code string | signature string | docstring string | loss_without_docstring float64 | loss_with_docstring float64 | factor float64 |
|---|---|---|---|---|---|
p = OptionParser(grasstruth.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
james, = args
fp = open(james)
pairs = set()
for row in fp:
atoms = row.split()
genes = []
idx = {}
for i, a in enumerate(atoms)... | def grasstruth(args) | %prog grasstruth james-pan-grass.txt
Prepare truth pairs for 4 grasses. | 3.154462 | 2.948055 | 1.070015 |
p = OptionParser(synfind.__doc__)
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
lastfile = args[0]
bedfiles = args[1:]
fp = open(lastfile)
filteredlast = lastfile + ".filtered"
fw = open(filteredlast, "w")
for row in fp:
b = Bla... | def synfind(args) | %prog synfind all.last *.bed
Prepare input for SynFind. | 2.587844 | 2.415478 | 1.071359 |
p = OptionParser(yeasttruth.__doc__)
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
pillars = args[0]
gffiles = args[1:]
aliases = {}
pivot = {}
for gffile in gffiles:
is_pivot = op.basename(gffile).startswith("Sa... | def yeasttruth(args) | %prog yeasttruth Pillars.tab *.gff
Prepare pairs data for 14 yeasts. | 2.644542 | 2.511631 | 1.052918 |
from matplotlib_venn import venn2
p = OptionParser(venn.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="9x9")
if len(args) < 1:
sys.exit(not p.print_help())
bcs = args
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes([0, 0, 1, 1])
pad = .02
... | def venn(args) | %prog venn *.benchmark
Display benchmark results as Venn diagram. | 3.211409 | 3.181002 | 1.009559 |
p = OptionParser(coge.__doc__)
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
gffs = args
for gff in gffs:
atoms = op.basename(gff).split(".")
gid = atoms[-2]
assert gid.startswith("gid")
gid = get_number(gid)
gen... | def coge(args) | %prog coge *.gff
Prepare coge datasets. | 4.656058 | 4.241477 | 1.097744 |
p = OptionParser(benchmark.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
pf, bedfile = args
truth = pf + ".truth"
synfind = pf + ".synfind"
mcscanx = pf + ".mcscanx"
iadhore = pf + ".iadhore"
orthofinder = pf + ".orthofinder"
... | def benchmark(args) | %prog benchmark at bedfile
Compare SynFind, MCScanx, iADHoRe and OrthoFinder against the truth. | 2.577518 | 2.19212 | 1.17581 |
p = OptionParser(cyntenator.__doc__)
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
lastfile = args[0]
fp = open(lastfile)
filteredlastfile = lastfile + ".blast"
fw = open(filteredlastfile, "w")
for row in fp:
b = BlastLine(row)
... | def cyntenator(args) | %prog cyntenator athaliana.athaliana.last athaliana.bed
Prepare input for Cyntenator. | 3.852404 | 3.64122 | 1.057998 |
p = OptionParser(iadhore.__doc__)
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
lastfile = args[0]
bedfiles = args[1:]
blast_table = "blast_table.txt"
fp = open(lastfile)
seen = set()
for row in fp:
c = BlastLine(row)
a,... | def iadhore(args) | %prog iadhore athaliana.athaliana.last athaliana.bed
Wrap around iADHoRe. | 3.101125 | 3.054992 | 1.015101 |
p = OptionParser(athalianatruth.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
atxt, bctxt = args
g = Grouper()
pairs = set()
for txt in (atxt, bctxt):
extract_groups(g, pairs, txt)
fw = open("pairs", "w")
for pair in ... | def athalianatruth(args) | %prog athalianatruth J_a.txt J_bc.txt
Prepare pairs data for At alpha/beta/gamma. | 2.825551 | 2.660889 | 1.061883 |
p = OptionParser(mcscanx.__doc__)
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
blastfile = args[0]
bedfiles = args[1:]
prefix = "_".join(op.basename(x)[:2] for x in bedfiles)
symlink(blastfile, prefix + ".blast")
allbedfile = prefix + ".gf... | def mcscanx(args) | %prog mcscanx athaliana.athaliana.last athaliana.bed
Wrap around MCScanX. | 2.915783 | 2.839213 | 1.026969 |
p = OptionParser(grass._doc__)
p.set_verbose()
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
master, james = args
fp = open(master)
next(fp)
master_store = defaultdict(set)
for row in fp:
atoms = row.split()
s = set()
... | def grass(args) | %prog grass coge_master_table.txt james.txt
Validate SynFind pan-grass set against James. This set can be generated:
https://genomevolution.org/r/fhak | 2.9049 | 2.791263 | 1.040711 |
p = OptionParser(ecoli.__doc__)
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
master, querybed = args
fp = open(master)
header = next(fp)
assert header[0] == '#'
qorg = header.strip().split("\t")[1]
qorg = qorg.spli... | def ecoli(args) | %prog ecoli coge_master_table.txt query.bed
Perform gene presence / absence analysis in Ecoli master spreadsheet. Ecoli
spresheets can be downloaded below:
Ecoli K12 MG1655 (K) as query
Regenerate this analysis: https://genomevolution.org/r/fggo
Ecoli O157:H7 EDL933 (O) as query
Regenerate th... | 4.19033 | 4.09256 | 1.02389 |
p = OptionParser(cartoon.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="10x7")
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes([0, 0, 1, 1])
# Panel A
A = CartoonRegion(41)
A.draw(root, .35, .85, strip=False, color=False)
x1, x2 = A.x1, A.x2
lsg = ... | def cartoon(args) | %prog synteny.py
Generate cartoon illustration of SynFind. | 3.619598 | 3.581594 | 1.010611 |
from jcvi.formats.base import split
p = OptionParser(parallel.__doc__)
p.set_home("maker")
p.set_tmpdir(tmpdir="tmp")
p.set_grid_opts(array=True)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
genome, NN = args
threaded = opts.threaded... | def parallel(args) | %prog parallel genome.fasta N
Partition the genome into parts and run separately. This is useful if MAKER
is to be run on the grid. | 4.754758 | 4.493413 | 1.058162 |
from jcvi.formats.gff import merge as gmerge
p = OptionParser(merge.__doc__)
p.set_home("maker")
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
outdir, outputgff = args
fsnames, suffix = get_fsnames(outdir)
nfs = len(fsnames)
cmd = op.... | def merge(args) | %prog merge outdir output.gff
Follow-up command after grid jobs are completed after parallel(). | 6.340092 | 6.054099 | 1.04724 |
from jcvi.utils.counter import Counter
p = OptionParser(validate.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
outdir, genome = args
counter = Counter()
fsnames, suffix = get_fsnames(outdir)
dsfile = "{0}{1}/{0}.maker.output/{0}... | def validate(args) | %prog validate outdir genome.fasta
Validate current folder after MAKER run and check for failures. Failed batch
will be written to a directory for additional work. | 4.380803 | 4.107612 | 1.066508 |
from jcvi.formats.bed import evaluate
from jcvi.formats.gff import make_index
p = OptionParser(evaluate.__doc__)
p.add_option("--type", default="CDS",
help="list of features to extract, use comma to separate (e.g."
