def print_stats(qrycovered, refcovered, id_pct):
from jcvi.utils.cbook import thousands
m1 = "Reference coverage: {0} bp".format(thousands(refcovered))
m2 = "Query coverage: {0} bp".format(thousands(qrycovered))
m3 = "Identity: {0:.1f}%".format(id_pct)
print >> sys.stderr, "\n".join((m1, m2, m3))
def print_stats(qrycovered, refcovered, id_pct):
from jcvi.utils.cbook import thousands
m1 = "Reference coverage: {0} bp".format(thousands(refcovered))
m2 = "Query coverage: {0} bp".format(thousands(qrycovered))
m3 = "Identity: {0:.1f}%".format(id_pct)
print >> sys.stderr, "\n".join((m1, m2, m3))
def summary(args):
"""
%prog summary bedfile
Sum the total lengths of the intervals.
"""
from jcvi.utils.cbook import SummaryStats
p = OptionParser(summary.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(p.print_help())
bedfile, = args
bed = Bed(bedfile)
stats = SummaryStats([x.span for x in bed])
print >> sys.stderr, "Total seqids: {0}".format(len(bed.seqids))
print >> sys.stderr, "Total ranges: {0}".format(len(bed))
total_bases = bed.sum(unique=False)
unique_bases = bed.sum()
print >> sys.stderr, "Total unique bases: {0} bp".format(
thousands(unique_bases))
print >> sys.stderr, "Total bases: {0} bp".format(thousands(total_bases))
print >> sys.stderr, "Estimated coverage: {0:.1f}x".\
format(total_bases * 1. / unique_bases)
print >> sys.stderr, stats
def summary(args):
"""
%prog summary bedfile
Sum the total lengths of the intervals.
"""
from jcvi.utils.cbook import SummaryStats
p = OptionParser(summary.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(p.print_help())
bedfile, = args
bed = Bed(bedfile)
stats = SummaryStats([x.span for x in bed])
print >> sys.stderr, "Total seqids: {0}".format(len(bed.seqids))
print >> sys.stderr, "Total ranges: {0}".format(len(bed))
total_bases = bed.sum(unique=False)
unique_bases = bed.sum()
print >> sys.stderr, "Total unique bases: {0} bp".format(thousands(unique_bases))
print >> sys.stderr, "Total bases: {0} bp".format(thousands(total_bases))
print >> sys.stderr, "Estimated coverage: {0:.1f}x".\
format(total_bases * 1. / unique_bases)
print >> sys.stderr, stats
def summary(args):
"""
%prog summary bedfile
Sum the total lengths of the intervals.
"""
import numpy as np
p = OptionParser(summary.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(p.print_help())
bedfile, = args
bed = Bed(bedfile)
spans = np.array([x.span for x in bed])
avg = int(np.average(spans))
std = int(np.std(spans))
print >> sys.stderr, "Total seqids: {0}".format(len(bed.seqids))
print >> sys.stderr, "Total ranges: {0}".format(len(bed))
total_bases = bed.sum(unique=False)
unique_bases = bed.sum()
print >> sys.stderr, "Total unique bases: {0} bp".format(thousands(unique_bases))
print >> sys.stderr, "Total bases: {0} bp".format(thousands(total_bases))
print >> sys.stderr, "Estimated coverage: {0:.1f}x".format(total_bases * 1.0 / unique_bases)
print >> sys.stderr, "Average spans: {0}, stdev: {1}".format(avg, std)
def report(self):
print >> sys.stderr, "Total seqids: {0}".format(self.nseqids)
print >> sys.stderr, "Total ranges: {0}".format(self.nfeats)
print >> sys.stderr, "Total unique bases: {0} bp".format(thousands(self.unique_bases))
print >> sys.stderr, "Total bases: {0} bp".format(thousands(self.total_bases))
print >> sys.stderr, "Estimated coverage: {0:.1f}x".format(self.coverage)
print >> sys.stderr, self.stats
maxspan, maxaccn = max(self.mspans)
minspan, minaccn = min(self.mspans)
print >> sys.stderr, "Longest: {0} ({1})".format(maxaccn, maxspan)
print >> sys.stderr, "Shortest: {0} ({1})".format(minaccn, minspan)
def fillstats(args):
"""
%prog fillstats genome.fill
Build stats on .fill file from GapCloser.
"""
from jcvi.utils.cbook import SummaryStats, percentage, thousands
p = OptionParser(fillstats.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
(fillfile, ) = args
fp = open(fillfile)
scaffolds = 0
gaps = []
for row in fp:
if row[0] == ">":
scaffolds += 1
continue
fl = FillLine(row)
gaps.append(fl)
print("{0} scaffolds in total".format(scaffolds), file=sys.stderr)
closed = [x for x in gaps if x.closed]
closedbp = sum(x.before for x in closed)
notClosed = [x for x in gaps if not x.closed]
notClosedbp = sum(x.before for x in notClosed)
totalgaps = len(closed) + len(notClosed)
print(
"Closed gaps: {0} size: {1} bp".format(
percentage(len(closed), totalgaps), thousands(closedbp)),
file=sys.stderr,
)
ss = SummaryStats([x.after for x in closed])
print(ss, file=sys.stderr)
ss = SummaryStats([x.delta for x in closed])
print("Delta:", ss, file=sys.stderr)
print(
"Remaining gaps: {0} size: {1} bp".format(
percentage(len(notClosed), totalgaps), thousands(notClosedbp)),
file=sys.stderr,
)
ss = SummaryStats([x.after for x in notClosed])
print(ss, file=sys.stderr)
def report(self):
print >> sys.stderr, "Total seqids: {0}".format(self.nseqids)
print >> sys.stderr, "Total ranges: {0}".format(self.nfeats)
print >> sys.stderr, "Total unique bases: {0} bp".format(
thousands(self.unique_bases))
print >> sys.stderr, "Total bases: {0} bp".format(
thousands(self.total_bases))
print >> sys.stderr, "Estimated coverage: {0:.1f}x".format(
self.coverage)
print >> sys.stderr, self.stats
maxspan, maxaccn = max(self.mspans)
minspan, minaccn = min(self.mspans)
print >> sys.stderr, "Longest: {0} ({1})".format(maxaccn, maxspan)
print >> sys.stderr, "Shortest: {0} ({1})".format(minaccn, minspan)
def fillstats(args):
"""
%prog fillstats genome.fill
Build stats on .fill file from GapCloser.
"""
from jcvi.utils.cbook import SummaryStats, percentage, thousands
p = OptionParser(fillstats.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
fillfile, = args
fp = open(fillfile)
scaffolds = 0
gaps = []
for row in fp:
if row[0] == ">":
scaffolds += 1
continue
fl = FillLine(row)
gaps.append(fl)
print >> sys.stderr, "{0} scaffolds in total".format(scaffolds)
closed = [x for x in gaps if x.closed]
closedbp = sum(x.before for x in closed)
notClosed = [x for x in gaps if not x.closed]
notClosedbp = sum(x.before for x in notClosed)
totalgaps = len(closed) + len(notClosed)
totalbp = closedbp + notClosedbp
print >> sys.stderr, "Closed gaps: {0} size: {1} bp".\
format(percentage(len(closed), totalgaps), thousands(closedbp))
ss = SummaryStats([x.after for x in closed])
print >> sys.stderr, ss
ss = SummaryStats([x.delta for x in closed])
print >> sys.stderr, "Delta:", ss
print >> sys.stderr, "Remaining gaps: {0} size: {1} bp".\
format(percentage(len(notClosed), totalgaps), thousands(notClosedbp))
ss = SummaryStats([x.after for x in notClosed])
print >> sys.stderr, ss
def summary(args):
"""
%prog summary bedfile
Sum the total lengths of the intervals.
"""
from jcvi.utils.cbook import SummaryStats
p = OptionParser(summary.__doc__)
p.add_option("--sizes", default=False, action="store_true",
help="Write .sizes file")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
bedfile, = args
bed = Bed(bedfile)
mspans = [(x.span, x.accn) for x in bed]
if opts.sizes:
sizesfile = bedfile + ".sizes"
fw = open(sizesfile, "w")
for span, accn in mspans:
print >> fw, span
fw.close()
logging.debug("Spans written to `{0}`.".format(sizesfile))
spans, accns = zip(*mspans)
stats = SummaryStats(spans)
print >> sys.stderr, "Total seqids: {0}".format(len(bed.seqids))
print >> sys.stderr, "Total ranges: {0}".format(len(bed))
total_bases = bed.sum(unique=False)
unique_bases = bed.sum()
print >> sys.stderr, "Total unique bases: {0} bp".format(thousands(unique_bases))
print >> sys.stderr, "Total bases: {0} bp".format(thousands(total_bases))
print >> sys.stderr, "Estimated coverage: {0:.1f}x".\
format(total_bases * 1. / unique_bases)
print >> sys.stderr, stats
maxspan, maxaccn = max(mspans)
minspan, minaccn = min(mspans)
print >> sys.stderr, "Longest: {0} ({1})".format(maxaccn, maxspan)
print >> sys.stderr, "Shortest: {0} ({1})".format(minaccn, minspan)
def scaffold(args):
"""
%prog scaffold scaffold.fasta synteny.blast synteny.sizes synteny.bed
physicalmap.blast physicalmap.sizes physicalmap.bed
As evaluation of scaffolding, visualize external line of evidences:
* Plot synteny to an external genome
* Plot alignments to physical map
* Plot alignments to genetic map (TODO)
Each trio defines one panel to be plotted. blastfile defines the matchings
between the evidences vs scaffolds. Then the evidence sizes, and evidence
bed to plot dot plots.
This script will plot a dot in the dot plot in the corresponding location
the plots are one contig/scaffold per plot.
