code string | signature string | docstring string | loss_without_docstring float64 | loss_with_docstring float64 | factor float64 |
|---|---|---|---|---|---|
from jcvi.assembly.soap import prepare
logging.debug("Work on {0} ({1})".format(pf, ','.join(p)))
asm = "{0}.closed.scafSeq".format(pf)
if not need_update(p, asm):
logging.debug("Assembly found: {0}. Skipped.".format(asm))
return
slink(p, pf, tag, extra)
cwd = os.getcwd()... | def soap_trios(p, pf, tag, extra) | Take one pair of reads and 'widow' reads after correction and run SOAP. | 6.864745 | 6.688527 | 1.026346 |
p = OptionParser(soapX.__doc__)
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
folder, tag = args[:2]
extra = args[2:]
extra = [get_abs_path(x) for x in extra]
tag = tag.split(",")
for p, pf in iter_project(folder, n=3):
soap_trios(p... | def soapX(args) | %prog soapX folder tag [*.fastq]
Run SOAP on a folder of paired reads and apply tag before assembly.
Optional *.fastq in the argument list will be symlinked in each folder and
co-assembled. | 4.510181 | 3.695247 | 1.220536 |
p = OptionParser(correctX.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
folder, tag = args
tag = tag.split(",")
for p, pf in iter_project(folder):
correct_pairs(p, pf, tag) | def correctX(args) | %prog correctX folder tag
Run ALLPATHS correction on a folder of paired reads and apply tag. | 3.86094 | 2.920025 | 1.322228 |
p = OptionParser(allpathsX.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
folder, tag = args
tag = tag.split(",")
for p, pf in iter_project(folder):
assemble_pairs(p, pf, tag) | def allpathsX(args) | %prog allpathsX folder tag
Run assembly on a folder of paired reads and apply tag (PE-200, PE-500).
Allow multiple tags separated by comma, e.g. PE-350,TT-1050 | 3.991196 | 3.081041 | 1.295405 |
p = OptionParser(stats.__doc__)
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
folder, = args
statsfiles = iglob(folder, "*.stats")
after_equal = lambda x: x.split("=")[-1]
header = "Library Assembled_reads Contigs".split()
... | def stats(args) | %prog stats folder
Generate table summarizing .stats files. | 3.521761 | 3.418794 | 1.030118 |
p = OptionParser(mcluster.__doc__)
add_consensus_options(p)
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
consensusfiles = args
minlength = opts.minlength
cpus = opts.cpus
pf = opts.prefix
pctid = find_pctid(consensusfi... | def mcluster(args) | %prog mcluster *.consensus
Cluster across samples using consensus sequences. | 3.205901 | 3.124623 | 1.026012 |
p = OptionParser(mconsensus.__doc__)
p.add_option("--allele_counts", default="allele_counts",
help="Directory to generate allele counts")
add_consensus_options(p)
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
consensusfiles = args
... | def mconsensus(args) | %prog mconsensus *.consensus
Call consensus along the stacks from cross-sample clustering. | 3.582958 | 3.552646 | 1.008532 |
p = OptionParser(consensus.__doc__)
p.add_option("--ploidy", default=2, type="int",
help="Number of haplotypes per locus")
add_consensus_options(p)
p.set_verbose()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
clustSfile, = ar... | def consensus(args) | %prog consensus clustSfile
Call consensus along the stacks. Tabulate bases at each site, tests for
errors according to error rate, calls consensus. | 3.478853 | 3.35002 | 1.038458 |
S, nreps = zip(*S)
S = np.array([list(x) for x in S])
rows, cols = S.shape
counts = []
for c in xrange(cols):
freq = [0] * NBASES
for b, nrep in zip(S[:, c], nreps):
freq[BASES.index(b)] += nrep
counts.append(freq)
return counts | def stack(S) | From list of bases at a site D, make counts of bases | 4.259558 | 3.903899 | 1.091103 |
cseq = seq.strip(GAPS)
leftjust = seq.index(cseq[0])
rightjust = seq.rindex(cseq[-1])
return leftjust, rightjust | def get_left_right(seq) | Find position of the first and last base | 4.899249 | 4.555274 | 1.075511 |
C = ClustFile(f)
for data in C:
names, seqs, nreps = zip(*data)
total_nreps = sum(nreps)
# Depth filter
if total_nreps < mindepth:
continue
S = []
for name, seq, nrep in data:
# Append sequence * number of dereps
S.append(... | def cons(f, mindepth) | Makes a list of lists of reads at each site | 8.61644 | 7.891144 | 1.091913 |
C = defaultdict(int)
for d in N:
C[tuple(d)] += 1
return [i for i in C.items() if (0, 0, 0, 0) not in i] | def makeC(N) | Makes a dictionary with counts of base counts [x,x,x,x]:x,
speeds up Likelihood calculation | 4.219463 | 4.384814 | 0.96229 |
p = OptionParser(estimateHE.__doc__)
add_consensus_options(p)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
clustSfile, = args
HEfile = clustSfile.rsplit(".", 1)[0] + ".HE"
if not need_update(clustSfile, HEfile):
logging.debug("File `{... | def estimateHE(args) | %prog estimateHE clustSfile
Estimate heterozygosity (H) and error rate (E). Idea borrowed heavily from
the PyRad paper. | 4.077169 | 3.660784 | 1.113742 |
matfile = op.join(datadir, "blosum80.mat")
cmd = "poa -read_fasta - -pir stdout {0} -tolower -silent -hb -fuse_all".format(matfile)
p = Popen(cmd, shell=True, stdin=PIPE, stdout=PIPE, stderr=STDOUT, close_fds=True)
s = ""
for i, j in zip(names, seqs):
s += "\n".join((i, j)) + "\n"
r... | def alignfast(names, seqs) | Performs MUSCLE alignments on cluster and returns output as string | 5.51488 | 5.646791 | 0.97664 |