"'five_prime_UTR,CDS,three_prime_UTR') [default: %default]"... | def batcheval(args) | %prog batcheval model.ids gff_file evidences.bed fastafile
Get the accuracy for a list of models against evidences in the range of the
genes. For example:
$ %prog batcheval all.gff3 isoforms.ids proteins.bed scaffolds.fasta
Outfile contains the scores for the models can be found in models.scores | 3.338075 | 3.009954 | 1.109012 |
bed_file = get_bed_file(gff_file, stype, key)
cmd = "intersectBed -a {0} -b {1} -wao".format(split_bed, bed_file)
cmd += " | cut -f4,10"
p = popen(cmd)
splits = defaultdict(set)
for row in p:
a, b = row.split()
splits[a].add(b)
return splits | def get_splits(split_bed, gff_file, stype, key) | Use intersectBed to find the fused gene => split genes mappings. | 2.808934 | 2.614559 | 1.074343 |
from jcvi.formats.bed import evaluate
bed_file = get_bed_file(gff_file, type, key)
b = evaluate([bed_file, evidences_bed, sizesfile, "--query={0}".format(query)])
return b | def get_accuracy(query, gff_file, evidences_bed, sizesfile, type, key) | Get sensitivity, specificity and accuracy given gff_file, and a query range
that look like "chr1:1-10000". | 4.979709 | 5.055483 | 0.985011 |
from jcvi.formats.bed import Bed
p = OptionParser(split.__doc__)
p.add_option("--key", default="Name",
help="Key in the attributes to extract predictor.gff [default: %default]")
p.add_option("--parents", default="match",
help="list of features to extract, use comma to separ... | def split(args) | %prog split split.bed evidences.bed predictor1.gff predictor2.gff fastafile
Split MAKER models by checking against predictors (such as AUGUSTUS and
FGENESH). For each region covered by a working model. Find out the
combination of predictors that gives the best accuracy against evidences
(such as PASA).... | 2.668696 | 2.443971 | 1.091951 |
p = OptionParser(datastore.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
ds, = args
fp = open(ds)
for row in fp:
fn = row.strip()
assert op.exists(fn)
pp, logfile = op.split(fn)
flog = open(fn)
for ... | def datastore(args) | %prog datastore datastore.log > gfflist.log
Generate a list of gff filenames to merge. The `datastore.log` file can be
generated by something like:
$ find
/usr/local/scratch/htang/EVM_test/gannotation/maker/1132350111853_default/i1/
-maxdepth 4 -name "*datastore*.log" > datastore.log | 3.739252 | 3.642381 | 1.026596 |
from jcvi.formats.base import DictFile
p = OptionParser(libsvm.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
csvfile, prefixids = args
d = DictFile(prefixids)
fp = open(csvfile)
next(fp)
for row in fp:
atoms = row.spl... | def libsvm(args) | %prog libsvm csvfile prefix.ids
Convert csv file to LIBSVM format. `prefix.ids` contains the prefix mapping.
Ga -1
Gr 1
So the feature in the first column of csvfile get scanned with the prefix
and mapped to different classes. Formatting spec:
http://svmlight.joachims.org/ | 3.100321 | 2.757751 | 1.124221 |
N = len(nodes)
E = len(edges)
A = np.zeros((E, N), dtype=int)
for i, (a, b, distance) in enumerate(edges):
A[i, a] = 1
A[i, b] = -1
K = np.eye(E, dtype=int)
L = np.array([x[-1] for x in edges])
s = spring_system(A, K, L)
return np.array([0] + [int(round(x, 0)) for... | def determine_positions(nodes, edges) | Construct the problem instance to solve the positions of contigs.
The input for spring_system() is A, K, L, which looks like the following.
A = np.array([[1, -1, 0], [0, 1, -1], [1, 0, -1]])
K = np.eye(3, dtype=int)
L = np.array([1, 2, 3])
For example, A-B distance 1, B-C distance 2, A-C distance ... | 3.101441 | 2.536464 | 1.222742 |
N = len(nodes)
M = np.zeros((N, N), dtype=float)
for a, b, w in edges:
M[a, b] += w
M = symmetrize(M)
return get_signs(M, cutoff=cutoff, validate=False) | def determine_signs(nodes, edges, cutoff=1e-10) | Construct the orientation matrix for the pairs on N molecules.
>>> determine_signs([0, 1, 2], [(0, 1, 1), (0, 2, -1), (1, 2, -1)])
