def test_too_big_hmms():
test_repeat = repeat.Repeat(msa=TEST_RESULT_REPEAT_MSA_LONG)
test_hmm = HMM.create(input_format='repeat', repeat=test_repeat)
test_seq = sequence.Sequence(TEST_SEQUENCE_A)
test_optimized_repeat = test_seq.detect([test_hmm])
assert type(test_optimized_repeat) == repeat_list.RepeatList
assert len(test_optimized_repeat.repeats) == 0
test_repeat = repeat.Repeat(msa=TEST_RESULT_REPEAT_MSA_SUPER_LONG)
test_hmm = HMM.create(input_format='repeat', repeat=test_repeat)
test_seq = sequence.Sequence(TEST_SEQUENCE_SUPER_LONG_A)
test_optimized_repeat = test_seq.detect([test_hmm])
assert type(test_optimized_repeat) == repeat_list.RepeatList
assert len(test_optimized_repeat.repeats) == 0
def test_sequence_pickle():
test_seq = sequence.Sequence(TEST_SEQUENCE)
test_pickle = os.path.join(path(), "test.pickle")
test_seq.write(test_pickle, 'pickle')
test_seq_new = sequence.Sequence.create(test_pickle, 'pickle')
assert test_seq.seq == test_seq_new.seq
test_repeat = repeat.Repeat(msa=TEST_REPEAT_MSA_DOUBLE)
test_hmm = HMM.create(input_format='repeat', repeat=test_repeat)
test_optimized_repeat = test_seq.detect([test_hmm])
test_seq.set_repeatlist(test_optimized_repeat, TEST_SEQUENCE_TAG)
assert type(test_optimized_repeat) == repeat_list.RepeatList
assert list(test_seq.d_repeatlist.keys()) == [TEST_SEQUENCE_TAG]
assert type(
test_seq.d_repeatlist[TEST_SEQUENCE_TAG]) == repeat_list.RepeatList
assert test_seq.d_repeatlist[TEST_SEQUENCE_TAG].repeats
test_retrieved_repeatlist = test_seq.get_repeatlist(TEST_SEQUENCE_TAG)
assert test_retrieved_repeatlist == test_optimized_repeat
test_seq.write(test_pickle, 'pickle')
test_seq_new = sequence.Sequence.create(test_pickle, 'pickle')
assert test_seq.d_repeatlist.keys() == test_seq_new.d_repeatlist.keys()
assert test_seq.d_repeatlist[TEST_SEQUENCE_TAG].repeats[
0].msa == test_seq_new.d_repeatlist[TEST_SEQUENCE_TAG].repeats[0].msa
if os.path.exists(test_pickle):
os.remove(test_pickle)
def test_detect_TRUST():
test_seq = sequence.Sequence(TEST_SEQUENCE_Q9BRR0)
predicted_repeats = repeat_detection_run.run_detector(seq_records = [test_seq], detectors = ["TRUST"])[0]['TRUST']
# Warning: TRUST finds these results ONLY with the BLOSUM50 substitution matrix.
assert len(predicted_repeats) == 3
# Warning: TRUST finds these results ONLY with the BLOSUM50 substitution matrix.
assert predicted_repeats[0].msa == ['HLREDIAQIP---TCAEAGE---QEGRLQR', 'KQKNATGGRR--HICHECGKSFAQSSGLSK', 'HRRIHTGEKP--YECEECGKAFIGSSALVI', 'HQRVHTGEKP--YECEECGKAFSHSSDLIK', 'HQRTHTGEKP--YECDDCGKTFSQSCSLLE', 'HHRIHTGEKP--YQCSMCGKAFRRSSHLLR', 'HQRIHTGDKN--VQEPEQGEAW--KSRM--', 'ESQLENVETPmsYKCNECERSFTQNTGLIE', 'HQKIHTGEKP--YQCNACGKGFTRISYLVQ']
def test_detect_repeats_denovo():
test_parameters = {"detection": {"detectors": ["TRUST"]}}
test_seq = sequence.Sequence(TEST_SEQUENCE_Q9BRR0)
test_optimized_repeat = test_seq.detect(denovo=True, **test_parameters)
assert type(test_optimized_repeat) == repeat_list.RepeatList
assert len(test_optimized_repeat.repeats) == 3
def test_serialize_repeat_list_tsv():
test_repeats = [repeat.Repeat(msa=i) for i in TEST_REPEATS[:2]]
test_seq = sequence.Sequence(TEST_SEQUENCE)
for i in test_repeats:
test_seq.repeat_in_sequence(i)
test_repeat_list = rl.RepeatList(repeats=test_repeats)
tsv = test_repeat_list.write("tsv", return_string=True)
assert type(tsv) == str
def test_serialize_repeat_list_tsv():
test_repeats = [repeat.Repeat(msa = i) for i in TEST_REPEATS]
test_seq = sequence.Sequence(TEST_SEQUENCE)
for i in test_repeats:
test_seq.repeat_in_sequence(i)
test_repeat_list = rl.RepeatList(repeats = test_repeats)
