code string | signature string | docstring string | loss_without_docstring float64 | loss_with_docstring float64 | factor float64 |
|---|---|---|---|---|---|
from jcvi.formats.blast import chain_HSPs
p = OptionParser(overlap.__doc__)
p.add_option("--dir", default=os.getcwd(),
help="Download sequences to dir [default: %default]")
p.add_option("--suffix", default="fasta",
help="Suffix of the sequence file in dir [default: %default... | def overlap(args) | %prog overlap <a|a.fasta> <b|b.fasta>
Check overlaps between two fasta records. The arguments can be genBank IDs
instead of FASTA files. In case of IDs, the sequences will be downloaded
first. | 2.918242 | 2.857532 | 1.021246 |
p = OptionParser(certificate.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
tpffile, certificatefile = args
fastadir = "fasta"
tpf = TPF(tpffile)
data = check_certificate(certificatefile)
fw = must_open(certificatefile, "w")
... | def certificate(args) | %prog certificate tpffile certificatefile
Generate certificate file for all overlaps in tpffile. tpffile can be
generated by jcvi.formats.agp.tpf().
North chr1 2 0 AC229737.8 telomere 58443
South chr1 2 1 AC229737.8 AC202463.29 58443 37835 58443 + Non-terminal
Each line describe... | 5.966173 | 5.004024 | 1.192275 |
p = OptionParser(neighbor.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
agpfile, componentID = args
fastadir = "fasta"
cmd = "grep"
cmd += " --color -C2 {0} {1}".format(componentID, agpfile)
sh(cmd)
agp = AGP(agpfile)
ao... | def neighbor(args) | %prog neighbor agpfile componentID
Check overlaps of a particular component in agpfile. | 4.249483 | 3.751676 | 1.132689 |
from jcvi.formats.base import DictFile
p = OptionParser(agp.__doc__)
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
tpffile, certificatefile, agpfile = args
orientationguide = DictFile(tpffile, valuepos=2)
cert = Certificate(certificatefile)
... | def agp(args) | %prog agp tpffile certificatefile agpfile
Build agpfile from overlap certificates.
Tiling Path File (tpf) is a file that lists the component and the gaps.
It is a three-column file similar to below, also see jcvi.formats.agp.tpf():
telomere chr1 na
AC229737.8 chr1 +
AC202463.29... | 4.055129 | 3.119316 | 1.300006 |
aLhang, aRhang = self.qstart - 1, self.asize - self.qstop
bLhang, bRhang = self.sstart - 1, self.bsize - self.sstop
if self.orientation == '-':
bLhang, bRhang = bRhang, bLhang
if self.qreverse:
aLhang, aRhang = aRhang, aLhang
bLhang, bRhang = ... | def get_hangs(self) | Determine the type of overlap given query, ref alignment coordinates
Consider the following alignment between sequence a and b:
aLhang \ / aRhang
\------------/
/------------\
bLhang / \ bRhang
Terminal overlap: a before b, b be... | 2.782713 | 2.294577 | 1.212735 |
print(aclr, bclr, file=sys.stderr)
otype = self.otype
if otype == 1:
if aclr.orientation == '+':
aclr.end = self.qstop
else:
aclr.start = self.qstart
if bclr.orientation == '+':
bclr.start = self.sstop ... | def update_clr(self, aclr, bclr) | Zip the two sequences together, using "left-greedy" rule
============= seqA
||||
====(===============) seqB | 2.925367 | 2.857793 | 1.023646 |
aLhang, aRhang, bLhang, bRhang = self.get_hangs()
achar = ">"
bchar = "<" if self.orientation == '-' else ">"
if self.qreverse:
achar = "<"
bchar = {">" : "<", "<" : ">"}[bchar]
print(aLhang, aRhang, bLhang, bRhang, file=sys.stderr)
widt... | def print_graphic(self) | >>>>>>>>>>>>>>>>>>> seqA (alen)
||||||||
<<<<<<<<<<<<<<<<<<<<< seqB (blen) | 3.03035 | 2.87799 | 1.05294 |
fw = must_open(filename, "w")
for aid, bb in groupby(self.lines, key=lambda x: x.aid):
bb = list(bb)
north, south = bb
aid = north.aid
assert aid == south.aid
aphase = north.aphase
chr = north.chr
size = north.... | def write_AGP(self, filename, orientationguide={}) | For each component, we have two overlaps: North and South.