array([ 1, 1, -1]) | 3.460259 | 3.877702 | 0.892348 |
# Is this a symmetric matrix?
assert is_symmetric(M), "the matrix is not symmetric:\n{0}".format(str(M))
N, x = M.shape
# eigh() works on symmetric matrix (Hermitian)
w, v = np.linalg.eigh(M)
m = np.argmax(w)
mv = v[:, m]
f = lambda x: (x if abs(x) > cutoff else 0)
mv = [f(x) f... | def get_signs(M, cutoff=1e-10, validate=True, ambiguous=True) | Given a numpy array M that contains pairwise orientations, find the largest
eigenvalue and associated eigenvector and return the signs for the
eigenvector. This should correspond to the original orientations for the
individual molecule. In the first example below, let's say 3 molecules A, B
and C, A-B:s... | 5.168143 | 5.298134 | 0.975465 |
# Linear equation is A'KAx = -A'KL
C = np.dot(A.T, K)
left = np.dot(C, A)
right = - np.dot(C, L)
left = left[1:, 1:]
right = right[1:]
x = np.linalg.solve(left, right)
return x | def spring_system(A, K, L) | Solving the equilibrium positions of the objects, linked by springs of
length L, stiffness of K, and connectivity matrix A. Then solving:
F_nodes = -A'KAx - A'KL = 0
In the context of scaffolding, lengths (L) are inferred by mate inserts,
stiffness (K) is inferred via the number of links, connectivity... | 4.700365 | 4.598763 | 1.022093 |
p = OptionParser(fix.__doc__)
p.add_option("--ignore_sym_pat", default=False, action="store_true",
help="Do not fix names matching symbol patterns i.e." + \
" names beginning or ending with gene symbols or a series of numbers." + \
" e.g. `ARM repeat superfamily protein`, `beta-hexo... | def fix(args) | %prog fix ahrd.csv > ahrd.fixed.csv
Fix ugly names from Uniprot. | 5.867056 | 5.490234 | 1.068635 |
p = OptionParser(merge.__doc__)
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
csvfiles = args
cf = csvfiles[0]
fp = open(cf)
for row in fp:
if row.startswith("Protein"):
break
header = row.rstrip()
print(header)
... | def merge(args) | %prog merge output/*.csv > ahrd.csv
Merge AHRD results, remove redundant headers, empty lines, etc. If there are
multiple lines containing the same ID (first column). Then whatever comes
the first will get retained. | 2.830992 | 2.734676 | 1.03522 |
p = OptionParser(batch.__doc__)
ahrd_weights = { "blastp": [0.5, 0.3, 0.2],
"blastx": [0.6, 0.4, 0.0]
}
blast_progs = tuple(ahrd_weights.keys())
p.add_option("--path", default="~/code/AHRD/",
help="Path where AHRD is installed [default: %de... | def batch(args) | %prog batch splits output
The arguments are two folders.
Input FASTA sequences are in splits/.
Output csv files are in output/.
Must have folders swissprot/, tair/, trembl/ that contains the respective
BLAST output. Once finished, you can run, for example:
$ parallel java -Xmx2g -jar ~/code/A... | 3.946648 | 3.587261 | 1.100184 |
if gff3:
s = s.replace('+', 'PlusSign')
d = parse_qs(s, keep_attr_order=keep_attr_order)
for key in d:
d[key][0] = unquote(d[key][0].replace('PlusSign', '+').replace('"', ''))
else:
attributes = s.split(";")
d = DefaultOrderedDict(list) if keep_a... | def make_attributes(s, gff3=True, keep_attr_order=True) | In GFF3, the last column is typically:
ID=cds00002;Parent=mRNA00002;
In GFF2, the last column is typically:
Gene 22240.t000374; Note "Carbonic anhydrase" | 3.084357 | 3.37514 | 0.913846 |
from jcvi.utils.range import Range
if score or id:
_score = score if score else obj.score
_id = id if id else obj.id
return Range(seqid=obj.seqid, start=obj.start, end=obj.end, \
score=_score, id=_id)
elif strand:
return (obj.seqid, obj.start, obj.end, obj.s... | def to_range(obj, score=None, id=None, strand=None) | Given a gffutils object, convert it to a range object | 2.339034 | 2.341828 | 0.998807 |
p = OptionParser(addparent.__doc__)
p.add_option("--childfeat", default="CDS", help="Type of children feature")
p.add_option("--parentfeat", default="mRNA", help="Type of merged feature")
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help()... | def addparent(args) | %prog addparent file.gff
Merge sister features and infer parents. | 2.512474 | 2.440731 | 1.029394 |
_strand = 1 if strand == '+' else -1
return fasta.sequence({'chr': seqid, 'start': start, 'stop': stop, \
'strand': _strand}) | def _fasta_slice(fasta, seqid, start, stop, strand) | Return slice of fasta, given (seqid, start, stop, strand) | 4.024476 | 3.937519 | 1.022084 |
if len(codon) != 3:
return False
if type == 'start':
if codon != 'ATG':
return False
elif type == 'stop':
if not any(_codon == codon for _codon in ('TGA', 'TAG', 'TAA')):
return False
else:
logging.error("`{0}` is not a valid codon type. ".fo... | def is_valid_codon(codon, type='start') | Given a codon sequence, check if it is a valid start/stop codon | 2.671605 | 2.659463 | 1.004566 |
s, e = codon_span[0], codon_span[1]
while True:
if (type == 'start' and strand == '+') or \
(type == 'stop' and strand == '-'):
s, e = s - 3, e - 3
else:
s, e = s + 3, e + 3
codon = _fasta_slice(genome, seqid, s, e, strand)
is_valid = is_... | def scan_for_valid_codon(codon_span, strand, seqid, genome, type='start') | Given a codon span, strand and reference seqid, scan upstream/downstream
to find a valid in-frame start/stop codon | 2.222182 | 2.146001 | 1.035499 |
p = OptionParser(sizes.__doc__)
p.set_outfile()
p.add_option("--parents", dest="parents", default="mRNA",
help="parent feature(s) for which size is to be calculated")
p.add_option("--child", dest="child", default="CDS",
help="child feature to use for size calculations")
... | def sizes(args) | %prog sizes gffile
Given a gff file of features, calculate the sizes of chosen parent feature
based on summation of sizes of child features.
For example, for parent 'mRNA' and child 'CDS' feature types, calcuate sizes of
mRNA by summing the sizes of the disjoint CDS parts. | 3.274271 | 2.877501 | 1.137887 |
from jcvi.utils.grouper import Grouper
from itertools import combinations
p = OptionParser(cluster.__doc__)
p.add_option("--slop", default=False, action="store_true",
help="allow minor variation in terminal 5'/3' UTR" + \
" start/stop position [default: %default]")
... | def cluster(args) | %prog cluster gffile
Given a gff file of gene structures (multiple transcripts per gene locus),
cluster/consolidate all transcripts based on shared splicing structure.