"""
from jcvi.graphics.base import set_image_options
from jcvi.utils.iter import grouper
p = OptionParser(scaffold.__doc__)
p.add_option("--cutoff", type="int", default=1000000,
help="Plot scaffolds with size larger than [default: %default]")
p.add_option("--highlights",
help="A set of regions in BED format to highlight [default: %default]")
opts, args, iopts = set_image_options(p, args, figsize="14x8", dpi=150)
if len(args) < 4 or len(args) % 3 != 1:
sys.exit(not p.print_help())
highlights = opts.highlights
scafsizes = Sizes(args[0])
trios = list(grouper(3, args[1:]))
trios = [(a, Sizes(b), Bed(c)) for a, b, c in trios]
if highlights:
hlbed = Bed(highlights)
for scaffoldID, scafsize in scafsizes.iter_sizes():
if scafsize < opts.cutoff:
continue
logging.debug("Loading {0} (size={1})".format(scaffoldID,
thousands(scafsize)))
tmpname = scaffoldID + ".sizes"
tmp = open(tmpname, "w")
tmp.write("{0}\t{1}".format(scaffoldID, scafsize))
tmp.close()
tmpsizes = Sizes(tmpname)
tmpsizes.close(clean=True)
if highlights:
subhighlights = list(hlbed.sub_bed(scaffoldID))
imagename = ".".join((scaffoldID, opts.format))
plot_one_scaffold(scaffoldID, tmpsizes, None, trios, imagename, iopts,
highlights=subhighlights)
def plot_one_scaffold(scaffoldID, ssizes, sbed, trios, imagename, iopts,
highlights=None):
ntrios = len(trios)
fig = plt.figure(1, (14, 8))
plt.cla()
plt.clf()
root = fig.add_axes([0, 0, 1, 1])
axes = [fig.add_subplot(1, ntrios, x) for x in range(1, ntrios + 1)]
scafsize = ssizes.get_size(scaffoldID)
for trio, ax in zip(trios, axes):
blastf, qsizes, qbed = trio
scaffolding(ax, scaffoldID, blastf, qsizes, ssizes, qbed, sbed,
highlights=highlights)
root.text(.5, .95, "{0} (size={1})".format(scaffoldID, thousands(scafsize)),
size=18, ha="center", color='b')
root.set_xlim(0, 1)
root.set_ylim(0, 1)
root.set_axis_off()
savefig(imagename, dpi=iopts.dpi, iopts=iopts)
def plot_one_scaffold(scaffoldID,
ssizes,
sbed,
trios,
imagename,
iopts,
highlights=None):
ntrios = len(trios)
fig = plt.figure(1, (14, 8))
plt.cla()
plt.clf()
root = fig.add_axes([0, 0, 1, 1])
axes = [fig.add_subplot(1, ntrios, x) for x in range(1, ntrios + 1)]
scafsize = ssizes.get_size(scaffoldID)
for trio, ax in zip(trios, axes):
blastf, qsizes, qbed = trio
scaffolding(ax,
scaffoldID,
blastf,
qsizes,
ssizes,
qbed,
sbed,
highlights=highlights)
root.text(
0.5,
0.95,
"{0} (size={1})".format(scaffoldID, thousands(scafsize)),
size=18,
ha="center",
color="b",
)
root.set_xlim(0, 1)
root.set_ylim(0, 1)
root.set_axis_off()
savefig(imagename, dpi=iopts.dpi, iopts=iopts)
def analyze_nbinom(self, K=23, maxiter=100):
"""Analyze the K-mer histogram using negative binomial distribution.
Args:
K (int, optional): K-mer size used when generating the histogram. Defaults to 23.
"""
from scipy.stats import nbinom
from scipy.optimize import minimize_scalar
from functools import lru_cache
method, xopt = "bounded", "xatol"
MAX_1CN_SIZE = 1e10
MAX_OPTIMIZED_SIZE = 9.9e9
# Generate bins for the decomposed negative binomial distributions
bins = [(i, i) for i in range(1, 9)
] # The first 8 CN are critical often determins ploidy
for i in (8, 16, 32, 64, 128, 256, 512): # 14 geometricly sized bins
a, b = i + 1, int(round(i * 2**0.5))
bins.append((a, b))
a, b = b + 1, i * 2
bins.append((a, b))
# Convert histogram to np array so we can index by CN
kf_ceil = max([cov for cov, _ in self.data])
N = kf_ceil + 1
hist = np.zeros(N, dtype=np.int)
for cov, count in self.data:
hist[cov] = count
# min1: find first minimum
_kf_min1 = 10
while _kf_min1 - 1 >= 2 and hist[_kf_min1 - 1] < hist[_kf_min1]:
_kf_min1 -= 1
while _kf_min1 <= kf_ceil and hist[_kf_min1 + 1] < hist[_kf_min1]:
_kf_min1 += 1
# max2: find absolute maximum mx2 above first minimum min1
_kf_max2 = _kf_min1
for kf in range(_kf_min1 + 1, int(0.8 * kf_ceil)):
if hist[kf] > hist[_kf_max2]:
_kf_max2 = kf
# Discard the last entry as that is usually an inflated number
hist = hist[:-1]
kf_range = np.arange(_kf_min1, len(hist), dtype=np.int)
P = hist[kf_range] * kf_range # Target distribution
print("==> Start nbinom method on range ({}, {})".format(
_kf_min1, len(hist)))
# Below is the optimization schemes, we optimize one variable at a time
@lru_cache(maxsize=None)
def nbinom_pmf_range(lambda_: int, rho: int, bin_id: int):
stacked = np.zeros(len(kf_range), dtype=np.float64)
lambda_ /= 100 # 2-digit precision
rho /= 100 # 2-digit precision
n = lambda_ / (rho - 1)
p = 1 / rho
start, end = bins[bin_id]
for i in range(start, end + 1):
stacked += nbinom.pmf(kf_range, n * i, p)
return stacked
def generative_model(G, lambda_, rho):
stacked = np.zeros(len(kf_range), dtype=np.float64)
lambda_ = int(round(lambda_ * 100))
rho = int(round(rho * 100))
for bin_id, g in enumerate(G):
stacked += g * nbinom_pmf_range(lambda_, rho, bin_id)
stacked *= kf_range
return stacked
def func(lambda_, rho, G):
stacked = generative_model(G, lambda_, rho)
return np.sum((P - stacked)**2) # L2 norm
def optimize_func(lambda_, rho, G):
# Iterate over all G
for i, g in enumerate(G):
G_i = optimize_func_Gi(lambda_, rho, G, i)
if (not 1 < G_i < MAX_OPTIMIZED_SIZE
): # Optimizer did not optimize this G_i
break
# Also remove the last bin since it is subject to marginal effect
G[i - 1] = 0
lambda_ = optimize_func_lambda_(lambda_, rho, G)
rho = optimize_func_rho(lambda_, rho, G)
score = func(lambda_, rho, G)
return lambda_, rho, G, score
def optimize_func_lambda_(lambda_, rho, G):
def f(arg):
return func(arg, rho, G)
res = minimize_scalar(f,
bounds=(_kf_min1, 100),
method=method,
options={xopt: 0.01})
return res.x
def optimize_func_rho(lambda_, rho, G):
def f(arg):
return func(lambda_, arg, G)
res = minimize_scalar(f,
bounds=(1.001, 5),
method=method,
options={xopt: 0.01})
return res.x
def optimize_func_Gi(lambda_, rho, G, i):
# Iterate a single G_i
def f(arg):
G[i] = arg
return func(lambda_, rho, G)
res = minimize_scalar(f,
bounds=(0, MAX_1CN_SIZE),
method=method,
options={xopt: 100})
return res.x
def run_optimization(termination=0.999, maxiter=100):
ll, rr, GG = l0, r0, G0
prev_score = np.inf
for i in range(maxiter):
print("Iteration", i + 1, file=sys.stderr)
ll, rr, GG, score = optimize_func(ll, rr, GG)
if score / prev_score > termination:
break
prev_score = score
if i % 10 == 0:
print(ll, rr, GG, score, file=sys.stderr)
print("Success!", file=sys.stderr)
# Remove bogus values that are close to the bounds
final_GG = [g for g in GG if 1 < g < MAX_OPTIMIZED_SIZE]
return ll, rr, final_GG
# Optimization - very slow
G0 = np.zeros(len(bins))
l0 = _kf_max2
r0 = 1.5
print(l0, r0, G0, file=sys.stderr)
ll, rr, GG = run_optimization(maxiter=maxiter)
print(ll, rr, GG, file=sys.stderr)
# Ready for genome summary
m = "\n==> Kmer (K={0}) Spectrum Analysis\n".format(K)
genome_size = int(round(self.totalKmers / ll))
inferred_genome_size = 0
for i, g in enumerate(GG):
start, end = bins[i]
mid = (start + end) / 2
inferred_genome_size += g * mid * (end - start + 1)
inferred_genome_size = int(round(inferred_genome_size))
genome_size = max(genome_size, inferred_genome_size)
m += "Genome size estimate = {0}\n".format(thousands(genome_size))
copy_series = []
copy_messages = []
for i, g in enumerate(GG):
start, end = bins[i]
mid = (start + end) / 2
copy_num = start if start == end else "{}-{}".format(start, end)
g_copies = int(round(g * mid * (end - start + 1)))
copy_series.append((mid, copy_num, g_copies, g))
copy_message = "CN {}: {:.1f} Mb ({:.1f} percent)".format(
copy_num, g_copies / 1e6, g_copies * 100 / genome_size)
copy_messages.append(copy_message)
m += copy_message + "\n"
if genome_size > inferred_genome_size:
g_copies = genome_size - inferred_genome_size
copy_num = "{}+".format(end + 1)
copy_series.append(
(end + 1, copy_num, g_copies, g_copies / (end + 1)))
m += "CN {}: {:.1f} Mb ({:.1f} percent)\n".format(
copy_num, g_copies / 1e6, g_copies * 100 / genome_size)
# Determine ploidy
def determine_ploidy(copy_series, threshold=0.15):
counts_so_far = 1
ploidy_so_far = 0
for mid, copy_num, g_copies, g in copy_series:
if g_copies / counts_so_far < threshold:
break
counts_so_far += g_copies
ploidy_so_far = mid
return int(ploidy_so_far)
ploidy = determine_ploidy(copy_series)
self.ploidy = ploidy
self.ploidy_message = "Ploidy: {}".format(ploidy)
m += self.ploidy_message + "\n"
self.copy_messages = copy_messages[:ploidy]
# Repeat content
def calc_repeats(copy_series, ploidy, genome_size):
unique = 0
for mid, copy_num, g_copies, g in copy_series:
if mid <= ploidy:
unique += g_copies
else:
break
return 1 - unique / genome_size
repeats = calc_repeats(copy_series, ploidy, genome_size)
self.repetitive = "Repeats: {:.1f} percent".format(repeats * 100)
m += self.repetitive + "\n"
# SNP rate
def calc_snp_rate(copy_series, ploidy, genome_size, K):
# We can calculate the SNP rate s, assuming K-mer of length K:
# s = 1-(1-L/G)^(1/K)
# L: # of unique K-mers under 'het' peak
# G: genome size
# K: K-mer length
L = 0
for mid, copy_num, g_copies, g in copy_series:
if mid < ploidy:
L += g
else:
break
return 1 - (1 - L / genome_size)**(1 / K)
snp_rate = calc_snp_rate(copy_series, ploidy, genome_size, K)
self.snprate = "SNP rate: {:.2f} percent".format(snp_rate * 100)
m += self.snprate + "\n"
print(m, file=sys.stderr)
self.lambda_ = ll
return {
"generative_model": generative_model,
"Gbins": GG,
"lambda": ll,
"rho": rr,
"kf_range": kf_range,
}
def histogram(args):
"""
%prog histogram meryl.histogram species K
Plot the histogram based on meryl K-mer distribution, species and N are
only used to annotate the graphic.
