def group(args):
"""
%prog group anchorfiles
Group the anchors into ortho-groups. Can input multiple anchor files.
"""
p = OptionParser(group.__doc__)
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
anchorfiles = args
groups = Grouper()
for anchorfile in anchorfiles:
ac = AnchorFile(anchorfile)
for a, b, idx in ac.iter_pairs():
groups.join(a, b)
logging.debug("Created {0} groups with {1} members.".\
format(len(groups), groups.num_members))
outfile = opts.outfile
fw = must_open(outfile, "w")
for g in groups:
print >> fw, ",".join(sorted(g))
fw.close()
return outfile
def get_2D_overlap(chain, eclusters):
"""
Implements a sweep line algorithm, that has better running time than naive O(n^2):
assume block has x_ends, and y_ends for the bounds
1. sort x_ends, and take a sweep line to scan the x_ends
2. if left end, test y-axis intersection of current block with `active` set;
also put this block in the `active` set
3. if right end, remove block from the `active` set
"""
mergeables = Grouper()
active = set()
x_ends = []
for i, (range_x, range_y, score) in enumerate(eclusters):
chr, left, right = range_x
x_ends.append((chr, left, 0, i)) # 0/1 for left/right-ness
x_ends.append((chr, right, 1, i))
x_ends.sort()
chr_last = ""
for chr, pos, left_right, i in x_ends:
if chr != chr_last:
active.clear()
if left_right == 0:
active.add(i)
for x in active:
# check y-overlap
if range_overlap(eclusters[x][1], eclusters[i][1]):
mergeables.join(x, i)
else: # right end
active.remove(i)
chr_last = chr
return mergeables
def find_synteny_region(query, sbed, data, window, cutoff, colinear=False):
"""
Get all synteny blocks for a query, algorithm is single linkage
anchors are a window centered on query
Two categories of syntenic regions depending on what query is:
(Syntelog): syntenic region is denoted by the syntelog
(Gray gene): syntenic region is marked by the closest flanker
"""
regions = []
ysorted = sorted(data, key=lambda x: x[1])
g = Grouper()
a, b = tee(ysorted)
next(b, None)
for ia, ib in izip(a, b):
pos1, pos2 = ia[1], ib[1]
if pos2 - pos1 < window and sbed[pos1].seqid == sbed[pos2].seqid:
g.join(ia, ib)
for group in sorted(g):
(qflanker, syntelog), (far_flanker, far_syntelog), flanked = \
get_flanker(group, query)
# run a mini-dagchainer here, take the direction that gives us most anchors
if colinear:
y_indexed_group = [(y, i) for i, (x, y) in enumerate(group)]
lis = longest_increasing_subsequence(y_indexed_group)
lds = longest_decreasing_subsequence(y_indexed_group)
if len(lis) >= len(lds):
track = lis
orientation = "+"
else:
track = lds
orientation = "-"
group = [group[i] for (y, i) in track]
xpos, ypos = zip(*group)
score = min(len(set(xpos)), len(set(ypos)))
if qflanker == query:
gray = "S"
else:
gray = "G" if not flanked else "F"
score -= 1 # slight penalty for not finding syntelog
if score < cutoff:
continue
# y-boundary of the block
left, right = group[0][1], group[-1][1]
# this characterizes a syntenic region (left, right).
# syntelog is -1 if it's a gray gene
syn_region = (syntelog, far_syntelog, left,
right, gray, orientation, score)
regions.append(syn_region)
return sorted(regions, key=lambda x: -x[-1]) # decreasing synteny score
def get_2D_overlap(chain, eclusters):
"""
Implements a sweep line algorithm, that has better running time than naive O(n^2):
assume block has x_ends, and y_ends for the bounds
1. sort x_ends, and take a sweep line to scan the x_ends
2. if left end, test y-axis intersection of current block with `active` set;
also put this block in the `active` set
3. if right end, remove block from the `active` set
"""
mergeables = Grouper()
active = set()
x_ends = []
for i, (range_x, range_y, score) in enumerate(eclusters):
chr, left, right = range_x
x_ends.append((chr, left, 0, i)) # 0/1 for left/right-ness
x_ends.append((chr, right, 1, i))
x_ends.sort()
chr_last = ""
for chr, pos, left_right, i in x_ends:
if chr != chr_last: active.clear()
if left_right == 0:
active.add(i)
for x in active:
# check y-overlap
if range_overlap(eclusters[x][1], eclusters[i][1]):
mergeables.join(x, i)
else: # right end
active.remove(i)
chr_last = chr
return mergeables
def group(args):
"""
%prog group anchorfiles
Group the anchors into ortho-groups. Can input multiple anchor files.
"""
p = OptionParser(group.__doc__)
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
anchorfiles = args
groups = Grouper()
for anchorfile in anchorfiles:
ac = AnchorFile(anchorfile)
for a, b in ac.iter_pairs():
groups.join(a, b)
ngroups = len(groups)
nmembers = sum(len(x) for x in groups)
logging.debug("Created {0} groups with {1} members.".\
format(ngroups, nmembers))
for g in groups:
print ",".join(sorted(g))
def synteny_scan(points, xdist, ydist, N):
"""
This is the core single linkage algorithm which behaves in O(n):
iterate through the pairs, foreach pair we look back on the
adjacent pairs to find links
"""
clusters = Grouper()
n = len(points)
points.sort()
for i in xrange(n):
for j in xrange(i - 1, -1, -1):
# x-axis distance
del_x = points[i][0] - points[j][0]
if del_x > xdist:
break
# y-axis distance
del_y = points[i][1] - points[j][1]
if abs(del_y) > ydist:
continue
# otherwise join
clusters.join(points[i], points[j])
# select clusters that are at least >=N
clusters = [sorted(cluster) for cluster in list(clusters) \
if _score(cluster) >= N]
return clusters
def iter_partitions(self, cutoff=.3, gtr=True):
from jcvi.utils.grouper import Grouper
if gtr:
names = self.gnames
fp = open(self.gtrfile)
else:
names = self.anames
fp = open(self.atrfile)
reader = csv.reader(fp, delimiter="\t")
grouper = Grouper()
for g in map(GTRLine._make, reader):
d = float(g.dist)
if d < cutoff:
continue
grouper.join(g.parent, g.left_child, g.right_child)
parents = {}
for i, group in enumerate(grouper):
for g in group:
parents[g] = i
partitions = [[parents.get(a, x), x] for a, x in names]
for key, parts in groupby(partitions, key=lambda x: x[0]):
yield list(x[1] for x in parts)
def synteny_scan(points, xdist, ydist, N):
"""
This is the core single linkage algorithm which behaves in O(n):
iterate through the pairs, foreach pair we look back on the
adjacent pairs to find links
"""
clusters = Grouper()
n = len(points)
points.sort()
for i in xrange(n):
for j in xrange(i - 1, -1, -1):
# x-axis distance
del_x = points[i][0] - points[j][0]
if del_x > xdist:
break
# y-axis distance
del_y = points[i][1] - points[j][1]
if abs(del_y) > ydist:
continue
# otherwise join
clusters.join(points[i], points[j])
# select clusters that are at least >=N
clusters = [sorted(cluster) for cluster in list(clusters) \
if _score(cluster) >= N]
return clusters
def group(args):
"""
%prog group anchorfiles
Group the anchors into ortho-groups. Can input multiple anchor files.
"""
p = OptionParser(group.__doc__)
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
anchorfiles = args
groups = Grouper()
for anchorfile in anchorfiles:
ac = AnchorFile(anchorfile)
for a, b, idx in ac.iter_pairs():
groups.join(a, b)
logging.debug("Created {0} groups with {1} members.".\
format(len(groups), groups.num_members))
outfile = opts.outfile
fw = must_open(outfile, "w")
for g in groups:
print >> fw, ",".join(sorted(g))
fw.close()
return outfile
def pile(args):
"""
%prog pile abedfile bbedfile > piles
Call intersectBed on two bedfiles.