If `slop` is enabled, clustering/consolidation will collapse any variation
in terminal UTR lengths, keeping only the longest as repres... | 2.358458 | 2.27633 | 1.036079 |
from jcvi.formats.base import SetFile
from jcvi.formats.bed import BedSummary
from jcvi.utils.table import tabulate
p = OptionParser(summary.__doc__)
p.add_option("--isoform", default=False, action="store_true",
help="Find longest isoform of each id")
p.add_option("--ids",... | def summary(args) | %prog summary gffile
Print summary stats for features of different types. | 2.746888 | 2.674602 | 1.027027 |
from Bio.Alphabet import generic_dna
try:
from BCBio import GFF
except ImportError:
print("You need to install dep first: $ easy_install bcbio-gff", file=sys.stderr)
p = OptionParser(gb.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.prin... | def gb(args) | %prog gb gffile fastafile
Convert GFF3 to Genbank format. Recipe taken from:
<http://www.biostars.org/p/2492/> | 2.598323 | 2.422902 | 1.072401 |
from jcvi.formats.fasta import longestorf
p = OptionParser(orient.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
ingff3, fastafile = args
idsfile = fastafile.rsplit(".", 1)[0] + ".orf.ids"
if need_update(fastafile, idsfile):
l... | def orient(args) | %prog orient in.gff3 features.fasta > out.gff3
Change the feature orientations based on translation. This script is often
needed in fixing the strand information after mapping RNA-seq transcripts.
You can generate the features.fasta similar to this command:
$ %prog load --parents=EST_match --children... | 3.276163 | 3.210238 | 1.020536 |
p = OptionParser(rename.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
ingff3, switch = args
switch = DictFile(switch)
gff = Gff(ingff3)
for g in gff:
id, = g.attributes["ID"]
newname = switch.get(id, id)
g.att... | def rename(args) | %prog rename in.gff3 switch.ids > reindexed.gff3
Change the IDs within the gff3. | 2.848674 | 2.359897 | 1.207118 |
p = OptionParser(parents.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
gff_file, idsfile = args
g = make_index(gff_file)
fp = open(idsfile)
for row in fp:
cid = row.strip()
b = next(g.parents(cid, 1))
print("\... | def parents(args) | %prog parents gffile models.ids
Find the parents given a list of IDs in "models.ids". | 3.82469 | 3.235641 | 1.18205 |
p = OptionParser(filter.__doc__)
p.add_option("--type", default="mRNA",
help="The feature to scan for the attributes [default: %default]")
g1 = OptionGroup(p, "Filter by identity/coverage attribute values")
g1.add_option("--id", default=95, type="float",
help="Minimum ... | def filter(args) | %prog filter gffile > filtered.gff
Filter the gff file based on criteria below:
(1) feature attribute values: [Identity, Coverage].
You can get this type of gff by using gmap
$ gmap -f 2 ....
(2) Total bp length of child features | 2.386064 | 2.303233 | 1.035963 |
p = OptionParser(gapsplit.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
gffile, = args
gff = Gff(gffile)
for g in gff:
if re.match("EST_match", g.type):
match = re.search(r'\S+ (\d+) \d+ ([\s{1}\-])', g.... | def gapsplit(args) | %prog gapsplit gffile > split.gff
Read in the gff (normally generated by GMAP) and print it out after splitting
each feature into one parent and multiple child features based on alignment
information encoded in CIGAR string. | 2.458143 | 2.390766 | 1.028182 |
p = OptionParser(fixboundaries.__doc__)
p.add_option("--type", default="gene", type="str",
help="Feature type for which to adjust boundaries")
p.add_option("--child_ftype", default="mRNA", type="str",
help="Child featuretype(s) to use for identifying boundaries")
p... | def fixboundaries(args) | %prog fixboundaries gffile --type="gene" --child_ftype="mRNA" > gffile.fixed
Adjust the boundary coordinates of parents features based on
range chained child features, extracting their min and max values | 2.792766 | 2.356482 | 1.185142 |
p = OptionParser(liftover.__doc__)
p.add_option("--tilesize", default=50000, type="int",
help="The size for each tile [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
gffile, = args
gff = Gff(gffile)
for g i... | def liftover(args) | %prog liftover gffile > liftover.gff
Adjust gff coordinates based on tile number. For example,
"gannotation.asmbl.000095.7" is the 8-th tile on asmbl.000095. | 2.474503 | 2.16694 | 1.141934 |
from jcvi.utils.range import Range, range_piles
ranges = [Range(a.seqid, a.start, a.end, 0, i) \
for i, a in enumerate(allgenes)]
for pile in range_piles(ranges):
yield [allgenes[x] for x in pile] | def get_piles(allgenes) | Before running uniq, we need to compute all the piles. The piles are a set
of redundant features we want to get rid of. Input are a list of GffLines
features. Output are list of list of features distinct "piles". | 4.430434 | 4.237797 | 1.045457 |
f1c, f2c = list(dbx1.children(f1, featuretype=featuretype, order_by='start')), \
list(dbx2.children(f2, featuretype=featuretype, order_by='start'))
lf1c, lf2c = len(f1c), len(f2c)
if match_nchildren(f1c, f2c):
if lf1c > 0 and lf2c > 0:
exclN = set()
if featu... | def match_subfeats(f1, f2, dbx1, dbx2, featuretype=None, slop=False) | Given 2 gffutils features located in 2 separate gffutils databases,
iterate through all subfeatures of a certain type and check whether
they are identical or not
The `slop` parameter allows for variation in the terminal UTR region | 2.889957 | 2.848741 | 1.014468 |
supported_modes = ("span", "score")
p = OptionParser(uniq.__doc__)
p.add_option("--type", default="gene",
help="Types of features to non-redundify [default: %default]")
p.add_option("--mode", default="span", choices=supported_modes,
help="Pile mode [default: %defau... | def uniq(args) | %prog uniq gffile > uniq.gff
Remove redundant gene models. For overlapping gene models, take the longest
gene. A second scan takes only the genes selected.
--mode controls whether you want larger feature, or higher scoring feature.
