code string | signature string | docstring string | loss_without_docstring float64 | loss_with_docstring float64 | factor float64 |
|---|---|---|---|---|---|
if not HAVE_MPL: # pragma: no cover
raise RuntimeError('matplotlib not available.')
cmap = LinearSegmentedColormap.from_list(self.name,
self.mpl_colors, **kwargs)
return cmap | def get_mpl_colormap(self, **kwargs) | A color map that can be used in matplotlib plots. Requires matplotlib
to be importable. Keyword arguments are passed to
`matplotlib.colors.LinearSegmentedColormap.from_list`. | 4.205011 | 4.08694 | 1.02889 |
from ipythonblocks import BlockGrid
grid = BlockGrid(self.number, 1, block_size=block_size)
for block, color in zip(grid, self.colors):
block.rgb = color
grid.show() | def show_as_blocks(self, block_size=100) | Show colors in the IPython Notebook using ipythonblocks.
Parameters
----------
block_size : int, optional
Size of displayed blocks. | 6.412446 | 6.653701 | 0.963741 |
url = 'http://colorbrewer2.org/index.html?type={0}&scheme={1}&n={2}'
return url.format(self.type.lower(), self.name, self.number) | def colorbrewer2_url(self) | URL that can be used to view the color map at colorbrewer2.org. | 2.987949 | 2.848918 | 1.048802 |
from jcvi.formats.fasta import summary as fsummary
from jcvi.utils.cbook import percentage, human_size
p = OptionParser(summary.__doc__)
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
chainfile, oldfasta, newfasta = args
chain = Chain(chainfil... | def summary(args) | %prog summary old.new.chain old.fasta new.fasta
Provide stats of the chain file. | 2.761256 | 2.488626 | 1.109551 |
from jcvi.formats.agp import AGP
from jcvi.formats.sizes import Sizes
p = OptionParser(fromagp.__doc__)
p.add_option("--novalidate", default=False, action="store_true",
help="Do not validate AGP")
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.p... | def fromagp(args) | %prog fromagp agpfile componentfasta objectfasta
Generate chain file from AGP format. The components represent the old
genome (target) and the objects represent new genome (query). | 2.717917 | 2.485977 | 1.093299 |
p = OptionParser(blat.__doc__)
p.add_option("--minscore", default=100, type="int",
help="Matches minus mismatches gap penalty [default: %default]")
p.add_option("--minid", default=98, type="int",
help="Minimum sequence identity [default: %default]")
p.set_cpus()
... | def blat(args) | %prog blat old.fasta new.fasta
Generate psl file using blat. | 3.308975 | 3.131968 | 1.056516 |
from jcvi.formats.sizes import Sizes
p = OptionParser(frompsl.__doc__)
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
pslfile, oldfasta, newfasta = args
pf = oldfasta.split(".")[0]
# Chain together alignments from using axtChain
chainfile... | def frompsl(args) | %prog frompsl old.new.psl old.fasta new.fasta
Generate chain file from psl file. The pipeline is describe in:
<http://genomewiki.ucsc.edu/index.php/Minimal_Steps_For_LiftOver> | 2.937448 | 2.777003 | 1.057776 |
atoms = row.strip().split("\t")
name1, name2, coverage, identity, nmismatch, ngap, \
start1, end1, strand1, start2, end2, strand2, score = atoms
identity = identity.replace("%", "")
hitlen = coverage.split("/")[1]
score = float(score)
same_strand = (strand1 == strand2)
if no... | def lastz_to_blast(row) | Convert the lastz tabular to the blast tabular, see headers above
Obsolete after LASTZ version 1.02.40 | 3.589094 | 3.519085 | 1.019894 |
bfasta_fn, afasta_fn, outfile, lastz_bin, extra, mask, format = t
ref_tags = [Darkspace]
qry_tags = [Darkspace]
ref_tags, qry_tags = add_mask(ref_tags, qry_tags, mask=mask)
lastz_cmd = Lastz_template.format(lastz_bin, bfasta_fn, ref_tags, \
afa... | def lastz_2bit(t) | Used for formats other than BLAST, i.e. lav, maf, etc. which requires the
database file to contain a single FASTA record. | 4.34037 | 4.445572 | 0.976336 |
p = OptionParser(main.__doc__)
supported_formats = tuple(x.strip() for x in \
"lav, lav+text, axt, axt+, maf, maf+, maf-, sam, softsam, "\
"sam-, softsam-, cigar, BLASTN, BLASTN-, differences, rdotplot, text".split(','))
p.add_option("--format", default="BLASTN-", choices=supported_fo... | def main() | %prog database.fa query.fa [options]
Run LASTZ similar to the BLAST interface, and generates -m8 tabular format | 4.698961 | 4.608009 | 1.019738 |
p = OptionParser(augustus.__doc__)
p.add_option("--species", default="maize",
help="Use species model for prediction")
p.add_option("--hintsfile", help="Hint-guided AUGUSTUS")
p.add_option("--nogff3", default=False, action="store_true",
help="Turn --gff3=off")
... | def augustus(args) | %prog augustus fastafile
Run parallel AUGUSTUS. Final results can be reformatted using
annotation.reformat.augustus(). | 3.82969 | 3.634778 | 1.053624 |
p = OptionParser(star.__doc__)
p.add_option("--single", default=False, action="store_true",
help="Single end mapping")
p.set_fastq_names()
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
folder, reference = args
... | def star(args) | %prog star folder reference
Run star on a folder with reads. | 3.29791 | 3.177873 | 1.037773 |
p = OptionParser(cufflinks.__doc__)
p.add_option("--gtf", help="Reference annotation [default: %default]")
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
folder, reference = args
cpus = opts.cpus
gtf = opts.gtf
transcripts ... | def cufflinks(args) | %prog cufflinks folder reference
Run cufflinks on a folder containing tophat results. | 2.506116 | 2.454472 | 1.021041 |
from jcvi.apps.bowtie import check_index
from jcvi.formats.fastq import guessoffset
p = OptionParser(tophat.__doc__)
p.add_option("--gtf", help="Reference annotation [default: %default]")
p.add_option("--single", default=False, action="store_true",
help="Single end mapping")
... | def tophat(args) | %prog tophat folder reference
Run tophat on a folder of reads. | 2.595776 | 2.542535 | 1.02094 |
from scipy.stats import hmean
return int(round(hmean(np.clip(a, a_min, a_max)))) | def hmean_int(a, a_min=5778, a_max=1149851) | Harmonic mean of an array, returns the closest int | 3.125829 | 2.778952 | 1.124823 |
counts = np.zeros(BB, dtype=int)
for x in a:
c = int(round(math.log(x, phi)))
if c < lb:
c = lb
if c > ub:
c = ub
counts[c - lb] += 1
return counts | def golden_array(a, phi=1.61803398875, lb=LB, ub=UB) | Given list of ints, we aggregate similar values so that it becomes an
array of multiples of phi, where phi is the golden ratio.
