PNAS paper. The anchorfile defines a list of synteny blocks,
based on which the genome on one or both axis can be chopped up into pieces
and clustered.
"""
from jcvi.utils.range import Range
p = OptionParser(cluster.__doc__)
p.set_beds()
p.add_option("--minsize",
default=10,
type="int",
help="Only segment using blocks >= size [default: %default]")
p.add_option("--path",
default="~/scratch/bin",
help="Path to the CLUSTER 3.0 binary [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
blastfile, anchorfile = args
qbed, sbed, qorder, sorder, is_self = check_beds(blastfile, p, opts)
minsize = opts.minsize
ac = AnchorFile(anchorfile)
qranges, sranges = [], []
qextra = [x[1:] for x in qbed.get_breaks()]
sextra = [x[1:] for x in sbed.get_breaks()]
id = 0
for block in ac.iter_blocks(minsize=minsize):
q, s = zip(*block)[:2]
q = [qorder[x][0] for x in q]
s = [sorder[x][0] for x in s]
minq, maxq = min(q), max(q)
mins, maxs = min(s), max(s)
id += 1
qr = Range("0", minq, maxq, maxq - minq, id)
sr = Range("0", mins, maxs, maxs - mins, id)
qranges.append(qr)
sranges.append(sr)
qpads = list(get_segments(qranges, qextra))
spads = list(get_segments(sranges, sextra))
suffix = ".pad.bed"
qpf = opts.qbed.split(".")[0]
spf = opts.sbed.split(".")[0]
qpadfile = qpf + suffix
spadfile = spf + suffix
qnpads, qpadnames = write_PAD_bed(qpadfile, qpf, qpads, qbed)
snpads, spadnames = write_PAD_bed(spadfile, spf, spads, sbed)
qpadbed, spadbed = Bed(qpadfile), Bed(spadfile)
logmp = make_arrays(blastfile, qpadbed, spadbed, qpadnames, spadnames)
m, n = logmp.shape
matrixfile = ".".join((qpf, spf, "logmp.txt"))
fw = open(matrixfile, "w")
header = ["o"] + spadnames
print("\t".join(header), file=fw)
for i in xrange(m):
row = [qpadnames[i]] + ["{0:.1f}".format(x) for x in logmp[i, :]]
print("\t".join(row), file=fw)
fw.close()
# Run CLUSTER 3.0 (Pearson correlation, average linkage)
cmd = op.join(opts.path, "cluster")
cmd += " -g 2 -e 2 -m a -f {0}".format(matrixfile)
pf = matrixfile.rsplit(".", 1)[0]
cdtfile = pf + ".cdt"
if need_update(matrixfile, cdtfile):
sh(cmd)
def cluster(args):
"""
%prog cluster blastfile anchorfile --qbed qbedfile --sbed sbedfile
Cluster the segments and form PAD. This is the method described in Tang et
al. (2010) PNAS paper. The anchorfile defines a list of synteny blocks,
based on which the genome on one or both axis can be chopped up into pieces
and clustered.
