Louis BECQUEY / biorseo

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Louis BECQUEY
72060257 2 parents 2e2ec263 73734e9d

Merge branch 'master' of https://github.com/persalteas/biominserter

Inline Side-by-side
Showing 2 changed files with 123 additions and 120 deletions
......@@ -549,7 +549,7 @@ class BiorseoInstance:
cmd = ["java", "-jar", jar3dexec, filename, ILmotifDir+"/all.txt",
loop.header[1:]+".ILloop.csv", loop.header[1:]+".ILseq.csv"]
nowhere = open(devnull, 'w')
logfile = open("log_of_the_run.sh", 'a')
logfile = open(biorseoDir + "/log_of_the_run.sh", 'a')
logfile.write(' '.join(cmd))
logfile.write("\n")
logfile.close()
......@@ -789,7 +789,7 @@ class BiorseoInstance:
if c == 0:
seq = l[:-1].upper()
if is_canonical_nts(seq):
header = header.replace('/', '_').replace('\'','').replace('(','').replace(')','')
header = header.replace('/', '_').replace('\'','').replace('(','').replace(')','').replace(' ','_')
RNAcontainer.append(RNA(header, seq))
if not path.isfile(self.outputf + header + ".fa"):
rna = open(self.outputf + header + ".fa", "w")
......@@ -845,4 +845,4 @@ class BiorseoInstance:
self.joblist.append(Job(command=command, priority=priority, timeout=3600, how_many_in_parallel=3))
BiorseoInstance(opts)
BiorseoInstance(opts)
\ No newline at end of file
......
......@@ -9,7 +9,7 @@ from matplotlib import colors
from math import sqrt
from multiprocessing import Pool, cpu_count, Manager
import multiprocessing
import ast
import ast, time
# ================== DEFINITION OF THE PATHS ==============================
......@@ -77,9 +77,8 @@ class MyPool(multiprocessing.pool.Pool):
kwargs['context'] = NoDaemonContext()
super(MyPool, self).__init__(*args, **kwargs)
exit()
def execute_job(j):
def execute_job(j):
if j.checkFunc_ is not None:
if j.checkFunc_(*j.checkArgs_):
running_stats[2] += 1
......@@ -223,6 +222,7 @@ def launch_JAR3D_worker(loop):
return insertion_sites
def launch_JAR3D(seq_, basename):
time1 = time.time()
rnasubopt_preds = []
# Extracting probable loops from RNA-subopt structures
rna = open(outputDir + basename + ".subopt", "r")
......@@ -270,9 +270,10 @@ def launch_JAR3D(seq_, basename):
positions.append("-,-")
resultsfile.write(','.join(positions)+'\n')
resultsfile.close()
time2 = time.time()
print("<%s | %.3fs" % (basename, time2-time1))
def launch_BayesPairing(module_type, seq_, header_, basename):
chdir(bypdir)
cmd = ["python3","parse_sequences.py","-seq",outputDir + basename + ".fa", "-d", module_type, "-interm","1"]
......@@ -281,6 +282,7 @@ def launch_BayesPairing(module_type, seq_, header_, basename):
logfile.write("\n")
logfile.close()
chdir(bypdir)
out = subprocess.check_output(cmd).decode('utf-8')
BypLog = out.split('\n')
idx = 0
......@@ -1176,43 +1178,43 @@ for instance in RNAcontainer:
instance.evaluate()
x_PK = [
[ rna.biokop.avg_mcc for rna in RNAcontainer if len(rna.biokop.predictions)],
[ rna.biokop.avg_mcc for rna in RNAcontainer if len(rna.biokop.predictions)],
[ rna.biorseoRawA.avg_mcc for rna in RNAcontainer if len(rna.biorseoRawA.predictions)],
[ rna.biorseoRawB.avg_mcc for rna in RNAcontainer if len(rna.biorseoRawB.predictions)],
[ rna.biorseoBayesPairA.avg_mcc for rna in RNAcontainer if len(rna.biorseoBayesPairA.predictions)],
[ rna.biorseoBayesPairB.avg_mcc for rna in RNAcontainer if len(rna.biorseoBayesPairB.predictions)],
[ rna.biorseoBayesPairC.avg_mcc for rna in RNAcontainer if len(rna.biorseoBayesPairC.predictions)],
[ rna.biorseoBayesPairD.avg_mcc for rna in RNAcontainer if len(rna.biorseoBayesPairD.predictions)],
[ rna.biorseoBGSUJAR3DA.avg_mcc for rna in RNAcontainer if len(rna.biorseoBGSUJAR3DA.predictions)],
[ rna.biorseoBGSUJAR3DB.avg_mcc for rna in RNAcontainer if len(rna.biorseoBGSUJAR3DB.predictions)],
[ rna.biorseoBGSUJAR3DC.avg_mcc for rna in RNAcontainer if len(rna.biorseoBGSUJAR3DC.predictions)],
[ rna.biorseoBGSUJAR3DD.avg_mcc for rna in RNAcontainer if len(rna.biorseoBGSUJAR3DD.predictions)],
[ rna.biorseoBGSUBayesPairA.avg_mcc for rna in RNAcontainer if len(rna.biorseoBGSUBayesPairA.predictions)],