phi ^ 14 = 843
phi ^ 33 = 7881196
So the array of counts go between 843 to 788196. One triva is that the
exponents of phi gets closer to integers as N grows. See interest... | 3.142025 | 3.56808 | 0.880593 |
from .chic import score_evaluate_M
t, stour, tour_score, active_sizes, M = arg
stour_score, = score_evaluate_M(stour, active_sizes, M)
delta_score = tour_score - stour_score
log10d = np.log10(delta_score) if delta_score > 1e-9 else -9
return t, log10d | def prune_tour_worker(arg) | Worker thread for CLMFile.prune_tour() | 8.218111 | 8.308777 | 0.989088 |
p = OptionParser(heatmap.__doc__)
p.add_option("--resolution", default=500000, type="int",
help="Resolution when counting the links")
p.add_option("--chr", help="Plot this contig/chr only")
p.add_option("--nobreaks", default=False, action="store_true",
help="Do not... | def heatmap(args) | %prog heatmap input.npy genome.json
Plot heatmap based on .npy data file. The .npy stores a square matrix with
bins of genome, and cells inside the matrix represent number of links
between bin i and bin j. The `genome.json` contains the offsets of each
contig/chr so that we know where to draw boundary ... | 3.945331 | 3.710479 | 1.063294 |
p = OptionParser(mergemat.__doc__)
p.set_outfile(outfile="out")
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
npyfiles = args
A = np.load(npyfiles[0])
logging.debug("Load `{}`: matrix of shape {}:; sum={}"
.format(npyfiles[0],... | def mergemat(args) | %prog mergemat *.npy
Combine counts from multiple .npy data files. | 3.029465 | 2.747011 | 1.102822 |
import pysam
bamfile = pysam.AlignmentFile(bamfile, "rb")
seqsize = {}
for kv in bamfile.header["SQ"]:
if kv["LN"] < 10 * N:
continue
seqsize[kv["SN"]] = kv["LN"] / N + 1
allseqs = natsorted(seqsize.keys())
allseqsizes = np.array([seqsize[x] for x in allseqs])
... | def get_seqstarts(bamfile, N) | Go through the SQ headers and pull out all sequences with size
greater than the resolution settings, i.e. contains at least a few cells | 2.790582 | 2.814774 | 0.991405 |
b = np.ones(bins, dtype="float64")
b[0] = 100
for i in range(1, bins):
b[i] = b[i - 1] * ratio
bins = np.around(b).astype(dtype="int")
binsizes = np.diff(bins)
return bins, binsizes | def get_distbins(start=100, bins=2500, ratio=1.01) | Get exponentially sized | 3.037666 | 3.003142 | 1.011496 |
p = OptionParser(simulate.__doc__)
p.add_option("--genomesize", default=10000000, type="int",
help="Genome size")
p.add_option("--genes", default=1000, type="int",
help="Number of genes")
p.add_option("--contigs", default=100, type="int",
help="Num... | def simulate(args) | %prog simulate test
Simulate CLM and IDS files with given names.