seen = set()
filtered_names, filtered_seqs = [], []
for name, seq in zip(names, seqs):
samp = name.split(sep, 1)[0]
if samp in seen:
continue
seen.add(samp)
filtered_names.append(name)
filtered_seqs.append(seq)
nfiltered, nnames = len(filtered_na... | def filter_samples(names, seqs, sep='.') | When there are uncollapsed contigs within the same sample, only retain the
first seq, or the seq that is most abundant (with cluster_size). | 2.492549 | 2.494484 | 0.999224 |
p = OptionParser(cluster.__doc__)
add_consensus_options(p)
p.set_align(pctid=95)
p.set_outdir()
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
prefix = args[0]
fastqfiles = args[1:]
cpus = opts.cpus
pctid = opts.pcti... | def cluster(args) | %prog cluster prefix fastqfiles
Use `vsearch` to remove duplicate reads. This routine is heavily influenced
by PyRAD: <https://github.com/dereneaton/pyrad>. | 3.203557 | 3.118106 | 1.027405 |
p = OptionParser(align.__doc__)
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
clustfile, = args
parallel_musclewrap(clustfile, opts.cpus) | def align(args) | %prog align clustfile
Align clustfile to clustSfile. Useful for benchmarking aligners. | 3.97606 | 3.353732 | 1.185563 |
ax = np.array(x)
ay = np.array(y)
if title:
print(dark(title), file=sys.stderr)
az = ay * width / ay.max()
tx = [asciiaxis(x, digit=digit) for x in ax]
rjust = max([len(x) for x in tx]) + 1
for x, y, z in zip(tx, ay, az):
x = x.rjust(rjust)
y = y or ""
... | def asciiplot(x, y, digit=1, width=50, title=None, char="=") | Print out a horizontal plot using ASCII chars.
width is the textwidth (height) of the plot. | 4.347342 | 4.389501 | 0.990396 |
fig = plt.figure()
ax = fig.add_subplot(111)
xmax = 20 * (len(palette) + 1)
x1s = np.arange(0, xmax, 20)
xintervals = [10] * len(palette)
xx = zip(x1s, xintervals)
ax.broken_barh(xx, (5, 10), facecolors=palette)
ax.set_ylim(0, 20)
ax.set_xlim(0, xmax)
ax.set_axis_off()
... | def print_colors(palette, outfile="Palette.png") | print color palette (a tuple) to a PNG file for quick check | 2.762453 | 2.736808 | 1.00937 |
import random
from scipy import interpolate
if usepreset:
if 0 < N <= 5:
cmap = cm.gist_rainbow
elif N <= 20:
cmap = cm.Set1
else:
sys.exit(discrete_rainbow.__doc__)
cdict = cmap._segmentdata.copy()
# N colors
colors_i = np.linsp... | def discrete_rainbow(N=7, cmap=cm.Set1, usepreset=True, shuffle=False, \
plot=False) | Return a discrete colormap and the set of colors.
modified from
<http://www.scipy.org/Cookbook/Matplotlib/ColormapTransformations>
cmap: colormap instance, eg. cm.jet.
N: Number of colors.
Example
>>> x = resize(arange(100), (5,100))
>>> djet = cmap_discretize(cm.jet, 5)
>>> imshow(x,... | 3.428185 | 3.527971 | 0.971716 |
tc = "gray"
axt = ax.transAxes
yy = .95
for msg in messages:
ax.text(.95, yy, msg, color=tc, transform=axt, ha="right")
yy -= .05 | def write_messages(ax, messages) | Write text on canvas, usually on the top right corner. | 4.923167 | 4.442501 | 1.108197 |
'''
TODO: redundant with quickplot(), need to be refactored.
'''
if percentage:
total_length = sum(data.values())
data = dict((k, v * 100. / total_length) for (k, v) in data.items())
left, height = zip(*sorted(data.items()))
pad = max(height) * .01
c1, c2 = "darkslategray", ... | def quickplot_ax(ax, data, xmin, xmax, xlabel, title=None, ylabel="Counts",
counts=True, percentage=True, highlight=None) | TODO: redundant with quickplot(), need to be refactored. | 3.137947 | 2.916985 | 1.07575 |
plt.figure(1, (6, 6))
left, height = zip(*sorted(data.items()))
pad = max(height) * .01
if counts:
for l, h in zip(left, height):
if xmax and l > xmax:
break
plt.text(l, h + pad, str(h), color="darkslategray", size=8,
ha="center",... | def quickplot(data, xmin, xmax, xlabel, title, ylabel="Counts",
figname="plot.pdf", counts=True, print_stats=True) | Simple plotting function - given a dictionary of data, produce a bar plot
with the counts shown on the plot. | 2.888687 | 2.865021 | 1.00826 |
parts = au.split()
first = parts[0]
middle = [x for x in parts if x[-1] == '.']
middle = "".join(middle)
last = [x for x in parts[1:] if x[-1] != '.']
last = " ".join(last)
initials = "{0}.{1}".format(first[0], middle)
if first[-1] == '.': # Some people use full middle name
... | def get_name_parts(au) | Fares Z. Najar => last, first, initials
>>> get_name_parts("Fares Z. Najar")
('Najar', 'Fares', 'F.Z.') | 3.601522 | 3.718673 | 0.968496 |
from jcvi.formats.base import read_block
fp = open(lstfile)
all_authors = []
for header, seq in read_block(fp, "["):
seq = " ".join(seq)
authors = []
for au in seq.split(","):
au = au.strip()
if not au:
continue
au = strin... | def parse_names(lstfile) | This is the alternative format `lstfile`. In this format, there are two
sections, starting with [Sequence] and [Manuscript], respectively, then
followed by authors separated by comma. | 3.367142 | 3.314739 | 1.015809 |
p = OptionParser(names.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(p.print_help())
namelist, templatefile = args
# First check the alternative format
if open(namelist).read()[0] == '[':
out = parse_names(namelist)
make_template(templatefil... | def names(args) | %prog names namelist templatefile
Generate name blocks from the `namelist` file. The `namelist` file is
tab-delimited that contains >=4 columns of data. Three columns are mandatory.