=======
|||| South
====(=================) Current BAC
North ||||
===============
For the case that says "Non-terminal", the overlap will not be
con... | 3.652269 | 3.44891 | 1.058963 |
import hashlib
from jcvi.algorithms.formula import MAD_interval as confidence_interval
from jcvi.graphics.base import latex, plt, savefig, set2
p = OptionParser(gcdepth.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
sample_name, tag =... | def gcdepth(args) | %prog gcdepth sample_name tag
Plot GC content vs depth vs genomnic bins. Inputs are mosdepth output:
- NA12878_S1.mosdepth.global.dist.txt
- NA12878_S1.mosdepth.region.dist.txt
- NA12878_S1.regions.bed.gz
- NA12878_S1.regions.bed.gz.csi
- NA12878_S1.regions.gc.bed.gz
A sample mosdepth.sh s... | 3.979648 | 3.678486 | 1.081871 |
p = OptionParser(exonunion.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
gencodebed, = args
beds = BedTool(gencodebed)
# fields[3] is gene_id; fields[6] is gene_name
for g, gb in groupby(beds, key=lambda x: x.fields[3]):
gb = ... | def exonunion(args) | %prog exonunion gencode.v26.annotation.exon.bed
Collapse overlapping exons within the same gene. File
`gencode.v26.annotation.exon.bed` can be generated by:
$ zcat gencode.v26.annotation.gtf.gz | awk 'OFS="\t" {if ($3=="exon")
{print $1,$4-1,$5,$10,$12,$14,$16,$7}}' | tr -d '";' | 4.141375 | 4.135713 | 1.001369 |
from cyvcf2 import VCF
counter = Counter()
for v in VCF(vcffile):
tag = v.ID.split(":")[1]
counter[tag] += 1
return counter | def get_gain_loss_summary(vcffile) | Extract Canvas:GAIN/LOSS/REF/LOH tags | 4.46593 | 4.090415 | 1.091804 |
p = OptionParser(summarycanvas.__doc__)
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
for vcffile in args:
counter = get_gain_loss_summary(vcffile)
pf = op.basename(vcffile).split(".")[0]
print(pf + " " +
" ".join("{}:... | def summarycanvas(args) | %prog summarycanvas output.vcf.gz
Generate tag counts (GAIN/LOSS/REF/LOH) of segments in Canvas output. | 3.438965 | 2.698456 | 1.274419 |
from cStringIO import StringIO
from cyvcf2 import VCF
output = StringIO()
for v in VCF(vcffile):
chrom = v.CHROM
start = v.start
end = v.INFO.get('END') - 1
cn, = v.format('CN')[0]
print("\t".join(str(x) for x in (chrom, start, end, cn)), file=output)
b... | def parse_segments(vcffile) | Extract all copy number segments from a CANVAS file
VCF line looks like:
chr1 788879 Canvas:GAIN:chr1:788880-821005 N <CNV> 2 q10
SVTYPE=CNV;END=821005;CNVLEN=32126 RC:BC:CN:MCC 157:4:3:2 | 3.326536 | 3.468098 | 0.959182 |
if not counter:
return np.nan, np.nan
total = sum(v for k, v in counter.items())
mid = total / 2
weighted_sum = 0
items_seen = 0
median_found = False
for k, v in sorted(counter.items()):
weighted_sum += k * v
items_seen += v
if not median_found and items... | def counter_mean_and_median(counter) | Calculate the mean and median value of a counter | 2.62955 | 2.703175 | 0.972764 |
if not counter:
return "na"
return ",".join("{}:{}".format(*z) for z in sorted(counter.items())) | def counter_format(counter) | Pretty print a counter so that it appears as: "2:200,3:100,4:20" | 6.383723 | 4.84408 | 1.31784 |
p = OptionParser(gcn.__doc__)
p.set_cpus()
p.set_tmpdir(tmpdir="tmp")
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
exonbed = args[0]
canvasvcfs = args[1:]
tsvfile = opts.outfile
tmpdir = opts.tmpdir
mkdir(tmpdi... | def gcn(args) | %prog gcn gencode.v26.exonunion.bed data/*.vcf.gz
Compile gene copy njumber based on CANVAS results. | 4.104642 | 3.329911 | 1.232658 |
canvasvcf, exonbed, i = arg
logging.debug("Working on job {}: {}".format(i, canvasvcf))
samplekey = op.basename(canvasvcf).split(".")[0].rsplit('_', 1)[0]
d = {'SampleKey': samplekey}
exons = BedTool(exonbed)
cn = parse_segments(canvasvcf)
overlaps = exons.intersect(cn, wao=True)
g... | def vcf_to_df_worker(arg) | Convert CANVAS vcf to a dict, single thread | 3.925836 | 3.826679 | 1.025912 |
df = pd.DataFrame()
p = Pool(processes=cpus)
results = []
args = [(x, exonbed, i) for (i, x) in enumerate(canvasvcfs)]
r = p.map_async(vcf_to_df_worker, args,
callback=results.append)
r.wait()
for res in results:
df = df.append(res, ignore_index=True)
re... | def vcf_to_df(canvasvcfs, exonbed, cpus) | Compile a number of vcf files into tsv file for easy manipulation | 2.57602 | 2.50829 | 1.027002 |
tsvfile += suffix
columns = ["SampleKey"] + sorted(x for x in df.columns
if x.endswith(suffix))
tf = df.reindex_axis(columns, axis='columns')
tf.sort_values("SampleKey")
tf.to_csv(tsvfile, sep='\t', index=False, float_format='%.4g', na_rep="na")
print("T... | def df_to_tsv(df, tsvfile, suffix) | Serialize the dataframe as a tsv | 4.37793 | 4.508751 | 0.970985 |
from jcvi.graphics.base import savefig
p = OptionParser(coverage.__doc__)
opts, args, iopts = p.set_image_options(args, format="png")
if len(args) != 1:
sys.exit(not p.print_help())
covfile, = args
df = pd.read_csv(covfile, sep='\t', names=["Ref", "Position", "Depth"])
xlabe... | def coverage(args) | %prog coverage *.coverage
Plot coverage along chromosome. The coverage file can be generated with:
$ samtools depth a.bam > a.coverage
The plot is a simple line plot using matplotlib. | 3.109315 | 2.936401 | 1.058886 |
from jcvi.graphics.base import savefig
p = OptionParser(plot.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="8x7", format="png")