"""
from jcvi.utils.grouper import Grouper
p = OptionParser(pile.__doc__)
p.add_option("--minOverlap",
default=0,
type="int",
help="Minimum overlap required [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
abedfile, bbedfile = args
iw = intersectBed_wao(abedfile, bbedfile, minOverlap=opts.minOverlap)
groups = Grouper()
for a, b in iw:
groups.join(a.accn, b.accn)
ngroups = 0
for group in groups:
if len(group) > 1:
ngroups += 1
print "|".join(group)
logging.debug("A total of {0} piles (>= 2 members)".format(ngroups))
def find_synteny_region(query, sbed, data, window, cutoff, colinear=False):
"""
Get all synteny blocks for a query, algorithm is single linkage
anchors are a window centered on query
Two categories of syntenic regions depending on what query is:
(Syntelog): syntenic region is denoted by the syntelog
(Gray gene): syntenic region is marked by the closest flanker
"""
regions = []
ysorted = sorted(data, key=lambda x: x[1])
g = Grouper()
a, b = tee(ysorted)
next(b, None)
for ia, ib in zip(a, b):
pos1, pos2 = ia[1], ib[1]
if pos2 - pos1 < window and sbed[pos1].seqid == sbed[pos2].seqid:
g.join(ia, ib)
for group in sorted(g):
(qflanker, syntelog), (far_flanker, far_syntelog), flanked = \
get_flanker(group, query)
# run a mini-dagchainer here, take the direction that gives us most anchors
if colinear:
y_indexed_group = [(y, i) for i, (x, y) in enumerate(group)]
lis = longest_increasing_subsequence(y_indexed_group)
lds = longest_decreasing_subsequence(y_indexed_group)
if len(lis) >= len(lds):
track = lis
orientation = "+"
else:
track = lds
orientation = "-"
group = [group[i] for (y, i) in track]
xpos, ypos = zip(*group)
score = min(len(set(xpos)), len(set(ypos)))
if qflanker == query:
gray = "S"
else:
gray = "G" if not flanked else "F"
score -= 1 # slight penalty for not finding syntelog
if score < cutoff:
continue
# y-boundary of the block
left, right = group[0][1], group[-1][1]
# this characterizes a syntenic region (left, right).
# syntelog is -1 if it's a gray gene
syn_region = (syntelog, far_syntelog, left, right, gray, orientation,
score)
regions.append(syn_region)
return sorted(regions, key=lambda x: -x[-1]) # decreasing synteny score
def pile(args):
"""
%prog pile abedfile bbedfile > piles
Call intersectBed on two bedfiles.
"""
from jcvi.utils.grouper import Grouper
p = OptionParser(pile.__doc__)
p.add_option("--minOverlap", default=0, type="int",
help="Minimum overlap required [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
abedfile, bbedfile = args
iw = intersectBed_wao(abedfile, bbedfile, minOverlap=opts.minOverlap)
groups = Grouper()
for a, b in iw:
groups.join(a.accn, b.accn)
ngroups = 0
for group in groups:
if len(group) > 1:
ngroups += 1
print "|".join(group)
logging.debug("A total of {0} piles (>= 2 members)".format(ngroups))
def iter_partitions(self, cutoff=.3, gtr=True):
from jcvi.utils.grouper import Grouper
if gtr:
names = self.gnames
fp = open(self.gtrfile)
else:
names = self.anames
fp = open(self.atrfile)
reader = csv.reader(fp, delimiter="\t")
grouper = Grouper()
for g in map(GTRLine._make, reader):
d = float(g.dist)
if d < cutoff:
continue
grouper.join(g.parent, g.left_child, g.right_child)
parents = {}
for i, group in enumerate(grouper):
for g in group:
parents[g] = i
partitions = [[parents.get(a, x), x] for a, x in names]
for key, parts in groupby(partitions, key=lambda x: x[0]):
yield list(x[1] for x in parts)
def fuse(args):
"""
%prog fuse *.bed *.anchors
Fuse gene orders based on anchors file.
"""
from jcvi.algorithms.graph import BiGraph
p = OptionParser(fuse.__doc__)
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
bedfiles = [x for x in args if x.endswith(".bed")]
anchorfiles = [x for x in args if x.endswith(".anchors")]
# TODO: Use Markov clustering to sparsify the edges
families = Grouper()
for anchorfile in anchorfiles:
af = AnchorFile(anchorfile)
for a, b, block_id in af.iter_pairs():
families.join(a, b)
allowed = set(families.keys())
logging.debug("Total families: {}, Gene members: {}"
.format(len(families), len(allowed)))
# TODO: Use C++ implementation of BiGraph() when available
# For now just serialize this to the disk
G = BiGraph()
for bedfile in bedfiles:
bed = Bed(bedfile, include=allowed)
#add_bed_to_graph(G, bed, families)
print_edges(G, bed, families)
def group(args):
"""
%prog group anchorfiles
Group the anchors into ortho-groups. Can input multiple anchor files.
"""
p = OptionParser(group.__doc__)
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
anchorfiles = args
groups = Grouper()
for anchorfile in anchorfiles:
ac = AnchorFile(anchorfile)
for a, b in ac.iter_pairs():
groups.join(a, b)
ngroups = len(groups)
nmembers = sum(len(x) for x in groups)
logging.debug("Created {0} groups with {1} members.".\
format(ngroups, nmembers))
for g in groups:
print ",".join(sorted(g))
def fuse(args):
"""
%prog fuse *.bed *.anchors
Fuse gene orders based on anchors file.
"""
from jcvi.algorithms.graph import BiGraph
p = OptionParser(fuse.__doc__)
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
bedfiles = [x for x in args if x.endswith(".bed")]
anchorfiles = [x for x in args if x.endswith(".anchors")]
# TODO: Use Markov clustering to sparsify the edges
families = Grouper()
for anchorfile in anchorfiles:
af = AnchorFile(anchorfile)
for a, b, block_id in af.iter_pairs():
families.join(a, b)
allowed = set(families.keys())
logging.debug("Total families: {}, Gene members: {}".format(
len(families), len(allowed)))
# TODO: Use C++ implementation of BiGraph() when available
# For now just serialize this to the disk
for bedfile in bedfiles:
bed = Bed(bedfile, include=allowed)
print_edges(bed, families)
def athalianatruth(args):
"""
%prog athalianatruth J_a.txt J_bc.txt
Prepare pairs data for At alpha/beta/gamma.
"""
p = OptionParser(athalianatruth.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
atxt, bctxt = args
g = Grouper()
pairs = set()
for txt in (atxt, bctxt):
extract_groups(g, pairs, txt)
fw = open("pairs", "w")
for pair in sorted(pairs):
print("\t".join(pair), file=fw)
fw.close()
fw = open("groups", "w")
for group in list(g):
print(",".join(group), file=fw)
fw.close()
def main(blast_file, cds_file, bed_file, N=3):
# get the sizes for the CDS first
f = Fasta(cds_file)
sizes = dict(f.itersizes())
# retrieve the locations
bed = Bed(bed_file).order
# filter the blast file
g = Grouper()
fp = open(blast_file)
for row in fp:
b = BlastLine(row)
query_len = sizes[b.query]
subject_len = sizes[b.subject]
if b.hitlen < min(query_len, subject_len) / 2:
continue
query, subject = gene_name(b.query), gene_name(b.subject)
qi, q = bed[query]
si, s = bed[subject]
if q.seqid == s.seqid and abs(qi - si) <= N:
g.join(query, subject)
# dump the grouper
ngenes, nfamilies = 0, 0
families = []
for group in sorted(g):
if len(group) >= 2:
print ",".join(sorted(group))
ngenes += len(group)
nfamilies += 1
families.append(sorted(group))
longest_family = max(families, key=lambda x: len(x))
# generate reports
print >> sys.stderr, "Proximal paralogues (dist=%d):" % N
print >> sys.stderr, "Total %d genes in %d families" % (ngenes, nfamilies)
print >> sys.stderr, "Longest families (%d): %s" % (
len(longest_family), ",".join(longest_family))
def tandem_grouper(bed, blast_list, tandem_Nmax=10, flip=True):
if not flip:
simple_blast = [(b.query, (b.sseqid, b.si)) for b in blast_list if b.evalue < 1e-10]
else:
simple_blast = [(b.subject, (b.qseqid, b.qi)) for b in blast_list if b.evalue < 1e-10]
simple_blast.sort()
standems = Grouper()
for name, hits in groupby(simple_blast, key=lambda x: x[0]):
# these are already sorted.
hits = [x[1] for x in hits]
for ia, a in enumerate(hits[:-1]):
b = hits[ia + 1]
# on the same chr and rank difference no larger than tandem_Nmax
if b[1] - a[1] <= tandem_Nmax and b[0] == a[0]:
standems.join(a[1], b[1])
return standems
def main(blast_file, cds_file, bed_file, N=3):
# get the sizes for the CDS first
f = Fasta(cds_file)
sizes = dict(f.itersizes())
# retrieve the locations
bed = Bed(bed_file).order
# filter the blast file
g = Grouper()
fp = open(blast_file)
for row in fp:
b = BlastLine(row)
query_len = sizes[b.query]
subject_len = sizes[b.subject]
if b.hitlen < min(query_len, subject_len) / 2:
continue
query, subject = gene_name(b.query), gene_name(b.subject)
qi, q = bed[query]
si, s = bed[subject]
if q.seqid == s.seqid and abs(qi - si) <= N:
g.join(query, subject)
# dump the grouper
ngenes, nfamilies = 0, 0
families = []
for group in sorted(g):
if len(group) >= 2:
print ",".join(sorted(group))
ngenes += len(group)
nfamilies += 1
families.append(sorted(group))
longest_family = max(families, key=lambda x: len(x))
# generate reports
print >>sys.stderr, "Proximal paralogues (dist=%d):" % N
print >>sys.stderr, "Total %d genes in %d families" % (ngenes, nfamilies)
print >>sys.stderr, "Longest families (%d): %s" % (len(longest_family),
",".join(longest_family))
def tandem_grouper(bed, blast_list, tandem_Nmax=10, flip=True):
if not flip:
simple_blast = [(b.query, (b.sseqid, b.si)) \
for b in blast_list if b.evalue < 1e-10]
else:
simple_blast = [(b.subject, (b.qseqid, b.qi)) \
for b in blast_list if b.evalue < 1e-10]
simple_blast.sort()
standems = Grouper()
for name, hits in groupby(simple_blast, key=lambda x: x[0]):
# these are already sorted.
hits = [x[1] for x in hits]
for ia, a in enumerate(hits[:-1]):
b = hits[ia + 1]
# on the same chr and rank difference no larger than tandem_Nmax
if b[1] - a[1] <= tandem_Nmax and b[0] == a[0]:
standems.join(a[1], b[1])
return standems
def chain_HSPs(blastlines, xdist=100, ydist=100):
"""
Take a list of BlastLines (or a BlastSlow instance), and returns a list of
BlastLines.
