def draw_cytoband(ax,
chrom,
filename=datafile("hg38.band.txt"),
ymid=0.5,
width=0.99,
height=0.11):
import pandas as pd
bands = pd.read_csv(filename, sep="\t")
chrombands = bands[bands["#chrom"] == chrom]
data = []
for i, (chr, start, end, name, gie) in chrombands.iterrows():
data.append((chr, start, end, name, gie))
chromsize = max(x[2] for x in data)
scale = width * 1.0 / chromsize
xstart, ystart = (1 - width) / 2, ymid - height / 2
bp_to_pos = lambda x: xstart + x * scale
in_acen = False
for chr, start, end, name, gie in data:
color, alpha = get_color(gie)
bplen = end - start
if "acen" in gie:
if in_acen:
xys = [
(bp_to_pos(start), ymid),
(bp_to_pos(end), ystart),
(bp_to_pos(end), ystart + height),
]
else:
xys = [
(bp_to_pos(start), ystart),
(bp_to_pos(start), ystart + height),
(bp_to_pos(end), ymid),
]
p = Polygon(xys, closed=True, ec="k", fc=color, alpha=alpha)
in_acen = True
else:
p = Rectangle(
(bp_to_pos(start), ystart),
bplen * scale,
height,
ec="k",
fc=color,
alpha=alpha,
)
# print bp_to_pos(end)
ax.add_patch(p)
ax.text(
bp_to_pos((start + end) / 2),
ymid + height * 0.8,
name,
rotation=40,
color="lightslategray",
)
ax.text(0.5, ystart - height, chrom, size=16, ha="center", va="center")
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.set_axis_off()
def treds(args):
"""
%prog treds hli.tred.tsv
Compile allele_frequency for TREDs results. Write data.tsv, meta.tsv and
mask.tsv in one go.
"""
from jcvi.apps.base import datafile
p = OptionParser(treds.__doc__)
p.add_option("--csv",
default=False,
action="store_true",
help="Also write `meta.csv`")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
(tredresults, ) = args
df = pd.read_csv(tredresults, sep="\t")
tredsfile = datafile("TREDs.meta.csv")
tf = pd.read_csv(tredsfile)
tds = list(tf["abbreviation"])
ids = list(tf["id"])
tags = ["SampleKey"]
final_columns = ["SampleKey"]
afs = []
for td, id in zip(tds, ids):
tag1 = "{}.1".format(td)
tag2 = "{}.2".format(td)
if tag2 not in df:
afs.append("{}")
continue
tags.append(tag2)
final_columns.append(id)
a = np.array(list(df[tag1]) + list(df[tag2]))
counts = alleles_to_counts(a)
af = counts_to_af(counts)
afs.append(af)
tf["allele_frequency"] = afs
metafile = "TREDs_{}_SEARCH.meta.tsv".format(timestamp())
tf.to_csv(metafile, sep="\t", index=False)
logging.debug("File `{}` written.".format(metafile))
if opts.csv:
metacsvfile = metafile.rsplit(".", 1)[0] + ".csv"
tf.to_csv(metacsvfile, index=False)
logging.debug("File `{}` written.".format(metacsvfile))
pp = df[tags]
pp.columns = final_columns
datafile = "TREDs_{}_SEARCH.data.tsv".format(timestamp())
pp.to_csv(datafile, sep="\t", index=False)
logging.debug("File `{}` written.".format(datafile))
mask([datafile, metafile])
def locus(args):
"""
%prog locus bamfile
Extract selected locus from a list of TREDs for validation, and run lobSTR.
