def measure_rbp(entry):
import os
from time import time
from pysster import utils
output_folder = entry[4] + "_pysster/"
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
start = time()
# predict secondary structures
utils.predict_structures(entry[0], entry[0] + ".struct", annotate=True)
utils.predict_structures(entry[1], entry[1] + ".struct", annotate=True)
utils.predict_structures(entry[2], entry[2] + ".struct", annotate=True)
utils.predict_structures(entry[3], entry[3] + ".struct", annotate=True)
from pysster.Data import Data
from pysster.Model import Model
# load data
data = Data([entry[0] + ".struct", entry[1] + ".struct"], ("ACGU", "HIMS"))
data.train_val_test_split(
0.8, 0.1999
) # we need to have at least one test sequence, even though we have a separate test object
# training
params = {"kernel_len": 8}
model = Model(params, data)
model.train(data)
# load and predict test data
data_test = Data([entry[2] + ".struct", entry[3] + ".struct"],
("ACGU", "HIMS"))
predictions = model.predict(data_test, "all")
stop = time()
print("{}, time in seconds: {}".format(entry[4], stop - start))
# performance evaluation
labels = data_test.get_labels("all")
utils.plot_roc(labels, predictions, output_folder + "roc.pdf")
utils.plot_prec_recall(labels, predictions, output_folder + "prec.pdf")
# get motifs
activations = model.get_max_activations(data_test, "all")
_ = model.visualize_all_kernels(activations, data_test, output_folder)
# save model to drive
utils.save_model(model, "{}model.pkl".format(output_folder))
def test_utils_predict_structures(self):
# RNAfold and RNAlib bindings not available
skip = False
try:
from RNA import fold
except:
if which("RNAfold") == None:
try:
utils.predict_structures(
self.folder + "/data/rna_pred.fasta",
gettempdir() + "/test2.fasta", 2, False)
raise RuntimeError(
'predict_structures should have raised an error at this point, but did not'
)
except:
skip = True # we got an error, as expected
#annotate=False
if skip == True:
return
utils.predict_structures(self.folder + "/data/rna_pred.fasta",
gettempdir() + "/test2.fasta", 2, False)
if not isfile(gettempdir() + "/test2.fasta"): return
with open(self.folder + "/data/rna_pred_ref.fasta", 'rt') as handle:
ref = handle.read()
with open(gettempdir() + "/test2.fasta", 'rt') as handle:
comp = handle.read()
self.assertTrue(ref == comp)
remove(gettempdir() + "/test2.fasta")
#annotate=True
utils.predict_structures(self.folder + "/data/rna_pred.fasta",
gettempdir() + "/test2.fasta", 2, True)
if not isfile(gettempdir() + "/test2.fasta"): return
with open(self.folder + "/data/rna_pred_ref_annot.fasta",
'rt') as handle:
ref = handle.read()
with open(gettempdir() + "/test2.fasta", 'rt') as handle:
comp = handle.read()
self.assertTrue(ref == comp)
remove(gettempdir() + "/test2.fasta")
def main():
RBPs = [("data/pum2.train.positive.fasta",
"data/pum2.train.negative.fasta",
"data/pum2.test.positive.fasta",
"data/pum2.test.negative.fasta",
"PUM2"),
("data/qki.train.positive.fasta",
"data/qki.train.negative.fasta",
"data/qki.test.positive.fasta",
"data/qki.test.negative.fasta",
"QKI"),
("data/igf2bp123.train.positive.fasta",
"data/igf2bp123.train.negative.fasta",
"data/igf2bp123.test.positive.fasta",
"data/igf2bp123.test.negative.fasta",
"IGF2BP123"),
("data/srsf1.train.positive.fasta",
"data/srsf1.train.negative.fasta",