if len(args) != 3:
sys.exit(not p.print_help())
workdir, sample_key, chrs = args
chrs = chrs.split(",")
hmm = CopyNumberHMM(workdir... | def plot(args) | %prog plot workdir sample chr1,chr2
Plot some chromosomes for visual proof. Separate multiple chromosomes with
comma. Must contain folder workdir/sample-cn/. | 3.026196 | 2.73249 | 1.107487 |
p = OptionParser(sweep.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
workdir, sample_key = args
golden_ratio = (1 + 5 ** .5) / 2
cmd = "python -m jcvi.variation.cnv hmm {} {}".format(workdir, sample_key)
cmd += " --mu {:.5f} --sigma {... | def sweep(args) | %prog sweep workdir 102340_NA12878
Write a number of commands to sweep parameter space. | 2.913815 | 2.771933 | 1.051185 |
p = OptionParser(compare.__doc__)
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
truths = args[0]
cnvoutputs = args[1:]
cpus = min(len(cnvoutputs), opts.cpus)
p = Pool(processes=cpus)
results = []
files = [(x, truths) fo... | def compare(args) | %prog compare NA12878_array_hg38.bed *.seg
Compare cnv output to known ground truths. | 2.985197 | 2.29318 | 1.301771 |
p = OptionParser(cib.__doc__)
p.add_option("--prefix", help="Report seqids with this prefix only")
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
bamfile, samplekey = args
mkdir(samplekey)
bam = pysam.AlignmentFile(bamfile, "rb... | def cib(args) | %prog cib bamfile samplekey
Convert BAM to CIB (a binary storage of int8 per base). | 2.92452 | 2.636563 | 1.109217 |
p = OptionParser(batchcn.__doc__)
p.add_option("--upload", default="s3://hli-mv-data-science/htang/ccn",
help="Upload cn and seg results to s3")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
workdir, samples = args
upload = opts.u... | def batchcn(args) | %prog batchcn workdir samples.csv
Run CNV segmentation caller in batch mode. Scans a workdir. | 5.836182 | 5.249154 | 1.111833 |
p = OptionParser(hmm.__doc__)
p.add_option("--mu", default=.003, type="float",
help="Transition probability")
p.add_option("--sigma", default=.1, type="float",
help="Standard deviation of Gaussian emission distribution")
p.add_option("--threshold", default=1, type=... | def hmm(args) | %prog hmm workdir sample_key
Run CNV segmentation caller. The workdir must contain a subfolder called
`sample_key-cn` that contains CN for each chromosome. A `beta` directory
that contains scaler for each bin must also be present in the current
directory. | 3.635883 | 3.482521 | 1.044038 |
p = OptionParser(batchccn.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
csvfile, = args
mm = MakeManager()
pf = op.basename(csvfile).split(".")[0]
mkdir(pf)
header = next(open(csvfile))
header = None if header.strip().endswit... | def batchccn(args) | %prog batchccn test.csv
Run CCN script in batch. Write makefile. | 4.21366 | 4.039148 | 1.043205 |
p = OptionParser(mergecn.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
csvfile, = args
samples = [x.replace("-cn", "").strip().strip("/") for x in open(csvfile)]
betadir = "beta"
mkdir(betadir)
for seqid in allsomes:
names... | def mergecn(args) | %prog mergecn FACE.csv
Compile matrix of GC-corrected copy numbers. Place a bunch of folders in
csv file. Each folder will be scanned, one chromosomes after another. | 3.38746 | 3.30226 | 1.025801 |
# We need a way to go from compressed idices to original indices
P = Z.copy()
P[~np.isfinite(P)] = -1
_, mapping = np.unique(np.cumsum(P >= 0), return_index=True)
dZ = Z.compressed()
uniq, idx = np.unique(dZ, return_inverse=True)
segments = []
fo... | def annotate_segments(self, Z) | Report the copy number and start-end segment | 5.387792 | 5.424419 | 0.993248 |
if len(args) != 0:
sys.exit(not p.print_help())
s = InstanceSkeleton()
print("IP address:", s.private_ip_address, file=sys.stderr)
print("Instance type:", s.instance_type, file=sys.stderr) | def ip(args) | %prog describe
Show current IP address from JSON settings. | 4.626322 | 4.821877 | 0.959444 |
p = OptionParser(start.__doc__)
p.add_option("--ondemand", default=False, action="store_true",
help="Do we want a more expensive on-demand instance")
p.add_option("--profile", default="mvrad-datasci-role", help="Profile name")
p.add_option("--price", default=4.0, type=float, help="... | def start(args) | %prog start
Launch ec2 instance through command line. | 2.33479 | 2.290347 | 1.019405 |
p = OptionParser(stop.__doc__)
p.add_option("--profile", default="mvrad-datasci-role", help="Profile name")
opts, args = p.parse_args(args)
if len(args) != 0:
sys.exit(not p.print_help())
role(["htang"])
session = boto3.Session(profile_name=opts.profile)
client = session.clien... | def stop(args) | %prog stop
Stop EC2 instance. | 2.482464 | 2.418664 | 1.026378 |
p = OptionParser(rm.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
store, = args
contents = glob_s3(store)
for c in contents:
rm_s3(c) | def rm(args) | %prog rm "s3://hli-mv-data-science/htang/str/*.csv"
Remove a bunch of files. | 3.196016 | 2.991917 | 1.068217 |
p = OptionParser(cp.__doc__)
p.add_option("--force", default=False,
action="store_true", help="Force overwrite if exists")
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
store, folder = args
force = opts.force
... | def cp(args) | %prog cp "s3://hli-mv-data-science/htang/str/*.csv" .
