def A50(args):
"""
%prog A50 contigs_A.fasta contigs_B.fasta ...
Plots A50 graphics, see blog post (http://blog.malde.org/index.php/a50/)
"""
p = OptionParser(A50.__doc__)
p.add_option("--overwrite", default=False, action="store_true",
help="overwrite .rplot file if exists [default: %default]")
p.add_option("--cutoff", default=0, type="int", dest="cutoff",
help="use contigs above certain size [default: %default]")
p.add_option("--stepsize", default=10, type="int", dest="stepsize",
help="stepsize for the distribution [default: %default]")
opts, args = p.parse_args(args)
if not args:
sys.exit(p.print_help())
import numpy as np
from jcvi.utils.table import loadtable
stepsize = opts.stepsize # use stepsize to speed up drawing
rplot = "A50.rplot"
if not op.exists(rplot) or opts.overwrite:
fw = open(rplot, "w")
header = "\t".join(("index", "cumsize", "fasta"))
statsheader = ("Fasta", "L50", "N50", "Min", "Max", "Average", "Sum",
"Counts")
statsrows = []
print >>fw, header
for fastafile in args:
f = Fasta(fastafile, index=False)
ctgsizes = [length for k, length in f.itersizes()]
ctgsizes = np.array(ctgsizes)
a50, l50, n50 = calculate_A50(ctgsizes, cutoff=opts.cutoff)
cmin, cmax, cmean = min(ctgsizes), max(ctgsizes), np.mean(ctgsizes)
csum, counts = np.sum(ctgsizes), len(ctgsizes)
cmean = int(round(cmean))
statsrows.append((fastafile, l50, n50, cmin, cmax, cmean, csum,
counts))
logging.debug("`{0}` ctgsizes: {1}".format(fastafile, ctgsizes))
tag = "{0} (L50={1})".format(\
op.basename(fastafile).rsplit(".", 1)[0], l50)
logging.debug(tag)
for i, s in zip(xrange(0, len(a50), stepsize), a50[::stepsize]):
print >> fw, "\t".join((str(i), str(s / 1000000.), tag))
fw.close()
table = loadtable(statsheader, statsrows)
print >> sys.stderr, table
generate_plot(rplot)
def A50(args):
"""
%prog A50 contigs_A.fasta contigs_B.fasta ...
Plots A50 graphics, see blog post (http://blog.malde.org/index.php/a50/)
"""
p = OptionParser(A50.__doc__)
p.add_option("--overwrite", default=False, action="store_true",
help="overwrite .rplot file if exists [default: %default]")
p.add_option("--cutoff", default=0, type="int", dest="cutoff",
help="use contigs above certain size [default: %default]")
p.add_option("--stepsize", default=10, type="int", dest="stepsize",
help="stepsize for the distribution [default: %default]")
opts, args = p.parse_args(args)
if not args:
sys.exit(p.print_help())
import numpy as np
from jcvi.utils.table import loadtable
stepsize = opts.stepsize # use stepsize to speed up drawing
rplot = "A50.rplot"
if not op.exists(rplot) or opts.overwrite:
fw = open(rplot, "w")
header = "\t".join(("index", "cumsize", "fasta"))
statsheader = ("Fasta", "L50", "N50", "Min", "Max", "Average", "Sum",
"Counts")
statsrows = []
print >>fw, header
for fastafile in args:
f = Fasta(fastafile, index=False)
ctgsizes = [length for k, length in f.itersizes()]
ctgsizes = np.array(ctgsizes)
a50, l50, n50 = calculate_A50(ctgsizes, cutoff=opts.cutoff)
cmin, cmax, cmean = min(ctgsizes), max(ctgsizes), np.mean(ctgsizes)
csum, counts = np.sum(ctgsizes), len(ctgsizes)
cmean = int(round(cmean))
statsrows.append((fastafile, l50, n50, cmin, cmax, cmean, csum,
counts))
logging.debug("`{0}` ctgsizes: {1}".format(fastafile, ctgsizes))
tag = "{0} (L50={1})".format(\
op.basename(fastafile).rsplit(".", 1)[0], l50)
logging.debug(tag)
for i, s in zip(xrange(0, len(a50), stepsize), a50[::stepsize]):
print >> fw, "\t".join((str(i), str(s / 1000000.), tag))
fw.close()
table = loadtable(statsheader, statsrows)
print >> sys.stderr, table
generate_plot(rplot)
def count(args):
"""
%prog count *.gz
Count reads based on FASTQC results. FASTQC needs to be run on all the input
data given before running this command.
"""
from jcvi.utils.table import loadtable, write_csv
p = OptionParser(count.__doc__)
p.add_option("--dir",
help="Sub-directory where FASTQC was run [default: %default]")
p.add_option("--human",
default=False,
action="store_true",
help="Human friendly numbers [default: %default]")
p.set_table()
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
filenames = args
subdir = opts.dir
header = "Filename|Total Sequences|Sequence length|Total Bases".split("|")
rows = []
human = opts.human
for f in filenames:
folder = f.replace(".gz", "").rsplit(".", 1)[0] + "_fastqc"
if subdir:
folder = op.join(subdir, folder)
summaryfile = op.join(folder, "fastqc_data.txt")
fqcdata = FastQCdata(summaryfile, human=human)
row = [fqcdata[x] for x in header]
rows.append(row)
print >> sys.stderr, loadtable(header, rows)
write_csv(header,
rows,
sep=opts.sep,
filename=opts.outfile,
align=opts.align)
def count(args):
"""
%prog count *.gz
Count reads based on FASTQC results. FASTQC needs to be run on all the input
data given before running this command.
"""
from jcvi.utils.table import loadtable
p = OptionParser(count.__doc__)
p.add_option("--dir",
help="Sub-directory where FASTQC was run [default: %default]")
p.add_option("--human", default=False, action="store_true",
help="Human friendly numbers [default: %default]")
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
filenames = args
subdir = opts.dir
header = "Filename|Total Sequences|Sequence length|Total Bases".split("|")
rows = []
human = opts.human
for f in filenames:
folder = f.replace(".gz", "").rsplit(".", 1)[0] + "_fastqc"
if subdir:
folder = op.join(subdir, folder)
summaryfile = op.join(folder, "fastqc_data.txt")
fqcdata = FastQCdata(summaryfile, human=human)
row = [fqcdata[x] for x in header]
rows.append(row)
print >> sys.stderr, loadtable(header, rows)
fw = must_open(opts.outfile, "w")
data = [header] + rows
for d in data:
print >> fw, ",".join(str(x) for x in d)
