def run_locarnap(seqsin, numkept, cpus=1, foldless=False):
"""Runs locarna-p on a set of sequences in MinimalFastaParser format
[(header, seq), (header, seq)] and retgurns alignemtn and structure"""
seqs, headers = remove_duplicates(seqsin)
# blank headers to save memory
headers = 0
# make sure group has enough sequences before continuing
if len(seqs) < numkept and not foldless:
return "", ""
# headers come out in format Header_# so split to get # and sort by abundance
seqs.sort(reverse=True, key=lambda count: int(count[0].split("_")[1]))
# cut to numkept most abundant sequences
if len(seqs) > numkept:
seqs = seqs[:numkept]
return create_locarnap_alignment(seqs, RNA, struct=True, params={"--cpus": cpus})
def run_locarnap(seqsin, numkept, cpus=1,foldless=False):
'''Runs locarna-p on a set of sequences in MinimalFastaParser format
[(header, seq), (header, seq)] and returns alignment and structure'''
#make sure group has enough sequences before continuing
if len(seqsin) < numkept and not foldless:
return "", ""
if len(seqsin) == 1:
#raise ValueError("Need at least two sequences for locarna-p")
return LoadSeqs(data=seqsin, moltype=RNA), get_secondary_structure(seqsin[0][1])[1]
#headers come out in format Header_# so split to get # and sort by abundance
seqsin.sort(reverse=True, key=lambda count: int(count[0].split('_')[1]))
#cut to numkept most abundant sequences
if len(seqsin) > numkept:
seqsin = seqsin[:numkept]
aln, struct = create_locarnap_alignment(seqsin, RNA, struct=True, params={'--cpus': cpus})
struct = struct.replace('-', ".")
return aln, struct
otu = currotu[0]
print "==" + otu + "=="
print "Reading in 30 most abundant sequences"
# assuming that the fasta has more than 30 sequences in it. Safe assumption
# if this is a significant cluster
seqs = [(header, seq) for header, seq in MinimalFastaParser(open(currotu[1], "rU"))]
seqs, headers = remove_duplicates(seqs)
# blank headers to save memory
headers = 0
# headers come out in format Header_# so split to get # and sort by abundance
seqs.sort(reverse=True, key=lambda count: int(count[0].split("_")[1]))
# cut to 30 most abundant sequences
seqs = seqs[:30]
print "Running locarna-p on sequences"
args = {"--cpus": "24"}
aln, struct = create_locarnap_alignment(seqs, RNA, struct=True, params=args)
# create output folder for OTU
otufolder = "/Users/Ely/Desktop/Ely_selection/R7/lead_clusters/"
if not exists(otufolder):
mkdir(otufolder)
otufolder += otu
if not exists(otufolder):
mkdir(otufolder)
# print out alignment and structure in fasta and stockholm formats
alnout = open(otufolder + "/locarnap-aln.fasta", "w")
alnout.write(aln.toFasta() + "\n>SS_struct\n" + struct + "\n")
alnout.close()
alnout = open(otufolder + "/locarnap-aln.sto", "w")
struct_dict = {"SS_cons": struct}
alnout.write(stockholm_from_alignment(aln, GC_annotation=struct_dict))
alnout.close()