First name, middle initial and last name. First row is table header. For the
extra columns, the first column will go in t... | 3.867542 | 3.425513 | 1.12904 |
p = OptionParser(main.__doc__)
p.add_option("-g", "--graphic", default=False, action="store_true",
help="Create boilerplate for a graphic script")
opts, args = p.parse_args()
if len(args) != 1:
sys.exit(not p.print_help())
script, = args
imports = graphic_imports if op... | def main() | %prog scriptname.py
create a minimal boilerplate for a new script | 3.494385 | 3.375291 | 1.035284 |
sd = [ambiguous_dna_values[x] for x in s]
return ["".join(x) for x in list(product(*sd))] | def unpack_ambiguous(s) | List sequences with ambiguous characters in all possibilities. | 5.841689 | 4.912655 | 1.18911 |
p = OptionParser(split.__doc__)
p.set_outdir(outdir="deconv")
p.add_option("--nocheckprefix", default=False, action="store_true",
help="Don't check shared prefix [default: %default]")
p.add_option("--paired", default=False, action="store_true",
help="Paired-end dat... | def split(args) | %prog split barcodefile fastqfile1 ..
Deconvolute fastq files into subsets of fastq reads, based on the barcodes
in the barcodefile, which is a two-column file like:
ID01 AGTCCAG
Input fastqfiles can be several files. Output files are ID01.fastq,
ID02.fastq, one file per line in barcodefile.
... | 3.162382 | 3.047901 | 1.03756 |
p = OptionParser(merge.__doc__)
p.set_outdir(outdir="outdir")
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
folders = args
outdir = opts.outdir
mkdir(outdir)
files = flatten(glob("{0}/*.*.fastq".format(x)) for x in folders)
files = lis... | def merge(args) | %prog merge folder1 ...
Consolidate split contents in the folders. The folders can be generated by
the split() process and several samples may be in separate fastq files. This
program merges them. | 3.191673 | 2.864713 | 1.114134 |
_p = set()
for x in p:
_p |= set(range(x - tolerance, x + tolerance + 1))
return _p | def expand_alleles(p, tolerance=0) | Returns expanded allele set given the tolerance. | 3.958681 | 3.49059 | 1.134101 |
_p1 = expand_alleles(p1, tolerance=tolerance)
_p2 = expand_alleles(p2, tolerance=tolerance)
possible_progenies = set(tuple(sorted(x)) for x in product(_p1, _p2))
if x_linked: # Add all hemizygotes
possible_progenies |= set((x, x) for x in (set(_p1) | set(_p2)))
return possible_progenie... | def get_progenies(p1, p2, x_linked=False, tolerance=0) | Returns possible progenies in a trio. | 3.126399 | 2.886915 | 1.082955 |
p = OptionParser(mendelian_errors2.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="7x7", format="png")
if len(args) != 1:
sys.exit(not p.print_help())
csvfile, = args
fig, ax = plt.subplots(ncols=1, nrows=1,
figsize=(iopts.w, iopts.h))
ro... | def mendelian_errors2(args) | %prog mendelian_errors2 Trios.summary.csv
Plot Mendelian errors as calculated by mendelian(). File
`Trios.summary.csv` looks like:
Name,Motif,Inheritance,N_Correct,N_Error,N_missing,ErrorRate [N_Error / (N_Correct + N_Error))]
DM1,CTG,AD,790,12,0,1.5%
DM2,CCTG,AD,757,45,0,5.6%
DRPLA,CAG,AD,791... | 3.437916 | 3.297673 | 1.042528 |
call_to_ints = lambda x: tuple(int(_) for _ in x.split("|") if _ != ".")
tp1_sex, tp1_call = tp1[:2]
tp2_sex, tp2_call = tp2[:2]
tpp_sex, tpp_call = tpp[:2]
# tp1_evidence = sum(int(x) for x in tp1[2:])
# tp2_evidence = sum(int(x) for x in tp2[2:])
# tpp_evidence = sum(int(x) for x in t... | def mendelian_check(tp1, tp2, tpp, is_xlinked=False) | Compare TRED calls for Parent1, Parent2 and Proband. | 2.425333 | 2.359935 | 1.027712 |
return (rname == target_chr) and \
(target_start <= rstart <= target_end) | def in_region(rname, rstart, target_chr, target_start, target_end) | Quick check if a point is within the target region. | 2.752519 | 2.604005 | 1.057033 |
import pysam
from jcvi.utils.iter import pairwise
from jcvi.utils.grouper import Grouper
p = OptionParser(alts.__doc__)
p.set_outfile(outfile="TREDs.alts.csv")
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
treds = args
repo = TREDsRepo... | def alts(args) | %prog alts HD
Build alternative loci based on simulation data. | 3.054109 | 3.034291 | 1.006531 |
import seaborn as sns
p = OptionParser(depth.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="14x14")
if len(args) != 1:
sys.exit(not p.print_help())
tsvfile, = args
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2,