Copy files to folder. Accepts list of s3 addresses as input. | 2.815552 | 2.672009 | 1.053721 |
p = OptionParser(ls.__doc__)
p.add_option("--keys", help="List of keys to include")
p.add_option("--recursive", default=False, action="store_true",
help="Recursive search")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
store, = ar... | def ls(args) | %prog ls "s3://hli-mv-data-science/htang/str/*.vcf.gz"
List files with support for wildcards. | 3.102038 | 3.058249 | 1.014318 |
src_acct, src_username, dst_acct, dst_role = \
"205134639408 htang 114692162163 mvrad-datasci-role".split()
p = OptionParser(role.__doc__)
p.add_option("--profile", default="mvrad-datasci-role", help="Profile name")
p.add_option('--device',
default="arn:aws:iam::" + src... | def role(args) | %prog role htang
Change aws role. | 4.065366 | 3.827301 | 1.062202 |
p = OptionParser(query.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
locifile, contig = args
idx = build_index(locifile)
pos = idx[contig]
logging.debug("Contig {0} found at pos {1}".format(contig, pos))
fp = open(locifile)
fp... | def query(args) | %prog query out.loci contig
Random access to loci file. This script helps speeding up debugging. | 3.10677 | 2.718747 | 1.142721 |
from jcvi.assembly.geneticmap import bed as geneticmap_bed
from jcvi.apps.align import blat
from jcvi.formats.blast import bed as blast_bed, best
p = OptionParser(synteny.__doc__)
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
mstmapout, novo,... | def synteny(args) | %prog synteny mstmap.out novo.final.fasta reference.fasta
Plot MSTmap against reference genome. | 3.214167 | 2.963747 | 1.084494 |
from jcvi.assembly.geneticmap import MSTMatrix
p = OptionParser(mstmap.__doc__)
p.add_option("--population_type", default="RIL6",
help="Type of population, possible values are DH and RILd")
p.add_option("--missing_threshold", default=.5,
help="Missing threshold, .... | def mstmap(args) | %prog mstmap LMD50.snps.genotype.txt
Convert LMDs to MSTMAP input. | 4.208921 | 3.856867 | 1.09128 |
import numpy as np
from jcvi.utils.progressbar import ProgressBar, Percentage, Bar, ETA
p = OptionParser(weblogo.__doc__)
p.add_option("-N", default=10, type="int",
help="Count the first and last N bases")
p.add_option("--nreads", default=1000000, type="int",
... | def weblogo(args) | %prog weblogo [fastafile|fastqfile]
Extract base composition for reads | 2.469165 | 2.417541 | 1.021354 |
from jcvi.graphics.histogram import stem_leaf_plot
from jcvi.utils.cbook import SummaryStats
p = OptionParser(count.__doc__)
p.add_option("--csv", help="Write depth per contig to file")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
fastafile,... | def count(args) | %prog count cdhit.consensus.fasta
Scan the headers for the consensus clusters and count the number of reads. | 4.316698 | 4.190223 | 1.030183 |
from jcvi.assembly.kmer import jellyfish, histogram
from jcvi.assembly.preprocess import diginorm
from jcvi.formats.fasta import filter as fasta_filter, format
from jcvi.apps.cdhit import filter as cdhit_filter
p = OptionParser(novo.__doc__)
p.add_option("--technology", choices=("illumina"... | def novo(args) | %prog novo reads.fastq
Reference-free tGBS pipeline v1. | 3.297657 | 3.249632 | 1.014779 |
p = OptionParser(novo2.__doc__)
p.set_fastq_names()
p.set_align(pctid=95)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
trimmed, pf = args
pctid = opts.pctid
reads, samples = scan_read_files(trimmed, opts.names)
# Set up directory str... | def novo2(args) | %prog novo2 trimmed projectname
Reference-free tGBS pipeline v2. | 2.746471 | 2.677045 | 1.025934 |
p = OptionParser(snpplot.__doc__)
opts, args, iopts = p.set_image_options(args, format="png")
if len(args) != 1:
sys.exit(not p.print_help())
datafile, = args
# Read in CDT file
fp = open(datafile)
next(fp)
next(fp)
data = []
for row in fp:
atoms = row.spli... | def snpplot(args) | %prog counts.cdt
Illustrate the histogram per SNP site. | 2.937291 | 2.811296 | 1.044817 |
p = OptionParser(filterm4.__doc__)
p.add_option("--best", default=1, type="int", help="Only retain best N hits")
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
m4file, = args
best = opts.best
fp = open(m4file)
fw = must_... | def filterm4(args) | %prog filterm4 sample.m4 > filtered.m4
Filter .m4 file after blasr is run. As blasr takes a long time to run,
changing -bestn is undesirable. This screens the m4 file to retain top hits. | 2.586433 | 2.474248 | 1.045341 |
import json
p = OptionParser(spancount.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
fof, = args
fp = open(fof)
flist = [row.strip() for row in fp]
spanCount = "spanCount"
avgSpanBases = "avgSpanBases"
fw = open(spanCount... | def spancount(args) | %prog spancount list_of_fillingMetrics
Count span support for each gap. A file with paths of all fillingMetrics can
be built with Linux `find`.