[ rna.biorseoBGSUBayesPairB.avg_mcc for rna in RNAcontainer if len(rna.biorseoBGSUBayesPairB.predictions)],
[ rna.biorseoBGSUBayesPairC.avg_mcc for rna in RNAcontainer if len(rna.biorseoBGSUBayesPairC.predictions)],
[ rna.biorseoBGSUBayesPairD.avg_mcc for rna in RNAcontainer if len(rna.biorseoBGSUBayesPairD.predictions)]
[ rna.biokop.max_mcc for rna in RNAcontainer if len(rna.biokop.predictions)],
[ rna.biokop.max_mcc for rna in RNAcontainer if len(rna.biokop.predictions)],
[ rna.biorseoRawA.max_mcc for rna in RNAcontainer if len(rna.biorseoRawA.predictions)],
[ rna.biorseoRawB.max_mcc for rna in RNAcontainer if len(rna.biorseoRawB.predictions)],
[ rna.biorseoBayesPairA.max_mcc for rna in RNAcontainer if len(rna.biorseoBayesPairA.predictions)],
[ rna.biorseoBayesPairB.max_mcc for rna in RNAcontainer if len(rna.biorseoBayesPairB.predictions)],
[ rna.biorseoBayesPairC.max_mcc for rna in RNAcontainer if len(rna.biorseoBayesPairC.predictions)],
[ rna.biorseoBayesPairD.max_mcc for rna in RNAcontainer if len(rna.biorseoBayesPairD.predictions)],
[ rna.biorseoBGSUJAR3DA.max_mcc for rna in RNAcontainer if len(rna.biorseoBGSUJAR3DA.predictions)],
[ rna.biorseoBGSUJAR3DB.max_mcc for rna in RNAcontainer if len(rna.biorseoBGSUJAR3DB.predictions)],
[ rna.biorseoBGSUJAR3DC.max_mcc for rna in RNAcontainer if len(rna.biorseoBGSUJAR3DC.predictions)],
[ rna.biorseoBGSUJAR3DD.max_mcc for rna in RNAcontainer if len(rna.biorseoBGSUJAR3DD.predictions)],
[ rna.biorseoBGSUBayesPairA.max_mcc for rna in RNAcontainer if len(rna.biorseoBGSUBayesPairA.predictions)],
[ rna.biorseoBGSUBayesPairB.max_mcc for rna in RNAcontainer if len(rna.biorseoBGSUBayesPairB.predictions)],
[ rna.biorseoBGSUBayesPairC.max_mcc for rna in RNAcontainer if len(rna.biorseoBGSUBayesPairC.predictions)],
[ rna.biorseoBGSUBayesPairD.max_mcc for rna in RNAcontainer if len(rna.biorseoBGSUBayesPairD.predictions)]
]
RNAs_fully_predicted = [ x for x in RNAcontainer if x.has_complete_results(True)]
x_PK_fully = [
[ rna.biokop.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biokop.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoRawA.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoRawB.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBayesPairA.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBayesPairB.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBayesPairC.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBayesPairD.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUJAR3DA.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUJAR3DB.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUJAR3DC.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUJAR3DD.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUBayesPairA.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUBayesPairB.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUBayesPairC.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUBayesPairD.avg_mcc for rna in RNAs_fully_predicted],
[ rna.biokop.max_mcc for rna in RNAs_fully_predicted],
[ rna.biokop.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoRawA.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoRawB.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBayesPairA.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBayesPairB.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBayesPairC.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBayesPairD.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUJAR3DA.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUJAR3DB.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUJAR3DC.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUJAR3DD.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUBayesPairA.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUBayesPairB.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUBayesPairC.max_mcc for rna in RNAs_fully_predicted],
[ rna.biorseoBGSUBayesPairD.max_mcc for rna in RNAs_fully_predicted],
] # We ensure having the same number of RNAs in every sample by discarding the one for which computations did not ended/succeeded.