"""
from jcvi.utils.range import Range
p = OptionParser(cluster.__doc__)
p.set_beds()
p.add_option("--minsize", default=10, type="int",
help="Only segment using blocks >= size [default: %default]")
p.add_option("--path", default="~/scratch/bin",
help="Path to the CLUSTER 3.0 binary [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
blastfile, anchorfile = args
qbed, sbed, qorder, sorder, is_self = check_beds(blastfile, p, opts)
minsize = opts.minsize
ac = AnchorFile(anchorfile)
qranges, sranges = [], []
qextra = [x[1:] for x in qbed.get_breaks()]
sextra = [x[1:] for x in sbed.get_breaks()]
id = 0
for block in ac.iter_blocks(minsize=minsize):
q, s = zip(*block)[:2]
q = [qorder[x][0] for x in q]
s = [sorder[x][0] for x in s]
minq, maxq = min(q), max(q)
mins, maxs = min(s), max(s)
id += 1
qr = Range("0", minq, maxq, maxq - minq, id)
sr = Range("0", mins, maxs, maxs - mins, id)
qranges.append(qr)
sranges.append(sr)
qpads = list(get_segments(qranges, qextra))
spads = list(get_segments(sranges, sextra))
suffix = ".pad.bed"
qpf = opts.qbed.split(".")[0]
spf = opts.sbed.split(".")[0]
qpadfile = qpf + suffix
spadfile = spf + suffix
qnpads, qpadnames = write_PAD_bed(qpadfile, qpf, qpads, qbed)
snpads, spadnames = write_PAD_bed(spadfile, spf, spads, sbed)
qpadbed, spadbed = Bed(qpadfile), Bed(spadfile)
logmp = make_arrays(blastfile, qpadbed, spadbed, qpadnames, spadnames)
m, n = logmp.shape
matrixfile = ".".join((qpf, spf, "logmp.txt"))
fw = open(matrixfile, "w")
header = ["o"] + spadnames
print("\t".join(header), file=fw)
for i in xrange(m):
row = [qpadnames[i]] + ["{0:.1f}".format(x) for x in logmp[i, :]]
print("\t".join(row), file=fw)
fw.close()
# Run CLUSTER 3.0 (Pearson correlation, average linkage)
cmd = op.join(opts.path, "cluster")
cmd += " -g 2 -e 2 -m a -f {0}".format(matrixfile)
pf = matrixfile.rsplit(".", 1)[0]
cdtfile = pf + ".cdt"
if need_update(matrixfile, cdtfile):
sh(cmd)
def bed(args):
"""
%prog bed anchorsfile
Convert ANCHORS file to BED format.
"""
from collections import defaultdict
from jcvi.compara.synteny import AnchorFile, check_beds
from jcvi.formats.bed import Bed
from jcvi.formats.base import get_number
p = OptionParser(bed.__doc__)
p.add_option(
"--switch",
default=False,
action="store_true",
help="Switch reference and aligned map elements",
)
p.add_option("--scale",
type="float",
help="Scale the aligned map distance by factor")
p.set_beds()
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
(anchorsfile, ) = args
switch = opts.switch
scale = opts.scale
ac = AnchorFile(anchorsfile)
pairs = defaultdict(list)
for a, b, block_id in ac.iter_pairs():
pairs[a].append(b)
qbed, sbed, qorder, sorder, is_self = check_beds(anchorsfile, p, opts)
bd = Bed()
for q in qbed:
qseqid, qstart, qend, qaccn = q.seqid, q.start, q.end, q.accn
if qaccn not in pairs:
continue
for s in pairs[qaccn]:
si, s = sorder[s]
sseqid, sstart, send, saccn = s.seqid, s.start, s.end, s.accn
if switch:
qseqid, sseqid = sseqid, qseqid
qstart, sstart = sstart, qstart
qend, send = send, qend
qaccn, saccn = saccn, qaccn
if scale:
sstart /= scale
try:
newsseqid = get_number(sseqid)
except ValueError:
raise ValueError(
"`{0}` is on `{1}` with no number to extract".format(
saccn, sseqid))
bedline = "\t".join(
str(x) for x in (qseqid, qstart - 1, qend,