The simulator assumes several distributions:
- Links are distributed uniformly across genome
- Log10(link_size) are distributed normally
- Genes are distributed uniformly | 3.020748 | 3.017743 | 1.000996 |
qbedfile = pf + "tigs.bed"
sbedfile = pf + "chr.bed"
lastfile = "{}tigs.{}chr.last".format(pf, pf)
qbedfw = open(qbedfile, "w")
sbedfw = open(sbedfile, "w")
lastfw = open(lastfile, "w")
GeneContigs = np.searchsorted(ContigStarts, GenePositions) - 1
for i, (c, gstart) in enumerate(z... | def write_last_and_beds(pf, GenePositions, ContigStarts) | Write LAST file, query and subject BED files. | 2.779079 | 2.666475 | 1.04223 |
clm = defaultdict(list)
for start, end, linkstart, linkend in \
zip(ICLinkStartContigs, ICLinkEndContigs,
ICLinkStarts, ICLinkEnds):
start_a = ContigStarts[start]
start_b = start_a + ContigSizes[start]
end_a = ContigStarts[end]
end_b = end_a + Con... | def write_clm(pf, ICLinkStartContigs, ICLinkEndContigs,
ICLinkStarts, ICLinkEnds,
ContigStarts, ContigSizes) | Write CLM file from simulated data. | 1.920403 | 1.944493 | 0.987611 |
p = OptionParser(density.__doc__)
p.add_option("--save", default=False, action="store_true",
help="Write log densitites of contigs to file")
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
clmfile, = args
clm = CLMF... | def density(args) | %prog density test.clm
Estimate link density of contigs. | 3.549939 | 3.359465 | 1.056698 |
p = OptionParser(optimize.__doc__)
p.add_option("--skiprecover", default=False, action="store_true",
help="Do not import 'recover' contigs")
p.add_option("--startover", default=False, action="store_true",
help="Do not resume from existing tour file")
p.add_option("... | def optimize(args) | %prog optimize test.clm
Optimize the contig order and orientation, based on CLM file. | 4.116782 | 3.832673 | 1.074128 |
from .chic import score_evaluate_M
# Prepare input files
tour_contigs = clm.active_contigs
tour_sizes = clm.active_sizes
tour_M = clm.M
tour = clm.tour
signs = clm.signs
oo = clm.oo
def callback(tour, gen, phase, oo):
fitness = tour.fitness if hasattr(tour, "fitness") ... | def optimize_ordering(fwtour, clm, phase, cpus) | Optimize the ordering of contigs by Genetic Algorithm (GA). | 5.00914 | 4.835193 | 1.035975 |
# Prepare input files
tour_contigs = clm.active_contigs
tour = clm.tour
oo = clm.oo
print_tour(fwtour, tour, "FLIPALL{}".format(phase),
tour_contigs, oo, signs=clm.signs)
tag1 = clm.flip_whole(tour)
print_tour(fwtour, tour, "FLIPWHOLE{}".format(phase),
tou... | def optimize_orientations(fwtour, clm, phase, cpus) | Optimize the orientations of contigs by using heuristic flipping. | 3.747446 | 3.658745 | 1.024243 |
qbedfile, sbedfile = get_bed_filenames(lastfile, p, opts)
qbedfile = op.abspath(qbedfile)
sbedfile = op.abspath(sbedfile)
qbed = Bed(qbedfile, sorted=False)
contig_to_beds = dict(qbed.sub_beds())
# Create a separate directory for the subplots and movie
mkdir(odir, overwrite=True)
... | def prepare_synteny(tourfile, lastfile, odir, p, opts) | Prepare synteny plots for movie(). | 4.52211 | 4.314257 | 1.048178 |
tour = []
tour_o = []
for contig in row.split():
if contig[-1] in ('+', '-', '?'):
tour.append(contig[:-1])
tour_o.append(contig[-1])
else: # Unoriented
tour.append(contig)
tour_o.append('?')
return tour, tour_o | def separate_tour_and_o(row) | The tour line typically contains contig list like:
tig00044568+ tig00045748- tig00071055- tig00015093- tig00030900-
This function separates the names from the orientations. | 3.267538 | 2.90459 | 1.124957 |
row = open(tourfile).readlines()[-1]
_tour, _tour_o = separate_tour_and_o(row)
tour = []
tour_o = []
for tc, to in zip(_tour, _tour_o):
if tc not in clm.contigs:
logging.debug("Contig `{}` in file `{}` not found in `{}`"
.format(tc, tourfile, clm.id... | def iter_last_tour(tourfile, clm) | Extract last tour from tourfile. The clm instance is also passed in to see
if any contig is covered in the clm. | 3.557264 | 3.32237 | 1.070701 |
fp = open(tourfile)
i = 0
for row in fp:
if row[0] == '>':
label = row[1:].strip()
if label.startswith("GA"):
pf, j, score = label.split("-", 2)
j = int(j)
else:
j = 0
i += 1
else:
... | def iter_tours(tourfile, frames=1) | Extract tours from tourfile. Tourfile contains a set of contig
configurations, generated at each iteration of the genetic algorithm. Each
configuration has two rows, first row contains iteration id and score,
second row contains list of contigs, separated by comma. | 4.213403 | 4.18301 | 1.007266 |
p = OptionParser(movie.__doc__)
p.add_option("--frames", default=500, type="int",
help="Only plot every N frames")
p.add_option("--engine", default="ffmpeg", choices=("ffmpeg", "gifsicle"),
help="Movie engine, output MP4 or GIF")
p.set_beds()
opts, args, iopts ... | def movie(args) | %prog movie test.tour test.clm ref.contigs.last
Plot optimization history. | 4.673956 | 4.335537 | 1.078057 |
p = OptionParser(score.__doc__)
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
mdir, cdir, contigsfasta = args
orderingfiles = natsorted(iglob(mdir, "*.ordering"))
sizes = Sizes(contigsfasta)
contig_names = list(sizes.iter_name... | def score(args) | %prog score main_results/ cached_data/ contigsfasta
Score the current LACHESIS CLM. | 4.536965 | 4.356666 | 1.041385 |
tour = range(len(oo))
tour_sizes = np.array([sizes.sizes[x] for x in oo])
tour_M = M[oo, :][:, oo]
return tour, tour_sizes, tour_M | def prepare_ec(oo, sizes, M) | This prepares EC and converts from contig_id to an index. | 3.866329 | 3.507558 | 1.102285 |
sizes_oo = np.array([tour_sizes[x] for x in tour])
sizes_cum = np.cumsum(sizes_oo) - sizes_oo / 2
s = 0
size = len(tour)
for ia in xrange(size):
a = tour[ia]
for ib in xrange(ia + 1, size):
b = tour[ib]
links = tour_M[a, b]
dist = sizes_cum[ib... | def score_evaluate(tour, tour_sizes=None, tour_M=None) | SLOW python version of the evaluation function. For benchmarking
purposes only. Do not use in production. | 3.3235 | 3.348678 | 0.992481 |
p = OptionParser(movieframe.__doc__)
p.add_option("--label", help="Figure title")
p.set_beds()
p.set_outfile(outfile=None)
opts, args, iopts = p.set_image_options(args, figsize="16x8",
style="white", cmap="coolwarm",
... | def movieframe(args) | %prog movieframe tour test.clm contigs.ref.anchors
Draw heatmap and synteny in the same plot. | 4.014009 | 3.490465 | 1.149993 |
p = OptionParser(agp.__doc__)
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
odir, contigsfasta = args
fwagp = must_open(opts.outfile, 'w')
orderingfiles = natsorted(iglob(odir, "*.ordering"))
sizes = Sizes(contigsfasta).map... | def agp(args) | %prog agp main_results/ contigs.fasta
Generate AGP file based on LACHESIS output. | 4.272899 | 3.965281 | 1.077578 |
'''Converts the ContigOrdering file into AGP format
'''
contigorder = [(x.contig_name, x.strand) for x in self]
order_to_agp(obj, contigorder, sizes, fw,
gapsize=gapsize, gaptype=gaptype, evidence=evidence) | def write_agp(self, obj, sizes, fw=sys.stdout, gapsize=100,
gaptype="contig", evidence="map") | Converts the ContigOrdering file into AGP format | 6.359537 | 4.213562 | 1.509302 |
'''IDS file has a list of contigs that need to be ordered. 'recover',
keyword, if available in the third column, is less confident.