$ (find assembly -name "fillingMetrics.json" -print > list_of_fillMetrics 2>
/dev/null &) | 3.132198 | 2.988448 | 1.048102 |
from jcvi.formats.base import write_file
from jcvi.formats.fasta import format
p = OptionParser(patch.__doc__)
p.add_option("--cleanfasta", default=False, action="store_true",
help="Clean FASTA to remove description [default: %default]")
p.add_option("--highqual", default=Fals... | def patch(args) | %prog patch reference.fasta reads.fasta
Run PBJelly with reference and reads. | 3.633767 | 3.456843 | 1.051181 |
p = OptionParser(prepare.__doc__ + FastqNamings)
p.add_option("-K", default=51, type="int", help="K-mer size")
p.set_cpus(cpus=32)
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
genomesize = float(args[0]) / 1000
fnames = args[1:]
for x in f... | def prepare(args) | %prog prepare genomesize *.fastq
Prepare MERACULOUS configuation file. Genome size should be entered in Mb. | 4.636864 | 4.170582 | 1.111803 |
from jcvi.apps.fetch import batch_taxonomy
list_of_taxids = [str(x) for x in list_of_taxids]
return list(batch_taxonomy(list_of_taxids)) | def get_names(list_of_taxids) | >>> mylist = [3702, 3649, 3694, 3880]
>>> get_names(mylist)
['Arabidopsis thaliana', 'Carica papaya', 'Populus trichocarpa', 'Medicago truncatula'] | 4.343782 | 4.754012 | 0.913709 |
from jcvi.apps.fetch import batch_taxids
return [int(x) for x in batch_taxids(list_of_names)] | def get_taxids(list_of_names) | >>> mylist = ['Arabidopsis thaliana', 'Carica papaya']
>>> get_taxids(mylist)
[1, 2] | 6.228539 | 6.724813 | 0.926203 |
from ete2 import Tree
t = TaxIDTree(list_of_taxids)
t = Tree(str(t), format=8)
ancestor = t.get_common_ancestor(*t.get_leaves())
return ancestor.name | def MRCA(list_of_taxids) | This gets the most recent common ancester (MRCA) for a list of taxids
>>> mylist = [3702, 3649, 3694, 3880]
>>> MRCA(mylist)
'rosids' | 4.877177 | 6.150789 | 0.792935 |
assert isinstance(taxid, int)
t = TaxIDTree(taxid)
try:
return "Viridiplantae" in str(t)
except AttributeError:
raise ValueError("{0} is not a valid ID".format(taxid)) | def isPlantOrigin(taxid) | Given a taxid, this gets the expanded tree which can then be checked to
see if the organism is a plant or not
>>> isPlantOrigin(29760)
True | 5.456123 | 5.706039 | 0.956201 |
p = OptionParser(newick.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
idsfile, = args
mylist = [x.strip() for x in open(idsfile) if x.strip()]
print(get_taxids(mylist))
t = TaxIDTree(mylist)
print(t) | def newick(args) | %prog newick idslist
Query a list of IDs to retrieve phylogeny. | 3.213061 | 2.892598 | 1.110787 |
pf = op.basename(bedfile).split(".")[0]
minibamfile = minibam or op.basename(bamfile).replace(".bam", ".{}.bam".format(pf))
minisamfile = minibam.replace(".bam", ".sam")
baifile = minibamfile + ".bai"
if op.exists(baifile):
sh("rm {}".format(baifile))
cmd = "samtools view -H {} > {... | def get_minibam_bed(bamfile, bedfile, minibam=None) | samtools view -L could do the work, but it is NOT random access. Here we
are processing multiple regions sequentially. See also:
https://www.biostars.org/p/49306/ | 3.010196 | 3.125143 | 0.963219 |
p = OptionParser(fastq.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
bamfile, pf = args
singletons = pf + ".se.fastq"
a = pf + ".read1.fastq"
b = pf + ".read2.fastq"
cmd = "samtools collate -uOn 128 {} tmp-prefix".format(bamfile... | def fastq(args) | %prog fastq bamfile prefix
Convert BAM files to paired FASTQ files. | 4.154544 | 3.969392 | 1.046645 |
p = OptionParser(mini.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
bamfile, region = args
get_minibam(bamfile, region) | def mini(args) | %prog mini bamfile region
Extract mini-bam for a single region. | 2.866166 | 2.009571 | 1.426258 |
p = OptionParser(noclip.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
bamfile, = args
noclipbam = bamfile.replace(".bam", ".noclip.bam")
cmd = "samtools view -h {} | awk -F '\t' '($6 !~ /H|S/)'".format(bamfile)
cmd += " | samtools vie... | def noclip(args) | %prog noclip bamfile
Remove clipped reads from BAM. | 2.648163 | 2.466251 | 1.07376 |
p = OptionParser(append.__doc__)
p.add_option("--prepend", help="Prepend string to read names")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
bamfile, = args
prepend = opts.prepend
icmd = "samtools view -h {0}".format(bamfile)
bamfile = b... | def append(args) | %prog append bamfile
Append /1 or /2 to read names. Useful for using the Tophat2 bam file for
training AUGUSTUS gene models. | 2.759181 | 2.551936 | 1.081211 |
p = OptionParser(bed.__doc__)
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
bedfile = args[0]
bamfiles = args[1:]
for bamfile in bamfiles:
cmd = "bamToBed -i {0}".format(bamfile)
sh(cmd, outfile=bedfile, append=True) | def bed(args) | %prog bed bedfile bamfiles
Convert bam files to bed. | 2.48405 | 2.17779 | 1.140629 |
from jcvi.apps.grid import MakeManager
p = OptionParser(merge.__doc__)
p.set_sep(sep="_", help="Separator to group per prefix")
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
merged_bams = args[0]
bamdirs = args[1:]
mkdir(merged_bams)
... | def merge(args) | %prog merge merged_bams bams1_dir bams2_dir ...
Merge BAM files. Treat the bams with the same prefix as a set.