"{0}:{1}".format(newsseqid, sstart)))
bd.add(bedline)
bd.print_to_file(filename=opts.outfile, sorted=True)
def dotplot_main(args):
p = OptionParser(__doc__)
p.set_beds()
p.add_option(
"--synteny",
default=False,
action="store_true",
help="Run a fast synteny scan and display blocks",
)
p.add_option("--cmaptext",
help="Draw colormap box on the bottom-left corner")
p.add_option(
"--vmin",
dest="vmin",
type="float",
default=0,
help="Minimum value in the colormap",
)
p.add_option(
"--vmax",
dest="vmax",
type="float",
default=2,
help="Maximum value in the colormap",
)
p.add_option(
"--nmax",
dest="sample_number",
type="int",
default=10000,
help="Maximum number of data points to plot",
)
p.add_option(
"--minfont",
type="int",
default=4,
help="Do not render labels with size smaller than",
)
p.add_option("--colormap",
help="Two column file, block id to color mapping")
p.add_option(
"--colororientation",
action="store_true",
default=False,
help="Color the blocks based on orientation, similar to mummerplot",
)
p.add_option(
"--nosort",
default=False,
action="store_true",
help="Do not sort the seqids along the axes",
)
p.add_option("--nosep",
default=False,
action="store_true",
help="Do not add contig lines")
p.add_option("--title", help="Title of the dot plot")
p.set_dotplot_opts()
p.set_outfile(outfile=None)
opts, args, iopts = p.set_image_options(args,
figsize="9x9",
style="dark",
dpi=90,
cmap="copper")
if len(args) != 1:
sys.exit(not p.print_help())
(anchorfile, ) = args
qbed, sbed, qorder, sorder, is_self = check_beds(anchorfile,
p,
opts,
sorted=(not opts.nosort))
palette = opts.colormap
if palette:
palette = Palette(palettefile=palette)
elif opts.colororientation:
palette = Palette.from_block_orientation(anchorfile, qbed, sbed)
cmaptext = opts.cmaptext
if anchorfile.endswith(".ks"):
from jcvi.apps.ks import KsFile
logging.debug("Anchors contain Ks values")
cmaptext = cmaptext or "*Ks* values"
anchorksfile = anchorfile + ".anchors"
if need_update(anchorfile, anchorksfile):
ksfile = KsFile(anchorfile)
ksfile.print_to_anchors(anchorksfile)
anchorfile = anchorksfile
if opts.skipempty:
ac = AnchorFile(anchorfile)
if is_self:
qseqids = sseqids = set()
else:
qseqids, sseqids = set(), set()
for pair in ac.iter_pairs():
q, s = pair[:2]
qi, q = qorder[q]
si, s = sorder[s]
qseqids.add(q.seqid)
sseqids.add(s.seqid)
if is_self:
qbed = sbed = subset_bed(qbed, qseqids)
else:
qbed = subset_bed(qbed, qseqids)
sbed = subset_bed(sbed, sseqids)
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes([0, 0, 1, 1]) # the whole canvas
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # the dot plot
dotplot(
anchorfile,
qbed,
sbed,
fig,
root,
ax,
vmin=opts.vmin,
vmax=opts.vmax,
is_self=is_self,
synteny=opts.synteny,
cmap_text=opts.cmaptext,
cmap=iopts.cmap,
genomenames=opts.genomenames,
sample_number=opts.sample_number,
minfont=opts.minfont,
palette=palette,
sep=(not opts.nosep),
sepcolor=set1[int(opts.theme)],
title=opts.title,
stdpf=(not opts.nostdpf),
chpf=(not opts.nochpf),
)
image_name = opts.outfile or (op.splitext(anchorfile)[0] + "." +
opts.format)
savefig(image_name, dpi=iopts.dpi, iopts=iopts)
fig.clear()
def omgprepare(args):
"""
%prog omgprepare ploidy anchorsfile blastfile
Prepare to run Sankoff's OMG algorithm to get orthologs.
