def buildIndex(K, inputs, output):
"""
Create a new k-mer index. The FASTA files named in the list
`inputs` are read in and the `K` length k-mers and their reverse
complements are extracted and collated to create an index that
maps from k-mer to sequence number (numbering from 0). The
`names` member of the KmerIndex object can be used to retrieve
the name from the sequence number.
"""
seqs = []
for inp in inputs:
with openFile(inp) as f:
seqs += list(readFasta(f))
S = []
nms = []
lens = array.array('I', [])
for i in xrange(len(seqs)):
(nm, seq) = seqs[i]
nms.append(nm)
xs = list(kmers(K, seq, True))
xs.sort()
uniq(xs)
seqs[i] = [nm, xs]
lens.append(len(xs))
S += xs
S.sort()
uniq(S)
S = sparse(2*K, S)
T = array.array('I', [0 for i in xrange(S.count() + 1)])
for i in xrange(len(seqs)):
for x in seqs[i][1]:
r = S.rank(x)
T[r] += 1
t0 = 0
for i in xrange(len(T)):
t1 = t0 + T[i]
T[i] = t0
t0 = t1
T0 = [c for c in T]
U = array.array('H', [0 for i in xrange(t0)])
for i in xrange(len(seqs)):
for x in seqs[i][1]:
r = S.rank(x)
U[T0[r]] = i
T0[r] += 1
with container(output, 'w') as z:
writeKmers(K, S.xs, z)
n = write32(z, T, 'offsets')
z.meta['T'] = n
n = write16(z, U, 'postings')
z.meta['U'] = n
n = write32(z, lens, 'lens')
z.meta['lens'] = n
z.meta['names'] = nms
def test_kmers():
s = "CCTCGTACGCCATATTTTCGCATTTCACGTACGTATTGTTTTTGCAACATAATTACCTATTCTCTTTTGGGGGGGGTTTTAGGCATTCCATTTAATNGCTTTTCTTTTAATGCATGGAGTTTTTCCCATTCATCCTTTGATATATTATCTTTACTTGCTTCGAAGTCTNTTGCTGTGAGATGTATATCTTCTGGATGGATTTGTTTACGTTCTTTTGTTACTGGATCTATAGTAAATGGAATCATTTCCTT"
k = 25
xs = list(basics.kmers(k, s, False))
ys = []
for i in range(len(s) - k + 1):
y = basics.kmer(s[i:i + k])
if y is not None:
ys.append(y)
assert xs == ys
from pykmer.basics import kmers
from pykmer.file import readFasta
from pykmer.misc import uniq
import pykmer.kset as kset
import sys
if len(sys.argv) < 4:
print >> sys.stderr, "usage: make-kset.py <K> <output-filename> <input-FASTA>...."
sys.exit(1)
K = int(sys.argv[1])
xs = []
for fn in sys.argv[3:]:
with open(fn) as f:
for (nm, seq) in readFasta(f):
xs += list(kmers(K, seq, True))
xs.sort()
uniq(xs)
kset.write(K, xs, sys.argv[2])
# file.
#
xs = {}
with gzip.open(sys.argv[2]) as f:
# Now parse the FASTA file, to get the <name, sequence> pairs.
for (nm, seq) in readFasta(f):
# From each sequence extract k-mers.
#
# Note that the kmers() function is a generator - it uses
# yield to return each k-mer in turn rather than composing
# them in to a list or a set.
#
# The third argument is a boolean flag that indicates whether
# or not to return reverse complement versions of the k-mers
# as well as the "forward" ones in the sequence.
for x in kmers(K, seq, True):
# If this is the first instance of this k-mer
# initialize an entry in the dict.
if x not in xs:
xs[x] = 0
# Add this k-mer instance
xs[x] += 1
# Step 2
# Iterate over the dictionary, and compile the k-mer frequency
# histogram.
#
hist = {}
for (x,f) in xs.iteritems():
if f not in hist:
hist[f] = 0