"""
p = OptionParser(histogram.__doc__)
p.add_option(
"--vmin",
dest="vmin",
default=1,
type="int",
help="minimum value, inclusive",
)
p.add_option(
"--vmax",
dest="vmax",
default=100,
type="int",
help="maximum value, inclusive",
)
p.add_option(
"--pdf",
default=False,
action="store_true",
help="Print PDF instead of ASCII plot",
)
p.add_option(
"--method",
choices=("nbinom", "allpaths"),
default="nbinom",
help=
"'nbinom' - slow but more accurate for het or polyploid genome; 'allpaths' - fast and works for homozygous enomes",
)
p.add_option(
"--maxiter",
default=100,
type="int",
help="Max iterations for optimization. Only used with --method nbinom",
)
p.add_option("--coverage",
default=0,
type="int",
help="Kmer coverage [default: auto]")
p.add_option(
"--nopeaks",
default=False,
action="store_true",
help="Do not annotate K-mer peaks",
)
opts, args, iopts = p.set_image_options(args, figsize="7x7")
if len(args) != 3:
sys.exit(not p.print_help())
histfile, species, N = args
method = opts.method
vmin, vmax = opts.vmin, opts.vmax
ascii = not opts.pdf
peaks = not opts.nopeaks and method == "allpaths"
N = int(N)
if histfile.rsplit(".", 1)[-1] in ("mcdat", "mcidx"):
logging.debug("CA kmer index found")
histfile = merylhistogram(histfile)
ks = KmerSpectrum(histfile)
method_info = ks.analyze(K=N, maxiter=opts.maxiter, method=method)
Total_Kmers = int(ks.totalKmers)
coverage = opts.coverage
Kmer_coverage = ks.lambda_ if not coverage else coverage
Genome_size = int(round(Total_Kmers * 1.0 / Kmer_coverage))
Total_Kmers_msg = "Total {0}-mers: {1}".format(N, thousands(Total_Kmers))
Kmer_coverage_msg = "{0}-mer coverage: {1:.1f}x".format(N, Kmer_coverage)
Genome_size_msg = "Estimated genome size: {0:.1f} Mb".format(Genome_size /
1e6)
Repetitive_msg = ks.repetitive
SNPrate_msg = ks.snprate
for msg in (Total_Kmers_msg, Kmer_coverage_msg, Genome_size_msg):
print(msg, file=sys.stderr)
x, y = ks.get_xy(vmin, vmax)
title = "{0} {1}-mer histogram".format(species, N)
if ascii:
asciiplot(x, y, title=title)
return Genome_size
plt.figure(1, (iopts.w, iopts.h))
plt.bar(x, y, fc="#b2df8a", lw=0)
# Plot the negative binomial fit
if method == "nbinom":
generative_model = method_info["generative_model"]
GG = method_info["Gbins"]
ll = method_info["lambda"]
rr = method_info["rho"]
kf_range = method_info["kf_range"]
stacked = generative_model(GG, ll, rr)
plt.plot(
kf_range,
stacked,
":",
color="#6a3d9a",
lw=2,
)
ax = plt.gca()
if peaks: # Only works for method 'allpaths'
t = (ks.min1, ks.max1, ks.min2, ks.max2, ks.min3)
tcounts = [(x, y) for x, y in ks.counts if x in t]
if tcounts:
x, y = zip(*tcounts)
tcounts = dict(tcounts)
plt.plot(x, y, "ko", lw=3, mec="k", mfc="w")
ax.text(ks.max1, tcounts[ks.max1], "SNP peak")
ax.text(ks.max2, tcounts[ks.max2], "Main peak")
ymin, ymax = ax.get_ylim()
ymax = ymax * 7 / 6
if method == "nbinom":
# Plot multiple CN locations, CN1, CN2, ... up to ploidy
cn_color = "#a6cee3"
for i in range(1, ks.ploidy + 1):
x = i * ks.lambda_
plt.plot((x, x), (0, ymax), "-.", color=cn_color)
plt.text(
x,
ymax * 0.95,
"CN{}".format(i),
ha="right",
va="center",
color=cn_color,
rotation=90,
)
messages = [
Total_Kmers_msg,
Kmer_coverage_msg,
Genome_size_msg,
Repetitive_msg,
SNPrate_msg,
]
if method == "nbinom":
messages += [ks.ploidy_message] + ks.copy_messages
write_messages(ax, messages)
ax.set_title(markup(title))
ax.set_xlim((0, vmax))
ax.set_ylim((0, ymax))
adjust_spines(ax, ["left", "bottom"], outward=True)
xlabel, ylabel = "Coverage (X)", "Counts"
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
set_human_axis(ax)
imagename = histfile.split(".")[0] + "." + iopts.format
savefig(imagename, dpi=100)
return Genome_size
def analyze_allpaths(self, ploidy=2, K=23, covmax=1000000):
"""
Analyze Kmer spectrum, calculations derived from
allpathslg/src/kmers/KmerSpectra.cc
"""
from math import sqrt
data = self.data
kf_ceil = max(K for (K, c) in data)
if kf_ceil > covmax:
exceeds = sum(1 for (K, c) in data if K > covmax)
logging.debug("A total of {0} distinct K-mers appear > "
"{1} times. Ignored ...".format(exceeds, covmax))
kf_ceil = covmax
nkf = kf_ceil + 1
a = [0] * nkf
for kf, c in data:
if kf > kf_ceil:
continue
a[kf] = c
ndk = a # number of distinct kmers
nk = [k * c for k, c in enumerate(a)] # number of kmers
cndk = [0] * nkf # cumulative number of distinct kmers
cnk = [0] * nkf # cumulative number of kmers
for kf in range(1, nkf):
cndk[kf] = cndk[kf - 1] + 0.5 * (ndk[kf - 1] + ndk[kf])
cnk[kf] = cnk[kf - 1] + 0.5 * (nk[kf - 1] + nk[kf])
# Separate kmer spectrum in 5 regions based on the kf
# 1 ... kf_min1 : bad kmers with low frequency
# kf_min1 ... kf_min2 : good kmers CN = 1/2 (SNPs)
# kf_min2 ... kf_min3 : good kmers CN = 1
# kf_min3 ... kf_hi : good kmers CN > 1 (repetitive)
# kf_hi ... inf : bad kmers with high frequency
# min1: find first minimum
_kf_min1 = 10
while _kf_min1 - 1 >= 2 and nk[_kf_min1 - 1] < nk[_kf_min1]:
_kf_min1 -= 1
while _kf_min1 <= kf_ceil and nk[_kf_min1 + 1] < nk[_kf_min1]:
_kf_min1 += 1
# max2: find absolute maximum mx2 above first minimum min1
_kf_max2 = _kf_min1
for kf in range(_kf_min1 + 1, int(0.8 * kf_ceil)):
if nk[kf] > nk[_kf_max2]:
_kf_max2 = kf
# max2: resetting max2 for cases of very high polymorphism
if ploidy == 2:
ndk_half = ndk[_kf_max2 // 2]
ndk_double = ndk[_kf_max2 * 2]
if ndk_double > ndk_half:
_kf_max2 *= 2
# max1: SNPs local maximum max1 as half global maximum max2
_kf_max1 = _kf_max2 // 2
# min2: SNPs local minimum min2 between max1 and max2
_kf_min2 = (_kf_max1 * (2 * ndk[_kf_max1] + ndk[_kf_max2]) //
(ndk[_kf_max1] + ndk[_kf_max2]))
# min1: refine between min1 and max2/2
for kf in range(_kf_min1 + 1, _kf_max1):
if nk[kf] < nk[_kf_min1]:
_kf_min1 = kf
# min3: not a minimum, really. upper edge of main peak
_kf_min3 = _kf_max2 * 3 // 2
print("kfs:",
_kf_min1,
_kf_max1,
_kf_min2,
_kf_max2,
_kf_min3,
file=sys.stderr)
self.min1 = _kf_min1
self.max1 = _kf_max1
self.min2 = _kf_min2
self.max2 = _kf_max2
self.min3 = _kf_min3
self.lambda_ = self.max2 # Main peak
# Define maximum kf above which we neglect data
_kf_hi = (_kf_max2 * sqrt(4 * ndk[2 * _kf_max2] * _kf_max2)
if 2 * _kf_max2 < len(ndk) else _kf_max2 *
sqrt(4 * ndk[len(ndk) - 1] * _kf_max2))
_kf_hi = int(_kf_hi)
if _kf_hi > kf_ceil:
_kf_hi = kf_ceil
_nk_total = cnk[len(cnk) - 1]
_nk_bad_low_kf = cnk[_kf_min1]
_nk_good_uniq = cnk[_kf_min3] - cnk[_kf_min2]
_nk_bad_high_kf = _nk_total - cnk[_kf_hi]
_ndk_good_snp = cndk[_kf_min2] - cndk[_kf_min1]
_ndk_good_uniq = cndk[_kf_min3] - cndk[_kf_min2]
# kmer coverage C_k
_kf_ave_uniq = _nk_good_uniq * 1.0 / _ndk_good_uniq
_genome_size = (_nk_total - _nk_bad_low_kf -
_nk_bad_high_kf) / _kf_ave_uniq
_genome_size_unique = _ndk_good_uniq + _ndk_good_snp / 2
_genome_size_repetitive = _genome_size - _genome_size_unique
_coverage = _nk_total / _genome_size if _genome_size else 0
# SNP rate estimation, assumes uniform distribution of SNPs over the
# genome and accounts for the reduction in SNP kmer counts when
# polymorphism is very high
if ploidy == 2:
_d_SNP = (1.0 /
(1.0 -
(1.0 - 0.5 * _ndk_good_snp / _genome_size)**(1.0 / K))
if _ndk_good_snp > 0 else 1000000)
G = int(_genome_size)
G1 = int(_genome_size_unique)
GR = int(_genome_size_repetitive)
coverage = int(_coverage)
m = "Kmer (K={0}) Spectrum Analysis\n".format(K)
m += "Genome size estimate = {0}\n".format(thousands(G))
m += "Genome size estimate CN = 1 = {0} ({1})\n".format(
thousands(G1), percentage(G1, G))
m += "Genome size estimate CN > 1 = {0} ({1})\n".format(
thousands(GR), percentage(GR, G))
m += "Coverage estimate: {0} x\n".format(coverage)
self.repetitive = "Repeats: {0} percent".format(GR * 100 // G)
if ploidy == 2:
d_SNP = int(_d_SNP)
self.snprate = "SNP rate ~= 1/{0}".format(d_SNP)
else:
self.snprate = "SNP rate not computed (Ploidy = {0})".format(
ploidy)
m += self.snprate + "\n"
self.genomesize = int(round(self.totalKmers * 1.0 / self.max2))
print(m, file=sys.stderr)
return {}
def histogram(args):
"""
%prog histogram [reads.fasta|reads.fastq]
Plot read length distribution for reads. The plot would be similar to the
one generated by SMRT-portal, for example:
http://blog.pacificbiosciences.com/2013/10/data-release-long-read-shotgun.html
Plot has two axes - corresponding to pdf and cdf, respectively. Also adding
number of reads, average/median, N50, and total length.