"""
from jcvi.formats.blast import cscore
from jcvi.formats.base import DictFile
p = OptionParser(omgprepare.__doc__)
p.add_option("--norbh", action="store_true",
help="Disable RBH hits [default: %default]")
p.add_option("--pctid", default=0, type="int",
help="Percent id cutoff for RBH hits [default: %default]")
p.add_option("--cscore", default=90, type="int",
help="C-score cutoff for RBH hits [default: %default]")
p.set_stripnames()
p.set_beds()
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
ploidy, anchorfile, blastfile = args
norbh = opts.norbh
pctid = opts.pctid
cs = opts.cscore
qbed, sbed, qorder, sorder, is_self = check_beds(anchorfile, p, opts)
fp = open(ploidy)
genomeidx = dict((x.split()[0], i) for i, x in enumerate(fp))
fp.close()
ploidy = DictFile(ploidy)
geneinfo(qbed, qorder, genomeidx, ploidy)
geneinfo(sbed, sorder, genomeidx, ploidy)
pf = blastfile.rsplit(".", 1)[0]
cscorefile = pf + ".cscore"
cscore([blastfile, "-o", cscorefile, "--cutoff=0", "--pct"])
ac = AnchorFile(anchorfile)
pairs = set((a, b) for a, b, i in ac.iter_pairs())
logging.debug("Imported {0} pairs from `{1}`.".format(len(pairs), anchorfile))
weightsfile = pf + ".weights"
fp = open(cscorefile)
fw = open(weightsfile, "w")
npairs = 0
for row in fp:
a, b, c, pct = row.split()
c, pct = float(c), float(pct)
c = int(c * 100)
if (a, b) not in pairs:
if norbh:
continue
if c < cs:
continue
if pct < pctid:
continue
c /= 10 # This severely penalizes RBH against synteny
print >> fw, "\t".join((a, b, str(c)))
npairs += 1
fw.close()
logging.debug("Write {0} pairs to `{1}`.".format(npairs, weightsfile))
def omgprepare(args):
"""
%prog omgprepare ploidy anchorsfile blastfile
Prepare to run Sankoff's OMG algorithm to get orthologs.
"""
from jcvi.formats.blast import cscore
from jcvi.formats.base import DictFile
p = OptionParser(omgprepare.__doc__)
p.add_option("--norbh", action="store_true", help="Disable RBH hits")
p.add_option(
"--pctid", default=0, type="int", help="Percent id cutoff for RBH hits"
)
p.add_option("--cscore", default=90, type="int", help="C-score cutoff for RBH hits")
p.set_stripnames()
p.set_beds()
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
ploidy, anchorfile, blastfile = args
norbh = opts.norbh
pctid = opts.pctid
cs = opts.cscore
qbed, sbed, qorder, sorder, is_self = check_beds(anchorfile, p, opts)
fp = open(ploidy)
genomeidx = dict((x.split()[0], i) for i, x in enumerate(fp))
fp.close()
ploidy = DictFile(ploidy)
geneinfo(qbed, qorder, genomeidx, ploidy)
geneinfo(sbed, sorder, genomeidx, ploidy)
pf = blastfile.rsplit(".", 1)[0]
cscorefile = pf + ".cscore"
cscore([blastfile, "-o", cscorefile, "--cutoff=0", "--pct"])
ac = AnchorFile(anchorfile)
pairs = set((a, b) for a, b, i in ac.iter_pairs())
logging.debug("Imported {0} pairs from `{1}`.".format(len(pairs), anchorfile))
weightsfile = pf + ".weights"
fp = open(cscorefile)
fw = open(weightsfile, "w")
npairs = 0
for row in fp:
a, b, c, pct = row.split()
c, pct = float(c), float(pct)
c = int(c * 100)
if (a, b) not in pairs:
if norbh:
continue
if c < cs:
continue
if pct < pctid:
continue
c /= 10 # This severely penalizes RBH against synteny
print("\t".join((a, b, str(c))), file=fw)
npairs += 1
fw.close()
logging.debug("Write {0} pairs to `{1}`.".format(npairs, weightsfile))
def ancestral(args):
"""
%prog ancestral vplanifoliaA.vplanifoliaA.anchors > vplanifoliaA_blocks.bed
Paint 14 chromosomes following alpha WGD.