tig00015093 46912
tig00035238 46779 recover
tig00030900 119291
'''
idsfile = self.idsfile
logging.debug("Pars... | def parse_ids(self, skiprecover) | IDS file has a list of contigs that need to be ordered. 'recover',
keyword, if available in the third column, is less confident.
tig00015093 46912
tig00035238 46779 recover
tig00030900 119291 | 5.742777 | 2.573421 | 2.231573 |
active = self.active
densities = defaultdict(int)
for (at, bt), links in self.contacts.items():
if not (at in active and bt in active):
continue
densities[at] += links
densities[bt] += links
logdensities = {}
for x, d ... | def calculate_densities(self) | Calculate the density of inter-contig links per base. Strong contigs
considered to have high level of inter-contig links in the current
partition. | 4.135475 | 3.668313 | 1.127351 |
if tourfile and (not op.exists(tourfile)):
logging.debug("Tourfile `{}` not found".format(tourfile))
tourfile = None
if tourfile:
logging.debug("Importing tourfile `{}`".format(tourfile))
tour, tour_o = iter_last_tour(tourfile, self)
... | def activate(self, tourfile=None, minsize=10000, backuptour=True) | Select contigs in the current partition. This is the setup phase of the
algorithm, and supports two modes:
- "de novo": This is useful at the start of a new run where no tours
available. We select the strong contigs that have significant number
of links to other contigs in the parti... | 4.47128 | 4.32212 | 1.034511 |
from .chic import score_evaluate_M
return score_evaluate_M(tour, self.active_sizes, self.M) | def evaluate_tour_M(self, tour) | Use Cythonized version to evaluate the score of a current tour | 17.293421 | 14.16977 | 1.220445 |
from .chic import score_evaluate_P
return score_evaluate_P(tour, self.active_sizes, self.P) | def evaluate_tour_P(self, tour) | Use Cythonized version to evaluate the score of a current tour,
with better precision on the distance of the contigs. | 18.051754 | 12.620217 | 1.430384 |
from .chic import score_evaluate_Q
return score_evaluate_Q(tour, self.active_sizes, self.Q) | def evaluate_tour_Q(self, tour) | Use Cythonized version to evaluate the score of a current tour,
taking orientation into consideration. This may be the most accurate
evaluation under the right condition. | 18.32765 | 13.546539 | 1.35294 |
if self.signs is None: # First run
score = 0
else:
old_signs = self.signs[:self.N]
score, = self.evaluate_tour_Q(tour)
# Remember we cannot have ambiguous orientation code (0 or '?') here
self.signs = get_signs(self.O, validate=False, ambigu... | def flip_all(self, tour) | Initialize the orientations based on pairwise O matrix. | 7.615099 | 7.002648 | 1.08746 |
score, = self.evaluate_tour_Q(tour)
self.signs = -self.signs
score_flipped, = self.evaluate_tour_Q(tour)
if score_flipped > score:
tag = ACCEPT
else:
self.signs = -self.signs
tag = REJECT
self.flip_log("FLIPWHOLE", score, score... | def flip_whole(self, tour) | Test flipping all contigs at the same time to see if score improves. | 4.928718 | 4.935844 | 0.998556 |
n_accepts = n_rejects = 0
any_tag_ACCEPT = False
for i, t in enumerate(tour):
if i == 0:
score, = self.evaluate_tour_Q(tour)
self.signs[t] = -self.signs[t]
score_flipped, = self.evaluate_tour_Q(tour)
if score_flipped > scor... | def flip_one(self, tour) | Test flipping every single contig sequentially to see if score
improves. | 3.282876 | 3.237176 | 1.014117 |
while True:
tour_score, = self.evaluate_tour_M(tour)
logging.debug("Starting score: {}".format(tour_score))
active_sizes = self.active_sizes
M = self.M
args = []
for i, t in enumerate(tour):
stour = tour[:i] + tour[... | def prune_tour(self, tour, cpus) | Test deleting each contig and check the delta_score; tour here must
be an array of ints. | 4.75538 | 4.558024 | 1.043299 |
N = self.N
tig_to_idx = self.tig_to_idx
M = np.zeros((N, N), dtype=int)
for (at, bt), links in self.contacts.items():
if not (at in tig_to_idx and bt in tig_to_idx):
continue
ai = tig_to_idx[at]
bi = tig_to_idx[bt]
... | def M(self) | Contact frequency matrix. Each cell contains how many inter-contig
links between i-th and j-th contigs. | 2.853749 | 2.476113 | 1.152511 |
N = self.N
tig_to_idx = self.tig_to_idx
O = np.zeros((N, N), dtype=int)
for (at, bt), (strandedness, md, mh) in self.orientations.items():
if not (at in tig_to_idx and bt in tig_to_idx):
continue
ai = tig_to_idx[at]
bi = tig_to... | def O(self) | Pairwise strandedness matrix. Each cell contains whether i-th and j-th
contig are the same orientation +1, or opposite orientation -1. | 3.519616 | 3.082044 | 1.141975 |
N = self.N
tig_to_idx = self.tig_to_idx
P = np.zeros((N, N, 2), dtype=int)
for (at, bt), (strandedness, md, mh) in self.orientations.items():
if not (at in tig_to_idx and bt in tig_to_idx):
continue
ai = tig_to_idx[at]
bi = tig... | def P(self) | Contact frequency matrix with better precision on distance between
contigs. In the matrix M, the distance is assumed to be the distance
between mid-points of two contigs. In matrix Q, however, we compute
harmonic mean of the links for the orientation configuration that is
shortest. This ... | 2.902641 | 2.689924 | 1.079079 |
N = self.N
tig_to_idx = self.tig_to_idx
signs = self.signs
Q = np.ones((N, N, BB), dtype=int) * -1 # Use -1 as the sentinel
for (at, bt), k in self.contacts_oriented.items():
if not (at in tig_to_idx and bt in tig_to_idx):
continue
