def path(args):
"""
%prog path input.bed scaffolds.fasta
Construct golden path given a set of genetic maps. The respective weight for
each map is given in file `weights.txt`. The map with the highest weight is
considered the pivot map. The final output is an AGP file that contains
ordered scaffolds.
"""
oargs = args
p = OptionParser(path.__doc__)
p.add_option("-w",
"--weightsfile",
default="weights.txt",
help="Use weights from file")
p.add_option("--distance",
default="rank",
choices=distance_choices,
help="Distance function when building initial consensus")
p.add_option("--linkage",
default="double",
choices=linkage_choices,
help="Linkage function when building initial consensus")
p.add_option("--gapsize",
default=100,
type="int",
help="Insert gaps of size between scaffolds")
p.add_option("--ngen",
default=500,
type="int",
help="Iterations in GA, more ~ slower")
p.add_option("--npop",
default=100,
type="int",
help="Population size in GA, more ~ slower")
p.add_option("--seqid", help="Only run partition with this seqid")
p.add_option("--links",
default=10,
type="int",
help="Only plot matchings more than")
p.add_option("--noplot",
default=False,
action="store_true",
help="Do not visualize the alignments")
p.set_cpus(cpus=16)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
inputbed, fastafile = args
pf = inputbed.rsplit(".", 1)[0]
bedfile = pf + ".bed"
weightsfile = opts.weightsfile
gapsize = opts.gapsize
ngen = opts.ngen
npop = opts.npop
cpus = opts.cpus
if sys.version_info[:2] < (2, 7):
logging.debug("Python version: {0}. CPUs set to 1.".\
format(sys.version.splitlines()[0].strip()))
cpus = 1
function = get_function(opts.distance)
cc = Map(bedfile, function)
mapnames = cc.mapnames
allseqids = cc.seqids
weights = Weights(weightsfile, mapnames)
pivot = weights.pivot
ref = weights.ref
linkage = opts.linkage
oseqid = opts.seqid
logging.debug("Linkage function: {0}-linkage".format(linkage))
linkage = {
"single": min,
"double": double_linkage,
"complete": max,
"average": np.mean,
"median": np.median
}[linkage]
# Partition the linkage groups into consensus clusters
C = Grouper()
# Initialize the partitions
for mlg in cc.mlgs:
C.join(mlg)
logging.debug("Partition LGs based on {0}".format(ref))
for mapname in mapnames:
if mapname == ref:
continue
# Compute co-occurrence between LG pairs
G = defaultdict(int)
for s in allseqids:
s = Scaffold(s, cc)
s.add_LG_pairs(G, (ref, mapname))
# Convert edge list to adj list
nodes = defaultdict(list)
for (a, b), w in G.items():
nodes[a].append((b, w))
# Find the best ref LG every non-ref LG matches to
for n, neighbors in nodes.items():
if n.split("-")[0] == ref:
continue
neighbors = dict(neighbors)
best_neighbor, best_value = best_no_ambiguous(neighbors, n)
if best_neighbor is None:
continue
C.join(n, best_neighbor)
partitions = defaultdict(list)
# Partition the scaffolds and assign them to one consensus
for s in allseqids:
s = Scaffold(s, cc)
seqid = s.seqid
counts = {}
for mlg, count in s.mlg_counts.items():
consensus = C[mlg]
mapname = mlg.split("-")[0]
mw = weights[mapname]
if consensus not in counts:
counts[consensus] = 0
counts[consensus] += count * mw
best_consensus, best_value = best_no_ambiguous(counts, seqid)
if best_consensus is None:
continue
partitions[best_consensus].append(seqid)
# Perform OO within each partition
agpfile = pf + ".chr.agp"
tourfile = pf + ".tour"
sizes = Sizes(fastafile).mapping
fwagp = must_open(agpfile, "w")
fwtour = must_open(tourfile, "w")
solutions = []
for lgs, scaffolds in sorted(partitions.items()):
if oseqid and oseqid not in lgs:
continue
tag = "|".join(lgs)
lgs_maps = set(x.split("-")[0] for x in lgs)
if pivot not in lgs_maps:
logging.debug("Skipping {0} ...".format(tag))
continue
logging.debug("Working on {0} ...".format(tag))
s = ScaffoldOO(lgs,
scaffolds,
cc,
pivot,
weights,
sizes,
function=function,
linkage=linkage,
ngen=ngen,
npop=npop,
cpus=cpus)
for fw in (sys.stderr, fwtour):
print >> fw, ">{0} ({1})".format(s.object, tag)
print >> fw, " ".join("".join(x) for x in s.tour)
solutions.append(s)
fwtour.close()
# meta-data about the run parameters
command = "# COMMAND: python -m jcvi.assembly.allmaps path {0}".\
format(" ".join(oargs))
comment = "Generated by ALLMAPS v{0} ({1})\n{2}".\
format(version, get_today(), command)
AGP.print_header(fwagp, comment=comment)
for s in sorted(solutions, key=lambda x: x.object):
order_to_agp(s.object,
s.tour,
sizes,
fwagp,
gapsize=gapsize,
gaptype="map")
fwagp.close()
logging.debug("AGP file written to `{0}`.".format(agpfile))
logging.debug("Tour file written to `{0}`.".format(tourfile))
build([inputbed, fastafile])
summaryfile = pf + ".summary.txt"
summary([inputbed, fastafile, "--outfile={0}".format(summaryfile)])
if not opts.noplot:
plotall([inputbed, "--links={0}".format(opts.links)])
def path(args):
"""
%prog path input.bed scaffolds.fasta
Construct golden path given a set of genetic maps. The respective weight for
each map is given in file `weights.txt`. The map with the highest weight is
considered the pivot map. The final output is an AGP file that contains
ordered scaffolds.