... | def depth(args) | %prog depth DP.tsv
Plot read depths across all TREDs. | 3.201663 | 3.04475 | 1.051536 |
p = OptionParser(mendelian_errors.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="6x6")
if len(args) != 1:
sys.exit(not p.print_help())
csvfile, = args
fig, ax = plt.subplots(ncols=1, nrows=1,
figsize=(iopts.w, iopts.h))
root = fig.add_ax... | def mendelian_errors(args) | %prog mendelian_errors STR-Mendelian-errors.csv
Plot Mendelian errors as calculated by mendelian(). File
`STR-Mendelian-errors.csv` looks like:
,Duos - Mendelian errors,Trios - Mendelian errors
SCA36,1.40%,0.60%
ULD,0.30%,1.50%
BPES,0.00%,1.80%
One TRED disease per line, followed by duo ... | 2.710426 | 2.58465 | 1.048663 |
self_key = ["ChildSelf"]
keys = family.keys()
spouse_key = [x for x in keys if ("spouse" in x.lower())]
assert len(spouse_key) <= 1
parent_keys = [x for x in keys if \
("parent" in x.lower()) and ("grand" not in x.lower())]
sib_keys = [x for x in keys if ("sibling" in x.... | def extract_trios(family) | Identify all trios/duos inside a family, where a family contains dictionary
of relationship: individual, for example:
{
"ChildSelf": "176531498",
"DzTwin": "176531497",
"Parent": "176449143"
} | 3.457097 | 3.008599 | 1.149072 |
df = pd.read_csv(tsvfile, sep="\t", index_col=0, dtype={"SampleKey": str})
return df | def read_tred_tsv(tsvfile) | Read the TRED table into a dataframe. | 3.893492 | 3.781091 | 1.029727 |
p = OptionParser(mendelian.__doc__)
p.add_option("--tolerance", default=0, type="int",
help="Tolernace for differences")
p.set_verbose()
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
triosjson, tredtsv = args
verbose = opts.ve... | def mendelian(args) | %prog mendelian trios_candidate.json hli.20170424.tred.tsv
Calculate Mendelian errors based on trios and duos. | 2.93967 | 2.717172 | 1.081886 |
p = OptionParser(mini.__doc__)
p.add_option("--pad", default=20000, type="int",
help="Add padding to the STR reigons")
p.add_option("--treds", default=None,
help="Extract specific treds, use comma to separate")
p.set_outfile()
opts, args = p.parse_args(args)
... | def mini(args) | %prog mini bamfile minibamfile
Prepare mini-BAMs that contain only the STR loci. | 3.494931 | 2.879089 | 1.213902 |
p = OptionParser(likelihood.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="10x5",
style="white", cmap="coolwarm")
if len(args) != 0:
sys.exit(not p.print_help())
fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1,
... | def likelihood(args) | %prog likelihood
Plot likelihood surface. Look for two files in the current folder:
- 100_100.log, haploid model
- 100_20.log, diploid model | 3.072207 | 2.815662 | 1.091114 |
from matplotlib import gridspec
p = OptionParser(likelihood2.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="10x5",
style="white", cmap="coolwarm")
if len(args) != 1:
sys.exit(not p.print_help())
jsonfile, = args
fig = plt.figure(fi... | def likelihood2(args) | %prog likelihood2 100_20.json
Plot the likelihood surface and marginal distributions. | 2.750187 | 2.524802 | 1.089268 |
from matplotlib import gridspec
p = OptionParser(likelihood3.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="10x10",
style="white", cmap="coolwarm")
if len(args) != 2:
sys.exit(not p.print_help())
jsonfile1, jsonfile2 = args
fig = pl... | def likelihood3(args) | %prog likelihood3 140_20.json 140_70.json
Plot the likelihood surface and marginal distributions for two settings. | 2.457937 | 2.289191 | 1.073714 |
p = OptionParser(diagram.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="8x4", format="png")
if len(args) != 0:
sys.exit(not p.print_help())
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes([0, 0, 1, 1])
# Gauge on top, this is log-scale
yy = .7
... | def diagram(args) | %prog diagram
Plot the predictive power of various evidences. | 3.933568 | 3.89674 | 1.009451 |
p = OptionParser(allelefreqall.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
reportfile, = args
treds, df = read_treds(reportfile)
# Prepare 5 pages, each page with 6 distributions
treds = sorted(treds)
count = 6
pdfs = []
... | def allelefreqall(args) | %prog allelefreqall HN_Platinum_Gold.20180525.tsv.report.txt
Plot all 30 STR allele frequencies. | 4.114099 | 4.084572 | 1.007229 |
p = OptionParser(allelefreq.__doc__)
p.add_option("--nopanels", default=False, action="store_true",
help="No panel labels A, B, ...")