tsv = rl_io.serialize_repeat_list_tsv(test_repeat_list)
assert type(tsv) == str
def test_detect_repeats_with_repeat():
test_repeat = repeat.Repeat(msa=TEST_REPEAT_MSA_DOUBLE)
test_hmm = HMM.create(input_format='repeat', repeat=test_repeat)
test_seq = sequence.Sequence(TEST_SEQUENCE)
test_optimized_repeat = test_seq.detect([test_hmm])
assert type(test_optimized_repeat) == repeat_list.RepeatList
assert len(test_optimized_repeat.repeats) == 1
assert test_optimized_repeat.repeats[
0].msa == TEST_RESULT_REPEAT_MSA_DOUBLE
test_repeat = repeat.Repeat(msa=TEST_REPEAT_MSA_SINGLE)
test_hmm = HMM.create(input_format='repeat', repeat=test_repeat)
test_optimized_repeat = test_seq.detect([test_hmm])
assert type(test_optimized_repeat) == repeat_list.RepeatList
assert len(test_optimized_repeat.repeats) == 1
assert test_optimized_repeat.repeats[
0].msa == TEST_RESULT_REPEAT_MSA_SINGLE
def workflow(sequences_file,
hmm_annotation_file,
hmm_dir,
result_file,
result_file_serialized,
format,
max_time,
time_interval=3600,
next_time=3600,
**kwargs):
''' Annotate sequences with TRs from multiple sources, test and refine annotations.
Save the annotations in a pickle.
Args:
sequences_file (str): Path to the pickle file containing a list of ``Sequence``
instances.
hmm_dir (str): Path to directory where all HMMs are stored as .pickles
result_file (str): Path to the result file.
max_time (str): Max run time in seconds
Raises:
Exception: If the pickle ``sequences_file`` cannot be loaded
Exception: if the hmm_dir does not exist
'''
start = datetime.datetime.now()
max_time, time_interval, next_time = int(max_time), int(
time_interval), int(next_time)
try:
l_sequence = Fasta(sequences_file)
except:
raise Exception(
"Cannot load putative pickle file sequences_file: {}".format(
sequences_file))
if not os.path.isdir(hmm_dir):
try:
os.makedirs(hmm_dir)
except:
raise Exception(
"hmm_dir does not exists and could not be created: {}".format(
hmm_dir))
try:
with open(hmm_annotation_file, 'rb') as fh:
dHMM_annotation = pickle.load(fh)
except:
raise Exception(
"Cannot load hmm_annotation_file: {}".format(hmm_annotation_file))
basic_filter = CONFIG['filter']['basic']['dict']
basic_filter_tag = CONFIG['filter']['basic']['tag']
# Load previous results
try:
if not os.path.isdir(os.path.dirname(result_file)):
os.makedirs(os.path.dirname(result_file))
except:
raise Exception(
"Could not create path to result_file directory: {}".format(
os.path.dirname(result_file)))
try:
with open(result_file, 'rb') as fh:
dResults = pickle.load(fh)
except:
LOG.debug(
"Could not load previous results file - perhaps non existant: {}".
format(result_file))
dResults = {}
dHMM = {}
for iS_pyfaidx in l_sequence:
# If sequence is already included in results: continue.
if iS_pyfaidx.name in dResults:
continue
elapsed_time = (datetime.datetime.now() - start).seconds
if elapsed_time > max_time or elapsed_time > next_time:
with open(result_file, 'wb') as fh:
pickle.dump(dResults, fh)
next_time = next_time + time_interval
iS = sequence.Sequence(seq=str(iS_pyfaidx), name=iS_pyfaidx.name)
LOG.debug("Work on sequence {}".format(iS))
# 1. annotate_de_novo()
denovo_repeat_list = iS.detect(denovo=True,
repeat={"calc_pvalue": True})
LOG.debug(denovo_repeat_list.repeats)
for iTR in denovo_repeat_list.repeats:
iTR.model = None
# 2. annotate_TRs_from_hmmer()
if iS.name in dHMM_annotation:
lHMM = dHMM_annotation[iS.name]
infoNRuns = len(lHMM)
LOG.debug(
"{} Viterbi runs need to be performed.".format(infoNRuns))
lHMM = set(lHMM)
infoNHMM = len(lHMM)
LOG.debug(
"These derive from {} independent HMMs.".format(infoNHMM))