"""
key = lambda x: (x.query, x.subject)
blastlines.sort(key=key)
clusters = Grouper()
for qs, points in groupby(blastlines, key=key):
points = sorted(list(points), \
key=lambda x: (x.qstart, x.qstop, x.sstart, x.sstop))
n = len(points)
for i in xrange(n):
a = points[i]
clusters.join(a)
for j in xrange(i + 1, n):
b = points[j]
if a.orientation != b.orientation:
continue
# x-axis distance
del_x = get_distance(a, b)
if del_x > xdist:
continue
# y-axis distance
del_y = get_distance(a, b, xaxis=False)
if del_y > ydist:
continue
# otherwise join
clusters.join(a, b)
chained_hsps = []
for c in clusters:
chained_hsps.append(combine_HSPs(c))
chained_hsps = sorted(chained_hsps, key=lambda x: -x.score)
return chained_hsps
def fuse(args):
"""
%prog fuse *.bed *.anchors
Fuse gene orders based on anchors file.
"""
p = OptionParser(fuse.__doc__)
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
bedfiles = [x for x in args if x.endswith(".bed")]
anchorfiles = [x for x in args if x.endswith(".anchors")]
aligned_genes = Grouper()
for anchorfile in anchorfiles:
af = AnchorFile(anchorfile)
for a, b, block_id in af.iter_pairs():
aligned_genes.join(a, b)
print list(aligned_genes)
logging.debug("Total aligned genes: {}".format(len(aligned_genes)))
def chain_HSPs(blast, xdist=100, ydist=100):
"""
Take a list of BlastLines (or a BlastSlow instance), and returns a list of
BlastLines.
"""
key = lambda x: (x.query, x.subject)
blast.sort(key=key)
clusters = Grouper()
for qs, points in groupby(blast, key=key):
points = sorted(list(points), \
key=lambda x: (x.qstart, x.qstop, x.sstart, x.sstop))
n = len(points)
for i in xrange(n):
a = points[i]
clusters.join(a)
for j in xrange(i + 1, n):
b = points[j]
# x-axis distance
del_x = get_distance(a, b)
if del_x > xdist:
break
# y-axis distance
del_y = get_distance(a, b, xaxis=False)
if del_y > ydist:
continue
# otherwise join
clusters.join(a, b)
chained_hsps = [combine_HSPs(x) for x in clusters]
key = lambda x: (x.query, -x.score if x.has_score else 0)
chained_hsps = sorted(chained_hsps, key=key)
return chained_hsps
def chain_HSPs(blastlines, xdist=100, ydist=100):
"""
Take a list of BlastLines (or a BlastSlow instance), and returns a list of
BlastLines.
"""
key = lambda x: (x.query, x.subject)
blastlines.sort(key=key)
clusters = Grouper()
for qs, points in groupby(blastlines, key=key):
points = sorted(list(points), \
key=lambda x: (x.qstart, x.qstop, x.sstart, x.sstop))
n = len(points)
for i in xrange(n):
a = points[i]
clusters.join(a)
for j in xrange(i + 1, n):
b = points[j]
if a.orientation != b.orientation:
continue
# x-axis distance
del_x = get_distance(a, b)
if del_x > xdist:
continue
# y-axis distance
del_y = get_distance(a, b, xaxis=False)
if del_y > ydist:
continue
# otherwise join
clusters.join(a, b)
chained_hsps = []
for c in clusters:
chained_hsps.append(combine_HSPs(c))
chained_hsps = sorted(chained_hsps, key=lambda x: -x.score)
return chained_hsps
def chain_HSPs(blast, xdist=100, ydist=100):
"""
Take a list of BlastLines (or a BlastSlow instance), and returns a list of
BlastLines.
"""
key = lambda x: (x.query, x.subject)
blast.sort(key=key)
clusters = Grouper()
for qs, points in groupby(blast, key=key):
points = sorted(
list(points), key=lambda x: (x.qstart, x.qstop, x.sstart, x.sstop)
)
n = len(points)
for i in range(n):
a = points[i]
clusters.join(a)
for j in range(i + 1, n):
b = points[j]
# x-axis distance
del_x = get_distance(a, b)
if del_x > xdist:
break
# y-axis distance
del_y = get_distance(a, b, xaxis=False)
if del_y > ydist:
continue
# otherwise join
clusters.join(a, b)
chained_hsps = [combine_HSPs(x) for x in clusters]
key = lambda x: (x.query, -x.score if x.has_score else 0)
chained_hsps = sorted(chained_hsps, key=key)
return chained_hsps
def napus(args):
"""
%prog napus napus.bed brapa.boleracea.i1.blocks diploid.napus.fractionation
Extract napus gene loss vs diploid ancestors. We are looking specifically
for anything that has the pattern:
BR - BO or BR - BO
| |
AN CN
Step 1: extract BR - BO syntenic pairs
Step 2: get diploid gene retention patterns from BR or BO as query
Step 3: look for if AN or CN is NS(non-syntenic) or NF(not found) and
specifically with NS, the NS location is actually the homeologous site.
Step 4: categorize gene losses into singleton, or segmental (defined as
consecutive losses with a maximum skip of 1
"""
from jcvi.utils.cbook import SummaryStats
p = OptionParser(napus.__doc__)
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
napusbed, brbo, dpnp = args
retention = {}
fp = open(dpnp)
for row in fp:
seqid, query, hit = row.split()
retention[query] = hit
order = Bed(napusbed).order
quartetsfile = "quartets"
fp = open(brbo)
fw = open(quartetsfile, "w")
AL = "AN LOST"
CL = "CN LOST"
for row in fp:
br, bo = row.split()
if '.' in (br, bo):
continue
an, cn = retention[br], retention[bo]
row = "\t".join((br, bo, an, cn))
if '.' in (an, cn):
#print row
continue
# label loss candidates
antag, anrange = get_tag(an, order)
cntag, cnrange = get_tag(cn, order)
if range_overlap(anrange, cnrange):
if (antag, cntag) == ("NS", None):
row = row + "\t{0}|{1}".format(AL, br)
if (antag, cntag) == (None, "NS"):
row = row + "\t{0}|{1}".format(CL, bo)
print >> fw, row
fw.close()
logging.debug("Quartets and gene losses written to `{0}`.".\
format(quartetsfile))
# Parse the quartets file to extract singletons vs.segmental losses
fp = open(quartetsfile)
fw = open(quartetsfile + ".summary", "w")
data = [x.rstrip().split("\t") for x in fp]
skip = 1 # max distance between losses
g = Grouper()
losses = [(len(x) == 5) for x in data]
for i, d in enumerate(losses):
if not d:
continue
g.join(i, i)
itag = data[i][-1].split("|")[0]
for j in xrange(i + 1, i + skip + 1):
jtag = data[j][-1].split("|")[0]
if j < len(losses) and losses[j] and itag == jtag:
g.join(i, j)
losses = list(g)
singletons = [x for x in losses if len(x) == 1]
segments = [x for x in losses if len(x) > 1]
ns, nm = len(singletons), len(segments)
assert len(losses) == ns + nm
grab_tag = lambda pool, tag: \
[x for x in pool if all(data[z][-1].startswith(tag) for z in x)]
an_loss_singletons = grab_tag(singletons, AL)
cn_loss_singletons = grab_tag(singletons, CL)
als, cls = len(an_loss_singletons), len(cn_loss_singletons)
an_loss_segments = grab_tag(segments, AL)
cn_loss_segments = grab_tag(segments, CL)
alm, clm = len(an_loss_segments), len(cn_loss_segments)
mixed = len(segments) - alm - clm
assert mixed == 0
logging.debug("Singletons: {0} (AN LOSS: {1}, CN LOSS: {2})".\
format(ns, als, cls))
logging.debug("Segments: {0} (AN LOSS: {1}, CN LOSS: {2})".\
format(nm, alm, clm))
print >> sys.stderr, SummaryStats([len(x) for x in losses])
for x in singletons + segments:
print >> fw, "### LENGTH =", len(x)
for i in x:
print >> fw, "\t".join(data[i])
fw.close()
def segment(args):
"""
%prog segment loss.ids bedfile
Merge adjacent gene loss into segmental loss.
Then based on the segmental loss, estimate amount of DNA loss in base pairs.
Two estimates can be given:
- conservative: just within the start and end of a single gene
- aggressive: extend the deletion track to the next gene
The real deletion size is within these estimates.