--best controls how many redundant features to keep, e.g. 10 for est2g... | 3.105157 | 2.960523 | 1.048854 |
valid_sort_methods = ("unix", "topo")
p = OptionParser(sort.__doc__)
p.add_option("--method", default="unix", choices=valid_sort_methods,
help="Specify sort method [default: %default]")
p.add_option("-i", dest="inplace", default=False, action="store_true",
help="I... | def sort(args) | %prog sort gffile
Sort gff file using plain old unix sort based on [chromosome, start coordinate].
or topologically based on hierarchy of features using the gt (genometools) toolkit | 3.110766 | 2.903384 | 1.071428 |
p = OptionParser(fromgtf.__doc__)
p.add_option("--transcript_id", default="transcript_id",
help="Field name for transcript [default: %default]")
p.add_option("--gene_id", default="gene_id",
help="Field name for gene [default: %default]")
p.add_option("--augustus", ... | def fromgtf(args) | %prog fromgtf gtffile
Convert gtf to gff file. In gtf, the "transcript_id" will convert to "ID=",
the "transcript_id" in exon/CDS feature will be converted to "Parent=". | 2.229429 | 2.170307 | 1.027241 |
p = OptionParser(frombed.__doc__)
p.add_option("--type", default="match",
help="GFF feature type [default: %default]")
p.add_option("--source", default="default",
help="GFF source qualifier [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1... | def frombed(args) | %prog frombed bed_file [--options] > gff_file
Convert bed to gff file. In bed, the accn will convert to key='ID'
Default type will be `match` and default source will be `source` | 2.65457 | 2.154772 | 1.231949 |
p = OptionParser(fromsoap.__doc__)
p.add_option("--type", default="nucleotide_match",
help="GFF feature type [default: %default]")
p.add_option("--source", default="soap",
help="GFF source qualifier [default: %default]")
p.set_fixchrnames(orgn="maize")
p.set_out... | def fromsoap(args) | %prog fromsoap soapfile > gff_file | 3.687697 | 3.341352 | 1.103654 |
p = OptionParser(gtf.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
gffile, = args
gff = Gff(gffile)
transcript_info = AutoVivification()
for g in gff:
if g.type.endswith(("RNA", "transcript")):
if "ID" in g.attribu... | def gtf(args) | %prog gtf gffile
Convert gff to gtf file. In gtf, only exon/CDS features are important. The
first 8 columns are the same as gff, but in the attributes field, we need to
specify "gene_id" and "transcript_id". | 2.350284 | 2.284036 | 1.029005 |
p = OptionParser(merge.__doc__)
p.add_option("--seq", default=False, action="store_true",
help="Print FASTA sequences at the end")
p.set_outfile()
opts, args = p.parse_args(args)
nargs = len(args)
if nargs < 1:
sys.exit(not p.print_help())
if nargs == 1:
... | def merge(args) | %prog merge gffiles
Merge several gff files into one. When only one file is given, it is assumed
to be a file with a list of gff files. | 2.880445 | 2.677744 | 1.075698 |
p = OptionParser(extract.__doc__)
p.add_option("--contigs",
help="Extract features from certain contigs [default: %default]")
p.add_option("--names",
help="Extract features with certain names [default: %default]")
p.add_option("--types", type="str", default=None,
... | def extract(args) | %prog extract gffile
--contigs: Extract particular contig(s) from the gff file. If multiple contigs are
involved, use "," to separate, e.g. "contig_12,contig_150"; or provide a file
with multiple contig IDs, one per line
--names: Process particular ID(s) from the gff file. If multiple IDs are
invol... | 3.188984 | 2.936364 | 1.086031 |
p = OptionParser(split.__doc__)
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
gffile, outdir = args
mkdir(outdir)
g = Gff(gffile)
seqids = g.seqids
for s in seqids:
outfile = op.join(outdir, s + ".gff")
extract([gffile, ... | def split(args) | %prog split gffile outdir
Split the gff into one contig per file. Will also take sequences if the file
contains FASTA sequences. | 3.162964 | 2.681286 | 1.179644 |
p = OptionParser(note.__doc__)
p.add_option("--type", default=None,
help="Only process certain types, multiple types allowed with comma")
p.add_option("--attribute", default="Parent,Note",
help="Attribute field to extract, multiple fields allowd with comma")
p.add_option("--... | def note(args) | %prog note gffile > tabfile
Extract certain attribute field for each feature. | 3.046747 | 2.753163 | 1.106635 |
from tempfile import mkstemp
from pybedtools import BedTool
from jcvi.utils.cbook import SummaryStats
p = OptionParser(splicecov.__doc__)
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
gfffile, juncsbed, = args
tagged ... | def splicecov(args) | %prog splicecov annotation.gff3 junctions.bed
Given an annotation GFF file (containing introns) and a
TopHat junctions.bed file (preprocessed using formats.bed.juncs(),
each intron gets tagged with the JUNC identifier and read coverage.
Output is a summary table listing for each gene locus, the isofor... | 2.786511 | 2.699027 | 1.032413 |
'''
%prog bed gff_file [--options]
Parses the start, stop locations of the selected features out of GFF and
generate a bed file
'''
from jcvi.utils.cbook import gene_name
p = OptionParser(bed.__doc__)
p.add_option("--type", dest="type", default="gene",
help="Feature type to... | def bed(args) | %prog bed gff_file [--options]
Parses the start, stop locations of the selected features out of GFF and
generate a bed file | 2.638214 | 2.374987 | 1.110833 |
import gffutils
db_file = gff_file + ".db"
if need_update(gff_file, db_file):
if op.exists(db_file):
os.remove(db_file)
logging.debug("Indexing `{0}`".format(gff_file))
gffutils.create_db(gff_file, db_file, merge_strategy="create_unique")
else:
logging.d... | def make_index(gff_file) | Make a sqlite database for fast retrieval of features. | 2.340239 | 2.372663 | 0.986334 |
p = OptionParser(children.__doc__)
p.add_option("--parents", default="gene",
help="list of features to extract, use comma to separate (e.g."
"'gene,mRNA') [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
gff... | def children(args) | %prog children gff_file
Get the children that have the same parent. | 3.408213 | 3.064181 | 1.112275 |
# can request upstream sequence only from the following valid sites
valid_upstream_sites = ["TSS", "TrSS"]
upstream_site, upstream_len = None, None
flag, error_msg = None, None
parents, children = None, None
if re.match(r'upstream', feature):
parents, children = "mRNA", "CDS"
... | def parse_feature_param(feature) | Take the --feature param (coming from gff.load() and parse it.
Returns feature, parents and children terms.