# Load all HMM pickles needed for the particular sequence.
for hmm_ID in lHMM:
if hmm_ID not in dHMM:
dHMM[hmm_ID] = hmm.HMM.create(file_format="pickle",
file=os.path.join(
hmm_dir,
hmm_ID + ".pickle"))
pfam_repeat_list = iS.detect([dHMM[hmm_ID] for hmm_ID in lHMM],
repeat={"calc_pvalue": True})
for iTR, hmm_ID in zip(pfam_repeat_list.repeats, lHMM):
iTR.model = hmm_ID
iTR.TRD = "PFAM"
else:
pfam_repeat_list = None
# 3. merge_and_basic_filter()
all_repeat_list = denovo_repeat_list + pfam_repeat_list
iS.set_repeatlist(all_repeat_list, REPEAT_LIST_TAG)
iS.set_repeatlist(denovo_repeat_list, DE_NOVO_ALL_TAG)
iS.set_repeatlist(pfam_repeat_list, PFAM_ALL_TAG)
rl_tmp = iS.get_repeatlist(REPEAT_LIST_TAG)
if iS.get_repeatlist(REPEAT_LIST_TAG):
for iB in basic_filter.values():
rl_tmp = rl_tmp.filter(**iB)
else:
rl_tmp = iS.get_repeatlist(REPEAT_LIST_TAG)
iS.set_repeatlist(rl_tmp, basic_filter_tag)
iS.set_repeatlist(rl_tmp.intersection(iS.get_repeatlist(PFAM_ALL_TAG)),
PFAM_TAG)
iS.set_repeatlist(
rl_tmp.intersection(iS.get_repeatlist(DE_NOVO_ALL_TAG)),
DE_NOVO_TAG)
# 4. calculate_overlap()
# Perform common ancestry overlap filter and keep PFAMs
criterion_pfam_fixed = {
"func_name": "none_overlapping_fixed_repeats",
"rl_fixed": iS.get_repeatlist(PFAM_TAG),
"overlap_type": "common_ancestry"
}
iS.d_repeatlist[DE_NOVO_TAG] = iS.get_repeatlist(DE_NOVO_TAG).filter(
**criterion_pfam_fixed)
# Choose only the most convincing de novo TRs
criterion_filter_order = {
"func_name":
"none_overlapping",
"overlap": ("common_ancestry", None),
"l_criterion": [("pvalue", "phylo_gap01"),
("divergence", "phylo_gap01")]
}
iS.d_repeatlist[DE_NOVO_TAG] = iS.get_repeatlist(DE_NOVO_TAG).filter(
**criterion_filter_order)
# 5. refine_denovo()
denovo_final = []
denovo_refined = [None] * len(
iS.get_repeatlist(DE_NOVO_ALL_TAG).repeats)
for i, iTR in enumerate(iS.get_repeatlist(DE_NOVO_ALL_TAG).repeats):
if not iTR in iS.get_repeatlist(DE_NOVO_TAG).repeats:
continue
# Create HMM from TR
denovo_hmm = hmm.HMM.create(file_format='repeat', repeat=iTR)
# Run HMM on sequence
denovo_refined_rl = iS.detect(lHMM=[denovo_hmm])
append_refined = False
if denovo_refined_rl and denovo_refined_rl.repeats:
iTR_refined = denovo_refined_rl.repeats[0]
iTR_refined.TRD = iTR.TRD
iTR_refined.model = "cpHMM"