... | def Q(self) | Contact frequency matrix when contigs are already oriented. This is s a
similar matrix as M, but rather than having the number of links in the
cell, it points to an array that has the actual distances. | 4.355255 | 3.980851 | 1.094051 |
p = OptionParser(insertionpairs.__doc__)
p.add_option("--extend", default=10, type="int",
help="Allow insertion sites to match up within distance")
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
bedfile, = args
... | def insertionpairs(args) | %prog insertionpairs endpoints.bed
Pair up the candidate endpoints. A candidate exision point would contain
both left-end (LE) and right-end (RE) within a given distance.
-----------| |------------
-------| |--------
---------| |----------
(RE) (LE) | 3.120748 | 2.965388 | 1.052391 |
p = OptionParser(insertion.__doc__)
p.add_option("--mindepth", default=6, type="int",
help="Minimum depth to call an insertion")
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
bedfile, = args
mindepth = opts.min... | def insertion(args) | %prog insertion mic.mac.bed
Find IES based on mapping MIC reads to MAC genome. Output a bedfile with
'lesions' (stack of broken reads) in the MAC genome. | 2.395556 | 2.308559 | 1.037685 |
p.add_option("--distance", default=500, type="int",
help="Outer distance between the two ends [default: %default]")
p.add_option("--readlen", default=150, type="int",
help="Length of the read")
p.set_depth(depth=10)
p.set_outfile(outfile=None) | def add_sim_options(p) | Add options shared by eagle or wgsim. | 4.156691 | 3.976133 | 1.04541 |
p = OptionParser(wgsim.__doc__)
p.add_option("--erate", default=.01, type="float",
help="Base error rate of the read [default: %default]")
p.add_option("--noerrors", default=False, action="store_true",
help="Simulate reads with no errors [default: %default]")
p.add... | def wgsim(args) | %prog wgsim fastafile
Run dwgsim on fastafile. | 2.83584 | 2.746142 | 1.032663 |
p = OptionParser(cov.__doc__)
p.add_option("--order",
default="swede,kale,h165,yudal,aviso,abu,bristol,bzh",
help="The order to plot the tracks, comma-separated")
p.add_option("--reverse", default=False, action="store_true",
help="Plot the order in revers... | def cov(args) | %prog cov chrA01 chrC01 chr.sizes data AN.CN.1x1.lifted.anchors.simple
Plot coverage graphs between homeologs, the middle panel show the
homeologous gene pairs. Allow multiple chromosomes to multiple chromosomes. | 3.554698 | 3.369807 | 1.054867 |
p = OptionParser(fig4.__doc__)
p.add_option("--gauge_step", default=200000, type="int",
help="Step size for the base scale")
opts, args, iopts = p.set_image_options(args, figsize="9x7")
if len(args) != 2:
sys.exit(not p.print_help())
layout, datadir = args
layout =... | def fig4(args) | %prog fig4 layout data
Napus Figure 4A displays an example deleted region for quartet chromosomes,
showing read alignments from high GL and low GL lines. | 5.101378 | 4.908676 | 1.039258 |
import math
from jcvi.formats.bed import Bed
from jcvi.graphics.chromosome import HorizontalChromosome
from jcvi.graphics.base import kb_formatter
p = OptionParser(deletion.__doc__)
opts, args, iopts = p.set_image_options(args)
if len(args) != 3:
sys.exit(not p.print_help())
... | def deletion(args) | %prog deletion [deletion-genes|deletion-bases] C2-deletions boleracea.bed
Plot histogram for napus deletions. Can plot deletion-genes or
deletion-bases. The three largest segmental deletions will be highlighted
along with a drawing of the C2 chromosome. | 3.395955 | 3.262703 | 1.040841 |
p = OptionParser(ploidy.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="8x7")
if len(args) != 2:
sys.exit(not p.print_help())
seqidsfile, klayout = args
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes([0, 0, 1, 1])
Karyotype(fig, root, seqidsfile,... | def ploidy(args) | %prog ploidy seqids layout
Build a figure that calls graphics.karyotype to illustrate the high ploidy
of B. napus genome. | 3.013797 | 2.797278 | 1.077403 |
from jcvi.graphics.base import red_purple as default_cm
p = OptionParser(expr.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="8x5")
if len(args) != 4:
sys.exit(not p.print_help())
block, exp, layout, napusbed = args
fig = plt.figure(1, (iopts.w, iopts.h))
roo... | def expr(args) | %prog expr block exp layout napus.bed
Plot a composite figure showing synteny and the expression level between
homeologs in two tissues - total 4 lists of values. block file contains the
gene pairs between AN and CN. | 3.059624 | 2.86284 | 1.068737 |
p = OptionParser(pasteprepare.__doc__)
p.add_option("--flank", default=5000, type="int",
help="Get the seq of size on two ends [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
goodfasta, = args
flank = opts.flan... | def pasteprepare(args) | %prog pasteprepare bacs.fasta
Prepare sequences for paste. | 3.35168 | 3.230469 | 1.037521 |
from jcvi.formats.bed import uniq
p = OptionParser(paste.__doc__)
p.add_option("--maxsize", default=300000, type="int",
help="Maximum size of patchers to be replaced [default: %default]")
p.add_option("--prefix", help="Prefix of the new object [default: %default]")
p.set_rclip(rcli... | def paste(args) | %prog paste flanks.bed flanks_vs_assembly.blast backbone.fasta
Paste in good sequences in the final assembly. | 4.693723 | 4.387397 | 1.06982 |
p = OptionParser(eject.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