"""
from jcvi.formats.sam import get_minibam
# See `Format-lobSTR-database.ipynb` for a list of TREDs for validation
INCLUDE = [
"HD", "SBMA", "SCA1", "SCA2", "SCA8", "SCA17", "DM1", "DM2", "FXTAS"
]
db_choices = ("hg38", "hg19")
p = OptionParser(locus.__doc__)
p.add_option("--tred", choices=INCLUDE, help="TRED name")
p.add_option("--ref",
choices=db_choices,
default="hg38",
help="Reference genome")
p.set_home("lobstr")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
(bamfile, ) = args
ref = opts.ref
lhome = opts.lobstr_home
tred = opts.tred
tredsfile = datafile("TREDs.meta.csv")
tf = pd.read_csv(tredsfile, index_col=0)
row = tf.ix[tred]
tag = "repeat_location"
ldb = "TREDs"
if ref == "hg19":
tag += "." + ref
ldb += "-" + ref
seqid, start_end = row[tag].split(":")
PAD = 1000
start, end = start_end.split("-")
start, end = int(start) - PAD, int(end) + PAD
region = "{}:{}-{}".format(seqid, start, end)
minibamfile = get_minibam(bamfile, region)
c = seqid.replace("chr", "")
cmd, vcf = allelotype_on_chr(minibamfile, c, lhome, ldb)
sh(cmd)
parser = LobSTRvcf(columnidsfile=None)
parser.parse(vcf, filtered=False)
items = parser.items()
if not items:
print("No entry found!", file=sys.stderr)
return
k, v = parser.items()[0]
print("{} => {}".format(tred, v.replace(",", "/")), file=sys.stderr)
def locus(args):
"""
%prog locus bamfile
Extract selected locus from a list of TREDs for validation, and run lobSTR.
"""
from jcvi.formats.sam import get_minibam
# See `Format-lobSTR-database.ipynb` for a list of TREDs for validation
INCLUDE = ["HD", "SBMA", "SCA1", "SCA2", "SCA8", "SCA17", "DM1", "DM2",
"FXTAS"]
db_choices = ("hg38", "hg19")
p = OptionParser(locus.__doc__)
p.add_option("--tred", choices=INCLUDE,
help="TRED name")
p.add_option("--ref", choices=db_choices, default="hg38",
help="Reference genome")
p.set_home("lobstr")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
bamfile, = args
ref = opts.ref
lhome = opts.lobstr_home
tred = opts.tred
tredsfile = datafile("TREDs.meta.csv")
tf = pd.read_csv(tredsfile, index_col=0)
row = tf.ix[tred]
tag = "repeat_location"
ldb = "TREDs"
if ref == "hg19":
tag += "." + ref
ldb += "-" + ref
seqid, start_end = row[tag].split(":")
PAD = 1000
start, end = start_end.split('-')
start, end = int(start) - PAD, int(end) + PAD
region = "{}:{}-{}".format(seqid, start, end)
minibamfile = get_minibam(bamfile, region)
c = seqid.replace("chr", "")
cmd, vcf = allelotype_on_chr(minibamfile, c, lhome, ldb)
sh(cmd)
parser = LobSTRvcf(columnidsfile=None)
parser.parse(vcf, filtered=False)
items = parser.items()
if not items:
print("No entry found!", file=sys.stderr)
return
k, v = parser.items()[0]
print("{} => {}".format(tred, v.replace(',', '/')), file=sys.stderr)
def treds(args):
"""
%prog treds hli.tred.tsv
Compile allele_frequency for TREDs results. Write data.tsv, meta.tsv and
mask.tsv in one go.
"""
p = OptionParser(treds.__doc__)
p.add_option("--csv", default=False, action="store_true",
help="Also write `meta.csv`")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
tredresults, = args
df = pd.read_csv(tredresults, sep="\t")
tredsfile = datafile("TREDs.meta.csv")
tf = pd.read_csv(tredsfile)
tds = list(tf["abbreviation"])
ids = list(tf["id"])
tags = ["SampleKey"]
final_columns = ["SampleKey"]
afs = []
for td, id in zip(tds, ids):
tag1 = "{}.1".format(td)
tag2 = "{}.2".format(td)
if tag2 not in df:
afs.append("{}")
continue
tags.append(tag2)
final_columns.append(id)