"""
from jcvi.formats.base import SetFile
p = OptionParser(segment.__doc__)
p.add_option("--chain", default=1, type="int",
help="Allow next N genes to be chained [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
idsfile, bedfile = args
bed = Bed(bedfile)
order = bed.order
ids = SetFile(idsfile)
losses = Grouper()
skip = opts.chain
for i, a in enumerate(bed):
a = a.accn
for j in xrange(i + 1, i + 1 + skip):
if j >= len(bed):
break
b = bed[j].accn
if a in ids:
losses.join(a, a)
if a in ids and b in ids:
losses.join(a, b)
losses = list(losses)
singletons = [x for x in losses if len(x) == 1]
segments = [x for x in losses if len(x) > 1]
ns, nm, nt = len(singletons), len(segments), len(losses)
assert ns + nm == nt
# Summary for all segments
for x in sorted(singletons) + sorted(segments):
print "\t".join(str(x) for x in ("|".join(sorted(x)), len(x),
estimate_size(x, bed, order)))
# Find longest segment stretch
if segments:
mx, maxsegment = max([(len(x), x) for x in segments])
print >> sys.stderr, "Longest stretch: run of {0} genes".format(mx)
print >> sys.stderr, " {0}".format("|".join(sorted(maxsegment)))
seg_asize = sum(estimate_size(x, bed, order) for x in segments)
seg_bsize = sum(estimate_size(x, bed, order, conservative=False) \
for x in segments)
else:
seg_asize = seg_bsize = 0
sing_asize = sum(estimate_size(x, bed, order) for x in singletons)
sing_bsize = sum(estimate_size(x, bed, order, conservative=False) \
for x in singletons)
total_asize = sing_asize + seg_asize
total_bsize = sing_bsize + seg_bsize
print >> sys.stderr, "Singleton ({0}): {1} - {2} bp".\
format(ns, sing_asize, sing_bsize)
print >> sys.stderr, "Segment ({0}): {1} - {2} bp".\
format(nm, seg_asize, seg_bsize)
print >> sys.stderr, "Total ({0}): {1} - {2} bp".\
format(nt, total_asize, total_bsize)
print >> sys.stderr, "Average ({0}): {1} bp".\
format(nt, (total_asize + total_bsize) / 2)
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 tandem_main(blast_file, cds_file, bed_file, N=3, P=50, is_self=True, \
evalue=.01, strip_name=".", ofile=sys.stderr, genefam=False):
if genefam:
N = 1e5
# get the sizes for the CDS first
f = Fasta(cds_file)
sizes = dict(f.itersizes())
# retrieve the locations
bed = Bed(bed_file)
order = bed.order
if is_self:
# filter the blast file
g = Grouper()
fp = open(blast_file)
for row in fp:
b = BlastLine(row)
query_len = sizes[b.query]
subject_len = sizes[b.subject]
if b.hitlen < min(query_len, subject_len) * P / 100.:
continue
query = gene_name(b.query, strip_name)
subject = gene_name(b.subject, strip_name)
qi, q = order[query]
si, s = order[subject]
if abs(qi - si) <= N and b.evalue <= evalue:
if genefam:
g.join(query, subject)
elif q.seqid == s.seqid:
g.join(query, subject)
else:
homologs = Grouper()
fp = open(blast_file)
for row in fp:
b = BlastLine(row)
query_len = sizes[b.query]
subject_len = sizes[b.subject]
if b.hitlen < min(query_len, subject_len) * P / 100.:
continue
if b.evalue > evalue:
continue
query = gene_name(b.query, strip_name)
subject = gene_name(b.subject, strip_name)
homologs.join(query, subject)
if genefam:
g = homologs
else:
g = Grouper()
for i, atom in enumerate(bed):
for x in range(1, N + 1):
if all([i-x >= 0, bed[i-x].seqid == atom.seqid, \
homologs.joined(bed[i-x].accn, atom.accn)]):
leni = sizes[bed[i].accn]
lenx = sizes[bed[i - x].accn]
if abs(leni - lenx) > max(leni, lenx) * (1 - P / 100.):
continue
g.join(bed[i - x].accn, atom.accn)
# dump the grouper
fw = must_open(ofile, "w")
ngenes, nfamilies = 0, 0
families = []
for group in sorted(g):
if len(group) >= 2:
print >> fw, ",".join(sorted(group))
ngenes += len(group)
nfamilies += 1
families.append(sorted(group))
longest_family = max(families, key=lambda x: len(x))
# generate reports
print >> sys.stderr, "Proximal paralogues (dist=%d):" % N
print >> sys.stderr, "Total %d genes in %d families" % (ngenes, nfamilies)
print >> sys.stderr, "Longest families (%d): %s" % (
len(longest_family), ",".join(longest_family))
return families
def consolidate(args):
"""
%prog consolidate gffile1 gffile2 ... > consolidated.out
Given 2 or more gff files generated by pasa annotation comparison,
iterate through every gene locus and identify all cases of same and
different isoforms across the different input datasets.
"""
from jcvi.formats.base import longest_unique_prefix
from jcvi.formats.gff import make_index
from jcvi.utils.cbook import AutoVivification
from jcvi.utils.grouper import Grouper
from itertools import combinations, product
p = OptionParser(consolidate.__doc__)
p.add_option("--slop", default=False, action="store_true",
help="allow minor variation in terminal 5'/3' UTR" + \
" start/stop position [default: %default]")
p.set_outfile()
opts, args = p.parse_args(args)
slop = opts.slop
if len(args) < 2:
sys.exit(not p.print_help())
gffdbx = {}
gene_coords = {}
mrna = AutoVivification()
for gffile in args:
dbn = longest_unique_prefix(gffile, args)
gffdbx[dbn] = make_index(gffile)
for gene in gffdbx[dbn].features_of_type('gene',
order_by=('seqid', 'start')):
if gene.id not in gene_coords:
gene_coords[gene.id] = []
gene_coords[gene.id].extend([gene.start, gene.stop])
c = list(gffdbx[dbn].children(gene,
featuretype='mRNA',
order_by='start'))
if len(c) > 0:
mrna[gene.id][dbn] = c
fw = must_open(opts.outfile, "w")
print >> fw, "##gff-version 3"
summary = ["id"]
summary.extend(gffdbx.keys())
print >> sys.stderr, "\t".join(str(x) for x in summary)
for gene in mrna:
g = Grouper()
dbns = list(combinations(mrna[gene], 2))
if len(dbns) > 0:
for dbn1, dbn2 in dbns:
for mrna1, mrna2 in product(mrna[gene][dbn1],
mrna[gene][dbn2]):
g.join((dbn1, mrna1.id))
g.join((dbn2, mrna2.id))
fUTR, tUTR = None, None
if match_subfeats(mrna1, mrna2, gffdbx[dbn1],
gffdbx[dbn2]):
fUTR = match_subfeats(mrna1, mrna2, gffdbx[dbn1], gffdbx[dbn2], \
featuretype='five_prime_UTR', slop=slop)
tUTR = match_subfeats(mrna1, mrna2, gffdbx[dbn1], gffdbx[dbn2], \
featuretype='three_prime_UTR', slop=slop)
if fUTR and tUTR:
g.join((dbn1, mrna1.id), (dbn2, mrna2.id))
else:
for dbn1 in mrna[gene]:
for mrna1 in mrna[gene][dbn1]:
g.join((dbn1, mrna1.id))
dbn = mrna[gene].keys()[0]
gene_coords[gene].sort()
_gene = gffdbx[dbn][gene]
_gene.start, _gene.stop = gene_coords[gene][0], gene_coords[gene][-1]
print >> fw, _gene
logging.debug(list(g))
for group in g:
dbs, mrnas = [el[0] for el in group], [el[1] for el in group]
d, m = dbs[0], mrnas[0]
if slop:
mlen = 0
for D, M in zip(dbs, mrnas):
_mrna = gffdbx[D][M]
_mlen = (_mrna.stop - _mrna.start) + 1
if _mlen > mlen:
d, m, mlen = D, M, _mlen
dbid, _mrnaid = "".join(str(x) for x in set(dbs)), []
_mrnaid = [x for x in mrnas if x not in _mrnaid]
mrnaid = "{0}:{1}".format(dbid, "-".join(_mrnaid))
_mrna = gffdbx[d][m]
_mrna.attributes['ID'] = [mrnaid]
children = gffdbx[d].children(m, order_by='start')
print >> fw, _mrna
for child in children:
child.attributes['ID'] = ["{0}:{1}".format(dbid, child.id)]
child.attributes['Parent'] = [mrnaid]
print >> fw, child
summary = [mrnaid]
summary.extend(['Y' if db in set(dbs) else 'N' for db in gffdbx])
print >> sys.stderr, "\t".join(str(x) for x in summary)
fw.close()
def renumber(args):
"""
%prog renumber Mt35.consolidated.bed > tagged.bed
Renumber genes for annotation updates.