"""
from jcvi.utils.cbook import human_size, thousands, SUFFIXES
from jcvi.formats.fastq import fasta
from jcvi.graphics.histogram import stem_leaf_plot
from jcvi.graphics.base import (
plt,
markup,
human_formatter,
human_base_formatter,
savefig,
set2,
set_ticklabels_helvetica,
)
p = OptionParser(histogram.__doc__)
p.set_histogram(vmax=50000,
bins=100,
xlabel="Read length",
title="Read length distribution")
p.add_option("--ylabel1",
default="Counts",
help="Label of y-axis on the left")
p.add_option(
"--color",
default="0",
choices=[str(x) for x in range(8)],
help="Color of bars, which is an index 0-7 in brewer set2",
)
opts, args, iopts = p.set_image_options(args, figsize="6x6", style="dark")
if len(args) != 1:
sys.exit(not p.print_help())
(fastafile, ) = args
fastafile, qualfile = fasta([fastafile, "--seqtk"])
sizes = Sizes(fastafile)
all_sizes = sorted(sizes.sizes)
xmin, xmax, bins = opts.vmin, opts.vmax, opts.bins
left, height = stem_leaf_plot(all_sizes, xmin, xmax, bins)
plt.figure(1, (iopts.w, iopts.h))
ax1 = plt.gca()
width = (xmax - xmin) * 0.5 / bins
color = set2[int(opts.color)]
ax1.bar(left, height, width=width, linewidth=0, fc=color, align="center")
ax1.set_xlabel(markup(opts.xlabel))
ax1.set_ylabel(opts.ylabel1)
ax2 = ax1.twinx()
cur_size = 0
total_size, l50, n50 = sizes.summary
cdf = {}
hsize = human_size(total_size)
tag = hsize[-2:]
unit = 1000**SUFFIXES[1000].index(tag)
for x in all_sizes:
if x not in cdf:
cdf[x] = (total_size - cur_size) * 1.0 / unit
cur_size += x
x, y = zip(*sorted(cdf.items()))
ax2.plot(x, y, "-", color="darkslategray")
ylabel2 = "{0} above read length".format(tag)
ax2.set_ylabel(ylabel2)
for ax in (ax1, ax2):
set_ticklabels_helvetica(ax)
ax.set_xlim((xmin - width / 2, xmax + width / 2))
tc = "gray"
axt = ax1.transAxes
xx, yy = 0.95, 0.95
ma = "Total bases: {0}".format(hsize)
mb = "Total reads: {0}".format(thousands(len(sizes)))
mc = "Average read length: {0}bp".format(thousands(np.mean(all_sizes)))
md = "Median read length: {0}bp".format(thousands(np.median(all_sizes)))
me = "N50 read length: {0}bp".format(thousands(l50))
for t in (ma, mb, mc, md, me):
print(t, file=sys.stderr)
ax1.text(xx, yy, t, color=tc, transform=axt, ha="right")
yy -= 0.05
ax1.set_title(markup(opts.title))
# Seaborn removes ticks for all styles except 'ticks'. Now add them back:
ax1.tick_params(
axis="x",
direction="out",
length=3,
left=False,
right=False,
top=False,
bottom=True,
)
ax1.xaxis.set_major_formatter(human_base_formatter)
ax1.yaxis.set_major_formatter(human_formatter)
figname = sizes.filename + ".pdf"
savefig(figname)
def histogram(args):
"""
%prog histogram [reads.fasta|reads.fastq]
Plot read length distribution for reads. The plot would be similar to the
one generated by SMRT-portal, for example:
http://blog.pacificbiosciences.com/2013/10/data-release-long-read-shotgun.html
Plot has two axes - corresponding to pdf and cdf, respectively. Also adding
number of reads, average/median, N50, and total length.
"""
from jcvi.utils.cbook import human_size, thousands, SUFFIXES
from jcvi.formats.fastq import fasta
from jcvi.graphics.histogram import stem_leaf_plot
from jcvi.graphics.base import plt, markup, human_formatter, \
human_base_formatter, savefig, set2, set_ticklabels_helvetica
p = OptionParser(histogram.__doc__)
p.set_histogram(vmax=50000, bins=100, xlabel="Read length",
title="Read length distribution")
p.add_option("--ylabel1", default="Counts",
help="Label of y-axis on the left")
p.add_option("--color", default='0', choices=[str(x) for x in range(8)],
help="Color of bars, which is an index 0-7 in brewer set2")
opts, args, iopts = p.set_image_options(args, figsize="6x6", style="dark")
if len(args) != 1:
sys.exit(not p.print_help())
fastafile, = args
fastafile, qualfile = fasta([fastafile, "--seqtk"])
sizes = Sizes(fastafile)
all_sizes = sorted(sizes.sizes)
xmin, xmax, bins = opts.vmin, opts.vmax, opts.bins
left, height = stem_leaf_plot(all_sizes, xmin, xmax, bins)
plt.figure(1, (iopts.w, iopts.h))
ax1 = plt.gca()
width = (xmax - xmin) * .5 / bins
color = set2[int(opts.color)]
ax1.bar(left, height, width=width, linewidth=0, fc=color, align="center")
ax1.set_xlabel(markup(opts.xlabel))
ax1.set_ylabel(opts.ylabel1)
ax2 = ax1.twinx()
cur_size = 0
total_size, l50, n50 = sizes.summary
cdf = {}
hsize = human_size(total_size)
tag = hsize[-2:]
unit = 1000 ** SUFFIXES[1000].index(tag)
for x in all_sizes:
if x not in cdf:
cdf[x] = (total_size - cur_size) * 1. / unit
cur_size += x
x, y = zip(*sorted(cdf.items()))
ax2.plot(x, y, '-', color="darkslategray")
ylabel2 = "{0} above read length".format(tag)
ax2.set_ylabel(ylabel2)
for ax in (ax1, ax2):
set_ticklabels_helvetica(ax)
ax.set_xlim((xmin - width / 2, xmax + width / 2))
tc = "gray"
axt = ax1.transAxes
xx, yy = .95, .95
ma = "Total bases: {0}".format(hsize)
mb = "Total reads: {0}".format(thousands(len(sizes)))
mc = "Average read length: {0}bp".format(thousands(np.mean(all_sizes)))
md = "Median read length: {0}bp".format(thousands(np.median(all_sizes)))
me = "N50 read length: {0}bp".format(thousands(l50))
for t in (ma, mb, mc, md, me):
print >> sys.stderr, t
ax1.text(xx, yy, t, color=tc, transform=axt, ha="right")
yy -= .05
ax1.set_title(markup(opts.title))
# Seaborn removes ticks for all styles except 'ticks'. Now add them back:
ax1.tick_params(axis="x", direction="out", length=3,
left=False, right=False, top=False, bottom=True)
ax1.xaxis.set_major_formatter(human_base_formatter)
ax1.yaxis.set_major_formatter(human_formatter)
figname = sizes.filename + ".pdf"
savefig(figname)
def dotplot(anchorfile, qbed, sbed, fig, root, ax, vmin=0, vmax=1,
is_self=False, synteny=False, cmap_text=None, cmap="copper",
genomenames=None, sample_number=10000, minfont=5, palette=None,
chrlw=.1, title=None, sep=True, sepcolor="g", stdpf=True):
fp = open(anchorfile)
# add genome names
if genomenames:
gx, gy = genomenames.split("_")
else:
to_ax_label = lambda fname: op.basename(fname).split(".")[0]
gx, gy = [to_ax_label(x.filename) for x in (qbed, sbed)]
gx, gy = markup(gx), markup(gy)
qorder = qbed.order
sorder = sbed.order
data = []
if cmap_text:
logging.debug("Capping values within [{0:.1f}, {1:.1f}]"\
.format(vmin, vmax))
block_id = 0
for row in fp:
atoms = row.split()
block_color = None
if row[0] == "#":
block_id += 1
if palette:
block_color = palette.get(block_id, "k")
continue
# first two columns are query and subject, and an optional third column
if len(atoms) < 2:
continue
query, subject = atoms[:2]
value = atoms[-1]
if cmap_text:
try:
value = float(value)
except ValueError:
value = vmax
if value < vmin:
continue
if value > vmax:
continue
else:
value = 0
if query not in qorder:
continue
if subject not in sorder:
continue
qi, q = qorder[query]
si, s = sorder[subject]
nv = value if block_color is None else block_color
data.append((qi, si, nv))
if is_self: # Mirror image
data.append((si, qi, nv))
npairs = downsample(data, sample_number=sample_number)
x, y, c = zip(*data)
if palette:
ax.scatter(x, y, c=c, edgecolors="none", s=2, lw=0)
else:
ax.scatter(x, y, c=c, edgecolors="none", s=2, lw=0, cmap=cmap,
vmin=vmin, vmax=vmax)
if synteny:
clusters = batch_scan(data, qbed, sbed)
draw_box(clusters, ax)
if cmap_text:
draw_cmap(root, cmap_text, vmin, vmax, cmap=cmap)
xsize, ysize = len(qbed), len(sbed)
logging.debug("xsize=%d ysize=%d" % (xsize, ysize))
qbreaks = qbed.get_breaks()
sbreaks = sbed.get_breaks()
xlim, ylim = plot_breaks_and_labels(fig, root, ax, gx, gy, xsize, ysize,
qbreaks, sbreaks, sep=sep, chrlw=chrlw,
sepcolor=sepcolor, minfont=minfont, stdpf=stdpf)
# create a diagonal to separate mirror image for self comparison
if is_self:
ax.plot(xlim, (0, ysize), 'm-', alpha=.5, lw=2)
if palette: # bottom-left has the palette, if available
colors = palette.colors
xstart, ystart = .1, .05
for category, c in sorted(colors.items()):
root.add_patch(Rectangle((xstart, ystart), .03, .02, lw=0, fc=c))
root.text(xstart + .04, ystart, category, color=c)
xstart += .1
if title is None:
title = "Inter-genomic comparison: {0} vs {1}".format(gx, gy)
if is_self:
title = "Intra-genomic comparison within {0}".format(gx)
npairs /= 2
title += " ({0} gene pairs)".format(thousands(npairs))
root.set_title(title, x=.5, y=.96, color="k")
if title:
logging.debug("Dot plot title: {}".format(title))
normalize_axes(root)
def summary(args):
"""