Also returns length of upstream sequence (and start site) requested
If erroneous, returns a flag and error message to be displayed on exit | 3.330456 | 2.988718 | 1.114343 |
if uSite == "TSS":
(upstream_start, upstream_stop) = \
(feat.start - uLen, feat.start - 1) \
if feat.strand == "+" else \
(feat.end + 1, feat.end + uLen)
elif uSite == "TrSS":
children = []
for c in gffdb.children(feat.id, 1):
... | def get_upstream_coords(uSite, uLen, seqlen, feat, children_list, gffdb) | Subroutine takes upstream site, length, reference sequence length,
parent mRNA feature (GffLine object), list of child feature types
and a GFFutils.GFFDB object as the input
If upstream of TSS is requested, use the parent feature coords
to extract the upstream sequence
If upstream of TrSS is reque... | 2.406862 | 2.239722 | 1.074625 |
p = OptionParser(bed12.__doc__)
p.add_option("--parent", default="mRNA",
help="Top feature type [default: %default]")
p.add_option("--block", default="exon",
help="Feature type for regular blocks [default: %default]")
p.add_option("--thick", default="CDS",
help="... | def bed12(args) | %prog bed12 gffile > bedfile
Produce bed12 file for coding features. The exons will be converted to blocks.
The CDS range will be shown between thickStart to thickEnd. For reference,
bed format consists of the following fields:
1. chrom
2. chromStart
3. chromEnd
4. name
5. score
6.... | 2.177887 | 2.009732 | 1.08367 |
sig_elems = [self.seqid, self.source, self.type, \
self.start, self.end, self.strand, \
self.phase]
if re.search("exon|CDS|UTR", self.type):
parent = self.get_attr("Parent")
if parent:
(locus, iso) = atg_name(parent... | def signature(self) | create a unique signature for any GFF line based on joining
columns 1,2,3,4,5,7,8 (into a comma separated string) | 4.999623 | 4.476635 | 1.116826 |
p = OptionParser(main.__doc__)
p.add_option("--refids", help="Use subset of contigs in the ref")
p.add_option("--refcov", default=.01, type="float",
help="Minimum reference coverage [default: %default]")
p.add_option("--all", default=False, action="store_true",
hel... | def main(args) | %prog deltafile
Plot one query. Extract the references that have major matches to this
query. Control "major" by option --refcov. | 3.481893 | 3.206017 | 1.086049 |
p = OptionParser(bed.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(p.print_help())
flist = args
prefix = flist[0].split(".")[0]
j = 0
for f in flist:
reader = Maf(f).reader
for rec in reader:
a, b = rec.components
... | def bed(args) | %prog bed maffiles > out.bed
Convert a folder of maf alignments to the bed features
then useful to check coverage, etc. | 3.893902 | 3.707149 | 1.050377 |
'''
%prog blast maffiles > out.blast
From a folder of .maf files, generate .blast file with tabular format.
'''
p = OptionParser(blast.__doc__)
opts, args = p.parse_args(args)
if len(args) == 0:
sys.exit(p.print_help())
flist = args
for f in flist:
maf_to_blast8(f... | def blast(args) | %prog blast maffiles > out.blast
From a folder of .maf files, generate .blast file with tabular format. | 4.370489 | 2.25994 | 1.933896 |
indexes = interval_index_file.Indexes()
in_handle = open(filename)
reader = maf.Reader(in_handle)
while True:
pos = reader.file.tell()
rec = next(reader)
if rec is None:
break
for c in rec.components:
... | def build_index(self, filename, indexfile) | Recipe from Brad Chapman's blog
<http://bcbio.wordpress.com/2009/07/26/sorting-genomic-alignments-using-python/> | 4.716141 | 4.494425 | 1.049331 |
size = len(candidate)
prob = random.random()
if prob > .5: # Inversion
p = random.randint(0, size-1)
q = random.randint(0, size-1)
if p > q:
p, q = q, p
q += 1
s = candidate[p:q]
x = candidate[:p] + s[::-1] + candidate[q:]
return cr... | def genome_mutation(candidate) | Return the mutants created by inversion mutation on the candidates.
This function performs inversion or insertion. It randomly chooses two
locations along the candidate and reverses the values within that
slice. Insertion is done by popping one item and insert it back at random
position. | 2.829352 | 2.634816 | 1.073833 |
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
if halloffame is not None:
halloffa... | def eaSimpleConverge(population, toolbox, cxpb, mutpb, ngen, stats=None,
halloffame=None, callback=None, verbose=True) | This algorithm reproduce the simplest evolutionary algorithm as
presented in chapter 7 of [Back2000]_.
Modified to allow checking if there is no change for ngen, as a simple
rule for convergence. Interface is similar to eaSimple(). However, in
eaSimple, ngen is total number of iterations; in eaSimpleCo... | 1.693926 | 1.776109 | 0.953729 |
from jcvi.formats.fasta import Fasta
from jcvi.formats.bed import Bed
from jcvi.utils.cbook import fill
p = OptionParser(frombed.__doc__)
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
bedfile, contigfasta, readfasta = args
prefix = bedfil... | def frombed(args) | %prog frombed bedfile contigfasta readfasta
Convert read placement to contig format. This is useful before running BAMBUS. | 2.999459 | 2.804577 | 1.069487 |
p = OptionParser(main.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
contigfile, = args
bedfile = contigfile.rsplit(".", 1)[0] + ".bed"
fw = open(bedfile, "w")
c = ContigFile(contigfile)
for rec in c.iter_records():
for r ... | def bed(args) | %prog bed contigfile
Prints out the contigs and their associated reads. | 3.03594 | 2.800821 | 1.083947 |
'''
runs the command:
`tesseract_cmd` `input_filename` `output_filename_base`
returns the exit status of tesseract, as well as tesseract's stderr output
'''
command = [tesseract_cmd, input_filename, output_filename_base]
if lang is not None:
command += ['-l', lang]
if bo... | def run_tesseract(input_filename, output_filename_base, lang=None, boxes=False) | runs the command:
`tesseract_cmd` `input_filename` `output_filename_base`
returns the exit status of tesseract, as well as tesseract's stderr output | 3.760585 | 2.402981 | 1.564966 |
'''
returns all lines in the error_string that start with the string "error"
'''
lines = error_string.splitlines()
error_lines = tuple(line for line in lines if line.find('Error') >= 0)
if len(error_lines) > 0:
return '\n'.join(error_lines)
else:
return error_string.strip() | def get_errors(error_string) | returns all lines in the error_string that start with the string "error" | 3.510636 | 2.564074 | 1.369163 |
''' returns a temporary file-name '''
# prevent os.tmpname from printing an error...
stderr = sys.stderr
try:
sys.stderr = cStringIO.StringIO()
return os.tempnam(None, 'tess_')
finally:
sys.stderr = stderr | def tempnam() | returns a temporary file-name | 7.023331 | 7.057039 | 0.995223 |
'''
Runs tesseract on the specified image. First, the image is written to disk,
and then the tesseract command is run on the image. Resseract's result is
read, and the temporary files are erased.