Output the commands first. | 2.943633 | 2.713904 | 1.084649 |
p = OptionParser(count.__doc__)
p.add_option("--type", default="exon",
help="Only count feature type")
p.set_cpus(cpus=8)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
bamfile, gtf = args
cpus = opts.cpus
pf = bamfile.spli... | def count(args) | %prog count bamfile gtf
Count the number of reads mapped using `htseq-count`. | 2.699282 | 2.489758 | 1.084154 |
p = OptionParser(coverage.__doc__)
p.add_option("--format", default="bigwig",
choices=("bedgraph", "bigwig", "coverage"),
help="Output format")
p.add_option("--nosort", default=False, action="store_true",
help="Do not sort BAM")
p.set_outfile()
... | def coverage(args) | %prog coverage fastafile bamfile
Calculate coverage for BAM file. BAM file will be sorted unless with
--nosort. | 2.754091 | 2.551785 | 1.07928 |
p = OptionParser(fpkm.__doc__)
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
fastafile = args[0]
bamfiles = args[1:]
# Create a DUMMY gff file for cuffdiff
gffile = fastafile.rsplit(".", 1)[0] + ".gff"
if need_update(fastafile, gffile):
... | def fpkm(args) | %prog fpkm fastafile *.bam
Calculate FPKM values from BAM file. | 3.197411 | 3.005013 | 1.064026 |
import jcvi.formats.bed
p = OptionParser(pairs.__doc__)
p.set_pairs()
opts, targs = p.parse_args(args)
if len(targs) != 1:
sys.exit(not p.print_help())
samfile, = targs
bedfile = samfile.rsplit(".", 1)[0] + ".bed"
if need_update(samfile, bedfile):
cmd = "bamToBed ... | def pairs(args) | See __doc__ for OptionParser.set_pairs(). | 3.201763 | 2.990763 | 1.070551 |
p = OptionParser(consensus.__doc__)
p.add_option("--fasta", default=False, action="store_true",
help="Generate consensus FASTA sequences [default: %default]")
p.add_option("--mask", default=0, type="int",
help="Mask bases with quality lower than")
opts, args = p.parse_args(a... | def consensus(args) | %prog consensus fastafile bamfile
Convert bam alignments to consensus FASTQ/FASTA. | 3.445184 | 3.160402 | 1.09011 |
from jcvi.apps.grid import Jobs
valid_callers = ("mpileup", "freebayes")
p = OptionParser(vcf.__doc__)
p.set_outfile(outfile="out.vcf.gz")
p.add_option("--nosort", default=False, action="store_true",
help="Do not sort the BAM files")
p.add_option("--caller", default="mpile... | def vcf(args) | %prog vcf fastafile bamfiles > out.vcf.gz
Call SNPs on bam files. | 2.538849 | 2.377097 | 1.068046 |
import pysam
from jcvi.utils.natsort import natsorted
p = OptionParser(chimera.__doc__)
p.set_verbose()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
samfile, = args
samfile = pysam.AlignmentFile(samfile)
rstore = defaultdict(list)
... | def chimera(args) | %prog chimera bamfile
Parse BAM file from `bwasw` and list multi-hit reads and breakpoints. | 2.972091 | 2.838223 | 1.047166 |
p = OptionParser(index.__doc__)
p.add_option("--fasta", dest="fasta", default=None,
help="add @SQ header to the BAM file [default: %default]")
p.add_option("--unique", default=False, action="store_true",
help="only retain uniquely mapped reads [default: %default]")
p.set_cpu... | def index(args) | %prog index samfile/bamfile
If SAM file, convert to BAM, sort and then index, using SAMTOOLS | 2.178547 | 2.106593 | 1.034157 |
import pysam
from jcvi.apps.grid import Jobs
p = OptionParser(mapped.__doc__)
p.set_sam_options(extra=False)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(p.print_help())
samfile, = args
view_opts = []
oext, mopts = (".sam", ["-S"]) \
if sam... | def mapped(args) | %prog mapped sam/bamfile
Given an input sam/bam file, output a sam/bam file containing only the mapped reads.
Optionally, extract the unmapped reads into a separate file | 3.412084 | 3.344686 | 1.020151 |
p = OptionParser(pair.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(p.print_help())
def callback(s):
print(s.pairline)
Sam(args[0], callback=callback) | def pair(args) | %prog pair samfile
Parses the sam file and retrieve in pairs format,
query:pos ref:pos | 3.742981 | 3.943785 | 0.949083 |
seq, cigar = a.seq, a.cigar
start = 0
subseqs = []
npadded = 0
if cigar is None:
return None, npadded
for operation, length in cigar:
end = start if operation == 2 else start + length
if operation == 0: # match
subseq = seq[start:end]
elif oper... | def cigar_to_seq(a, gap='*') | Accepts a pysam row.
cigar alignment is presented as a list of tuples (operation,length). For
example, the tuple [ (0,3), (1,5), (0,2) ] refers to an alignment with 3
matches, 5 insertions and another 2 matches.
Op BAM Description
M 0 alignment match (can be a sequence match or mismatch)
I 1 i... | 2.593589 | 2.645809 | 0.980263 |
p = OptionParser(dump.__doc__)
p.add_option("--dir",
help="Working directory [default: %default]")
p.add_option("--nosim", default=False, action="store_true",
help="Do not simulate qual to 50 [default: %default]")
opts, args = p.parse_args(args)
if len(args) !=... | def dump(args) | %prog dump fastbfile
Export ALLPATHS fastb file to fastq file. Use --dir to indicate a previously
run allpaths folder. | 3.979665 | 3.703243 | 1.074643 |
p = OptionParser(fixpairs.__doc__)
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
pairsfile, sep, sd = args
newpairsfile = pairsfile.rsplit(".", 1)[0] + ".new.pairs"
sep = int(sep)
sd = int(sd)
p = PairsFile(pairsfile)
p.fixLibraryStat... | def fixpairs(args) | %prog fixpairs pairsfile sep sd
Fix pairs library stats. This is sometime useful to modify library stats,
for example, the separation between paired reads after importing the data. | 2.830066 | 2.018937 | 1.40176 |
p = OptionParser(fill.__doc__)
p.add_option("--stretch", default=3, type="int",
help="MAX_STRETCH to pass to FillFragments [default: %default]")
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
fastb, = args
assert ... | def fill(args) | %prog fill frag_reads_corr.fastb
Run FillFragments on `frag_reads_corr.fastb`. | 4.327679 | 3.523796 | 1.22813 |
fp = open(fastqfile)
currentID = 0
npairs = nfrags = 0
for x, lib in izip(p.r1, p.libs):
while currentID != x:
fragsfw.writelines(islice(fp, 4)) # Exhaust the iterator
currentID += 1
nfrags += 1
a = list(islice(fp, 4))
b = list(islice(fp,... | def extract_pairs(fastqfile, p1fw, p2fw, fragsfw, p, suffix=False) | Take fastqfile and array of pair ID, extract adjacent pairs to outfile.