%prog summary txtfile fastafile
The txtfile can be generated by: %prog mstmap --noheader --freq=0
Tabulate on all possible combinations of genotypes and provide results
in a nicely-formatted table. Give a fastafile for SNP rate (average
# of SNPs per Kb).
Only three-column file is supported:
locus_id intra- genotype inter- genotype
"""
from jcvi.utils.cbook import thousands
from jcvi.utils.table import tabulate
p = OptionParser(summary.__doc__)
p.add_option("--counts",
help="Print SNP counts in a txt file [default: %default]")
p.add_option("--bed",
help="Print SNPs locations in a bed file [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
txtfile, fastafile = args
bedfw = open(opts.bed, "w") if opts.bed else None
fp = open(txtfile)
header = fp.next().split() # Header
snps = defaultdict(list) # contig => list of loci
combinations = defaultdict(int)
intraSNPs = interSNPs = 0
distinctSet = set() # set of genes that show A-B pattern
ref, alt = header[1:3]
snpcounts, goodsnpcounts = defaultdict(int), defaultdict(int)
for row in fp:
atoms = row.split()
assert len(atoms) == 3, \
"Only three-column file is supported"
locus, intra, inter = atoms
ctg, pos = locus.rsplit(".", 1)
pos = int(pos)
snps[ctg].append(pos)
snpcounts[ctg] += 1
if intra == 'X':
intraSNPs += 1
if inter in ('B', 'X'):
interSNPs += 1
if intra == 'A' and inter == 'B':
distinctSet.add(ctg)
goodsnpcounts[ctg] += 1
# Tabulate all possible combinations
intra = ref + "-" + intra
inter = alt + "-" + inter
combinations[(intra, inter)] += 1
if bedfw:
print >> bedfw, "\t".join(str(x) for x in \
(ctg, pos - 1, pos, locus))
if bedfw:
logging.debug("SNP locations written to `{0}`.".format(opts.bed))
bedfw.close()
nsites = sum(len(x) for x in snps.values())
sizes = Sizes(fastafile)
bpsize = sizes.totalsize
snprate = lambda a: a * 1000. / bpsize
m = "Dataset `{0}` contains {1} contigs ({2} bp).\n".\
format(fastafile, len(sizes), thousands(bpsize))
m += "A total of {0} SNPs within {1} contigs ({2} bp).\n".\
format(nsites, len(snps),
thousands(sum(sizes.mapping[x] for x in snps.keys())))
m += "SNP rate: {0:.1f}/Kb, ".format(snprate(nsites))
m += "IntraSNPs: {0} ({1:.1f}/Kb), InterSNPs: {2} ({3:.1f}/Kb)".\
format(intraSNPs, snprate(intraSNPs), interSNPs, snprate(interSNPs))
print >> sys.stderr, m
print >> sys.stderr, tabulate(combinations)
leg = "Legend: A - homozygous same, B - homozygous different, X - heterozygous"
print >> sys.stderr, leg
tag = (ref + "-A", alt + "-B")
distinctSNPs = combinations[tag]
tag = str(tag).replace("'", "")
print >> sys.stderr, "A total of {0} disparate {1} SNPs in {2} contigs.".\
format(distinctSNPs, tag, len(distinctSet))
if not opts.counts:
return
snpcountsfile = opts.counts
fw = open(snpcountsfile, "w")
header = "\t".join(("Contig", "#_SNPs", "#_AB_SNP"))
print >> fw, header
assert sum(snpcounts.values()) == nsites
assert sum(goodsnpcounts.values()) == distinctSNPs
for ctg in sorted(snps.keys()):
snpcount = snpcounts[ctg]
goodsnpcount = goodsnpcounts[ctg]
print >> fw, "\t".join(str(x) for x in (ctg, snpcount, goodsnpcount))
fw.close()
logging.debug("SNP counts per contig is written to `{0}`.".\
format(snpcountsfile))
def coverage(args):
"""
%prog coverage fastafile ctg bedfile1 bedfile2 ..
Plot coverage from a set of BED files that contain the read mappings. The
paired read span will be converted to a new bedfile that contain the happy
mates. ctg is the chr/scf/ctg that you want to plot the histogram on.
If the bedfiles already contain the clone spans, turn on --spans.
"""
from jcvi.formats.bed import mates, bedpe
p = OptionParser(coverage.__doc__)
p.add_option("--ymax",
default=None,
type="int",
help="Limit ymax [default: %default]")
p.add_option(
"--spans",
default=False,
action="store_true",
help="BED files already contain clone spans [default: %default]")
opts, args, iopts = p.set_image_options(args, figsize="8x5")
if len(args) < 3:
sys.exit(not p.print_help())
fastafile, ctg = args[0:2]
bedfiles = args[2:]
sizes = Sizes(fastafile)
size = sizes.mapping[ctg]
plt.figure(1, (iopts.w, iopts.h))
ax = plt.gca()
bins = 100 # smooth the curve
lines = []
legends = []
not_covered = []
yy = .9
for bedfile, c in zip(bedfiles, "rgbcky"):
if not opts.spans:
pf = bedfile.rsplit(".", 1)[0]
matesfile = pf + ".mates"
if need_update(bedfile, matesfile):
matesfile, matesbedfile = mates([bedfile, "--lib"])
bedspanfile = pf + ".spans.bed"
if need_update(matesfile, bedspanfile):
bedpefile, bedspanfile = bedpe(
[bedfile, "--span", "--mates={0}".format(matesfile)])
bedfile = bedspanfile
bedsum = Bed(bedfile).sum(seqid=ctg)
notcoveredbases = size - bedsum
legend = bedfile.split(".")[0]
msg = "{0}: {1} bp not covered".format(legend,
thousands(notcoveredbases))
not_covered.append(msg)
print >> sys.stderr, msg
ax.text(.1, yy, msg, color=c, size=9, transform=ax.transAxes)
yy -= .08
cov = Coverage(bedfile, sizes.filename)
x, y = cov.get_plot_data(ctg, bins=bins)
line, = ax.plot(x, y, '-', color=c, lw=2, alpha=.5)
lines.append(line)
legends.append(legend)
leg = ax.legend(lines, legends, shadow=True, fancybox=True)
leg.get_frame().set_alpha(.5)
ylabel = "Average depth per {0}Kb".format(size / bins / 1000)
ax.set_xlim(0, size)
ax.set_ylim(0, opts.ymax)
ax.set_xlabel(ctg)
ax.set_ylabel(ylabel)
set_human_base_axis(ax)
figname = "{0}.{1}.pdf".format(fastafile, ctg)
savefig(figname, dpi=iopts.dpi, iopts=iopts)
def dotplot(anchorfile, qbed, sbed, fig, root, ax, vmin=0, vmax=1,
is_self=False, synteny=False, cmap_text=None, cmap="copper",
genomenames=None, sample_number=10000, minfont=5, palette=None,
chrlw=.01, title=None, sepcolor="gainsboro"):
fp = open(anchorfile)
qorder = qbed.order
sorder = sbed.order
data = []
if cmap_text:
logging.debug("Capping values within [{0:.1f}, {1:.1f}]"\
.format(vmin, vmax))
block_id = 0
for row in fp:
atoms = row.split()
block_color = None
if row[0] == "#":
block_id += 1
if palette:
block_color = palette.get(block_id, "k")
continue
# first two columns are query and subject, and an optional third column
if len(atoms) < 2:
continue
query, subject = atoms[:2]
value = atoms[-1]
if cmap_text:
try:
value = float(value)
except ValueError:
value = vmax
if value < vmin:
continue
if value > vmax:
continue
else:
value = 0
if query not in qorder:
continue
if subject not in sorder:
continue
qi, q = qorder[query]
si, s = sorder[subject]
nv = value if block_color is None else block_color
data.append((qi, si, nv))
if is_self: # Mirror image
data.append((si, qi, nv))
npairs = len(data)
# Only show random subset
if npairs > sample_number:
logging.debug("Showing a random subset of {0} data points (total {1}) " \
"for clarity.".format(sample_number, npairs))
data = sample(data, sample_number)
# the data are plotted in this order, the least value are plotted
# last for aesthetics
#if not palette:
# data.sort(key=lambda x: -x[2])
x, y, c = zip(*data)
if palette:
ax.scatter(x, y, c=c, edgecolors="none", s=2, lw=0)
else:
ax.scatter(x, y, c=c, edgecolors="none", s=2, lw=0, cmap=cmap,
vmin=vmin, vmax=vmax)
if synteny:
clusters = batch_scan(data, qbed, sbed)
draw_box(clusters, ax)
if cmap_text:
draw_cmap(root, cmap_text, vmin, vmax, cmap=cmap)
xsize, ysize = len(qbed), len(sbed)
logging.debug("xsize=%d ysize=%d" % (xsize, ysize))
xlim = (0, xsize)
ylim = (ysize, 0) # invert the y-axis
# Tag to mark whether to plot chr name (skip small ones)
xchr_labels, ychr_labels = [], []
th = TextHandler(fig)
# plot the chromosome breaks
for (seqid, beg, end) in qbed.get_breaks():
xsize_ratio = abs(end - beg) * .8 / xsize
fontsize = th.select_fontsize(xsize_ratio)
seqid = "".join(seqid_parse(seqid)[:2])