'''
input_file_name = '%s.bmp' % tempnam()
output_file_name_base = tempnam()
if not boxes:... | def image_to_string(image, lang=None, boxes=False) | Runs tesseract on the specified image. First, the image is written to disk,
and then the tesseract command is run on the image. Resseract's result is
read, and the temporary files are erased. | 3.181112 | 2.198546 | 1.446916 |
import pandas as pd
p = OptionParser(mitosomatic.__doc__)
p.add_option("--minaf", default=.005, type="float",
help="Minimum allele fraction")
p.add_option("--maxaf", default=.1, type="float",
help="Maximum allele fraction")
opts, args = p.parse_args(args)
... | def mitosomatic(args) | %prog mitosomatic t.piledriver
Find mito mosaic somatic mutations in piledriver results. | 2.42432 | 2.446278 | 0.991024 |
p = OptionParser(bed.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
delt, = args
dt = Delly(delt)
dt.write_bed("del.bed") | def bed(args) | %prog bed del.txt
Convert `del.txt` to BED format. DELLY manual here:
<http://www.embl.de/~rausch/delly.html>
Deletion:
chr, start, end, size, #supporting_pairs, avg._mapping_quality, deletion_id
chr1, 10180, 10509, 329, 75, 15.8667, Deletion_Sample_00000000 | 3.998654 | 3.428037 | 1.166456 |
from jcvi.formats.vcf import VcfLine
from six.moves.urllib.parse import parse_qsl
p = OptionParser(mitocompile.__doc__)
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
vcfs = args
print("\t".join("vcf samplekey depth seqid pos alt svlen pe sr".s... | def mitocompile(args) | %prog mitcompile *.vcf.gz
Extract information about deletions in vcf file. | 3.638021 | 3.526044 | 1.031757 |
p = OptionParser(mito.__doc__)
p.set_aws_opts(store="hli-mv-data-science/htang/mito-deletions")
p.add_option("--realignonly", default=False, action="store_true",
help="Realign only")
p.add_option("--svonly", default=False, action="store_true",
help="Run Realign => ... | def mito(args) | %prog mito chrM.fa input.bam
Identify mitochondrial deletions. | 3.545511 | 3.317906 | 1.068599 |
p = OptionParser(fcs.__doc__)
p.add_option("--cutoff", default=200,
help="Skip small components less than [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
fcsfile, = args
cutoff = opts.cutoff
fp = open(fcsfi... | def fcs(args) | %prog fcs fcsfile
Process the results from Genbank contaminant screen. An example of the file
looks like:
contig name, length, span(s), apparent source
contig0746 11760 1..141 vector
contig0751 14226 13476..14226 vector
contig0800 124133 30512..30559 primer/adapter | 2.88502 | 2.814128 | 1.025191 |
from jcvi.formats.base import must_open
p = OptionParser(asn.__doc__)
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) < 1:
sys.exit(not p.print_help())
fw = must_open(opts.outfile, "w")
for asnfile in args:
fp = open(asnfile)
ingeneralblock = Fals... | def asn(args) | %prog asn asnfiles
Mainly to get this block, and extract `str` field:
general {
db "TIGR" ,
tag
str "mtg2_12952" } ,
genbank {
accession "AC148996" , | 2.84407 | 2.612783 | 1.088521 |
from jcvi.formats.fasta import sequin
p = OptionParser(htgnew.__doc__)
p.add_option("--comment", default="",
help="Comments for this submission [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
fastafile, phasefile, ... | def htgnew(args) | %prog htgnew fastafile phasefile template.sbt
Prepare sqnfiles for submitting new Genbank HTG records.
`fastafile` contains the sequences.
`phasefile` contains the phase information, it is a two column file:
mth2-45h12 3
`template.sbt` is the Genbank submission template.
This function is... | 4.904938 | 4.534799 | 1.081622 |
rows, cols = get_rows_cols()
plate, splate = get_plate()
n96 = rows.index(c96[0]) * ncols / 2 + int(c96[1:])
q = "{0:02d}{1}".format(n96, "ABCD"[quad - 1])
return splate[q] | def convert_96_to_384(c96, quad, nrows=Nrows, ncols=Ncols) | Convert the 96-well number and quad number to 384-well number
>>> convert_96_to_384("B02", 1)
'C3'
>>> convert_96_to_384("H09", 4)
'P18' | 7.721169 | 8.131645 | 0.949521 |
p = OptionParser(t384.__doc__)
opts, args = p.parse_args(args)
plate, splate = get_plate()
fw = sys.stdout
for i in plate:
for j, p in enumerate(i):
if j != 0:
fw.write('|')
fw.write(p)
fw.write('\n') | def t384(args) | %prog t384
Print out a table converting between 96 well to 384 well | 4.8772 | 4.384521 | 1.112368 |
s = "".join(s.split()[1:]).replace("/", ";")
a = parse_qs(s)
return a | def parse_description(s) | Returns a dictionary based on the FASTA header, assuming JCVI data | 11.196808 | 9.586869 | 1.167932 |
from jcvi.formats.base import FileMerger
from jcvi.formats.bed import mates
from jcvi.formats.contig import frombed
from jcvi.formats.fasta import join
from jcvi.utils.iter import grouper
p = OptionParser(scaffold.__doc__)
p.set_rclip(rclip=1)
p.add_option("--conf", help="BAMBUS c... | def scaffold(args) | %prog scaffold ctgfasta reads1.fasta mapping1.bed
reads2.fasta mapping2.bed ...