candidates, chrfasta = args
sizesfile = Sizes(chrfasta).filename
cbedfile = complementBed(candidates, sizesfile)
cbed = Bed(cbedfile)
for b in cbed:
b.a... | def eject(args) | %prog eject candidates.bed chr.fasta
Eject scaffolds from assembly, using the range identified by closest(). | 4.983202 | 3.701315 | 1.346333 |
p = OptionParser(closest.__doc__)
p.add_option("--om", default=False, action="store_true",
help="The bedfile is OM blocks [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
candidates, gapsbed, fastafile = args
si... | def closest(args) | %prog closest candidates.bed gaps.bed fastafile
Identify the nearest gaps flanking suggested regions. | 3.188433 | 2.867258 | 1.112015 |
from jcvi.formats.agp import mask, bed
from jcvi.formats.sizes import agp
p = OptionParser(insert.__doc__)
opts, args = p.parse_args(args)
if len(args) != 4:
sys.exit(not p.print_help())
candidates, gapsbed, chrfasta, unplacedfasta = args
refinedbed = refine([candidates, gaps... | def insert(args) | %prog insert candidates.bed gaps.bed chrs.fasta unplaced.fasta
Insert scaffolds into assembly. | 3.53912 | 3.216763 | 1.100212 |
from jcvi.formats.bed import uniq
from jcvi.utils.iter import pairwise
p = OptionParser(gaps.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
ombed, fastafile = args
ombed = uniq([ombed])
bed = Bed(ombed)
for a, b in pairwise(b... | def gaps(args) | %prog gaps OM.bed fastafile
Create patches around OM gaps. | 2.601868 | 2.39769 | 1.085156 |
p = OptionParser(tips.__doc__)
opts, args = p.parse_args(args)
if len(args) != 4:
sys.exit(not p.print_help())
pbedfile, cbedfile, sizesfile, bbfasta = args
pbed = Bed(pbedfile, sorted=False)
cbed = Bed(cbedfile, sorted=False)
complements = dict()
for object, beds in gro... | def tips(args) | %prog tips patchers.bed complements.bed original.fasta backbone.fasta
Append telomeric sequences based on patchers and complements. | 2.672838 | 2.517512 | 1.061698 |
p = OptionParser(fill.__doc__)
p.add_option("--extend", default=2000, type="int",
help="Extend seq flanking the gaps [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
gapsbed, badfasta = args
Ext = opts.extend
... | def fill(args) | %prog fill gaps.bed bad.fasta
Perform gap filling of one assembly (bad) using sequences from another. | 3.836496 | 3.389397 | 1.131911 |
from jcvi.apps.align import blast
from jcvi.formats.fasta import SeqIO
p = OptionParser(install.__doc__)
p.set_rclip(rclip=1)
p.add_option("--maxsize", default=300000, type="int",
help="Maximum size of patchers to be replaced [default: %default]")
p.add_option("--prefix", help=... | def install(args) | %prog install patchers.bed patchers.fasta backbone.fasta alt.fasta
Install patches into backbone, using sequences from alternative assembly.
The patches sequences are generated via jcvi.assembly.patch.fill().
The output is a bedfile that can be converted to AGP using
jcvi.formats.agp.frombed(). | 3.602128 | 3.380269 | 1.065634 |
p = OptionParser(refine.__doc__)
p.add_option("--closest", default=False, action="store_true",
help="In case of no gaps, use closest [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
breakpointsbed, gapsbed = args
... | def refine(args) | %prog refine breakpoints.bed gaps.bed
Find gaps within or near breakpoint region.
For breakpoint regions with no gaps, there are two options:
- Break in the middle of the region
- Break at the closest gap (--closest) | 2.534267 | 2.411638 | 1.050849 |
from jcvi.formats.bed import uniq
p = OptionParser(patcher.__doc__)
p.add_option("--backbone", default="OM",
help="Prefix of the backbone assembly [default: %default]")
p.add_option("--object", default="object",
help="New object name [default: %default]")
opts... | def patcher(args) | %prog patcher backbone.bed other.bed
Given optical map alignment, prepare the patchers. Use --backbone to suggest
which assembly is the major one, and the patchers will be extracted from
another assembly. | 4.08193 | 3.826408 | 1.066779 |
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")
... | 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. | 2.926023 | 2.679715 | 1.091916 |
p = OptionParser(stutter.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
vcf, = args
pf = op.basename(vcf).split(".")[0]
execid, sampleid = pf.split("_")
C = "vcftools --remove-filtered-all --min-meanDP 10"
C += " --gzvcf {} --out ... | def stutter(args) | %prog stutter a.vcf.gz
Extract info from lobSTR vcf file. Generates a file that has the following
fields:
CHR, POS, MOTIF, RL, ALLREADS, Q | 3.926034 | 3.676463 | 1.067884 |
p = OptionParser(filtervcf.__doc__)
p.set_home("lobstr", default="/mnt/software/lobSTR")
p.set_aws_opts(store="hli-mv-data-science/htang/str")
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
samples, = args
lhome = opts.lobstr_h... | def filtervcf(args) | %prog filtervcf NA12878.hg38.vcf.gz
Filter lobSTR VCF using script shipped in lobSTR. Input file can be a list
of vcf files. | 4.495221 | 4.091889 | 1.098568 |
p = OptionParser(meta.__doc__)
p.add_option("--cutoff", default=.5, type="float",
help="Percent observed required (chrY half cutoff)")
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) != 4:
sys.exit(not p.print_help())
binfile, sampleids, strids, wobed = ... | def meta(args) | %prog meta data.bin samples STR.ids STR-exons.wo.bed
Compute allele frequencies and prune sites based on missingness.