"""
from jcvi.algorithms.lis import longest_increasing_subsequence
from jcvi.utils.grouper import Grouper
p = OptionParser(renumber.__doc__)
p.set_annot_reformat_opts()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
bedfile, = args
pf = bedfile.rsplit(".", 1)[0]
abedfile = pf + ".a.bed"
bbedfile = pf + ".b.bed"
if need_update(bedfile, (abedfile, bbedfile)):
prepare(bedfile)
mbed = Bed(bbedfile)
g = Grouper()
for s in mbed:
accn = s.accn
g.join(*accn.split(";"))
bed = Bed(abedfile)
for chr, sbed in bed.sub_beds():
current_chr = chr_number(chr)
if not current_chr:
continue
ranks = []
gg = set()
for s in sbed:
accn = s.accn
achr, arank = atg_name(accn)
if achr != current_chr:
continue
ranks.append(arank)
gg.add(accn)
lranks = longest_increasing_subsequence(ranks)
print >> sys.stderr, current_chr, len(sbed), "==>", len(ranks), \
"==>", len(lranks)
granks = set(gene_name(current_chr, x, prefix=opts.prefix, \
pad0=opts.pad0, uc=opts.uc) for x in lranks) | \
set(gene_name(current_chr, x, prefix=opts.prefix, \
pad0=opts.pad0, sep="te", uc=opts.uc) for x in lranks)
tagstore = {}
for s in sbed:
achr, arank = atg_name(s.accn)
accn = s.accn
if accn in granks:
tag = (accn, FRAME)
elif accn in gg:
tag = (accn, RETAIN)
else:
tag = (".", NEW)
tagstore[accn] = tag
# Find cases where genes overlap
for s in sbed:
accn = s.accn
gaccn = g[accn]
tags = [((tagstore[x][-1] if x in tagstore else NEW), x)
for x in gaccn]
group = [(PRIORITY.index(tag), x) for tag, x in tags]
best = min(group)[-1]
if accn != best:
tag = (best, OVERLAP)
else:
tag = tagstore[accn]
print "\t".join((str(s), "|".join(tag)))
def annotate(args):
"""
%prog annotate new.bed old.bed 2> log
Annotate the `new.bed` with features from `old.bed` for the purpose of
gene numbering.
Ambiguity in ID assignment can be resolved by either of the following 2 methods:
- `alignment`: make use of global sequence alignment score (calculated by `needle`)
- `overlap`: make use of overlap length (calculated by `intersectBed`)
Transfer over as many identifiers as possible while following guidelines:
http://www.arabidopsis.org/portals/nomenclature/guidelines.jsp#editing
Note: Following RegExp pattern describes the structure of the identifier
assigned to features in the `new.bed` file.
new_id_pat = re.compile(r"^\d+\.[cemtx]+\S+")
Examples: 23231.m312389, 23231.t004898, 23231.tRNA.144
Adjust the value of `new_id_pat` manually as per your ID naming conventions.
"""
from jcvi.utils.grouper import Grouper
valid_resolve_choices = ["alignment", "overlap"]
p = OptionParser(annotate.__doc__)
p.add_option("--resolve", default="alignment", choices=valid_resolve_choices,
help="Resolve ID assignment based on a certain metric" \
+ " [default: %default]")
p.add_option("--atg_name", default=False, action="store_true",
help="Specify is locus IDs in `new.bed` file follow ATG nomenclature" \
+ " [default: %default]")
g1 = OptionGroup(p, "Optional parameters (alignment):\n" \
+ "Use if resolving ambiguities based on sequence `alignment`")
g1.add_option("--pid", dest="pid", default=35., type="float",
help="Percent identity cutoff [default: %default]")
g1.add_option("--score", dest="score", default=250., type="float",
help="Alignment score cutoff [default: %default]")
p.add_option_group(g1)
g2 = OptionGroup(p, "Optional parameters (overlap):\n" \
+ "Use if resolving ambiguities based on `overlap` length\n" \
+ "Parameters equivalent to `intersectBed`")
g2.add_option("-f", dest="f", default=0.5, type="float",
help="Minimum overlap fraction (0.0 - 1.0) [default: %default]")
g2.add_option("-r", dest="r", default=False, action="store_true",
help="Require fraction overlap to be reciprocal [default: %default]")
g2.add_option("-s", dest="s", default=True, action="store_true",
help="Require same strandedness [default: %default]")
p.add_option_group(g2)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
nbedfile, obedfile = args
npf, opf = nbedfile.rsplit(".", 1)[0], obedfile.rsplit(".", 1)[0]
# Make consolidated.bed
cbedfile = "consolidated.bed"
if not os.path.isfile(cbedfile):
consolidate(nbedfile, obedfile, cbedfile)
else:
logging.warning("`{0}` already exists. Skipping step".format(cbedfile))
logging.warning("Resolving ID assignment ambiguity based on `{0}`".\
format(opts.resolve))
if opts.resolve == "alignment":
# Get pairs and prompt to run needle
pairsfile = "nw.pairs"
scoresfile = "nw.scores"
if not os.path.isfile(pairsfile):
get_pairs(cbedfile, pairsfile)
else:
logging.warning("`{0}` already exists. Checking for needle output".\
format(pairsfile))
# If needle scores do not exist, prompt user to run needle
if not os.path.isfile(scoresfile):
logging.error("`{0}` does not exist. Please process {1} using `needle`".\
format(scoresfile, pairsfile))
sys.exit()
else:
scoresfile = "ovl.scores"
# Calculate overlap length using intersectBed
calculate_ovl(nbedfile, obedfile, opts, scoresfile)
logging.warning("`{0}' exists. Storing scores in memory".\
format(scoresfile))
scores = read_scores(scoresfile, opts)
# Iterate through consolidated bed and
# filter piles based on score
abedline = {}
cbed = Bed(cbedfile)
g = Grouper()
for c in cbed:
accn = c.accn
g.join(*accn.split(";"))
nbedline = {}
nbed = Bed(nbedfile)
for line in nbed: nbedline[line.accn] = line
splits = set()
for chr, chrbed in nbed.sub_beds():
abedline, splits = annotate_chr(chr, chrbed, g, scores, nbedline, abedline, opts, splits)
if splits is not None:
abedline = process_splits(splits, scores, nbedline, abedline)
abedfile = npf + ".annotated.bed"
afh = open(abedfile, "w")
for accn in abedline:
print >> afh, abedline[accn]
afh.close()
sort([abedfile, "-i"])
def renumber(args):
"""
%prog renumber Mt35.consolidated.bed > tagged.bed
Renumber genes for annotation updates.
"""
from jcvi.algorithms.lis import longest_increasing_subsequence
from jcvi.utils.grouper import Grouper
p = OptionParser(renumber.__doc__)
p.set_annot_reformat_opts()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
bedfile, = args
pf = bedfile.rsplit(".", 1)[0]
abedfile = pf + ".a.bed"
bbedfile = pf + ".b.bed"
if need_update(bedfile, (abedfile, bbedfile)):
prepare(bedfile)
mbed = Bed(bbedfile)
g = Grouper()
for s in mbed:
accn = s.accn
g.join(*accn.split(";"))
bed = Bed(abedfile)
for chr, sbed in bed.sub_beds():
current_chr = chr_number(chr)
if not current_chr:
continue
ranks = []
gg = set()
for s in sbed:
accn = s.accn
achr, arank = atg_name(accn)
if achr != current_chr:
continue
ranks.append(arank)
gg.add(accn)
lranks = longest_increasing_subsequence(ranks)
print >> sys.stderr, current_chr, len(sbed), "==>", len(ranks), \
"==>", len(lranks)
granks = set(gene_name(current_chr, x, prefix=opts.prefix, \
pad0=opts.pad0, uc=opts.uc) for x in lranks) | \
set(gene_name(current_chr, x, prefix=opts.prefix, \
pad0=opts.pad0, sep="te", uc=opts.uc) for x in lranks)
tagstore = {}
for s in sbed:
achr, arank = atg_name(s.accn)
accn = s.accn
if accn in granks:
tag = (accn, FRAME)
elif accn in gg:
tag = (accn, RETAIN)
else:
tag = (".", NEW)
tagstore[accn] = tag
# Find cases where genes overlap
for s in sbed:
accn = s.accn
gaccn = g[accn]
tags = [((tagstore[x][-1] if x in tagstore else NEW), x) for x in gaccn]
group = [(PRIORITY.index(tag), x) for tag, x in tags]
best = min(group)[-1]
if accn != best:
tag = (best, OVERLAP)
else:
tag = tagstore[accn]
print "\t".join((str(s), "|".join(tag)))
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)])
def enrich(args):
"""
%prog enrich omgfile groups ntaxa > enriched.omg
Enrich OMG output by pulling genes misses by OMG.