print()
......@@ -1260,87 +1262,88 @@ test = stats.wilcoxon(x_PK_fully[0], x_PK_fully[11])
print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and Jar3dD are equal', p-value = ", test.pvalue)
# # ================== Print results for application cases =====================
# labels = ["Biokop","Biokop","RawA","RawB","BayesPairingA","BayesPairingB","BayesPairingC","BayesPairingD","JAR3DA","JAR3DB","JAR3DC","JAR3DD","BGSUBayesPairingA","BGSUBayesPairingB","BGSUBayesPairingC","BGSUBayesPairingD"]
# print("RNAsubopt",":",x_noPK[0])
# print("RNA-MOIP",":",x_noPK[1])
# for data, name in zip(x_PK, labels):
# print(name,":",data)
# labels = ["RNAsubopt","Biokop\t", "RNA-MoIP\t","RawA\t","RawB\t","BayesPairingA","BayesPairingB","BayesPairingC","BayesPairingD","JAR3DA\t","JAR3DB\t","JAR3DC\t","JAR3DD\t","BGSUBPairingA","BGSUBPairingB","BGSUBPairingC","BGSUBPairingD"]
# for r in RNAcontainer:
# print("\n",r.header_,"\nTrue structure:\t", r.true2d)
# for m, name in zip([r.rnasubopt, r.biokop, r.rnamoip,
# r.biorseoRawA,
# r.biorseoRawB,
# r.biorseoBayesPairA,
# r.biorseoBayesPairB,
# r.biorseoBayesPairC,
# r.biorseoBayesPairD,
# r.biorseoBGSUJAR3DA,
# r.biorseoBGSUJAR3DB,
# r.biorseoBGSUJAR3DC,
# r.biorseoBGSUJAR3DD,
# r.biorseoBGSUBayesPairA,
# r.biorseoBGSUBayesPairB,
# r.biorseoBGSUBayesPairC,
# r.biorseoBGSUBayesPairD ], labels):
# print(name+":\t",m.best_pred)
# ================= PLOTS OF RESULTS =======================================
merge = [ x_noPK[0], # RNA subopt
x_noPK[1], # RNA-MoIP
x_PK[0], # Biokop
x_PK[2], #biorseoRawA
x_PK[3], #biorseoRawB
x_PK[4], #biorseoBayesPairA
x_PK[5], #biorseoBayesPairB
x_PK[6], #biorseoBayesPairC
x_PK[7], #biorseoBayesPairD
x_PK[8], #biorseoBGSUJAR3DA
x_PK[9], #biorseoBGSUJAR3DB
x_PK[10], #biorseoBGSUJAR3DC
x_PK[11], #biorseoBGSUJAR3DD
x_PK[12], #biorseoBGSUBayesPairA
x_PK[13], #biorseoBGSUBayesPairB
x_PK[14], #biorseoBGSUBayesPairC
x_PK[15], #biorseoBGSUBayesPairD
]
# ================== Print results for application cases =====================
labels = ["Biokop","Biokop","RawA","RawB","BayesPairingA","BayesPairingB","BayesPairingC","BayesPairingD","JAR3DA","JAR3DB","JAR3DC","JAR3DD","BGSUBayesPairingA","BGSUBayesPairingB","BGSUBayesPairingC","BGSUBayesPairingD"]
print("RNAsubopt",":",x_noPK[0])
print("RNA-MOIP",":",x_noPK[1])
for data, name in zip(x_PK, labels):
print(name,":",data)
labels = ["RNAsubopt","Biokop\t", "RNA-MoIP\t","RawA\t","RawB\t","BayesPairingA","BayesPairingB","BayesPairingC","BayesPairingD","JAR3DA\t","JAR3DB\t","JAR3DC\t","JAR3DD\t","BGSUBPairingA","BGSUBPairingB","BGSUBPairingC","BGSUBPairingD"]