Filter subset of loci that satisfy:
1. no redundancy (unique chr:pos)
2. variable (n_alleles > 1)
3. low level of missing data (>= 50% autosomal + X, > 25% for Y)
Write meta f... | 4.435167 | 4.193947 | 1.057516 |
p = OptionParser(bin.__doc__)
p.add_option("--dtype", choices=("float32", "int32"),
help="dtype of the matrix")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
tsvfile, = args
dtype = opts.dtype
if dtype is None: # Guess
... | def bin(args) | %prog bin data.tsv
Conver tsv to binary format. | 2.408303 | 2.31014 | 1.042493 |
p = OptionParser(data.__doc__)
p.add_option("--notsv", default=False, action="store_true",
help="Do not write data.tsv")
opts, args = p.parse_args(args)
if len(args) != 4:
sys.exit(not p.print_help())
databin, sampleids, strids, metafile = args
final_columns, perc... | def data(args) | %prog data data.bin samples.ids STR.ids meta.tsv
Make data.tsv based on meta.tsv. | 4.363021 | 3.894823 | 1.12021 |
p = OptionParser(mask.__doc__)
opts, args = p.parse_args(args)
if len(args) not in (2, 4):
sys.exit(not p.print_help())
if len(args) == 4:
databin, sampleids, strids, metafile = args
df, m, samples, loci = read_binfile(databin, sampleids, strids)
mode = "STRs"
... | def mask(args) | %prog mask data.bin samples.ids STR.ids meta.tsv
OR
%prog mask data.tsv meta.tsv
Compute P-values based on meta and data. The `data.bin` should be the matrix
containing filtered loci and the output mask.tsv will have the same
dimension. | 4.154766 | 3.779213 | 1.099373 |
p = OptionParser(mergecsv.__doc__)
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
csvfiles = args
arrays = []
samplekeys = []
for csvfile in csvfiles:
samplekey = op.basename(csvfile).split(".")[0]
a = np.fromfile(csvfile, sep=",... | def mergecsv(args) | %prog mergecsv *.csv
Combine CSV into binary array. | 2.543311 | 2.357541 | 1.078798 |
p = OptionParser(compilevcf.__doc__)
p.add_option("--db", default="hg38", help="Use these lobSTR db")
p.add_option("--nofilter", default=False, action="store_true",
help="Do not filter the variants")
p.set_home("lobstr")
p.set_cpus()
p.set_aws_opts(store="hli-mv-data-scienc... | def compilevcf(args) | %prog compilevcf samples.csv
Compile vcf results into master spreadsheet. | 3.991339 | 3.927225 | 1.016326 |
from jcvi.utils.table import write_csv
p = OptionParser(ystr.__doc__)
p.set_home("lobstr")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
vcffile, = args
si = STRFile(opts.lobstr_home, db="hg38-named")
register = si.register
header = ... | def ystr(args) | %prog ystr chrY.vcf
Print out Y-STR info given VCF. Marker name extracted from tabfile. | 4.134412 | 4.023084 | 1.027672 |
p = OptionParser(liftover.__doc__)
p.add_option("--checkvalid", default=False, action="store_true",
help="Check minscore, period and length")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
refbed, fastafile = args
genome = pyfasta.F... | def liftover(args) | %prog liftover lobstr_v3.0.2_hg38_ref.bed hg38.upper.fa
LiftOver CODIS/Y-STR markers. | 3.216331 | 3.057996 | 1.051777 |
from jcvi.apps.base import iglob
cparams = "1 1 2 80 5 200 2000"
p = OptionParser(trf.__doc__)
p.add_option("--mismatch", default=31, type="int",
help="Mismatch and gap penalty")
p.add_option("--minscore", default=MINSCORE, type="int",
help="Minimum score to r... | def trf(args) | %prog trf outdir
Run TRF on FASTA files. | 3.095117 | 3.032254 | 1.020732 |
p = OptionParser(batchlobstr.__doc__)
p.add_option("--sep", default=",", help="Separator for building commandline")
p.set_home("lobstr", default="s3://hli-mv-data-science/htang/str-build/lobSTR/")
p.set_aws_opts(store="hli-mv-data-science/htang/str-data")
opts, args = p.parse_args(args)
if... | def batchlobstr(args) | %prog batchlobstr samples.csv
Run lobSTR sequentially on list of samples. Each line contains:
sample-name,s3-location | 5.047091 | 4.937597 | 1.022175 |
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("--tre... | def locus(args) | %prog locus bamfile
Extract selected locus from a list of TREDs for validation, and run lobSTR. | 4.847856 | 4.490833 | 1.0795 |
p = OptionParser(lobstrindex.__doc__)
p.add_option("--notreds", default=False, action="store_true",
help="Remove TREDs from the bed file")
p.set_home("lobstr")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
trfbed, fastafile = args... | def lobstrindex(args) | %prog lobstrindex hg38.trf.bed hg38.upper.fa
Make lobSTR index. Make sure the FASTA contain only upper case (so use
fasta.format --upper to convert from UCSC fasta). The bed file is generated
by str(). | 3.122686 | 2.972037 | 1.050689 |
p = OptionParser(close.__doc__)
p.set_home("gapfiller")
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
scaffolds = args[0]
libtxt = write_libraries(args[1:], aligner="bwa")
cmd = "perl " + op.join(opts.gapfiller_home, "GapFille... | def close(args) | %prog close scaffolds.fasta PE*.fastq