denovo_refined[i] = iTR_refined
# Check whether new and old TR overlap. Check whether new TR is
# significant. If not both, put unrefined TR into final.
if repeat_list.two_repeats_overlap("shared_char", iTR,
iTR_refined):
rl_tmp = repeat_list.RepeatList([iTR_refined])
LOG.debug(iTR_refined.msa)
for iB in basic_filter.values():
rl_tmp = rl_tmp.filter(**iB)
if rl_tmp.repeats:
append_refined = True
else:
denovo_refined[i] = False
if append_refined:
denovo_final.append(iTR_refined)
else:
denovo_final.append(iTR)
iS.set_repeatlist(repeat_list.RepeatList(denovo_refined),
DE_NOVO_REFINED_TAG)
iS.set_repeatlist(repeat_list.RepeatList(denovo_final),
DE_NOVO_FINAL_TAG)
iS.set_repeatlist(
iS.get_repeatlist(DE_NOVO_FINAL_TAG) + iS.get_repeatlist(PFAM_TAG),
FINAL_TAG)
dResults[iS.name] = iS
# 6.a Save results as pickle
with open(result_file, 'wb') as fh:
pickle.dump(dResults, fh)
# 6.b Save serialized results
with open(result_file_serialized, 'w') as fh_o:
if format == 'tsv':
header = [
"ID", "MSA", "begin", "pvalue", "l_effective", "n",
"n_effective", "TRD", "model"
]
fh_o.write("\t".join(header))
for iS in dResults.values():
for iTR in iS.get_repeatlist(FINAL_TAG).repeats:
if format == 'tsv':
try:
data = [
str(i) for i in [
iS.name, " ".join(iTR.msa), iTR.begin,
iTR.pvalue("phylo_gap01"), iTR.l_effective,
iTR.n, iTR.n_effective, iTR.TRD, iTR.model
]
]
except:
print(iTR)
raise Exception(
"(Could not save data for the above TR.)")
fh_o.write("\n" + "\t".join(data))
print("DONE")
def test_detect_repeats_with_hmm():
test_hmm = HMM.create(input_format='hmmer',
file=os.path.join(path(), TEST_FILE_WITH_ID))
test_seq = sequence.Sequence(TEST_SEQUENCE)
test_optimized_repeat = test_seq.detect([test_hmm])
def test_initialise_sequence():
test_seq = sequence.Sequence(TEST_SEQUENCE)
assert test_seq.seq == TEST_SEQUENCE
def find_protein_repeats(sequences_file, result_dir, pvalue_threshold = 0.05, divergence_threshold = 0.1, n_threshold = 2.5, l_threshold = 3):
"""
Finds tandem repeats (TRs) in the protein sequences provided in 'sequence_file'.
Filters the TRs according to the thresholds.
Saves the TRs in the 'result_dir' each individually with the protein identifier as filename.
Args:
sequence_file (str): whole path to file with >=1 protein fasta-sequence
result_dir (str): path where the individual TRAL results are supposed to be stored.
pvalue_threshold (int): p-value threshold for filtering
divergence_threshold (int): divergence threshold for filtering
n_threshold (int): minimun repeat unit count for filtering
l_threshold (int): maximum repeat unit length for filtering
Output:
For each protein sequence, a single file with its TRs is produced and stored in 'result_dir'.
"""
logging.config.fileConfig(config_file("logging.ini"))
log = logging.getLogger('root')
# define this as in the config
CONFIG_GENERAL = configuration.Configuration.instance().config
CONFIG = CONFIG_GENERAL["repeat_list"]
score = CONFIG["model"]
##########################################################################
# From .fasta to Class Sequence sequences
proteins = Fasta(sequences_file)
all_denovo_repeats = 0
all_filtered_repeats = 0
for pyfaidx in proteins:
seq_name = pyfaidx.name.split("|")[1]
# name is protein identifier
seq = sequence.Sequence(seq=str(pyfaidx), name=seq_name)
log.debug("Work on sequence {}".format(seq_name))
##########################################################################
# Getting TRs
denovo_list = seq.detect(denovo=True)
for TR in denovo_list.repeats:
TR.calculate_pvalues()
##########################################################################
# Filtering TRs
# add number of denovo found repeats
all_denovo_repeats += len(denovo_list.repeats)
# filtering for pvalue
denovo_list = denovo_list.filter(
"pvalue",
score,
pvalue_threshold)
# filtering for divergence
denovo_list = denovo_list.filter(
"divergence",
score,
divergence_threshold)
# filtering for number of repeat units
denovo_list = denovo_list.filter(
"attribute",
"n_effective",
"min",
n_threshold)
# filtering for length of repeat units
denovo_list = denovo_list.filter(
"attribute",
"l_effective",
"max",
l_threshold)
##########################################################################
# Building HMM with hmmbuild
# # De novo TRs were remastered with HMM
denovo_hmm = [hmm.HMM.create(input_format='repeat', repeat=iTR)
for iTR in denovo_list.repeats] # only possible with hmmbuild
denovo_list_remastered = seq.detect(lHMM=denovo_hmm)
##########################################################################
# Clustering
# De novo TRs were clustered for overlap (common ancestry). Only best =
# lowest p-Value and lowest divergence were retained.
denovo_list_remastered = denovo_list.filter(
"none_overlapping", ["common_ancestry"], {"pvalue": score, "divergence": score})