"""
p = OptionParser(enrich.__doc__)
p.add_option("--ghost", default=False, action="store_true",
help="Add ghost homologs already used [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
omgfile, groupsfile, ntaxa = args
ntaxa = int(ntaxa)
ghost = opts.ghost
# Get gene pair => weight mapping
weights = get_edges()
info = get_info()
# Get gene => taxon mapping
info = dict((k, v.split()[5]) for k, v in info.items())
groups = Grouper()
fp = open(groupsfile)
for row in fp:
members = row.strip().split(",")
groups.join(*members)
logging.debug("Imported {0} families with {1} members.".\
format(len(groups), groups.num_members))
seen = set()
omggroups = Grouper()
fp = open(omgfile)
for row in fp:
genes, idxs = row.split()
genes = genes.split(",")
seen.update(genes)
omggroups.join(*genes)
nmembers = omggroups.num_members
logging.debug("Imported {0} OMG families with {1} members.".\
format(len(omggroups), nmembers))
assert nmembers == len(seen)
alltaxa = set(str(x) for x in range(ntaxa))
recruited = []
fp = open(omgfile)
for row in fp:
genes, idxs = row.split()
genes = genes.split(",")
a = genes[0]
idxs = set(idxs.split(","))
missing_taxa = alltaxa - idxs
if not missing_taxa:
print row.rstrip()
continue
leftover = groups[a]
if not ghost:
leftover = set(leftover) - seen
if not leftover:
print row.rstrip()
continue
leftover_sorted_by_taxa = dict((k, \
[x for x in leftover if info[x] == k]) \
for k in missing_taxa)
#print genes, leftover
#print leftover_sorted_by_taxa
solutions = []
for solution in product(*leftover_sorted_by_taxa.values()):
score = sum(weights.get((a, b), 0) for a in solution for b in genes)
if score == 0:
continue
score += sum(weights.get((a, b), 0) for a, b in combinations(solution, 2))
solutions.append((score, solution))
#print solution, score
best_solution = max(solutions) if solutions else None
if best_solution is None:
print row.rstrip()
continue
#print "best ==>", best_solution
best_score, best_addition = best_solution
genes.extend(best_addition)
recruited.extend(best_addition)
genes = sorted([(info[x], x) for x in genes])
idxs, genes = zip(*genes)
if ghost: # decorate additions so it's clear that they were added
pgenes = []
for g in genes:
if g in recruited and g in seen:
pgenes.append("|{0}|".format(g))
else:
pgenes.append(g)
genes = pgenes
print "\t".join((",".join(genes), ",".join(idxs)))
if not ghost:
seen.update(best_addition)
logging.debug("Recruited {0} new genes.".format(len(recruited)))
def napus(args):
"""
%prog napus napus.bed brapa.boleracea.i1.blocks diploid.napus.fractionation
Extract napus gene loss vs diploid ancestors. We are looking specifically
for anything that has the pattern:
BR - BO or BR - BO
| |
AN CN
Step 1: extract BR - BO syntenic pairs
Step 2: get diploid gene retention patterns from BR or BO as query
Step 3: look for if AN or CN is NS(non-syntenic) or NF(not found) and
specifically with NS, the NS location is actually the homeologous site.
Step 4: categorize gene losses into singleton, or segmental (defined as
consecutive losses with a maximum skip of 1
"""
from jcvi.utils.cbook import SummaryStats
p = OptionParser(napus.__doc__)
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
napusbed, brbo, dpnp = args
retention = {}
fp = open(dpnp)
for row in fp:
seqid, query, hit = row.split()
retention[query] = hit
order = Bed(napusbed).order
quartetsfile = "quartets"
fp = open(brbo)
fw = open(quartetsfile, "w")
AL = "AN LOST"
CL = "CN LOST"
for row in fp:
br, bo = row.split()
if '.' in (br, bo):
continue
an, cn = retention[br], retention[bo]
row = "\t".join((br, bo, an, cn))
if '.' in (an, cn):
#print row
continue
# label loss candidates
antag, anrange = get_tag(an, order)
cntag, cnrange = get_tag(cn, order)
if range_overlap(anrange, cnrange):
if (antag, cntag) == ("NS", None):
row = row + "\t{0}|{1}".format(AL, br)
if (antag, cntag) == (None, "NS"):
row = row + "\t{0}|{1}".format(CL, bo)
print >> fw, row
fw.close()
logging.debug("Quartets and gene losses written to `{0}`.".\
format(quartetsfile))
# Parse the quartets file to extract singletons vs.segmental losses
fp = open(quartetsfile)
fw = open(quartetsfile + ".summary", "w")
data = [x.rstrip().split("\t") for x in fp]
skip = 1 # max distance between losses
g = Grouper()
losses = [(len(x) == 5) for x in data]
for i, d in enumerate(losses):
if not d:
continue
g.join(i, i)
itag = data[i][-1].split("|")[0]
for j in xrange(i + 1, i + skip + 1):
jtag = data[j][-1].split("|")[0]
if j < len(losses) and losses[j] and itag == jtag:
g.join(i, j)
losses = list(g)
singletons = [x for x in losses if len(x) == 1]
segments = [x for x in losses if len(x) > 1]
ns, nm = len(singletons), len(segments)
assert len(losses) == ns + nm
grab_tag = lambda pool, tag: \
[x for x in pool if all(data[z][-1].startswith(tag) for z in x)]
an_loss_singletons = grab_tag(singletons, AL)
cn_loss_singletons = grab_tag(singletons, CL)
als, cls = len(an_loss_singletons), len(cn_loss_singletons)
an_loss_segments = grab_tag(segments, AL)
cn_loss_segments = grab_tag(segments, CL)
alm, clm = len(an_loss_segments), len(cn_loss_segments)
mixed = len(segments) - alm - clm
assert mixed == 0
logging.debug("Singletons: {0} (AN LOSS: {1}, CN LOSS: {2})".\
format(ns, als, cls))
logging.debug("Segments: {0} (AN LOSS: {1}, CN LOSS: {2})".\
format(nm, alm, clm))
print >> sys.stderr, SummaryStats([len(x) for x in losses])
for x in singletons + segments:
print >> fw, "### LENGTH =", len(x)
for i in x:
print >> fw, "\t".join(data[i])
fw.close()
def tandem_main(blast_file, cds_file, bed_file, N=3, P=50, is_self=True, \
evalue=.01, strip_name=".", ofile=sys.stderr, genefam=False):
if genefam:
N = 1e5
# get the sizes for the CDS first
f = Fasta(cds_file)
sizes = dict(f.itersizes())
# retrieve the locations
bed = Bed(bed_file)
order = bed.order
if is_self:
# filter the blast file
g = Grouper()
fp = open(blast_file)
for row in fp:
b = BlastLine(row)
query_len = sizes[b.query]
subject_len = sizes[b.subject]
if b.hitlen < min(query_len, subject_len)*P/100.:
continue
query = gene_name(b.query, strip_name)
subject = gene_name(b.subject, strip_name)
qi, q = order[query]
si, s = order[subject]
if abs(qi - si) <= N and b.evalue <= evalue:
if genefam:
g.join(query, subject)
elif q.seqid == s.seqid:
g.join(query, subject)
else:
homologs = Grouper()
fp = open(blast_file)
for row in fp:
b = BlastLine(row)
query_len = sizes[b.query]
subject_len = sizes[b.subject]
if b.hitlen < min(query_len, subject_len)*P/100.:
continue
if b.evalue > evalue:
continue
query = gene_name(b.query, strip_name)
subject = gene_name(b.subject, strip_name)
homologs.join(query, subject)
if genefam:
g = homologs
else:
g = Grouper()
for i, atom in enumerate(bed):
for x in range(1, N+1):
if all([i-x >= 0, bed[i-x].seqid == atom.seqid, \
homologs.joined(bed[i-x].accn, atom.accn)]):
leni = sizes[bed[i].accn]
lenx = sizes[bed[i-x].accn]
if abs(leni - lenx) > max(leni, lenx)*(1-P/100.):
continue
g.join(bed[i-x].accn, atom.accn)
# dump the grouper
fw = must_open(ofile, "w")
ngenes, nfamilies = 0, 0
families = []
for group in sorted(g):
if len(group) >= 2:
print >>fw, ",".join(sorted(group))
ngenes += len(group)
nfamilies += 1
families.append(sorted(group))
longest_family = max(families, key=lambda x: len(x))
# generate reports
print >>sys.stderr, "Proximal paralogues (dist=%d):" % N
print >>sys.stderr, "Total %d genes in %d families" % (ngenes, nfamilies)
print >>sys.stderr, "Longest families (%d): %s" % (len(longest_family),
",".join(longest_family))
return families
def enrich(args):
"""
%prog enrich omgfile groups ntaxa > enriched.omg
Enrich OMG output by pulling genes misses by OMG.