a = np.array(list(df[tag1]) + list(df[tag2]))
counts = alleles_to_counts(a)
af = counts_to_af(counts)
afs.append(af)
tf["allele_frequency"] = afs
metafile = "TREDs_{}_SEARCH.meta.tsv".format(timestamp())
tf.to_csv(metafile, sep="\t", index=False)
logging.debug("File `{}` written.".format(metafile))
if opts.csv:
metacsvfile = metafile.rsplit(".", 1)[0] + ".csv"
tf.to_csv(metacsvfile, index=False)
logging.debug("File `{}` written.".format(metacsvfile))
pp = df[tags]
pp.columns = final_columns
datafile = "TREDs_{}_SEARCH.data.tsv".format(timestamp())
pp.to_csv(datafile, sep="\t", index=False)
logging.debug("File `{}` written.".format(datafile))
mask([datafile, metafile])
def read_treds(tredsfile=datafile("TREDs.meta.csv")):
if tredsfile.endswith(".csv"):
df = pd.read_csv(tredsfile)
treds = set(df["id"])
else:
df = pd.read_csv(tredsfile, sep="\t")
treds = set(df["abbreviation"])
logging.debug("Loaded {} treds from `{}`".format(len(treds), tredsfile))
return treds, df
def read_treds(tredsfile=datafile("TREDs.meta.csv")):
if tredsfile.endswith(".csv"):
df = pd.read_csv(tredsfile)
treds = set(df["id"])
else:
df = pd.read_csv(tredsfile, sep="\t")
treds = set(df["abbreviation"])
logging.debug("Loaded {} treds from `{}`".format(len(treds), tredsfile))
return treds, df
def make_STR_bed(filename="STR.bed", pad=0, treds=None):
tredsfile = datafile("TREDs.meta.csv")
tf = pd.read_csv(tredsfile)
tds = list(tf["abbreviation"])
regions = list(tf["repeat_location"])
fw = must_open(filename, "w")
extract_Y = False
for td, region in zip(tds, regions):
if treds and (td not in treds):
continue
c, startend = region.split(":")
extract_Y = extract_Y or (c == "chrY")
start, end = startend.split("-")
start, end = int(start), int(end)
print >> fw, "\t".join(str(x) for x in (c, start - pad, end + pad, td))
if not extract_Y:
return filename
UNIQY = datafile("chrY.hg38.unique_ccn.gc")
fp = open(UNIQY)
nregions = 0
for i, row in enumerate(fp):
# Some regions still have mapped reads, exclude a few
if i in (1, 4, 6, 7, 10, 11, 13, 16, 18, 19):
continue
if nregions >= 5:
break
c, start, end, gc = row.split()
start, end = int(start), int(end)
print >> fw, "\t".join(
str(x) for x in (c, start - pad, end + pad,
"chrY.unique_ccn.{}".format(nregions)))
nregions += 1
fw.close()
return filename
def draw_cytoband(ax, chrom, filename=datafile("hg38.band.txt"),
ymid=.5, width=.99, height=.11):
import pandas as pd
bands = pd.read_csv(filename, sep="\t")
chrombands = bands[bands["#chrom"] == chrom]
data = []
for i, (chr, start, end, name, gie) in chrombands.iterrows():
data.append((chr, start, end, name, gie))
chromsize = max(x[2] for x in data)
scale = width * 1. / chromsize
xstart, ystart = (1 - width) / 2, ymid - height / 2
bp_to_pos = lambda x: xstart + x * scale
in_acen = False
for chr, start, end, name, gie in data:
color, alpha = get_color(gie)
bplen = end - start
if "acen" in gie:
if in_acen:
xys = [(bp_to_pos(start), ymid), (bp_to_pos(end), ystart), (bp_to_pos(end), ystart + height)]
else:
xys = [(bp_to_pos(start), ystart), (bp_to_pos(start), ystart + height), (bp_to_pos(end), ymid)]
p = Polygon(xys, closed=True, ec='k', fc=color, alpha=alpha)
in_acen = True
else:
p = Rectangle((bp_to_pos(start), ystart), bplen * scale, height,
ec='k', fc=color, alpha=alpha)
#print bp_to_pos(end)
ax.add_patch(p)
ax.text(bp_to_pos((start + end) / 2), ymid + height * .8, name, rotation=40, color="lightslategray")
ax.text(.5, ystart - height, chrom, size=16, ha="center", va="center")
