clusters = {}
secs = time()
if exists(otufolder + "clusters.txt"):
print "sequences previously clustered"
clustersin = open(otufolder + "clusters.txt")
currclust = ""
for header, seq in MinimalFastaParser(clustersin):
if "cluster_" in header:
currclust = header
clusters[currclust] = []
else:
clusters[currclust].append((header,seq))
else:
print "Running uclust over sequences"
#cluster the initial sequences by sequence simmilarity
clusters = cluster_seqs(args.i, args.sim, folderout=args.o, gapopen='10.0/*TI', gapext='10.0')
#remove tiny clusters
#topop = []
#countarray = []
#for cluster in clusters:
# totalseqs = 0
# for seq in clusters[cluster]:
# totalseqs += int(seq[0].split("_")[1])
# if totalseqs < args.minseqs:
# topop.append(cluster)
# else:
# countarray.append((cluster,totalseqs))
#for remove in topop:
# clusters.pop(remove)
from cluster_seqs import cluster_seqs
from sys import argv
from cogent import RNA, LoadSeqs
from cogent.app.muscle_v38 import align_unaligned_seqs
from weblogolib import *
from os.path import exists
# test.py /path/to/filein.fasta /path/to/folderout/ #simmilarity
if __name__ == "__main__":
if argv[2][-1] != "/":
argv[2] += "/"
clusters = cluster_seqs(argv[1], argv[3], folderout=argv[2], gapopen='10.0/*TI', gapext='10.0')
#remove tiny clusters
topop = []
countarray = []
for cluster in clusters:
totalseqs = 0
for seq in clusters[cluster]:
totalseqs += int(seq[0].split("_")[1])
if totalseqs < 100:
topop.append(cluster)
else:
countarray.append((cluster,totalseqs))
for remove in topop:
clusters.pop(remove)
countarray.sort(reverse=True, key=lambda c: c[1])
for c in countarray:
print c[0] + "\t" + str(c[1])