"""
p = OptionParser(enrich.__doc__)
p.add_option("--ghost",
default=False,
action="store_true",
help="Add ghost homologs already used [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
omgfile, groupsfile, ntaxa = args
ntaxa = int(ntaxa)
ghost = opts.ghost
# Get gene pair => weight mapping
weights = get_edges()
info = get_info()
# Get gene => taxon mapping
info = dict((k, v.split()[5]) for k, v in info.items())
groups = Grouper()
fp = open(groupsfile)
for row in fp:
members = row.strip().split(",")
groups.join(*members)
logging.debug("Imported {0} families with {1} members.".\
format(len(groups), groups.num_members))
seen = set()
omggroups = Grouper()
fp = open(omgfile)
for row in fp:
genes, idxs = row.split()
genes = genes.split(",")
seen.update(genes)
omggroups.join(*genes)
nmembers = omggroups.num_members
logging.debug("Imported {0} OMG families with {1} members.".\
format(len(omggroups), nmembers))
assert nmembers == len(seen)
alltaxa = set(str(x) for x in range(ntaxa))
recruited = []
fp = open(omgfile)
for row in fp:
genes, idxs = row.split()
genes = genes.split(",")
a = genes[0]
idxs = set(idxs.split(","))
missing_taxa = alltaxa - idxs
if not missing_taxa:
print row.rstrip()
continue
leftover = groups[a]
if not ghost:
leftover = set(leftover) - seen
if not leftover:
print row.rstrip()
continue
leftover_sorted_by_taxa = dict((k, \
[x for x in leftover if info[x] == k]) \
for k in missing_taxa)
#print genes, leftover
#print leftover_sorted_by_taxa
solutions = []
for solution in product(*leftover_sorted_by_taxa.values()):
score = sum(
weights.get((a, b), 0) for a in solution for b in genes)
if score == 0:
continue
score += sum(
weights.get((a, b), 0) for a, b in combinations(solution, 2))
solutions.append((score, solution))
#print solution, score
best_solution = max(solutions) if solutions else None
if best_solution is None:
print row.rstrip()
continue
#print "best ==>", best_solution
best_score, best_addition = best_solution
genes.extend(best_addition)
recruited.extend(best_addition)
genes = sorted([(info[x], x) for x in genes])
idxs, genes = zip(*genes)
if ghost: # decorate additions so it's clear that they were added
pgenes = []
for g in genes:
if g in recruited and g in seen:
pgenes.append("|{0}|".format(g))
else:
pgenes.append(g)
genes = pgenes
print "\t".join((",".join(genes), ",".join(idxs)))
if not ghost:
seen.update(best_addition)
logging.debug("Recruited {0} new genes.".format(len(recruited)))
def annotate(args):
"""
%prog annotate new.bed old.bed 2> log
Annotate the `new.bed` with features from `old.bed` for the purpose of
gene numbering.
Ambiguity in ID assignment can be resolved by either of the following 2 methods:
- `alignment`: make use of global sequence alignment score (calculated by `needle`)
- `overlap`: make use of overlap length (calculated by `intersectBed`)
Transfer over as many identifiers as possible while following guidelines:
http://www.arabidopsis.org/portals/nomenclature/guidelines.jsp#editing
Note: Following RegExp pattern describes the structure of the identifier
assigned to features in the `new.bed` file.
new_id_pat = re.compile(r"^\d+\.[cemtx]+\S+")
Examples: 23231.m312389, 23231.t004898, 23231.tRNA.144
Adjust the value of `new_id_pat` manually as per your ID naming conventions.
"""
from jcvi.utils.grouper import Grouper
valid_resolve_choices = ["alignment", "overlap"]
p = OptionParser(annotate.__doc__)
p.add_option("--resolve", default="alignment", choices=valid_resolve_choices,
help="Resolve ID assignment based on a certain metric" \
+ " [default: %default]")
p.add_option("--atg_name", default=False, action="store_true",
help="Specify is locus IDs in `new.bed` file follow ATG nomenclature" \
+ " [default: %default]")
g1 = OptionGroup(p, "Optional parameters (alignment):\n" \
+ "Use if resolving ambiguities based on sequence `alignment`")
g1.add_option("--pid",
dest="pid",
default=35.,
type="float",
help="Percent identity cutoff [default: %default]")
g1.add_option("--score",
dest="score",
default=250.,
type="float",
help="Alignment score cutoff [default: %default]")
p.add_option_group(g1)
g2 = OptionGroup(p, "Optional parameters (overlap):\n" \
+ "Use if resolving ambiguities based on `overlap` length\n" \
+ "Parameters equivalent to `intersectBed`")
g2.add_option(
"-f",
dest="f",
default=0.5,
type="float",
help="Minimum overlap fraction (0.0 - 1.0) [default: %default]")
g2.add_option(
"-r",
dest="r",
default=False,
action="store_true",
help="Require fraction overlap to be reciprocal [default: %default]")
g2.add_option("-s",
dest="s",
default=True,
action="store_true",
help="Require same strandedness [default: %default]")
p.add_option_group(g2)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
nbedfile, obedfile = args
npf, opf = nbedfile.rsplit(".", 1)[0], obedfile.rsplit(".", 1)[0]
# Make consolidated.bed
cbedfile = "consolidated.bed"
if not os.path.isfile(cbedfile):
consolidate(nbedfile, obedfile, cbedfile)
else:
logging.warning("`{0}` already exists. Skipping step".format(cbedfile))
logging.warning("Resolving ID assignment ambiguity based on `{0}`".\
format(opts.resolve))
if opts.resolve == "alignment":
# Get pairs and prompt to run needle
pairsfile = "nw.pairs"
scoresfile = "nw.scores"
if not os.path.isfile(pairsfile):
get_pairs(cbedfile, pairsfile)
else:
logging.warning("`{0}` already exists. Checking for needle output".\
format(pairsfile))
# If needle scores do not exist, prompt user to run needle
if not os.path.isfile(scoresfile):
logging.error("`{0}` does not exist. Please process {1} using `needle`".\
format(scoresfile, pairsfile))
sys.exit()
else:
scoresfile = "ovl.scores"
# Calculate overlap length using intersectBed
calculate_ovl(nbedfile, obedfile, opts, scoresfile)
logging.warning("`{0}' exists. Storing scores in memory".\
format(scoresfile))
scores = read_scores(scoresfile, opts)
# Iterate through consolidated bed and
# filter piles based on score
abedline = {}
cbed = Bed(cbedfile)
g = Grouper()
for c in cbed:
accn = c.accn
g.join(*accn.split(";"))
nbedline = {}
nbed = Bed(nbedfile)
for line in nbed:
nbedline[line.accn] = line
splits = set()
for chr, chrbed in nbed.sub_beds():
abedline, splits = annotate_chr(chr, chrbed, g, scores, nbedline,
abedline, opts, splits)
if splits is not None:
abedline = process_splits(splits, scores, nbedline, abedline)
abedfile = npf + ".annotated.bed"
afh = open(abedfile, "w")
for accn in abedline:
print >> afh, abedline[accn]
afh.close()
sort([abedfile, "-i"])
def segment(args):
"""
%prog segment loss.ids bedfile
Merge adjacent gene loss into segmental loss.
Then based on the segmental loss, estimate amount of DNA loss in base pairs.
Two estimates can be given:
- conservative: just within the start and end of a single gene
- aggressive: extend the deletion track to the next gene
The real deletion size is within these estimates.