Run BAMBUS on set of contigs, reads and read mappings. | 3.93345 | 3.759332 | 1.046316 |
from jcvi.formats.fastq import shuffle, pairinplace, split
from jcvi.apps.base import getfilesize
p = OptionParser(diginorm.__doc__)
p.add_option("--single", default=False, action="store_true",
help="Single end reads")
p.add_option("--tablesize", help="Memory size")
p.add_... | def diginorm(args) | %prog diginorm fastqfile
Run K-mer based normalization. Based on tutorial:
<http://ged.msu.edu/angus/diginorm-2012/tutorial.html>
Assume input is either an interleaved pairs file, or two separate files.
To set up khmer:
$ git clone git://github.com/ged-lab/screed.git
$ git clone git://github.... | 3.442291 | 3.438977 | 1.000963 |
import math
from jcvi.formats.fasta import Fasta, SeqIO
from jcvi.formats.fastq import readlen, first, fasta
from jcvi.formats.blast import Blast
from jcvi.formats.base import FileShredder
from jcvi.apps.bowtie import align, get_samfile
from jcvi.apps.align import blast
p = Option... | def expand(args) | %prog expand bes.fasta reads.fastq
Expand sequences using short reads. Useful, for example for getting BAC-end
sequences. The template to use, in `bes.fasta` may just contain the junction
sequences, then align the reads to get the 'flanks' for such sequences. | 4.4566 | 4.348136 | 1.024945 |
from jcvi.apps.bowtie import BowtieLogFile, align
p = OptionParser(contamination.__doc__)
p.set_firstN()
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
ecoli, genome, fq = args
firstN_opt = "--firstN={0}".format(opts.firstN)
samfile, logfi... | def contamination(args) | %prog contamination Ecoli.fasta genome.fasta read.fastq
Check read contamination on a folder of paired reads. Use bowtie2 to compare
the reads against:
1. Ecoli.fsata - this will tell us the lower bound of contamination
2. genome.fasta - this will tell us the upper bound of contamination | 3.443541 | 3.246171 | 1.060801 |
choices = "prepare,align,filter,rmdup,genreads".split(",")
p = OptionParser(alignextend.__doc__)
p.add_option("--nosuffix", default=False, action="store_true",
help="Do not add /1/2 suffix to the read [default: %default]")
p.add_option("--rc", default=False, action="store_true",
... | def alignextend(args) | %prog alignextend ref.fasta read.1.fastq read.2.fastq
Wrapper around AMOS alignextend. | 3.195261 | 3.073931 | 1.039471 |
from jcvi.utils.table import loadtable, write_csv
p = OptionParser(count.__doc__)
p.add_option("--dir",
help="Sub-directory where FASTQC was run [default: %default]")
p.add_option("--human", default=False, action="store_true",
help="Human friendly numbers [default: ... | def count(args) | %prog count *.gz
Count reads based on FASTQC results. FASTQC needs to be run on all the input
data given before running this command. | 3.325669 | 3.271976 | 1.01641 |
p = OptionParser(hetsmooth.__doc__)
p.add_option("-K", default=23, type="int",
help="K-mer size [default: %default]")
p.add_option("-L", type="int",
help="Bottom threshold, first min [default: %default]")
p.add_option("-U", type="int",
help="Top th... | def hetsmooth(args) | %prog hetsmooth reads_1.fq reads_2.fq jf-23_0
Wrapper against het-smooth. Below is the command used in het-smooth manual.
$ het-smooth --kmer-len=23 --bottom-threshold=38 --top-threshold=220
--no-multibase-replacements --jellyfish-hash-file=23-mers.jf
reads_1.fq reads_2.fq | 3.177659 | 2.148694 | 1.478879 |
from jcvi.assembly.allpaths import prepare
from jcvi.assembly.base import FastqNamings
p = OptionParser(correct.__doc__ + FastqNamings)
p.add_option("--dir", default="data",
help="Working directory [default: %default]")
p.add_option("--fragsdedup", default=False, action="store_... | def correct(args) | %prog correct *.fastq
Correct the fastqfile and generated corrected fastqfiles. This calls
assembly.allpaths.prepare() to generate input files for ALLPATHS-LG. The
naming convention for your fastqfiles are important, and are listed below.
By default, this will correct all PE reads, and remove duplicat... | 3.256847 | 3.027355 | 1.075806 |
import re
import csv
p = OptionParser(fetch.__doc__)
p.add_option("--format", default="tab", choices=valid_formats,
help="download format [default: %default]")
p.add_option("--columns", default="entry name, protein names, genes,organism",
help="columns to down... | def fetch(args) | %prog fetch "query"
OR
%prog fetch queries.txt
Please provide a UniProt compatible `query` to retrieve data. If `query` contains
spaces, please remember to "quote" it.
You can also specify a `filename` which contains queries, one per line.
Follow this syntax <http://www.uniprot.org/help/t... | 3.110633 | 3.037267 | 1.024155 |
chr, se = s.split(":")
start, end = se.split("-")
start, end = int(start), int(end)
if start > end:
start, end = end, start
return Range(chr, start, end, 0, 0) | def range_parse(s) | >>> range_parse("chr1:1000-1")
Range(seqid='chr1', start=1, end=1000, score=0, id=0) | 3.141739 | 2.639445 | 1.190303 |
a_min, a_max = a
if a_min > a_max:
a_min, a_max = a_max, a_min
b_min, b_max = b
if b_min > b_max:
b_min, b_max = b_max, b_min
if a_max + extend < b_min or b_max + extend < a_min:
return None
i_min = max(a_min, b_min)
i_max = min(a_max, b_max)
if i_min > i_ma... | def range_intersect(a, b, extend=0) | Returns the intersection between two reanges.
>>> range_intersect((30, 45), (55, 65))
>>> range_intersect((48, 65), (45, 55))
[48, 55] | 1.632901 | 1.791242 | 0.911602 |
if not rset:
return None
a = rset[0]
for b in rset[1:]:
if not a:
return None
a = range_intersect(a, b)
return a | def ranges_intersect(rset) | Recursively calls the range_intersect() - pairwise version.
>>> ranges_intersect([(48, 65), (45, 55), (50, 56)])
[50, 55] | 2.908381 | 3.328662 | 0.873739 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.