for r in RNAcontainer:
print("\n",r.header_,"\nTrue structure:\t", r.true2d)
for m, name in zip([r.rnasubopt, r.biokop, r.rnamoip,
r.biorseoRawA,
r.biorseoRawB,
r.biorseoBayesPairA,
r.biorseoBayesPairB,
r.biorseoBayesPairC,
r.biorseoBayesPairD,
r.biorseoBGSUJAR3DA,
r.biorseoBGSUJAR3DB,
r.biorseoBGSUJAR3DC,
r.biorseoBGSUJAR3DD,
r.biorseoBGSUBayesPairA,
r.biorseoBGSUBayesPairB,
r.biorseoBGSUBayesPairC,
r.biorseoBGSUBayesPairD ], labels):
print(name+":\t",m.best_pred, "%.2f"% m.max_mcc, m.n_pred)
# # ================= PLOTS OF RESULTS =======================================
# merge = [
# x_PK[0], # Biokop
# x_noPK[0], # RNA subopt
# x_noPK[1], # RNA-MoIP
# x_PK[2], #biorseoRawA
# x_PK[3], #biorseoRawB
# x_PK[4], #biorseoBayesPairA
# x_PK[5], #biorseoBayesPairB
# x_PK[6], #biorseoBayesPairC
# x_PK[7], #biorseoBayesPairD
# x_PK[8], #biorseoBGSUJAR3DA
# x_PK[9], #biorseoBGSUJAR3DB
# x_PK[10], #biorseoBGSUJAR3DC
# x_PK[11], #biorseoBGSUJAR3DD
# x_PK[12], #biorseoBGSUBayesPairA
# x_PK[13], #biorseoBGSUBayesPairB
# x_PK[14], #biorseoBGSUBayesPairC
# x_PK[15], #biorseoBGSUBayesPairD
# ]
colors = [ 'blue', 'goldenrod', 'green',
'red',
'firebrick',
'limegreen',
'olive',
'forestgreen',
'lime',
'darkcyan',
'royalblue',
'navy',
'limegreen',
'olive',
'forestgreen',
'lime'
]
labels = [ "RNAsubopt",
"RNA-MoIP",
"Biokop",
"$f_{1A}$",
"$f_{1B}$",
"$f_{1A}$",
"$f_{1B}$",
"$f_{1C}$",
"$f_{1D}$",
"$f_{1A}$",
"$f_{1B}$",
"$f_{1C}$",
"$f_{1D}$",
"$f_{1A}$",
"$f_{1B}$",
"$f_{1C}$",
"$f_{1D}$"
]
# colors = [ 'green', 'blue', 'goldenrod',
# 'red',
# 'firebrick',
# 'limegreen',
# 'olive',
# 'forestgreen',
# 'lime',
# 'darkturquoise',
# 'darkcyan',
# 'royalblue',
# 'navy',
# 'limegreen',
# 'olive',
# 'forestgreen',
# 'lime'
# ]
# labels = [ "Biokop", "RNAsubopt",
# "RNA-MoIP",
# "$f_{1A}$",
# "$f_{1B}$",
# "$f_{1A}$",
# "$f_{1B}$",
# "$f_{1C}$",
# "$f_{1D}$",
# "$f_{1A}$",
# "$f_{1B}$",
# "$f_{1C}$",
# "$f_{1D}$",
# "$f_{1A}$",
# "$f_{1B}$",
# "$f_{1C}$",
# "$f_{1D}$"
# ]
# for y in [ i/10 for i in range(11) ]:
......@@ -1371,7 +1374,7 @@ labels = [ "RNAsubopt",
# # plt.axhline(y=0, color="black", linewidth=1)
# # plt.axhline(y=1, color="black", linewidth=1)
# plt.xticks([1.0+i for i in range(16)], labels[1:])
# plt.ylim((0.5, 1.01))
# plt.ylim((0.4, 1.01))
# plt.ylabel("MCC", fontsize=12)
# plt.subplots_adjust(left=0.05, right=0.95)
# # plt.title("Performance without pseudoknots (%d RNAs included)" % len(x_noPK_fully[0]))
......