xchr_labels.append((seqid, (beg + end) / 2, fontsize))
ax.plot([beg, beg], ylim, "-", lw=chrlw, color=sepcolor)
for (seqid, beg, end) in sbed.get_breaks():
ysize_ratio = abs(end - beg) * .8 / ysize
fontsize = th.select_fontsize(ysize_ratio)
seqid = "".join(seqid_parse(seqid)[:2])
ychr_labels.append((seqid, (beg + end) / 2, fontsize))
ax.plot(xlim, [beg, beg], "-", lw=chrlw, color=sepcolor)
# plot the chromosome labels
for label, pos, fontsize in xchr_labels:
pos = .1 + pos * .8 / xsize
if fontsize >= minfont:
root.text(pos, .91, latex(label), size=fontsize,
ha="center", va="bottom", rotation=45, color="grey")
# remember y labels are inverted
for label, pos, fontsize in ychr_labels:
pos = .9 - pos * .8 / ysize
if fontsize >= minfont:
root.text(.91, pos, latex(label), size=fontsize,
va="center", color="grey")
# create a diagonal to separate mirror image for self comparison
if is_self:
ax.plot(xlim, (0, ysize), 'm-', alpha=.5, lw=2)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
# add genome names
if genomenames:
gx, gy = genomenames.split("_")
else:
to_ax_label = lambda fname: op.basename(fname).split(".")[0]
gx, gy = [to_ax_label(x.filename) for x in (qbed, sbed)]
ax.set_xlabel(markup(gx), size=16)
ax.set_ylabel(markup(gy), size=16)
# beautify the numeric axis
for tick in ax.get_xticklines() + ax.get_yticklines():
tick.set_visible(False)
set_human_axis(ax)
plt.setp(ax.get_xticklabels() + ax.get_yticklabels(),
color='gray', size=10)
if palette: # bottom-left has the palette, if available
colors = palette.colors
xstart, ystart = .1, .05
for category, c in sorted(colors.items()):
root.add_patch(Rectangle((xstart, ystart), .03, .02, lw=0, fc=c))
root.text(xstart + .04, ystart, category, color=c)
xstart += .1
if not title:
title = "Inter-genomic comparison: {0} vs {1}".format(gx, gy)
if is_self:
title = "Intra-genomic comparison within {0}".format(gx)
npairs /= 2
title += " ({0} gene pairs)".format(thousands(npairs))
root.set_title(markup(title), x=.5, y=.96, color="k")
logging.debug(title)
root.set_xlim(0, 1)
root.set_ylim(0, 1)
root.set_axis_off()
def histogram(args):
"""
%prog histogram meryl.histogram species K
Plot the histogram based on meryl K-mer distribution, species and N are
only used to annotate the graphic. Find out totalKmers when running
kmer.meryl().
"""
p = OptionParser(histogram.__doc__)
p.add_option("--vmin", dest="vmin", default=1, type="int", help="minimum value, inclusive [default: %default]")
p.add_option("--vmax", dest="vmax", default=100, type="int", help="maximum value, inclusive [default: %default]")
p.add_option(
"--pdf", default=False, action="store_true", help="Print PDF instead of ASCII plot [default: %default]"
)
p.add_option("--coverage", default=0, type="int", help="Kmer coverage [default: auto]")
p.add_option("--nopeaks", default=False, action="store_true", help="Do not annotate K-mer peaks")
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
histfile, species, N = args
ascii = not opts.pdf
peaks = not opts.nopeaks
N = int(N)
if histfile.rsplit(".", 1)[-1] in ("mcdat", "mcidx"):
logging.debug("CA kmer index found")
histfile = meryl([histfile])
ks = KmerSpectrum(histfile)
ks.analyze(K=N)
Total_Kmers = int(ks.totalKmers)
coverage = opts.coverage
Kmer_coverage = ks.max2 if not coverage else coverage
Genome_size = int(round(Total_Kmers * 1.0 / Kmer_coverage))
Total_Kmers_msg = "Total {0}-mers: {1}".format(N, thousands(Total_Kmers))
Kmer_coverage_msg = "{0}-mer coverage: {1}".format(N, Kmer_coverage)
Genome_size_msg = "Estimated genome size: {0:.1f}Mb".format(Genome_size / 1e6)
Repetitive_msg = ks.repetitive
SNPrate_msg = ks.snprate
for msg in (Total_Kmers_msg, Kmer_coverage_msg, Genome_size_msg):
print >> sys.stderr, msg
x, y = ks.get_xy(opts.vmin, opts.vmax)
title = "{0} {1}-mer histogram".format(species, N)
if ascii:
asciiplot(x, y, title=title)
return Genome_size
plt.figure(1, (6, 6))
plt.plot(x, y, "g-", lw=2, alpha=0.5)
ax = plt.gca()
if peaks:
t = (ks.min1, ks.max1, ks.min2, ks.max2, ks.min3)
tcounts = [(x, y) for x, y in ks.counts if x in t]
if tcounts:
x, y = zip(*tcounts)
tcounts = dict(tcounts)
plt.plot(x, y, "ko", lw=2, mec="k", mfc="w")
ax.text(ks.max1, tcounts[ks.max1], "SNP peak", va="top")
ax.text(ks.max2, tcounts[ks.max2], "Main peak")
messages = [Total_Kmers_msg, Kmer_coverage_msg, Genome_size_msg, Repetitive_msg, SNPrate_msg]
write_messages(ax, messages)
ymin, ymax = ax.get_ylim()
ymax = ymax * 7 / 6
ax.set_title(markup(title))
ax.set_ylim((ymin, ymax))
xlabel, ylabel = "Coverage (X)", "Counts"
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
set_human_axis(ax)
imagename = histfile.split(".")[0] + ".pdf"
savefig(imagename, dpi=100)
return Genome_size
def histogram(args):
"""
%prog histogram meryl.histogram species K
Plot the histogram based on meryl K-mer distribution, species and N are
only used to annotate the graphic. Find out totalKmers when running
kmer.meryl().
"""
p = OptionParser(histogram.__doc__)
p.add_option("--vmin",
dest="vmin",
default=1,
type="int",
help="minimum value, inclusive [default: %default]")
p.add_option("--vmax",
dest="vmax",
default=100,
type="int",
help="maximum value, inclusive [default: %default]")
p.add_option("--pdf",
default=False,
action="store_true",
help="Print PDF instead of ASCII plot [default: %default]")
p.add_option("--coverage",
default=0,
type="int",
help="Kmer coverage [default: auto]")
p.add_option("--nopeaks",
default=False,
action="store_true",
help="Do not annotate K-mer peaks")
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
histfile, species, N = args
ascii = not opts.pdf
peaks = not opts.nopeaks
N = int(N)
ks = KmerSpectrum(histfile)
ks.analyze(K=N)
Total_Kmers = int(ks.totalKmers)
coverage = opts.coverage
Kmer_coverage = ks.max2 if not coverage else coverage
Genome_size = int(round(Total_Kmers * 1. / Kmer_coverage))
Total_Kmers_msg = "Total {0}-mers: {1}".format(N, thousands(Total_Kmers))
Kmer_coverage_msg = "{0}-mer coverage: {1}".format(N, Kmer_coverage)
Genome_size_msg = "Estimated genome size: {0:.1f}Mb".\
format(Genome_size / 1e6)
Repetitive_msg = ks.repetitive
SNPrate_msg = ks.snprate
for msg in (Total_Kmers_msg, Kmer_coverage_msg, Genome_size_msg):
print >> sys.stderr, msg
x, y = ks.get_xy(opts.vmin, opts.vmax)
title = "{0} genome {1}-mer histogram".format(species, N)
if ascii:
asciiplot(x, y, title=title)
return Genome_size
plt.figure(1, (6, 6))
plt.plot(x, y, 'g-', lw=2, alpha=.5)
ax = plt.gca()
if peaks:
t = (ks.min1, ks.max1, ks.min2, ks.max2, ks.min3)
tcounts = [(x, y) for x, y in ks.counts if x in t]
x, y = zip(*tcounts)
tcounts = dict(tcounts)
plt.plot(x, y, 'ko', lw=2, mec='k', mfc='w')
ax.text(ks.max1, tcounts[ks.max1], "SNP peak", va="top")
ax.text(ks.max2, tcounts[ks.max2], "Main peak")
tc = "gray"
axt = ax.transAxes
ax.text(.95, .95, Total_Kmers_msg, color=tc, transform=axt, ha="right")
ax.text(.95, .9, Kmer_coverage_msg, color=tc, transform=axt, ha="right")
ax.text(.95, .85, Genome_size_msg, color=tc, transform=axt, ha="right")
ax.text(.95, .8, Repetitive_msg, color=tc, transform=axt, ha="right")
ax.text(.95, .75, SNPrate_msg, color=tc, transform=axt, ha="right")
ymin, ymax = ax.get_ylim()
ymax = ymax * 7 / 6
ax.set_title(markup(title), color='r')
ax.set_ylim((ymin, ymax))
xlabel, ylabel = "Coverage (X)", "Counts"
ax.set_xlabel(xlabel, color='r')
ax.set_ylabel(ylabel, color='r')
set_human_axis(ax)
imagename = histfile.split(".")[0] + ".pdf"
savefig(imagename, dpi=100)
return Genome_size
def histogram(args):
"""
%prog histogram meryl.histogram species K
Plot the histogram based on meryl K-mer distribution, species and N are
only used to annotate the graphic.