p.add_option("--usereport", help="Use allele frequency in report file")
opts, args, iopts = p.set_image_options(args, figsize="9x13")
if len(ar... | def allelefreq(args) | %prog allelefreq HD,DM1,SCA1,SCA17,FXTAS,FRAXE
Plot the allele frequencies of some STRs. | 2.65593 | 2.625992 | 1.011401 |
p = OptionParser(simulate.__doc__)
p.add_option("--method", choices=("wgsim", "eagle"), default="eagle",
help="Read simulator")
p.add_option("--ref", default="hg38", choices=("hg38", "hg19"),
help="Reference genome version")
p.add_option("--tred", default="HD", hel... | def simulate(args) | %prog simulate run_dir 1 300
Simulate BAMs with varying inserts with dwgsim. The above command will
simulate between 1 to 300 CAGs in the HD region, in a directory called
`run_dir`. | 3.401989 | 3.316764 | 1.025695 |
p = OptionParser(mergebam.__doc__)
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) not in (2, 3):
sys.exit(not p.print_help())
if len(args) == 2:
idir1, outdir = args
dir1 = [idir1] if idir1.endswith(".bam") else iglob(idir1, "*.bam")
logging.debug("H... | def mergebam(args) | %prog mergebam dir1 homo_outdir
or
%prog mergebam dir1 dir2/20.bam het_outdir
Merge sets of BAMs to make diploid. Two modes:
- Homozygous mode: pair-up the bams in the two folders and merge
- Heterozygous mode: pair the bams in first folder with a particular bam | 2.362583 | 2.285603 | 1.033681 |
p = OptionParser(batchlobstr.__doc__)
p.add_option("--haploid", default="chrY,chrM",
help="Use haploid model for these chromosomes")
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
bamlist, = args
cmd = "python -m j... | def batchlobstr(args) | %prog batchlobstr bamlist
Run lobSTR on a list of BAMs. The corresponding batch command for TREDPARSE:
$ tred.py bamlist --haploid chr4 --workdir tredparse_results | 3.976732 | 3.606621 | 1.10262 |
from jcvi.variation.str import LobSTRvcf
p = OptionParser(compilevcf.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
folder, = args
vcf_files = iglob(folder, "*.vcf,*.vcf.gz")
for vcf_file in vcf_files:
try:
p = Lob... | def compilevcf(args) | %prog compilevcf dir
Compile vcf outputs into lists. | 4.953265 | 4.859082 | 1.019383 |
p = OptionParser(evidences.__doc__)
p.add_option("--csv", default="hli.20170328.tred.tsv",
help="TRED csv output to plot")
opts, args, iopts = p.set_image_options(args, format="pdf")
if len(args) != 0:
sys.exit(not p.print_help())
format = iopts.format
# Extract ... | def evidences(args) | %prog evidences
Plot distribution of evidences against two factors:
- Sample mean coverage
- Longer allele | 2.590734 | 2.572255 | 1.007184 |
import seaborn as sns
sns.set_context('talk')
plt.clf()
register = {"MeanCoverage": "Sample Mean Coverage",
"HD.FDP": "Depth of full spanning reads",
"HD.PDP": "Depth of partial spanning reads",
"HD.PEDP": "Depth of paired-end reads",
... | def draw_jointplot(figname, x, y, data=None, kind="reg", color=None,
xlim=None, ylim=None, format="pdf") | Wraps around sns.jointplot | 4.202741 | 4.260574 | 0.986426 |
a, b = s.split("|")
ai, aj = a.split("-")
bi, bj = b.split("-")
los = [int(ai), int(bi)]
his = [int(aj), int(bj)]
if exclude and exclude in los:
los.remove(exclude)
if exclude and exclude in his:
his.remove(exclude)
return max(los), max(his) | def get_lo_hi_from_CI(s, exclude=None) | Parse the confidence interval from CI.
>>> get_lo_hi_from_CI("20-20/40-60")
(40, 60) | 2.461694 | 2.867074 | 0.858608 |
p = OptionParser(compare.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="10x10")
if len(args) != 1:
sys.exit(not p.print_help())
datafile, = args
pf = datafile.rsplit(".", 1)[0]
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2,
... | def compare(args) | %prog compare Evaluation.csv
Compare performances of various variant callers on simulated STR datasets. | 3.800547 | 3.429311 | 1.108254 |
p = OptionParser(compare2.__doc__)
p.add_option('--maxinsert', default=300, type="int",
help="Maximum number of repeats")
add_simulate_options(p)
opts, args, iopts = p.set_image_options(args, figsize="10x5")
if len(args) != 0:
sys.exit(not p.print_help())
depth = ... | def compare2(args) | %prog compare2
Compare performances of various variant callers on simulated STR datasets. | 2.679372 | 2.579708 | 1.038634 |
p = OptionParser(power.__doc__)
p.add_option('--maxinsert', default=300, type="int",
help="Maximum number of repeats")
add_simulate_options(p)
opts, args, iopts = p.set_image_options(args, figsize="10x10", format="png")
if len(args) != 0:
sys.exit(not p.print_help())
... | def power(args) | %prog power
Compare performances of various variant callers on simulated STR datasets.
This compares the power of various evidence types. | 2.410269 | 2.356353 | 1.022881 |
p = OptionParser(tredparse.__doc__)
p.add_option('--maxinsert', default=300, type="int",
help="Maximum number of repeats")
add_simulate_options(p)
opts, args, iopts = p.set_image_options(args, figsize="10x10")
if len(args) != 0:
sys.exit(not p.print_help())
depth ... | def tredparse(args) | %prog tredparse
Compare performances of various variant callers on simulated STR datasets.
Adds coverage comparisons as panel C and D. | 1.910129 | 1.86906 | 1.021973 |
from jcvi.utils.cbook import percentage
if summary:
unique = len(data)
total = sum(data)
# Print out a distribution
print("Unique: {0}".format(percentage(unique, total)), file=sys.stderr)
bins = defaultdict(int)
for d in data:
logd = int(log(d, base))
... | def loghistogram(data, base=2, ascii=True, title="Counts", summary=False) | bins is a dictionary with key: log(x, base), value: counts. | 3.783378 | 3.657698 | 1.03436 |
'''
Generate stem and leaf plot given a collection of numbers
'''
assert bins > 0
range = vmax - vmin
step = range * 1. / bins
if isinstance(range, int):
step = int(ceil(step))
step = step or 1
bins = np.arange(vmin, vmax + step, step)
hist, bin_edges = np.histogram(dat... | def stem_leaf_plot(data, vmin, vmax, bins, digit=1, title=None) | Generate stem and leaf plot given a collection of numbers | 4.550087 | 3.976255 | 1.144315 |
if ascii:
return texthistogram([numberfile], vmin, vmax, title=title,
bins=bins, skip=skip, col=col, base=base)
data, vmin, vmax = get_data(numberfile, vmin, vmax, skip=skip, col=col)
outfile = numberfile + ".base{0}.{1}".format(base, outfmt) \
if base else numb... | def histogram(numberfile, vmin, vmax, xlabel, title, outfmt="pdf",
bins=50, skip=0, col=0, ascii=False, base=0, fill="white") | Generate histogram using number from numberfile, and only numbers in the
range of (vmin, vmax) | 4.436201 | 4.62039 | 0.960136 |
if ascii:
return texthistogram(numberfiles, vmin, vmax, title=title,
bins=bins, skip=skip)
newfile = "_".join(op.basename(x).split(".")[0] for x in numberfiles)
fw = open(newfile, "w")
print("{0}\tgrp".format(xlabel), file=fw)
if tags:
tags = tags.split(",")
... | def histogram_multiple(numberfiles, vmin, vmax, xlabel, title, outfmt="pdf",
tags=None, bins=20, skip=0, ascii=False,
facet=False, fill="white", prefix="") | Generate histogram using number from numberfile, and only numbers in the
range of (vmin, vmax). First combining multiple files. | 3.785789 | 3.727287 | 1.015696 |
allowed_format = ("emf", "eps", "pdf", "png", "ps", \
"raw", "rgba", "svg", "svgz")
p = OptionParser(main.__doc__)
p.add_option("--skip", default=0, type="int",
help="skip the first several lines [default: %default]")
p.add_option("--col", default=0, type="int", he... | def main() | %prog numbers1.txt number2.txt ...