##########################################################################
# Save Tandem Repeats
# Create output directory if not already exists.
try:
if not os.path.isdir(result_dir):
os.makedirs(result_dir)
except:
raise Exception(
"Could not create path to result directory: {}".format(
os.path.dirname(result_dir)))
# create filename
output_pickle_file = os.path.join(result_dir, seq_name + ".pkl")
output_tsv_file = os.path.join(result_dir, seq_name + ".tsv")
# save TR-file
denovo_list_remastered.write(output_format="pickle", file=output_pickle_file)
denovo_list_remastered.write(output_format="tsv", file=output_tsv_file)
#TODO save as fasta
all_filtered_repeats += len(denovo_list_remastered.repeats)
print("\n***", seq_name, "***")
print("denovo repeats:", len(denovo_list.repeats))
print("repeats after filtering and clustering:",
len(denovo_list_remastered.repeats))
for i in range(len(denovo_list_remastered.repeats)):
print(denovo_list_remastered.repeats[i])
return print("\nThere where {} repeats found de novo.".format(all_denovo_repeats), "After filtering and clustering there where only {} repeats left.\n".format(
all_filtered_repeats))
from pyfaidx import Fasta
# dowload proteinsequence from: https://www.uniprot.org/uniprot/P98179
##########################################################################
# From .fasta to Class Sequence sequences
proteins = Fasta(
"/home/matteo/polybox/MSc_ACLS/master_thesis/data/test_protein.fasta")
all_denovo_repeats = 0
all_filtered_repeats = 0
for pyfaidx in proteins:
seq_name = pyfaidx.name.split("|")[1]
# name is protein identifier
seq = sequence.Sequence(seq=str(pyfaidx), name=seq_name)
# Saving this sequences as binary files:
# with open(sequence_pkl, 'wb') as f:
# pickle.dump(seq, f)
##########################################################################
# Getting TRs
denovo_list = seq.detect(denovo=True)
# TODO: reinstall tral with all phylo files!
for TR in denovo_list.repeats:
TR.calculate_pvalues()
print(TR)
# Saving this sequences as binary files:
def test_detect_HHrepID():
test_seq = sequence.Sequence(TEST_SEQUENCE_Q9BRR0)
predicted_repeats = repeat_detection_run.run_detector(seq_records = [test_seq], detectors = ["HHrepID"])[0]['HHrepID']
assert len(predicted_repeats) == 1
assert predicted_repeats[0].msa == ['------------------IPTCAEAGEQ----', 'EGRLQRKQKNATGGRRHICHECGKSFAQ----', 'SSGLSKHRRIHTGEKPYECEECGKAFIG----', 'SSALVIHQRVHTGEKPYECEECGKAFSH----', 'SSDLIKHQRTHTGEKPYECDDCGKTFSQ----', 'SCSLLEHHRIHTGEKPYQCSMCGKAFRR----', 'SSHLLRHQRIHTGDKNVQEPEQGEAWKSRMES', '------QLENVETPMSYKCNECERSFTQ----', 'NTGLIEHQKIHTGEKPYQCNACGKGFTR----', 'ISYLVQHQRSHVG-------------------']
def test_detect_TREKS():
test_seq = sequence.Sequence(TEST_SEQUENCE_Q9BRR0)
predicted_repeats = repeat_detection_run.run_detector(seq_records = [test_seq], detectors = ["T-REKS"])[0]['T-REKS']
assert len(predicted_repeats) == 1
assert predicted_repeats[0].msa == ['C---G---KSFAQSSGLSKHRRIHTGEKPYECE-E', 'C---G---KAFIGSSALVIHQRVHTGEKPYECE-E', 'C---G---KAFSHSSDLIKHQRTHTGEKPYECD-D', 'C---G---KTFSQSCSLLEHHRIHTGEKPYQCS-M', 'C---G---KAFRRSSHLLRHQRIHTGDKNVQ-EPE', 'Q---G---EAW--KSRME-SQ-LENVETPMSYK--', 'C---NECERSFTQNTGLIEHQKIHTGEKPYQ----', 'CNACG---KGFTRISYLVQHQRSHVG-KNI-LS--']
def test_detect_XSTREAM():
test_seq = sequence.Sequence(TEST_SEQUENCE_Q9BRR0)
predicted_repeats = repeat_detection_run.run_detector(seq_records = [test_seq], detectors = ["XSTREAM"])[0]['XSTREAM']
assert len(predicted_repeats) == 1
assert predicted_repeats[0].msa == ['ECGKSFAQS-SGLSK-HRRIHTGEKPYECE', 'ECGKAFIGS-SALVI-HQRVHTGEKPYECE', 'ECGKAFSHS-SDL-IKHQRTHTGEKPYECD', 'DCGKTFSQSCSLLEH-H-RIHTGEKPY']