"""
p = OptionParser(histogram.__doc__)
p.add_option(
"--vmin",
dest="vmin",
default=1,
type="int",
help="minimum value, inclusive",
)
p.add_option(
"--vmax",
dest="vmax",
default=100,
type="int",
help="maximum value, inclusive",
)
p.add_option(
"--pdf",
default=False,
action="store_true",
help="Print PDF instead of ASCII plot",
)
p.add_option("--coverage",
default=0,
type="int",
help="Kmer coverage [default: auto]")
p.add_option(
"--nopeaks",
default=False,
action="store_true",
help="Do not annotate K-mer peaks",
)
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
histfile, species, N = args
ascii = not opts.pdf
peaks = not opts.nopeaks
N = int(N)
if histfile.rsplit(".", 1)[-1] in ("mcdat", "mcidx"):
logging.debug("CA kmer index found")
histfile = merylhistogram(histfile)
ks = KmerSpectrum(histfile)
ks.analyze(K=N)
Total_Kmers = int(ks.totalKmers)
coverage = opts.coverage
Kmer_coverage = ks.max2 if not coverage else coverage
Genome_size = int(round(Total_Kmers * 1.0 / Kmer_coverage))
Total_Kmers_msg = "Total {0}-mers: {1}".format(N, thousands(Total_Kmers))
Kmer_coverage_msg = "{0}-mer coverage: {1}".format(N, Kmer_coverage)
Genome_size_msg = "Estimated genome size: {0:.1f}Mb".format(Genome_size /
1e6)
Repetitive_msg = ks.repetitive
SNPrate_msg = ks.snprate
for msg in (Total_Kmers_msg, Kmer_coverage_msg, Genome_size_msg):
print(msg, file=sys.stderr)
x, y = ks.get_xy(opts.vmin, opts.vmax)
title = "{0} {1}-mer histogram".format(species, N)
if ascii:
asciiplot(x, y, title=title)
return Genome_size
plt.figure(1, (6, 6))
plt.plot(x, y, "g-", lw=2, alpha=0.5)
ax = plt.gca()
if peaks:
t = (ks.min1, ks.max1, ks.min2, ks.max2, ks.min3)
tcounts = [(x, y) for x, y in ks.counts if x in t]
if tcounts:
x, y = zip(*tcounts)
tcounts = dict(tcounts)
plt.plot(x, y, "ko", lw=2, mec="k", mfc="w")
ax.text(ks.max1, tcounts[ks.max1], "SNP peak", va="top")
ax.text(ks.max2, tcounts[ks.max2], "Main peak")
messages = [
Total_Kmers_msg,
Kmer_coverage_msg,
Genome_size_msg,
Repetitive_msg,
SNPrate_msg,
]
write_messages(ax, messages)
ymin, ymax = ax.get_ylim()
ymax = ymax * 7 / 6
ax.set_title(markup(title))
ax.set_ylim((ymin, ymax))
xlabel, ylabel = "Coverage (X)", "Counts"
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
set_human_axis(ax)
imagename = histfile.split(".")[0] + ".pdf"
savefig(imagename, dpi=100)
return Genome_size
def dotplot(anchorfile,
qbed,
sbed,
fig,
root,
ax,
vmin=0,
vmax=1,
is_self=False,
synteny=False,
cmap_text=None,
genomenames=None,
sample_number=10000,
minfont=5,
palette=None,
chrlw=.01,
title=None,
sepcolor="gainsboro"):
fp = open(anchorfile)
qorder = qbed.order
sorder = sbed.order
data = []
if cmap_text:
logging.debug("Normalize values to [%.1f, %.1f]" % (vmin, vmax))
block_id = 0
for row in fp:
atoms = row.split()
block_color = None
if row[0] == "#":
block_id += 1
if palette:
block_color = palette.get(block_id, "k")
continue
# first two columns are query and subject, and an optional third column
if len(atoms) < 2:
continue
query, subject = atoms[:2]
value = atoms[-1]
try:
value = float(value)
except ValueError:
value = vmax
if value < vmin:
value = vmin
if value > vmax:
value = vmax
if query not in qorder:
continue
if subject not in sorder:
continue
qi, q = qorder[query]
si, s = sorder[subject]
nv = vmax - value if block_color is None else block_color
data.append((qi, si, nv))
if is_self: # Mirror image
data.append((si, qi, nv))
npairs = len(data)
# Only show random subset
if npairs > sample_number:
logging.debug("Showing a random subset of {0} data points (total {1}) " \
"for clarity.".format(sample_number, npairs))
data = sample(data, sample_number)
# the data are plotted in this order, the least value are plotted
# last for aesthetics
if not palette:
data.sort(key=lambda x: -x[2])
default_cm = cm.copper
x, y, c = zip(*data)
if palette:
ax.scatter(x, y, c=c, edgecolors="none", s=2, lw=0)
else:
ax.scatter(x,
y,
c=c,
edgecolors="none",
s=2,
lw=0,
cmap=default_cm,
vmin=vmin,
vmax=vmax)
if synteny:
clusters = batch_scan(data, qbed, sbed)
draw_box(clusters, ax)
if cmap_text:
draw_cmap(root, cmap_text, vmin, vmax, cmap=default_cm, reverse=True)
xsize, ysize = len(qbed), len(sbed)
logging.debug("xsize=%d ysize=%d" % (xsize, ysize))
xlim = (0, xsize)
ylim = (ysize, 0) # invert the y-axis
# Tag to mark whether to plot chr name (skip small ones)
xchr_labels, ychr_labels = [], []
th = TextHandler(fig)
# plot the chromosome breaks
for (seqid, beg, end) in qbed.get_breaks():
xsize_ratio = abs(end - beg) * .8 / xsize
fontsize = th.select_fontsize(xsize_ratio)
seqid = "".join(seqid_parse(seqid)[:2])
xchr_labels.append((seqid, (beg + end) / 2, fontsize))
ax.plot([beg, beg], ylim, "-", lw=chrlw, color=sepcolor)
for (seqid, beg, end) in sbed.get_breaks():
ysize_ratio = abs(end - beg) * .8 / ysize
fontsize = th.select_fontsize(ysize_ratio)
seqid = "".join(seqid_parse(seqid)[:2])
ychr_labels.append((seqid, (beg + end) / 2, fontsize))
ax.plot(xlim, [beg, beg], "-", lw=chrlw, color=sepcolor)
# plot the chromosome labels
for label, pos, fontsize in xchr_labels:
pos = .1 + pos * .8 / xsize
if fontsize >= minfont:
root.text(pos,
.91,
latex(label),
size=fontsize,
ha="center",
va="bottom",
rotation=45,
color="grey")
# remember y labels are inverted
for label, pos, fontsize in ychr_labels:
pos = .9 - pos * .8 / ysize
if fontsize >= minfont:
root.text(.91,
pos,
latex(label),
size=fontsize,
va="center",
color="grey")
# create a diagonal to separate mirror image for self comparison
if is_self:
ax.plot(xlim, (0, ysize), 'm-', alpha=.5, lw=2)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
# add genome names
if genomenames:
gx, gy = genomenames.split("_")
else:
to_ax_label = lambda fname: op.basename(fname).split(".")[0]
gx, gy = [to_ax_label(x.filename) for x in (qbed, sbed)]
ax.set_xlabel(gx, size=16)
ax.set_ylabel(gy, size=16)
# beautify the numeric axis
for tick in ax.get_xticklines() + ax.get_yticklines():
tick.set_visible(False)
set_human_axis(ax)
plt.setp(ax.get_xticklabels() + ax.get_yticklabels(),
color='gray',
size=10)
if palette: # bottom-left has the palette, if available
colors = palette.colors
xstart, ystart = .1, .05
for category, c in sorted(colors.items()):
root.add_patch(Rectangle((xstart, ystart), .03, .02, lw=0, fc=c))
root.text(xstart + .04, ystart, category, color=c)
xstart += .1
if not title:
title = "Inter-genomic comparison: {0} vs {1}".format(gx, gy)
if is_self:
title = "Intra-genomic comparison within {0}".format(gx)
npairs /= 2
title += " ({0} gene pairs)".format(thousands(npairs))
root.set_title(title, x=.5, y=.96, color="k")
logging.debug(title)
root.set_xlim(0, 1)
root.set_ylim(0, 1)
root.set_axis_off()
def summary(args):
"""