"""
p = OptionParser(ancestral.__doc__)
p.set_beds()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
(anchorsfile, ) = args
qbed, sbed, qorder, sorder, is_self = check_beds(anchorsfile, p, opts)
# We focus on the following chromosome pairs
target_pairs = set((
(1, 1),
(1, 6),
(1, 8),
(1, 13),
(2, 4),
(3, 12),
(3, 14),
(5, 6),
(5, 8),
(7, 9),
(7, 11),
(9, 10),
(10, 11),
))
def get_target(achr, bchr):
if "chr" not in achr and "chr" not in bchr:
return None
achr, bchr = get_number(achr), get_number(bchr)
if achr > bchr:
achr, bchr = bchr, achr
if (achr, bchr) in target_pairs:
return achr, bchr
return None
def build_bedline(astart, aend, target_pair):
# target_name = "{:02d}-{:02d}".format(*target_pair)
target_name = [
str(x) for x in target_pair if x in (1, 2, 3, 5, 7, 10)
][0]
return "\t".join(
str(x)
for x in (astart.seqid, astart.start, aend.end, target_name))
# Iterate through the blocks, store any regions that has hits to one of the
# target_pairs
ac = AnchorFile(anchorsfile)
blocks = ac.blocks
outbed = Bed()
for i, block in enumerate(blocks):
a, b, scores = zip(*block)
a = [qorder[x] for x in a]
b = [sorder[x] for x in b]
astart, aend = min(a)[1], max(a)[1]
bstart, bend = min(b)[1], max(b)[1]
# Now convert to BED lines with new accn
achr, bchr = astart.seqid, bstart.seqid
target = get_target(achr, bchr)
if target is None:
continue
outbed.add(build_bedline(astart, aend, target))
outbed.add(build_bedline(bstart, bend, target))
outbed.print_to_file(sorted=True)
def main(args):
p = OptionParser(__doc__)
p.set_beds()
p.add_option("--quota", default="1:1",
help="`quota mapping` procedure -- screen blocks to constrain mapping"\
" (useful for orthology), "\
"put in the format like (#subgenomes expected for genome X):"\
"(#subgenomes expected for genome Y) "\
"[default: %default]")
p.add_option("--Nm", dest="Nmax", type="int", default=10,
help="distance cutoff to tolerate two blocks that are "\
"slightly overlapping (cutoff for `quota mapping`) "\
"[default: %default units (gene or bp dist)]")
supported_solvers = ("SCIP", "GLPK")
p.add_option("--self", dest="self_match",
action="store_true", default=False,
help="you might turn this on when screening paralogous blocks, "\
"esp. if you have reduced mirrored blocks into non-redundant set")
p.add_option("--solver",
default="SCIP",
choices=supported_solvers,
help="use MIP solver [default: %default]")
p.set_verbose(help="Show verbose solver output")
p.add_option("--screen",
default=False,
action="store_true",
help="generate new anchors file [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
qa_file, = args
qbed, sbed, qorder, sorder, is_self = check_beds(qa_file, p, opts)
# sanity check for the quota
if opts.quota:
try:
qa, qb = opts.quota.split(":")
qa, qb = int(qa), int(qb)
except:
print >> sys.stderr, "quota string should be the form x:x (2:4, 1:3, etc.)"
sys.exit(1)
if opts.self_match and qa != qb:
raise Exception, "when comparing genome to itself, " \
"quota must be the same number " \
"(like 1:1, 2:2) you have %s" % opts.quota
quota = (qa, qb)
self_match = opts.self_match
clusters = read_clusters(qa_file, qorder, sorder)
for cluster in clusters:
assert len(cluster) > 0
# below runs `quota mapping`
work_dir = op.join(op.dirname(op.abspath(qa_file)), "work")
selected_ids = solve_lp(clusters, quota, work_dir=work_dir, \
Nmax=opts.Nmax, self_match=self_match, \
solver=opts.solver, verbose=opts.verbose)
logging.debug("Selected {0} blocks.".format(len(selected_ids)))
prefix = qa_file.rsplit(".", 1)[0]
suffix = "{0}x{1}".format(qa, qb)
outfile = ".".join((prefix, suffix))
fw = must_open(outfile, "w")
print >> fw, ",".join(str(x) for x in selected_ids)
fw.close()
logging.debug("Screened blocks ids written to `{0}`.".format(outfile))
if opts.screen:
from jcvi.compara.synteny import screen
new_qa_file = ".".join((prefix, suffix, "anchors"))
largs = [qa_file, new_qa_file, "--ids", outfile]
if opts.qbed and opts.sbed:
largs += ["--qbed={0}".format(opts.qbed)]
largs += ["--sbed={0}".format(opts.sbed)]
screen(largs)