"""
oargs = args
p = OptionParser(path.__doc__)
p.add_option("-b", "--bedfile", help=SUPPRESS_HELP)
p.add_option("-s", "--fastafile", help=SUPPRESS_HELP)
p.add_option("-w", "--weightsfile", default="weights.txt",
help="Use weights from file")
p.add_option("--distance", default="rank", choices=distance_choices,
help="Distance function when building initial consensus")
p.add_option("--linkage", default="double", choices=linkage_choices,
help="Linkage function when building initial consensus")
p.add_option("--gapsize", default=100, type="int",
help="Insert gaps of size between scaffolds")
p.add_option("--ngen", default=500, type="int",
help="Iterations in GA, more ~ slower")
p.add_option("--npop", default=100, type="int",
help="Population size in GA, more ~ slower")
p.add_option("--seqid", help="Only run partition with this seqid")
p.add_option("--links", default=10, type="int",
help="Only plot matchings more than")
p.add_option("--noplot", default=False, action="store_true",
help="Do not visualize the alignments")
p.set_cpus(cpus=16)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
inputbed, fastafile = args
inputbed = opts.bedfile or inputbed
fastafile = opts.fastafile or fastafile
pf = inputbed.rsplit(".", 1)[0]
bedfile = pf + ".bed"
weightsfile = opts.weightsfile
gapsize = opts.gapsize
ngen = opts.ngen
npop = opts.npop
cpus = opts.cpus
if sys.version_info[:2] < (2, 7):
logging.debug("Python version: {0}. CPUs set to 1.".\
format(sys.version.splitlines()[0].strip()))
cpus = 1
function = get_function(opts.distance)
cc = Map(bedfile, function)
mapnames = cc.mapnames
allseqids = cc.seqids
weights = Weights(weightsfile, mapnames)
pivot = weights.pivot
ref = weights.ref
linkage = opts.linkage
oseqid = opts.seqid
logging.debug("Linkage function: {0}-linkage".format(linkage))
linkage = {"single": min, "double": double_linkage, "complete": max,
"average": np.mean, "median": np.median}[linkage]
# Partition the linkage groups into consensus clusters
C = Grouper()
# Initialize the partitions
for mlg in cc.mlgs:
C.join(mlg)
logging.debug("Partition LGs based on {0}".format(ref))
for mapname in mapnames:
if mapname == ref:
continue
# Compute co-occurrence between LG pairs
G = defaultdict(int)
for s in allseqids:
s = Scaffold(s, cc)
s.add_LG_pairs(G, (ref, mapname))
# Convert edge list to adj list
nodes = defaultdict(list)
for (a, b), w in G.items():
nodes[a].append((b, w))
# Find the best ref LG every non-ref LG matches to
for n, neighbors in nodes.items():
if n.split("-")[0] == ref:
continue
neighbors = dict(neighbors)
best_neighbor, best_value = best_no_ambiguous(neighbors, n)
if best_neighbor is None:
continue
C.join(n, best_neighbor)
partitions = defaultdict(list)
# Partition the scaffolds and assign them to one consensus
for s in allseqids:
s = Scaffold(s, cc)
seqid = s.seqid
counts = {}
for mlg, count in s.mlg_counts.items():
consensus = C[mlg]
mapname = mlg.split("-")[0]
mw = weights[mapname]
if consensus not in counts:
counts[consensus] = 0
counts[consensus] += count * mw
best_consensus, best_value = best_no_ambiguous(counts, seqid)
if best_consensus is None:
continue
partitions[best_consensus].append(seqid)
# Perform OO within each partition
agpfile = pf + ".chr.agp"
tourfile = pf + ".tour"
sizes = Sizes(fastafile).mapping
fwagp = must_open(agpfile, "w")
fwtour = must_open(tourfile, "w")
solutions = []
for lgs, scaffolds in sorted(partitions.items()):
if oseqid and oseqid not in lgs:
continue
tag = "|".join(lgs)
lgs_maps = set(x.split("-")[0] for x in lgs)
if pivot not in lgs_maps:
logging.debug("Skipping {0} ...".format(tag))
continue
logging.debug("Working on {0} ...".format(tag))
s = ScaffoldOO(lgs, scaffolds, cc, pivot, weights, sizes,
function=function, linkage=linkage,
ngen=ngen, npop=npop, cpus=cpus)
for fw in (sys.stderr, fwtour):
print >> fw, ">{0} ({1})".format(s.object, tag)
print >> fw, " ".join("".join(x) for x in s.tour)
solutions.append(s)
fwtour.close()
# meta-data about the run parameters
command = "# COMMAND: python -m jcvi.assembly.allmaps path {0}".\
format(" ".join(oargs))
comment = "Generated by ALLMAPS v{0} ({1})\n{2}".\
format(version, get_today(), command)
AGP.print_header(fwagp, comment=comment)
for s in sorted(solutions, key=lambda x: x.object):
order_to_agp(s.object, s.tour, sizes, fwagp, gapsize=gapsize,
gaptype="map")
fwagp.close()
logging.debug("AGP file written to `{0}`.".format(agpfile))
logging.debug("Tour file written to `{0}`.".format(tourfile))
build([inputbed, fastafile])
summaryfile = pf + ".summary.txt"
summary([inputbed, fastafile, "--outfile={0}".format(summaryfile)])
if not opts.noplot:
plotall([inputbed, "--links={0}".format(opts.links)])