Perform check on numbers when done. p1fw, p2fw is a list of file handles,
each for one end. p is a Pairs instance. | 3.009779 | 2.886416 | 1.042739 |
from jcvi.assembly.preprocess import run_FastbAndQualb2Fastq
p = OptionParser(pairs.__doc__)
p.add_option("--header", default=False, action="store_true",
help="Print header only [default: %default]")
p.add_option("--suffix", default=False, action="store_true",
help="Ad... | def pairs(args) | %prog pairs pairsfile <fastbfile|fastqfile>
Parse ALLPATHS pairs file, and write pairs IDs and single read IDs in
respective ids files: e.g. `lib1.pairs.fastq`, `lib2.pairs.fastq`,
and single `frags.fastq` (with single reads from lib1/2). | 3.058682 | 2.720573 | 1.124279 |
from jcvi.algorithms.graph import nx, topological_sort
p = OptionParser(log.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
g = nx.DiGraph()
logfile, = args
fp = open(logfile)
row = fp.readline()
incalling = False
basedb =... | def log(args) | %prog log logfile
Prepare a log of created files, ordered by their creation data. The purpose
for this script is to touch these files sequentially to reflect their build
order. On the JCVI scratch area, the files are touched regularly to avoid
getting deleted, losing their respective timestamps. Howeve... | 3.025777 | 2.861281 | 1.05749 |
mapping = self._mapping
set_a = mapping.setdefault(a, [a])
for arg in args:
set_b = mapping.get(arg)
if set_b is None:
set_a.append(arg)
mapping[arg] = set_a
elif set_b is not set_a:
if len(set_b) > len... | def join(self, a, *args) | Join given arguments into the same set. Accepts one or more arguments. | 2.136494 | 2.066672 | 1.033785 |
mapping = self._mapping
try:
return mapping[a] is mapping[b]
except KeyError:
return False | def joined(self, a, b) | Returns True if a and b are members of the same set. | 5.1512 | 4.301717 | 1.197475 |
from csv import reader
from xlwt import Workbook, easyxf
from jcvi.formats.base import flexible_cast
p = OptionParser(fromcsv.__doc__)
p.add_option("--noheader", default=False, action="store_true",
help="Do not treat the first row as header")
p.add_option("--rgb", default=... | def fromcsv(args) | %prog fromcsv csvfile
Convert csv file to EXCEL. | 2.425553 | 2.346614 | 1.033639 |
from xlrd import open_workbook
p = OptionParser(csv.__doc__)
p.set_sep(sep=',')
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
excelfile, = args
sep = opts.sep
csvfile = excelfile.rsplit(".", 1)[0] + ".csv"
wb = open_workbook(excelfile... | def csv(args) | %prog csv excelfile
Convert EXCEL to csv file. | 2.162119 | 1.981444 | 1.091184 |
from jcvi.utils.webcolors import color_diff
if isinstance(color, int):
return color
if color:
if isinstance(color, six.string_types):
rgb = map(int, color.split(','))
else:
rgb = color.Get()
logging.disable(... | def match_color_index(self, color) | Takes an "R,G,B" string or wx.Color and returns a matching xlwt
color. | 3.932989 | 3.219529 | 1.221604 |
if not self.unused_colors:
# If we somehow run out of colors, reset the color matcher.
self.reset()
used_colors = [c for c in self.xlwt_colors if c not in self.unused_colors]
result_color = max(self.unused_colors,
key=lambda c: min(self... | def get_unused_color(self) | Returns an xlwt color index that has not been previously returned by
this instance. Attempts to maximize the distance between the color and
all previously used colors. | 3.634894 | 3.011501 | 1.207004 |
import pyfasta
p = OptionParser(validate.__doc__)
p.add_option("--prefix", help="Add prefix to seqid")
opts, args = p.parse_args(args)
vcffile, fastafile = args
pf = opts.prefix
genome = pyfasta.Fasta(fastafile, record_class=pyfasta.MemoryRecord)
fp = must_open(vcffile)
match_... | def validate(args) | %prog validate input.vcf genome.fasta
Fasta validation of vcf file. | 3.212683 | 2.987828 | 1.075257 |
from six.moves.urllib.parse import parse_qs
p = OptionParser(uniq.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
vcffile, = args
fp = must_open(vcffile)
data = []
for row in fp:
if row[0] == '#':
print(row.stri... | def uniq(args) | %prog uniq vcffile
Retain only the first entry in vcf file. | 2.859519 | 2.571161 | 1.112151 |
from random import random
p = OptionParser(sample.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
vcffile, ratio = args
ratio = float(ratio)
fp = open(vcffile)
pf = vcffile.rsplit(".", 1)[0]
kept = pf + ".kept.vcf"
withheld... | def sample(args) | %prog sample vcffile 0.9
Sample subset of vcf file. | 2.277628 | 2.11774 | 1.075499 |
p = OptionParser(fromimpute2.__doc__)
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
impute2file, fastafile, chr = args
fasta = Fasta(fastafile)
print(get_vcfstanza(fastafile, fasta))
fp = open(impute2file)
seen = set()
for row in fp:
... | def fromimpute2(args) | %prog fromimpute2 impute2file fastafile 1
Convert impute2 output to vcf file. Imputed file looks like:
--- 1:10177:A:AC 10177 A AC 0.451 0.547 0.002 | 3.936787 | 3.635537 | 1.082862 |
p = OptionParser(refallele.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
vcffile, = args
fp = open(vcffile)
for row in fp:
if row[0] == '#':
continue
atoms = row.split()
marker = "{0}:{1}".format(*atoms... | def refallele(args) | %prog refallele vcffile > out.refAllele
Make refAllele file which can be used to convert PLINK file to VCF file. | 2.737516 | 2.509386 | 1.090911 |
from jcvi.formats.bed import BedLine
from jcvi.graphics.histogram import stem_leaf_plot