Print histogram of the data files. The data files contain one number per
line. If more than one file is inputted, the program will combine the
histograms into the same plot. | 3.243381 | 3.203633 | 1.012407 |
tlx, brx = [canvas2px(x, w, dpi) for x in (tlx, brx)]
tly, bry = [canvas2px(y, h, dpi) for y in (tly, bry)]
chr, bac_list = chr.split(':')
return '<area shape="rect" coords="' + \
",".join(str(x) for x in (tlx, tly, brx, bry)) \
+ '" href="#' + chr + '"' \
+ ' title... | def write_ImageMapLine(tlx, tly, brx, bry, w, h, dpi, chr, segment_start, segment_end) | Write out an image map area line with the coordinates passed to this
function
<area shape="rect" coords="tlx,tly,brx,bry" href="#chr7" title="chr7:100001..500001"> | 2.795164 | 2.469708 | 1.131779 |
p = OptionParser(batch.__doc__)
set_align_options(p)
p.set_sam_options()
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
ref_fasta, proj_dir, outdir = args
outdir = outdir.rstrip("/")
s3dir = None
if outdir.startswith("s3://"):
s... | def batch(args) | %proj batch database.fasta project_dir output_dir
Run bwa in batch mode. | 3.554724 | 3.451203 | 1.029995 |
p = OptionParser(index.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
dbfile, = args
check_index(dbfile) | def index(args) | %prog index database.fasta
Wrapper for `bwa index`. Same interface. | 2.642164 | 2.345026 | 1.12671 |
p.add_option("--bwa", default="bwa", help="Run bwa at this path")
p.add_option("--rg", help="Read group")
p.add_option("--readtype",
choices=("pacbio", "pbread", "ont2d", "intractg"),
help="Read type in bwa-mem")
p.set_cutoff(cutoff=800) | def set_align_options(p) | Used in align() and batch() | 7.068439 | 6.908607 | 1.023135 |
valid_modes = ("bwasw", "aln", "mem")
p = OptionParser(align.__doc__)
p.add_option("--mode", default="mem", choices=valid_modes, help="BWA mode")
set_align_options(p)
p.set_sam_options()
opts, args = p.parse_args(args)
mode = opts.mode
nargs = len(args)
if nargs not in (2, 3):... | def align(args) | %prog align database.fasta read1.fq [read2.fq]
Wrapper for three modes of BWA - mem (default), aln, bwasw (long reads). | 4.495648 | 4.058228 | 1.107786 |
dbfile, readfile = args
dbfile = check_index(dbfile)
saifile = check_aln(dbfile, readfile, cpus=opts.cpus)
samfile, _, unmapped = get_samfile(readfile, dbfile,
bam=opts.bam, unmapped=opts.unmapped)
if not need_update((dbfile, saifile), samfile):
l... | def samse(args, opts) | %prog samse database.fasta short_read.fastq
Wrapper for `bwa samse`. Output will be short_read.sam. | 5.516506 | 5.280905 | 1.044614 |
dbfile, read1file, read2file = args
dbfile = check_index(dbfile)
sai1file = check_aln(dbfile, read1file, cpus=opts.cpus)
sai2file = check_aln(dbfile, read2file, cpus=opts.cpus)
samfile, _, unmapped = get_samfile(read1file, dbfile,
bam=opts.bam, unmapped=o... | def sampe(args, opts) | %prog sampe database.fasta read1.fq read2.fq
Wrapper for `bwa sampe`. Output will be read1.sam. | 4.284782 | 3.892195 | 1.100865 |
dbfile, read1file = args[:2]
readtype = opts.readtype
pl = readtype or "illumina"
pf = op.basename(read1file).split(".")[0]
rg = opts.rg or r"@RG\tID:{0}\tSM:sm\tLB:lb\tPL:{1}".format(pf, pl)
dbfile = check_index(dbfile)
args[0] = dbfile
samfile, _, unmapped = get_samfile(read1file... | def mem(args, opts) | %prog mem database.fasta read1.fq [read2.fq]
Wrapper for `bwa mem`. Output will be read1.sam. | 5.403132 | 5.113921 | 1.056554 |
dbfile, readfile = args
dbfile = check_index(dbfile)
samfile, _, unmapped = get_samfile(readfile, dbfile,
bam=opts.bam, unmapped=opts.unmapped)
if not need_update(dbfile, samfile):
logging.error("`{0}` exists. `bwa bwasw` already run.".format(samfile)... | def bwasw(args, opts) | %prog bwasw database.fasta long_read.fastq
Wrapper for `bwa bwasw`. Output will be long_read.sam. | 5.497244 | 4.815858 | 1.141488 |
from jcvi.apps.base import mkdir
p = OptionParser(link.__doc__)
p.add_option("--dir",
help="Place links in a subdirectory [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
meta, = args
d = opts.dir
if d:... | def link(args) | %prog link metafile
Link source to target based on a tabular file. | 3.16386 | 2.964539 | 1.067235 |
p = OptionParser(touch.__doc__)
opts, args = p.parse_args(args)
fp = sys.stdin
for link_name in fp:
link_name = link_name.strip()
if not op.islink(link_name):
continue
if not op.exists(link_name):
continue
source = get_abs_path(link_name)
... | def touch(args) | find . -type l | %prog touch
Linux commands `touch` wouldn't modify mtime for links, this script can.