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.set_axis_off()
def __init__(self, filename=datafile("instance.json")):
super(InstanceSkeleton, self).__init__(filename)
self.spec = json.load(open(filename))
from math import log, ceil
from collections import Counter, defaultdict
from multiprocessing import Pool
from jcvi.utils.cbook import percentage, uniqify
from jcvi.formats.base import timestamp
from jcvi.formats.bed import natsorted
from jcvi.apps.grid import MakeManager
from jcvi.formats.base import LineFile, must_open
from jcvi.utils.aws import push_to_s3, pull_from_s3, check_exists_s3, ls_s3
from jcvi.apps.base import OptionParser, ActionDispatcher, mkdir, need_update, \
datafile, sh
REF = "hg38"
REPO = datafile("TREDs.meta.csv")
READLEN = 150
MINSCORE = 36
YSEARCH_HAPLOTYPE = """
DYS393 DYS390 DYS19/DYS394 DYS19b DYS391 DYS385a DYS385b DYS426 DYS388 DYS439
DYS389I DYS392 DYS389B DYS458 DYS459a/b DYS459a/b DYS455 DYS454 DYS447 DYS437
DYS448 DYS449 DYS464a/b/c/d DYS464a/b/c/d DYS464a/b/c/d DYS464a/b/c/d DYS464e DYS464f DYS464g DYS460
GATA-H4 YCAIIa YCAIIb DYS456 DYS607 DYS576 DYS570 CDYa CDYb DYS442
DYS438 DYS531 DYS578 DYS395S1a/b DYS395S1a/b DYS590 DYS537 DYS641 DYS472 DYS406S1
DYS511 DYS425 DYS413a DYS413b DYS557 DYS594 DYS436 DYS490 DYS534 DYS450
DYS444 DYS481 DYS520 DYS446 DYS617 DYS568 DYS487 DYS572 DYS640 DYS492
DYS565 DYS461 DYS462 GATA-A10 DYS635 GAAT1B07 DYS441 DYS445 DYS452 DYS463
DYS434 DYS435 DYS485 DYS494 DYS495 DYS505 DYS522 DYS533 DYS549 DYS556
DYS575 DYS589 DYS636 DYS638 DYS643 DYS714 DYS716 DYS717 DYS726 DXYS156-Y
""".split()
from math import log, ceil
from collections import Counter, defaultdict
from multiprocessing import Pool
from jcvi.utils.cbook import percentage, uniqify
from jcvi.formats.base import timestamp
from jcvi.formats.bed import natsorted
from jcvi.apps.grid import MakeManager
from jcvi.formats.base import LineFile, must_open
from jcvi.utils.aws import push_to_s3, pull_from_s3, check_exists_s3, ls_s3
from jcvi.apps.base import OptionParser, ActionDispatcher, mkdir, need_update, \
datafile, sh
REF = "hg38"
REPO = datafile("TREDs.meta.csv")
READLEN = 150
MINSCORE = 36
YSEARCH_HAPLOTYPE = """
DYS393 DYS390 DYS19/DYS394 DYS19b DYS391 DYS385a DYS385b DYS426 DYS388 DYS439
DYS389I DYS392 DYS389B DYS458 DYS459a/b DYS459a/b DYS455 DYS454 DYS447 DYS437
DYS448 DYS449 DYS464a/b/c/d DYS464a/b/c/d DYS464a/b/c/d DYS464a/b/c/d DYS464e DYS464f DYS464g DYS460
GATA-H4 YCAIIa YCAIIb DYS456 DYS607 DYS576 DYS570 CDYa CDYb DYS442
DYS438 DYS531 DYS578 DYS395S1a/b DYS395S1a/b DYS590 DYS537 DYS641 DYS472 DYS406S1
DYS511 DYS425 DYS413a DYS413b DYS557 DYS594 DYS436 DYS490 DYS534 DYS450
DYS444 DYS481 DYS520 DYS446 DYS617 DYS568 DYS487 DYS572 DYS640 DYS492
DYS565 DYS461 DYS462 GATA-A10 DYS635 GAAT1B07 DYS441 DYS445 DYS452 DYS463
DYS434 DYS435 DYS485 DYS494 DYS495 DYS505 DYS522 DYS533 DYS549 DYS556
DYS575 DYS589 DYS636 DYS638 DYS643 DYS714 DYS716 DYS717 DYS726 DXYS156-Y
""".split()
def __init__(self, filename=datafile("instance.json")):
super(InstanceSkeleton, self).__init__(filename)
self.spec = json.load(open(filename))
def get_hg38_chromsizes(filename=datafile("hg38.chrom.sizes")):
chromsizes = DictFile(filename)
chromsizes = dict((k, int(v)) for k, v in chromsizes.items())
return chromsizes
def read_treds(tredsfile=datafile("TREDs.meta.csv")):
df = pd.read_csv(tredsfile)
treds = set(df["id"])
logging.debug("Loaded {} treds from `{}`".format(len(treds), tredsfile))
return treds, df