Run GapFiller to fill gaps. | 4.01501 | 3.195921 | 1.256292 |
p = OptionParser(scaffold.__doc__)
p.set_aligner(aligner="bwa")
p.set_home("sspace")
p.set_cpus()
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
contigs = args[0]
libtxt = write_libraries(args[1:], aligner=opts.aligner)
# Requires getopt... | def scaffold(args) | %prog scaffold contigs.fasta MP*.fastq
Run SSPACE scaffolding. | 6.395324 | 5.470994 | 1.168951 |
p = OptionParser(agp.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
evidencefile, contigs = args
ef = EvidenceFile(evidencefile, contigs)
agpfile = evidencefile.replace(".evidence", ".agp")
ef.write_agp(agpfile) | def agp(args) | %prog agp evidencefile contigs.fasta
Convert SSPACE scaffold structure to AGP format. | 2.814865 | 2.048578 | 1.374058 |
p = OptionParser(embed.__doc__)
p.set_mingap(default=10)
p.add_option("--min_length", default=200, type="int",
help="Minimum length to consider [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
evidencefile, scaf... | def embed(args) | %prog embed evidencefile scaffolds.fasta contigs.fasta
Use SSPACE evidencefile to scaffold contigs into existing scaffold
structure, as in `scaffolds.fasta`. Contigs.fasta were used by SSPACE
directly to scaffold.
Rules:
1. Only update existing structure by embedding contigs small enough to fit.
... | 4.541344 | 4.336781 | 1.047169 |
align_choices = ("left", "center", "right")
p = OptionParser(gff.__doc__)
p.add_option("--align", default="left", choices=align_choices,
help="Horizontal alignment [default: %default]")
p.add_option("--noUTR", default=False, action="store_true",
help="Do not plot U... | def gff(args) | %prog gff *.gff
Draw exons for genes based on gff files. Each gff file should contain only
one gene, and only the "mRNA" and "CDS" feature will be drawn on the canvas. | 2.936266 | 2.829475 | 1.037742 |
from jcvi.formats.fastq import readlen
p = OptionParser(spades.__doc__)
opts, args = p.parse_args(args)
if len(args) == 0:
sys.exit(not p.print_help())
folder, = args
for p, pf in iter_project(folder):
rl = readlen([p[0], "--silent"])
# <http://spades.bioinf.spba... | def spades(args) | %prog spades folder
Run automated SPADES. | 3.733759 | 3.551172 | 1.051416 |
from jcvi.apps.bowtie import align
p = OptionParser(contamination.__doc__)
p.add_option("--mapped", default=False, action="store_true",
help="Retain contaminated reads instead [default: %default]")
p.set_cutoff(cutoff=800)
p.set_mateorientation(mateorientation="+-")
opts, ... | def contamination(args) | %prog contamination folder Ecoli.fasta
Remove contaminated reads. The FASTQ files in the folder will automatically
pair and filtered against Ecoli.fasta to remove contaminants using BOWTIE2. | 4.053464 | 3.699788 | 1.095594 |
p = OptionParser(pairs.__doc__)
p.set_firstN()
p.set_mates()
p.set_aligner()
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
cwd = os.getcwd()
aligner = opts.aligner
work = "-".join(("pairs", aligner))
mkdir(work)
from jcvi.form... | def pairs(args) | %prog pairs folder reference.fasta
Estimate insert size distribution. Compatible with a variety of aligners,
including BOWTIE and BWA. | 4.608087 | 4.383369 | 1.051266 |
p = OptionParser(allpaths.__doc__)
p.add_option("--ploidy", default="1", choices=("1", "2"),
help="Ploidy [default: %default]")
opts, args = p.parse_args(args)
if len(args) == 0:
sys.exit(not p.print_help())
folders = args
for pf in folders:
if not op.isdi... | def allpaths(args) | %prog allpaths folder1 folder2 ...
Run automated ALLPATHS on list of dirs. | 3.380087 | 3.398392 | 0.994614 |
p = OptionParser(prepare.__doc__)
p.add_option("--first", default=0, type="int",
help="Use only first N reads [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
jfile, = args
metafile = jfile + ".meta"
if nee... | def prepare(args) | %prog prepare jira.txt
Parse JIRA report and prepare input. Look for all FASTQ files in the report
and get the prefix. Assign fastq to a folder and a new file name indicating
the library type (e.g. PE-500, MP-5000, etc.).
Note that JIRA report can also be a list of FASTQ files. | 3.649271 | 3.411234 | 1.06978 |
slink(p, pf, tag)
assemble_dir(pf, target) | def assemble_pairs(p, pf, tag, target=["final.contigs.fasta"]) | Take one pair of reads and assemble to contigs.fasta. | 15.76053 | 16.326332 | 0.965344 |
from jcvi.assembly.preprocess import correct as cr
logging.debug("Work on {0} ({1})".format(pf, ','.join(p)))
itag = tag[0]
cm = ".".join((pf, itag))
targets = (cm + ".1.corr.fastq", cm + ".2.corr.fastq", \
pf + ".PE-0.corr.fastq")
if not need_update(p, targets):
lo... | def correct_pairs(p, pf, tag) | Take one pair of reads and correct to generate *.corr.fastq. | 4.920497 | 4.621193 | 1.064768 |
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