Use find to pipe in all the symlinks. | 4.039889 | 3.726762 | 1.084021 |
p = OptionParser(clean.__doc__)
opts, args = p.parse_args(args)
for link_name in os.listdir(os.getcwd()):
if not op.islink(link_name):
continue
logging.debug("remove symlink `{0}`".format(link_name))
os.unlink(link_name) | def clean(args) | %prog clean
Removes all symlinks from current folder | 3.776274 | 3.18476 | 1.185733 |
p = OptionParser(cp.__doc__)
fp = sys.stdin
for link_name in fp:
link_name = link_name.strip()
if not op.exists(link_name):
continue
source = get_abs_path(link_name)
link_name = op.basename(link_name)
if not op.exists(link_name):
os.syml... | def cp(args) | find folder -type l | %prog cp
Copy all the softlinks to the current folder, using absolute paths | 4.025498 | 3.663697 | 1.098753 |
from jcvi.utils.cbook import human_size
p = OptionParser(size.__doc__)
fp = sys.stdin
results = []
for link_name in fp:
link_name = link_name.strip()
if not op.islink(link_name):
continue
source = get_abs_path(link_name)
link_name = op.basename(li... | def size(args) | find folder -type l | %prog size
Get the size for all the paths that are pointed by the links | 3.46027 | 3.501798 | 0.988141 |
p = OptionParser(nucmer.__doc__)
opts, args = p.parse_args(args)
if len(args) != 5:
sys.exit(not p.print_help())
mapbed, mtrfasta, asmfasta, chr, idx = args
idx = int(idx)
m1 = 1000000
bedfile = "sample.bed"
bed = Bed()
bed.add("\t".join(str(x) for x in (chr, (idx - 1)... | def nucmer(args) | %prog nucmer mappings.bed MTR.fasta assembly.fasta chr1 3
Select specific chromosome region based on MTR mapping. The above command
will extract chr1:2,000,001-3,000,000. | 4.470879 | 3.997421 | 1.118441 |
from jcvi.apps.base import download
so_file_url = "http://obo.cvs.sourceforge.net/viewvc/obo/obo/ontology/genomic-proteomic/so.obo"
so_file = download(so_file_url, debug=False)
return GODag(so_file) | def load_GODag() | OBO file retrieved from http://obo.cvs.sourceforge.net/viewvc/obo/obo/ontology/genomic-proteomic/so.obo | 4.203075 | 2.188285 | 1.920716 |
if so is None:
so = load_GODag()
oterm = term
if term not in so.valid_names:
if "resolve" in method:
if "_" in term:
tparts = deque(term.split("_"))
tparts.pop() if "prefix" in method else tparts.popleft()
nterm = "_".join(tpa... | def validate_term(term, so=None, method="verify") | Validate an SO term against so.obo | 3.740386 | 3.688168 | 1.014158 |
p = OptionParser(agp.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
tablefile, = args
fp = open(tablefile)
for row in fp:
atoms = row.split()
hr = atoms[0]
scaf = atoms[1]
scaf_start = int(atoms[2]) + 1
... | def agp(args) | %prog agp Siirt_Female_pistachio_23May2017_table.txt
The table file, as prepared by Dovetail Genomics, is not immediately useful
to convert gene model coordinates, as assumed by formats.chain.fromagp().
This is a quick script to do such conversion. The file structure of this
table file is described in ... | 2.531113 | 2.428292 | 1.042343 |
p = OptionParser(traits.__doc__)
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
samples = []
for folder in args:
targets = iglob(folder, "*-traits.json")
if not targets:
continue
filename = targets[0]
js = jso... | def traits(args) | %prog traits directory
Make HTML page that reports eye and skin color. | 2.771375 | 2.53951 | 1.091303 |
p = OptionParser(regression.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="8x8")
if len(args) != 1:
sys.exit(not p.print_help())
tsvfile, = args
df = pd.read_csv(tsvfile, sep="\t")
chrono = "Chronological age (yr)"
pred = "Predicted age (yr)"
resdf = pd.Da... | def regression(args) | %prog regression postgenomic-s.tsv
Plot chronological vs. predicted age. | 3.351095 | 2.852391 | 1.174837 |
fig = plt.figure(1, size)
ax1 = plt.subplot2grid((2, 2), (0, 0))
ax2 = plt.subplot2grid((2, 2), (0, 1))
ax3 = plt.subplot2grid((2, 2), (1, 0))
ax4 = plt.subplot2grid((2, 2), (1, 1))
chemistry = ["V1", "V2", "V2.5", float("nan")]
colors = sns.color_palette("Set2", 8)
color_map = dict... | def composite_correlation(df, size=(12, 8)) | Plot composite correlation figure | 2.47629 | 2.4897 | 0.994613 |
p = OptionParser(correlation.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="12x8")
if len(args) != 1:
sys.exit(not p.print_help())
tsvfile, = args
df = pd.read_csv(tsvfile, sep="\t")
composite_correlation(df, size=(iopts.w, iopts.h))
outfile = tsvfile.rsplit("... | def correlation(args) | %prog correlation postgenomic-s.tsv
Plot correlation of age vs. postgenomic features. | 2.920494 | 2.444711 | 1.194617 |
df = df_orig.rename(columns={"hli_calc_age_sample_taken": "Age",
"hli_calc_gender": "Gender",
"eth7_max": "Ethnicity",
"MeanCoverage": "Mean coverage",
"Chemistry": "Sequencing chemistry",
"Releas... | def composite_qc(df_orig, size=(16, 12)) | Plot composite QC figures | 2.241422 | 2.233944 | 1.003347 |
# Construct the pairs of trait values
traitValuesAbsent = 0
nanValues = 0
genderSkipped = 0
twinValues = []
for a, b, t in triples:
if gender is not None and t != gender:
genderSkipped += 1
continue
if not (a in traits and b in traits):
tr... | def extract_twin_values(triples, traits, gender=None) | Calculate the heritability of certain traits in triplets.