%prog summary txtfile fastafile
The txtfile can be generated by: %prog mstmap --noheader --freq=0
Tabulate on all possible combinations of genotypes and provide results
in a nicely-formatted table. Give a fastafile for SNP rate (average
# of SNPs per Kb).
Only three-column file is supported:
locus_id intra- genotype inter- genotype
"""
from jcvi.utils.cbook import thousands
from jcvi.utils.table import tabulate
p = OptionParser(summary.__doc__)
p.add_option("--counts",
help="Print SNP counts in a txt file [default: %default]")
p.add_option("--bed",
help="Print SNPs locations in a bed file [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
txtfile, fastafile = args
bedfw = open(opts.bed, "w") if opts.bed else None
fp = open(txtfile)
header = fp.next().split() # Header
snps = defaultdict(list) # contig => list of loci
combinations = defaultdict(int)
intraSNPs = interSNPs = 0
distinctSet = set() # set of genes that show A-B pattern
ref, alt = header[1:3]
snpcounts, goodsnpcounts = defaultdict(int), defaultdict(int)
for row in fp:
atoms = row.split()
assert len(atoms) == 3, \
"Only three-column file is supported"
locus, intra, inter = atoms
ctg, pos = locus.rsplit(".", 1)
pos = int(pos)
snps[ctg].append(pos)
snpcounts[ctg] += 1
if intra == 'X':
intraSNPs += 1
if inter in ('B', 'X'):
interSNPs += 1
if intra == 'A' and inter == 'B':
distinctSet.add(ctg)
goodsnpcounts[ctg] += 1
# Tabulate all possible combinations
intra = ref + "-" + intra
inter = alt + "-" + inter
combinations[(intra, inter)] += 1
if bedfw:
print >> bedfw, "\t".join(str(x) for x in \
(ctg, pos - 1, pos, locus))
if bedfw:
logging.debug("SNP locations written to `{0}`.".format(opts.bed))
bedfw.close()
nsites = sum(len(x) for x in snps.values())
sizes = Sizes(fastafile)
bpsize = sizes.totalsize
snprate = lambda a: a * 1000. / bpsize
m = "Dataset `{0}` contains {1} contigs ({2} bp).\n".\
format(fastafile, len(sizes), thousands(bpsize))
m += "A total of {0} SNPs within {1} contigs ({2} bp).\n".\
format(nsites, len(snps),
thousands(sum(sizes.mapping[x] for x in snps.keys())))
m += "SNP rate: {0:.1f}/Kb, ".format(snprate(nsites))
m += "IntraSNPs: {0} ({1:.1f}/Kb), InterSNPs: {2} ({3:.1f}/Kb)".\
format(intraSNPs, snprate(intraSNPs), interSNPs, snprate(interSNPs))
print >> sys.stderr, m
print >> sys.stderr, tabulate(combinations)
leg = "Legend: A - homozygous same, B - homozygous different, X - heterozygous"
print >> sys.stderr, leg
tag = (ref + "-A", alt + "-B")
distinctSNPs = combinations[tag]
tag = str(tag).replace("'", "")
print >> sys.stderr, "A total of {0} disparate {1} SNPs in {2} contigs.".\
format(distinctSNPs, tag, len(distinctSet))
if not opts.counts:
return
snpcountsfile = opts.counts
fw = open(snpcountsfile, "w")
header = "\t".join(("Contig", "#_SNPs", "#_AB_SNP"))
print >> fw, header
assert sum(snpcounts.values()) == nsites
assert sum(goodsnpcounts.values()) == distinctSNPs
for ctg in sorted(snps.keys()):
snpcount = snpcounts[ctg]
goodsnpcount = goodsnpcounts[ctg]
print >> fw, "\t".join(str(x) for x in (ctg, snpcount, goodsnpcount))
fw.close()
logging.debug("SNP counts per contig is written to `{0}`.".\
format(snpcountsfile))
def analyze(self, ploidy=2, K=23, covmax=1000000):
"""
Analyze Kmer spectrum, calculations derived from
allpathslg/src/kmers/KmerSpectra.cc
"""
from math import sqrt
data = self.data
kf_ceil = max(K for (K, c) in data)
if kf_ceil > covmax:
exceeds = sum(1 for (K, c) in data if K > covmax)
logging.debug("A total of {0} distinct K-mers appear > " "{1} times. Ignored ...".format(exceeds, covmax))
kf_ceil = covmax
nkf = kf_ceil + 1
a = [0] * nkf
for kf, c in data:
if kf > kf_ceil:
continue
a[kf] = c
ndk = a # number of distinct kmers
nk = [k * c for k, c in enumerate(a)] # number of kmers
cndk = [0] * nkf # cumulative number of distinct kmers
cnk = [0] * nkf # cumulative number of kmers
for kf in xrange(1, nkf):
cndk[kf] = cndk[kf - 1] + 0.5 * (ndk[kf - 1] + ndk[kf])
cnk[kf] = cnk[kf - 1] + 0.5 * (nk[kf - 1] + nk[kf])
# Separate kmer spectrum in 5 regions based on the kf
# 1 ... kf_min1 : bad kmers with low frequency
# kf_min1 ... kf_min2 : good kmers CN = 1/2 (SNPs)
# kf_min2 ... kf_min3 : good kmers CN = 1
# kf_min3 ... kf_hi : good kmers CN > 1 (repetitive)
# kf_hi ... inf : bad kmers with high frequency
# min1: find first minimum
_kf_min1 = 10
while _kf_min1 - 1 >= 2 and nk[_kf_min1 - 1] < nk[_kf_min1]:
_kf_min1 -= 1
while _kf_min1 <= kf_ceil and nk[_kf_min1 + 1] < nk[_kf_min1]:
_kf_min1 += 1
# max2: find absolute maximum mx2 above first minimum min1
_kf_max2 = _kf_min1
for kf in xrange(_kf_min1 + 1, int(0.8 * kf_ceil)):
if nk[kf] > nk[_kf_max2]:
_kf_max2 = kf
# max2: resetting max2 for cases of very high polymorphism
if ploidy == 2:
ndk_half = ndk[_kf_max2 / 2]
ndk_double = ndk[_kf_max2 * 2]
if ndk_double > ndk_half:
_kf_max2 *= 2
# max1: SNPs local maximum max1 as half global maximum max2
_kf_max1 = _kf_max2 / 2
# min2: SNPs local minimum min2 between max1 and max2
_kf_min2 = _kf_max1 * (2 * ndk[_kf_max1] + ndk[_kf_max2]) / (ndk[_kf_max1] + ndk[_kf_max2])
# min1: refine between min1 and max2/2
for kf in xrange(_kf_min1 + 1, _kf_max1):
if nk[kf] < nk[_kf_min1]:
_kf_min1 = kf
# min3: not a minimum, really. upper edge of main peak
_kf_min3 = _kf_max2 * 3 / 2
print >> sys.stderr, "kfs:", _kf_min1, _kf_max1, _kf_min2, _kf_max2, _kf_min3
self.min1 = _kf_min1
self.max1 = _kf_max1
self.min2 = _kf_min2
self.max2 = _kf_max2
self.min3 = _kf_min3
# Define maximum kf above which we neglect data
_kf_hi = (
_kf_max2 * sqrt(4 * ndk[2 * _kf_max2] * _kf_max2)
if 2 * _kf_max2 < len(ndk)
else _kf_max2 * sqrt(4 * ndk[len(ndk) - 1] * _kf_max2)
)
_kf_hi = int(_kf_hi)
if _kf_hi > kf_ceil:
_kf_hi = kf_ceil
_nk_total = cnk[len(cnk) - 1]
_nk_bad_low_kf = cnk[_kf_min1]
_nk_good_uniq = cnk[_kf_min3] - cnk[_kf_min2]
_nk_bad_high_kf = _nk_total - cnk[_kf_hi]
_ndk_good_snp = cndk[_kf_min2] - cndk[_kf_min1]
_ndk_good_uniq = cndk[_kf_min3] - cndk[_kf_min2]
# kmer coverage C_k
_kf_ave_uniq = _nk_good_uniq * 1.0 / _ndk_good_uniq
_genome_size = (_nk_total - _nk_bad_low_kf - _nk_bad_high_kf) / _kf_ave_uniq
_genome_size_unique = _ndk_good_uniq + _ndk_good_snp / 2
_genome_size_repetitive = _genome_size - _genome_size_unique
_coverage = _nk_total / _genome_size if _genome_size else 0
# SNP rate estimation, assumes uniform distribution of SNPs over the
# genome and accounts for the reduction in SNP kmer counts when
# polymorphism is very high
if ploidy == 2:
_d_SNP = (
1.0 / (1.0 - (1.0 - 0.5 * _ndk_good_snp / _genome_size) ** (1.0 / K)) if _ndk_good_snp > 0 else 1000000
)
G = int(_genome_size)
G1 = int(_genome_size_unique)
GR = int(_genome_size_repetitive)
coverage = int(_coverage)
m = "Kmer (K={0}) Spectrum Analysis\n".format(K)
m += "Genome size estimate = {0}\n".format(thousands(G))
m += "Genome size estimate CN = 1 = {0} ({1})\n".format(thousands(G1), percentage(G1, G))
m += "Genome size estimate CN > 1 = {0} ({1})\n".format(thousands(GR), percentage(GR, G))
m += "Coverage estimate: {0} x\n".format(coverage)
self.repetitive = "Repeats: {0} percent".format(GR * 100 / G)
if ploidy == 2:
d_SNP = int(_d_SNP)
self.snprate = "SNP rate ~= 1/{0}".format(d_SNP)
else:
self.snprate = "SNP rate not computed (Ploidy = {0})".format(ploidy)
m += self.snprate + "\n"
self.genomesize = int(round(self.totalKmers * 1.0 / self.max2))
print >> sys.stderr, m
def coverage(args):
"""
%prog coverage fastafile ctg bedfile1 bedfile2 ..
Plot coverage from a set of BED files that contain the read mappings. The
paired read span will be converted to a new bedfile that contain the happy
mates. ctg is the chr/scf/ctg that you want to plot the histogram on.
If the bedfiles already contain the clone spans, turn on --spans.
"""
from jcvi.formats.bed import mates, bedpe
p = OptionParser(coverage.__doc__)
p.add_option("--ymax", default=None, type="int",
help="Limit ymax [default: %default]")
p.add_option("--spans", default=False, action="store_true",
help="BED files already contain clone spans [default: %default]")
opts, args, iopts = p.set_image_options(args, figsize="8x5")
if len(args) < 3:
sys.exit(not p.print_help())
fastafile, ctg = args[0:2]
bedfiles = args[2:]
sizes = Sizes(fastafile)
size = sizes.mapping[ctg]
plt.figure(1, (iopts.w, iopts.h))
ax = plt.gca()
bins = 100 # smooth the curve
lines = []
legends = []
not_covered = []
yy = .9
for bedfile, c in zip(bedfiles, "rgbcky"):
if not opts.spans:
pf = bedfile.rsplit(".", 1)[0]
matesfile = pf + ".mates"
if need_update(bedfile, matesfile):
matesfile, matesbedfile = mates([bedfile, "--lib"])
bedspanfile = pf + ".spans.bed"
if need_update(matesfile, bedspanfile):
bedpefile, bedspanfile = bedpe([bedfile, "--span",
"--mates={0}".format(matesfile)])
bedfile = bedspanfile
bedsum = Bed(bedfile).sum(seqid=ctg)
notcoveredbases = size - bedsum
legend = bedfile.split(".")[0]
msg = "{0}: {1} bp not covered".format(legend, thousands(notcoveredbases))
not_covered.append(msg)
print >> sys.stderr, msg
ax.text(.1, yy, msg, color=c, size=9, transform=ax.transAxes)
yy -= .08
cov = Coverage(bedfile, sizes.filename)
x, y = cov.get_plot_data(ctg, bins=bins)
line, = ax.plot(x, y, '-', color=c, lw=2, alpha=.5)
lines.append(line)
legends.append(legend)
leg = ax.legend(lines, legends, shadow=True, fancybox=True)
leg.get_frame().set_alpha(.5)
ylabel = "Average depth per {0}Kb".format(size / bins / 1000)
ax.set_xlim(0, size)
ax.set_ylim(0, opts.ymax)
ax.set_xlabel(ctg)
ax.set_ylabel(ylabel)
set_human_base_axis(ax)
figname ="{0}.{1}.pdf".format(fastafile, ctg)
savefig(figname, dpi=iopts.dpi, iopts=iopts)