"""
from jcvi.formats.base import SetFile
p = OptionParser(segment.__doc__)
p.add_option("--chain",
default=1,
type="int",
help="Allow next N genes to be chained [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
idsfile, bedfile = args
bed = Bed(bedfile)
order = bed.order
ids = SetFile(idsfile)
losses = Grouper()
skip = opts.chain
for i, a in enumerate(bed):
a = a.accn
for j in xrange(i + 1, i + 1 + skip):
if j >= len(bed):
break
b = bed[j].accn
if a in ids:
losses.join(a, a)
if a in ids and b in ids:
losses.join(a, b)
losses = list(losses)
singletons = [x for x in losses if len(x) == 1]
segments = [x for x in losses if len(x) > 1]
ns, nm, nt = len(singletons), len(segments), len(losses)
assert ns + nm == nt
# Summary for all segments
for x in sorted(singletons) + sorted(segments):
print "\t".join(
str(x) for x in ("|".join(sorted(x)), len(x),
estimate_size(x, bed, order)))
# Find longest segment stretch
if segments:
mx, maxsegment = max([(len(x), x) for x in segments])
print >> sys.stderr, "Longest stretch: run of {0} genes".format(mx)
print >> sys.stderr, " {0}".format("|".join(sorted(maxsegment)))
seg_asize = sum(estimate_size(x, bed, order) for x in segments)
seg_bsize = sum(estimate_size(x, bed, order, conservative=False) \
for x in segments)
else:
seg_asize = seg_bsize = 0
sing_asize = sum(estimate_size(x, bed, order) for x in singletons)
sing_bsize = sum(estimate_size(x, bed, order, conservative=False) \
for x in singletons)
total_asize = sing_asize + seg_asize
total_bsize = sing_bsize + seg_bsize
print >> sys.stderr, "Singleton ({0}): {1} - {2} bp".\
format(ns, sing_asize, sing_bsize)
print >> sys.stderr, "Segment ({0}): {1} - {2} bp".\
format(nm, seg_asize, seg_bsize)
print >> sys.stderr, "Total ({0}): {1} - {2} bp".\
format(nt, total_asize, total_bsize)
print >> sys.stderr, "Average ({0}): {1} bp".\
format(nt, (total_asize + total_bsize) / 2)
def group(args):
"""
%prog group tabfile > tabfile.grouped
Given a tab-delimited file, either group all elements within the file or
group the elements in the value column(s) based on the key (groupby) column
For example, convert this | into this
---------------------------------------
a 2 3 4 | a,2,3,4,5,6
a 5 6 | b,7,8
b 7 8 | c,9,10,11
c 9 |
c 10 11 |
If grouping by a particular column,
convert this | into this:
---------------------------------------------
a 2 3 4 | a 2,5 3,6 4
a 5 6 | b 7 8
b 7 8 | c 9,10 11
c 9 |
c 10 11 |
By default, it uniqifies all the grouped elements
"""
from jcvi.utils.cbook import AutoVivification
from jcvi.utils.grouper import Grouper
p = OptionParser(group.__doc__)
p.set_sep()
p.add_option("--groupby",
default=None,
type="int",
help="Default column to groupby")
p.add_option("--groupsep",
default=",",
help="Separator to join the grouped elements")
p.add_option(
"--nouniq",
default=False,
action="store_true",
help="Do not uniqify the grouped elements",
)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
(tabfile, ) = args
sep = opts.sep
groupby = opts.groupby
groupsep = opts.groupsep
cols = []
grouper = AutoVivification() if groupby is not None else Grouper()
fp = must_open(tabfile)
for row in fp:
row = row.rstrip()
atoms = row.split(sep)
if groupby is not None:
if len(cols) < len(atoms):
cols = [x for x in range(len(atoms))]
if groupby not in cols:
logging.error(
"groupby col index `{0}` is out of range".format(groupby))
sys.exit()
key = atoms[groupby]
for col in cols:
if col == groupby:
continue
if not grouper[key][col]:
grouper[key][col] = [] if opts.nouniq else set()
if col < len(atoms):
if groupsep in atoms[col]:
for atom in atoms[col].split(groupsep):
if opts.nouniq:
grouper[key][col].append(atom)
else:
grouper[key][col].add(atom)
else:
if opts.nouniq:
grouper[key][col].append(atoms[col])
else:
grouper[key][col].add(atoms[col])
else:
grouper.join(*atoms)
for key in grouper:
if groupby is not None:
line = []
for col in cols:
if col == groupby:
line.append(key)
elif col in grouper[key].keys():
line.append(groupsep.join(grouper[key][col]))
else:
line.append("na")
print(sep.join(line))
else:
print(groupsep.join(key))
def consolidate(args):
"""
%prog consolidate gffile1 gffile2 ... > consolidated.out
Given 2 or more gff files generated by pasa annotation comparison,
iterate through each locus (shared locus name or overlapping CDS)
and identify same/different isoforms (shared splicing structure)
across the input datasets.
If `slop` is enabled, consolidation will collapse any variation
in terminal UTR lengths, keeping the longest as representative.
"""
from jcvi.formats.base import longest_unique_prefix
from jcvi.formats.gff import make_index, match_subfeats
from jcvi.utils.cbook import AutoVivification
from jcvi.utils.grouper import Grouper
from itertools import combinations, product
supported_modes = ["name", "coords"]
p = OptionParser(consolidate.__doc__)
p.add_option("--slop", default=False, action="store_true",
help="allow minor variation in terminal 5'/3' UTR" + \
" start/stop position [default: %default]")
p.add_option("--inferUTR", default=False, action="store_true",
help="infer presence of UTRs from exon coordinates")
p.add_option("--mode", default="name", choices=supported_modes,
help="method used to determine overlapping loci")
p.add_option("--summary", default=False, action="store_true",
help="Generate summary table of consolidation process")
p.add_option("--clusters", default=False, action="store_true",
help="Generate table of cluster members after consolidation")
p.set_outfile()
opts, args = p.parse_args(args)
slop = opts.slop
inferUTR = opts.inferUTR
mode = opts.mode
if len(args) < 2:
sys.exit(not p.print_help())
gffdbx = {}
for gffile in args:
dbn = longest_unique_prefix(gffile, args)
gffdbx[dbn] = make_index(gffile)
loci = Grouper()
for dbn in gffdbx:
odbns = [odbn for odbn in gffdbx if dbn != odbn]
for gene in gffdbx[dbn].features_of_type('gene', order_by=('seqid', 'start')):
if mode == "name":
loci.join(gene.id, (gene.id, dbn))
else:
if (gene.id, dbn) not in loci:
loci.join((gene.id, dbn))
gene_cds = list(gffdbx[dbn].children(gene, \
featuretype='CDS', order_by=('start')))
gene_cds_start, gene_cds_stop = gene_cds[0].start, \
gene_cds[-1].stop
for odbn in odbns:
for ogene_cds in gffdbx[odbn].region(seqid=gene.seqid, \
start=gene_cds_start, end=gene_cds_stop, \
strand=gene.strand, featuretype='CDS'):
for ogene in gffdbx[odbn].parents(ogene_cds, featuretype='gene'):
loci.join((gene.id, dbn), (ogene.id, odbn))
gfeats = {}
mrna = AutoVivification()
for i, locus in enumerate(loci):
gene = "gene_{0:0{pad}}".format(i, pad=6) \
if mode == "coords" else None
for elem in locus:
if type(elem) == tuple:
_gene, dbn = elem
if gene is None: gene = _gene
g = gffdbx[dbn][_gene]
if gene not in gfeats:
gfeats[gene] = g
gfeats[gene].attributes['ID'] = [gene]
else:
if g.start < gfeats[gene].start:
gfeats[gene].start = g.start
if g.stop > gfeats[gene].stop:
gfeats[gene].stop = g.stop
c = list(gffdbx[dbn].children(_gene, featuretype='mRNA', order_by='start'))
if len(c) > 0:
mrna[gene][dbn] = c
fw = must_open(opts.outfile, "w")
print("##gff-version 3", file=fw)
seen = {}
if opts.summary:
summaryfile = "{0}.summary.txt".format(opts.outfile.rsplit(".")[0])
sfw = must_open(summaryfile, "w")
summary = ["id"]
summary.extend(gffdbx.keys())
print("\t".join(str(x) for x in summary), file=sfw)
if opts.clusters:
clustersfile = "{0}.clusters.txt".format(opts.outfile.rsplit(".")[0])
cfw = must_open(clustersfile, "w")
clusters = ["id", "dbns", "members", "trlens"]
print("\t".join(str(x) for x in clusters), file=cfw)
for gene in mrna:
g = Grouper()
dbns = list(combinations(mrna[gene], 2))
if len(dbns) > 0:
for dbn1, dbn2 in dbns:
dbx1, dbx2 = gffdbx[dbn1], gffdbx[dbn2]
for mrna1, mrna2 in product(mrna[gene][dbn1], mrna[gene][dbn2]):
mrna1s, mrna2s = mrna1.stop - mrna1.start + 1, \
mrna2.stop - mrna2.start + 1
g.join((dbn1, mrna1.id, mrna1s))
g.join((dbn2, mrna2.id, mrna2s))
if match_subfeats(mrna1, mrna2, dbx1, dbx2, featuretype='CDS'):
res = []
ftypes = ['exon'] if inferUTR else ['five_prime_UTR', 'three_prime_UTR']
for ftype in ftypes:
res.append(match_subfeats(mrna1, mrna2, dbx1, dbx2, featuretype=ftype, slop=slop))
if all(r == True for r in res):
g.join((dbn1, mrna1.id, mrna1s), (dbn2, mrna2.id, mrna2s))
else:
for dbn1 in mrna[gene]:
for mrna1 in mrna[gene][dbn1]:
g.join((dbn1, mrna1.id, mrna1.stop - mrna1.start + 1))
print(gfeats[gene], file=fw)
for group in g:
group.sort(key=lambda x: x[2], reverse=True)
dbs, mrnas = [el[0] for el in group], [el[1] for el in group]
d, m = dbs[0], mrnas[0]
dbid, _mrnaid = "|".join(str(x) for x in set(dbs)), []
for x in mrnas:
if x not in _mrnaid: _mrnaid.append(x)
mrnaid = "{0}|{1}".format(dbid, "-".join(_mrnaid))
if mrnaid not in seen:
seen[mrnaid] = 0
else:
seen[mrnaid] += 1
mrnaid = "{0}-{1}".format(mrnaid, seen[mrnaid])
_mrna = gffdbx[d][m]
_mrna.attributes['ID'] = [mrnaid]
_mrna.attributes['Parent'] = [gene]
children = gffdbx[d].children(m, order_by='start')
print(_mrna, file=fw)
for child in children:
child.attributes['ID'] = ["{0}|{1}".format(dbid, child.id)]
child.attributes['Parent'] = [mrnaid]
print(child, file=fw)
if opts.summary:
summary = [mrnaid]
summary.extend(['Y' if db in set(dbs) else 'N' for db in gffdbx])
print("\t".join(str(x) for x in summary), file=sfw)
if opts.clusters:
clusters = [mrnaid]
clusters.append(",".join(str(el[0]) for el in group))
clusters.append(",".join(str(el[1]) for el in group))
clusters.append(",".join(str(el[2]) for el in group))
print("\t".join(str(x) for x in clusters), file=cfw)
fw.close()
if opts.summary: sfw.close()
if opts.clusters: cfw.close()