Parameters
==========
triples: (a, b, "Female/Male") triples. The sample IDs are then used to query
the traits dictionary.
traits: sample_id => value dictionary
Returns
=======
tuples of size 2, that contain paired trai... | 2.99838 | 3.107881 | 0.964767 |
p = OptionParser(heritability.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="12x18")
if len(args) != 3:
sys.exit(not p.print_help())
combined, mz, dz = args
# Prepare twins data
def get_pairs(filename):
with open(filename) as fp:
for row in fp... | def heritability(args) | %prog pg.tsv MZ-twins.csv DZ-twins.csv
Plot composite figures ABCD on absolute difference of 4 traits,
EFGH on heritability of 4 traits. The 4 traits are:
telomere length, ccn.chrX, ccn.chrY, TRA.PPM | 4.214524 | 3.593874 | 1.172696 |
p = OptionParser(compile.__doc__)
p.set_outfile(outfile="age.tsv")
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
dfs = []
for folder in args:
ofolder = os.listdir(folder)
# telomeres
subdir = [x for x in ofolder if x.starts... | def compile(args) | %prog compile directory
Extract telomere length and ccn. | 2.904848 | 2.554879 | 1.13698 |
from random import choice
seq = Seq(''.join(choice('ACGT') for _ in xrange(size)))
s = SeqRecord(seq, id=name, description="Fake sequence")
SeqIO.write([s], fw, "fasta") | def simulate_one(fw, name, size) | Simulate a random sequence with name and size | 4.169788 | 3.774137 | 1.104832 |
p = OptionParser(simulate.__doc__)
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
idsfile, = args
fp = open(idsfile)
fw = must_open(opts.outfile, "w")
for row in fp:
name, size = row.split()
size = int(size)
... | def simulate(args) | %prog simulate idsfile
Simulate random FASTA file based on idsfile, which is a two-column
tab-separated file with sequence name and size. | 2.820316 | 2.221859 | 1.26935 |
p = OptionParser(gc.__doc__)
p.add_option("--binsize", default=500, type="int",
help="Bin size to use")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
fastafile, = args
binsize = opts.binsize
allbins = []
for name, seq in p... | def gc(args) | %prog gc fastafile
Plot G+C content distribution. | 2.657827 | 2.550005 | 1.042283 |
from jcvi.utils.cbook import SummaryStats
p = OptionParser(trimsplit.__doc__)
p.add_option("--minlength", default=1000, type="int",
help="Min length of contigs to keep")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
fastafile, = ... | def trimsplit(args) | %prog trimsplit fastafile
Split sequences at lower-cased letters and stretch of Ns. This is useful
at cleaning up the low quality bases for the QUIVER output. | 2.472383 | 2.359091 | 1.048024 |
from jcvi.formats.sizes import Sizes
p = OptionParser(qual.__doc__)
p.add_option("--qv", default=31, type="int",
help="Dummy qv score for extended bases")
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
fastafil... | def qual(args) | %prog qual fastafile
Generate dummy .qual file based on FASTA file. | 3.199175 | 2.91505 | 1.097468 |
p = OptionParser(info.__doc__)
p.add_option("--gaps", default=False, action="store_true",
help="Count number of gaps [default: %default]")
p.set_table()
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) == 0:
sys.exit(not p.print_help())
fastafiles ... | def info(args) | %prog info *.fasta
Run `sequence_info` on FASTA files. Generate a report per file. | 3.0887 | 2.713368 | 1.138327 |
p = OptionParser(fromtab.__doc__)
p.set_sep(sep=None)
p.add_option("--noheader", default=False, action="store_true",
help="Ignore first line")
p.add_option("--replace",
help="Replace spaces in name to char [default: %default]")
opts, args = p.parse_args(args)
... | def fromtab(args) | %prog fromtab tabfile fastafile
Convert 2-column sequence file to FASTA format. One usage for this is to
generatea `adapters.fasta` for TRIMMOMATIC. | 2.520715 | 2.327374 | 1.083072 |
p = OptionParser(longestorf.__doc__)
p.add_option("--ids", action="store_true",
help="Generate table with ORF info [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
fastafile, = args
pf = fastafile.rsplit(".", 1)... | def longestorf(args) | %prog longestorf fastafile
Find longest ORF for each sequence in fastafile. | 2.897203 | 2.803077 | 1.03358 |
from jcvi.utils.iter import grouper
p = OptionParser(ispcr.__doc__)
p.add_option("-r", dest="rclip", default=1, type="int",
help="pair ID is derived from rstrip N chars [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
... | def ispcr(args) | %prog ispcr fastafile
Reformat paired primers into isPcr query format, which is three column
format: name, forward, reverse | 3.108526 | 3.072927 | 1.011585 |
'''
parse a fasta-formatted file and returns header
can be a fasta file that contains multiple records.
'''
try:
fp = must_open(infile)
except:
fp = infile
# keep header
fa_iter = (x[1] for x in groupby(fp, lambda row: row[0] == '>'))
for header in fa_iter:
he... | def parse_fasta(infile, upper=False) | parse a fasta-formatted file and returns header
can be a fasta file that contains multiple records. | 4.364554 | 3.360394 | 1.298822 |
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