"data/srsf1.test.positive.fasta",
"data/srsf1.test.negative.fasta",
"SRSF1"),
("data/taf2n.train.positive.fasta",
"data/taf2n.train.negative.fasta",
"data/taf2n.test.positive.fasta",
"data/taf2n.test.negative.fasta",
"TAF2N"),
("data/nova.train.positive.fasta",
"data/nova.train.negative.fasta",
"data/nova.test.positive.fasta",
"data/nova.test.negative.fasta",
"NOVA")]
for entry in RBPs:
output_folder = entry[4] + "_pysster/"
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
start = time()
# predict secondary structures
utils.predict_structures(entry[0], entry[0]+".struct.gz", annotate=True)
utils.predict_structures(entry[1], entry[1]+".struct.gz", annotate=True)
utils.predict_structures(entry[2], entry[2]+".struct.gz", annotate=True)
utils.predict_structures(entry[3], entry[3]+".struct.gz", annotate=True)
# load data
data = Data([entry[0]+".struct.gz", entry[1]+".struct.gz"], ("ACGU", "HIMS"))
data.train_val_test_split(0.8, 0.1999) # we need to have at least one test sequence, even though we don't need it
print(data.get_summary())
# training
params = {"kernel_len": 8}
model = Model(params, data)
model.train(data)
# load and predict test data
data_test = Data([entry[2]+".struct.gz", entry[3]+".struct.gz"], ("ACGU", "HIMS"))
predictions = model.predict(data_test, "all")
stop = time()
print("{}, time in seconds: {}".format(entry[4], stop-start))
# performance evaluation
labels = data_test.get_labels("all")
utils.plot_roc(labels, predictions, output_folder+"roc.pdf")
utils.plot_prec_recall(labels, predictions, output_folder+"prec.pdf")
print(utils.get_performance_report(labels, predictions))
# get motifs
activations = model.get_max_activations(data_test, "all")
logos, scores = [], []
for kernel in range(model.params["kernel_num"]):
logo, score = model.visualize_kernel(activations, data_test, kernel, output_folder)
logos.append(logo)
scores.append(score)
# sort motifs by importance score
sorted_idx = [i[0] for i in sorted(enumerate(scores), key=lambda x:x[1])]
with open(output_folder+"kernel_scores.txt", "wt") as handle:
for x in sorted_idx:
print("kernel {:>3}: {:.3f}".format(x, scores[x]))
handle.write("kernel {:>3}: {:.3f}\n".format(x, scores[x]))
# save model to drive
utils.save_model(model, "{}model.pkl".format(output_folder))
alu[i].seq = alu[i].seq.transcribe()
rep = [rec for rec in SeqIO.parse("rep.fasta", "fasta") if rec.seq[150] == "A"]
for i in range(len(rep)):
rep[i].seq = rep[i].seq.transcribe()
nonrep = [
rec for rec in SeqIO.parse("nonrep.fasta", "fasta") if rec.seq[150] == "A"
]
for i in range(len(nonrep)):
nonrep[i].seq = nonrep[i].seq.transcribe()
random.seed(42)
SeqIO.write(random.sample(alu, 50000), "alu.fasta", "fasta")
SeqIO.write(random.sample(rep, 50000), "rep.fasta", "fasta")
SeqIO.write(random.sample(nonrep, 50000), "nonrep.fasta", "fasta")
print("predict secondary structures...")
utils.predict_structures("alu.fasta", "alu.fa.gz", 15, True)
utils.predict_structures("rep.fasta", "rep.fa.gz", 15, True)
utils.predict_structures("nonrep.fasta", "nonrep.fa.gz", 15, True)
print("clean up...")
os.remove("grch37.fasta")
os.remove("grch37.fasta.fai")
os.remove("rep.bed.gz")
os.remove("alu.fasta")
os.remove("rep.fasta")
os.remove("nonrep.fasta")
os.remove("alu.bed.gz")
os.remove("nonrep.bed.gz")
os.remove("rep.bed.gz")
os.remove("table1_full.txt.gz")