p = OptionParser(location.__doc__)
p.add_option("--dist", default=100, type="int",
help="Distance cutoff to call 5` and 3` [default: %default]")
opts, args = p.parse_args(args)
if len(... | def location(args) | %prog location bedfile fastafile
Given SNP locations, summarize the locations in the sequences. For example,
find out if there are more 3`-SNPs than 5`-SNPs. | 2.937487 | 2.825239 | 1.039731 |
atoms = s.split(":")
if len(atoms) < 3:
return g2x[atoms[0]]
inferred = atoms[0]
depth = int(atoms[depth_index])
if depth < mindepth:
return '-'
if inferred == '0/0':
return 'A'
if inferred == '0/1':
return '-' if nohet else 'X'
if inferred == '1/1':... | def encode_genotype(s, mindepth=3, depth_index=2, nohet=False) | >>> encode_genotype("1/1:128,18,0:6:18") # homozygote B
'B'
>>> encode_genotype("0/1:0,0,0:0:3") # missing data
'-'
>>> encode_genotype("0/1:128,0,26:7:22") # heterozygous A/B
'X' | 3.569856 | 3.442067 | 1.037126 |
from jcvi.assembly.geneticmap import MSTMatrix
p = OptionParser(mstmap.__doc__)
p.add_option("--dh", default=False, action="store_true",
help="Double haploid population, no het [default: %default]")
p.add_option("--freq", default=.2, type="float",
help="Allele mus... | def mstmap(args) | %prog mstmap bcffile/vcffile > matrixfile
Convert bcf/vcf format to mstmap input. | 3.727462 | 3.636404 | 1.025041 |
p = OptionParser(liftover.__doc__)
p.add_option("--newid", default=False, action="store_true",
help="Make new identifiers")
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
oldvcf, chainfile, newvcf = args
ul = UniqueLiftover(chainfi... | def liftover(args) | %prog liftover old.vcf hg19ToHg38.over.chain.gz new.vcf
Lift over coordinates in vcf file. | 2.872588 | 2.736664 | 1.049668 |
chromosome = str(chromosome)
position = int(position)
# Perform the liftover lookup, shift the position by 1 as pyliftover deals in 0-based co-ords
new = self.liftover.convert_coordinate(chromosome, position - 1)
# This has to be here as new will be NoneType when the c... | def liftover_cpra(self, chromosome, position, verbose=False) | Given chromosome, position in 1-based co-ordinates,
This will use pyliftover to liftover a CPRA, will return a (c,p) tuple or raise NonUniqueLiftover if no unique
and strand maintaining liftover is possible
:param chromosome: string with the chromosome as it's represented in the from_genome
... | 4.2365 | 4.229952 | 1.001548 |
p = OptionParser(multilineplot.__doc__)
p.add_option("--lines",
help="Features to plot in lineplot [default: %default]")
p.add_option("--colors",
help="List of colors matching number of input bed files")
p.add_option("--mode", default="span", choices=("span", "coun... | def multilineplot(args) | %prog multilineplot fastafile chr1
Combine multiple line plots in one vertical stack
Inputs must be BED-formatted.
--lines: traditional line plots, useful for plotting feature freq | 3.482716 | 3.383953 | 1.029186 |
p = OptionParser(stack.__doc__)
p.add_option("--top", default=10, type="int",
help="Draw the first N chromosomes [default: %default]")
p.add_option("--stacks",
default="Exons,Introns,DNA_transposons,Retrotransposons",
help="Features to plot in stackplo... | def stack(args) | %prog stack fastafile
Create landscape plots that show the amounts of genic sequences, and repetitive
sequences along the chromosomes. | 3.598276 | 3.525384 | 1.020676 |
from Bio.Emboss.Applications import NeedleCommandline
needle_cline = NeedleCommandline(asequence=fa, bsequence=fb,
gapopen=10, gapextend=0.5, outfile=needlefile)
stdout, stderr = needle_cline()
nh = NeedleHeader(needlefile)
FileShredder([fa, fb, needlefile], verbose=Fal... | def _needle(fa, fb, needlefile, a, b, results) | Run single needle job | 4.473867 | 4.529823 | 0.987647 |
from jcvi.formats.fasta import Fasta, SeqIO
p = OptionParser(needle.__doc__)
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
manager = mp.Manager()
results = manager.list()
needle_pool = mp.Pool(processes=mp.cpu_count())
pairsfile, apep, ... | def needle(args) | %prog needle nw.pairs a.pep.fasta b.pep.fasta
Take protein pairs and needle them
Automatically writes output file `nw.scores` | 2.380315 | 2.229596 | 1.067599 |
from jcvi.formats.base import SetFile, FileShredder
A, T, P = "ABINITIO_PREDICTION", "TRANSCRIPT", "PROTEIN"
# Stores default weights and types
Registry = {\
"maker": (A, 5),
"augustus_masked": (A, 1),
"snap_masked": (A, 1),
"genemark": (A, 1),
"est2genome":... | def maker(args) | %prog maker maker.gff3 genome.fasta
Prepare EVM inputs by separating tracks from MAKER. | 4.634776 | 4.281867 | 1.082419 |
p = OptionParser(tigrload.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
db, ev_type = args
runfile = "load.sh"
contents = EVMLOAD.format(db, ev_type)
write_file(runfile, contents) | def tigrload(args) | %prog tigrload db ev_type
Load EVM results into TIGR db. Actually, just write a load.sh script. The
ev_type should be set, e.g. "EVM1", "EVM2", etc. | 3.745573 | 2.443922 | 1.532608 |
p = OptionParser(pasa.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
pasa_db, fastafile = args
termexons = "pasa.terminal_exons.gff3"
if need_update(fastafile, termexons):
cmd = "$ANNOT_DEVEL/PASA2/scripts/pasa_asmbls_to_training_... | def pasa(args) | %prog pasa pasa_db fastafile
Run EVM in TIGR-only mode. | 7.071276 | 6.192741 | 1.141865 |
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