def getRandomizedSequenceCacheForVerticalPermutations(taxId):
global _caches
if (taxId, db.Sources.ShuffleCDS_vertical_permutation_1nt) in _caches:
cache = _caches[(taxId, db.Sources.ShuffleCDS_vertical_permutation_1nt)]
else:
# read all native sequences
protIds = []
cdss = []
for protId in SpeciesCDSSource(taxId):
cds = CDSHelper(taxId, protId)
if( cds.length()%3 != 0 ):
continue
seq = cds.sequence()
protIds.append(protId)
cdss.append(seq)
geneticCode = getSpeciesTranslationTable( taxId )
scpr = SynonymousCodonPermutingRandomization( geneticCode )
randomizer = lambda cdss: scpr.verticalPermutation( cdss )
cache = VerticalRandomizationCache(shuffleType=db.Sources.ShuffleCDS_vertical_permutation_1nt,
taxId=taxId,
nativeSeqsMap=dict(zip(protIds, cdss)),
geneticCode=geneticCode,
randomizer=randomizer )
_caches[(taxId, db.Sources.ShuffleCDS_vertical_permutation_1nt)] = cache
print(_caches.keys())
return cache
def writeSequenceToTempFile(taxId):
print("Fetching sequence for taxid={}".format(taxId))
allRecords = []
allCDSs = []
for protId in SpeciesCDSSource(taxId):
cds = CDSHelper(taxId, protId)
if (cds.length() % 3 != 0):
continue
seq = cds.sequence()
allCDSs.append(seq)
if (len(allCDSs) % 1000 == 999): print(".")
record = SeqRecord(Seq(''.join(allCDSs), NucleotideAlphabet),
id="allCDSs",
description="")
allRecords.append(record)
fout = NamedTemporaryFile(mode="w", delete=(not debugMode))
SeqIO.write(allRecords, fout.name,
"fasta") # write the full sequences into the file
return (len(allRecords), fout)
def calculateMissingWindowsForSequence(
self,
taxId,
protId,
seqIds,
requestedShuffleIds,
firstWindow,
lastWindowStart,
windowStep,
reference="begin",
shuffleType=db.Sources.ShuffleCDSv2_python):
timerForPreFolding.start()
logging.warning("Parameters: %d %s %s %s %d %d %s %d" %
(taxId, protId, seqIds, requestedShuffleIds,
lastWindowStart, windowStep, reference, shuffleType))
f = self._logfile
assert (len(seqIds) > 0)
assert (len(seqIds) == len(requestedShuffleIds))
optimalSpeciesGrowthTemperature = None
if (self._seriesSourceNumber ==
db.Sources.RNAfoldEnergy_SlidingWindow40_v2_native_temp):
(numericalProp, _) = getSpeciesTemperatureInfo(taxId)
optimalSpeciesGrowthTemperature = numericalProp[0]
if optimalSpeciesGrowthTemperature is None:
raise Exception(
"No temperature value for taxid={}, can't calculate native-temperature folding profile..."
.format(taxId))
else:
optimalSpeciesGrowthTemperature = float(
optimalSpeciesGrowthTemperature)
assert (optimalSpeciesGrowthTemperature >= -30.0
and optimalSpeciesGrowthTemperature <= 150.0)
if (reference != "begin" and reference != "end"):
timerForPreFolding.stop()
e = "Specificed profile reference '%s' is not supported! (" % reference
logging.error(e)
raise Exception(e)
# We will process all listed shuffle-ids for the following protein record
cds = CDSHelper(taxId, protId)
if (cds.length() < self._windowWidth):
e = "Refusing to process item %s because the sequence length (%d nt) is less than the window size (%d nt)\n" % (
itemToProcess, cds.length(), self._windowWidth)
f.write(e)
logging.error(e)
timerForPreFolding.stop()
raise Exception(e)
# Create a list of the windows we need to calculate for this CDS
if reference == "begin":
requestedWindowStarts = frozenset(
range(
0,
min(lastWindowStart + 1,
cds.length() - self._windowWidth - 1), windowStep))
if (len(requestedWindowStarts) == 0):
e = "No windows exist for calculation taxid=%d, protId=%s, CDS-length=%d, lastWindowStart=%d, windowStep=%d, windowWidth=%d - Skipping...\n" % (
taxId, protId, cds.length(), lastWindowStart, windowStep,
self._windowWidth)
f.write(e)
logging.error(e)
timerForPreFolding.stop()
raise Exception(e)
elif reference == "end":
lastPossibleWindowStart = cds.length(
) - self._windowWidth #+ 1 # disregard lastWindowStart when reference=="end"
#lastWindowCodonStart = (lastPossibleWindowStart-3)-(lastPossibleWindowStart-3)%3
#lastPossibleWindowStart = seqLength - windowWidth # + 1 # disregard lastWindowStart when reference=="end"
requestedWindowStarts = frozenset(
filter(
lambda x: x >= lastWindowStart,
range(lastPossibleWindowStart % windowStep,
lastPossibleWindowStart + 1, windowStep)))
#requestedWindowStarts = frozenset(range(lastWindowCodonStart % windowStep, lastWindowCodonStart, windowStep))
#pass
else:
assert (False)
# First, read available results (for all shuffle-ids) in JSON format
# Array is indexed by shuffle-id, so results not requested will be represented by None (as will requested items that have no results yet).
logging.info("DEBUG: requestedShuffleIds (%d items): %s\n" %
(len(requestedShuffleIds), requestedShuffleIds))
existingResults = cds.getCalculationResult2(self._seriesSourceNumber,
requestedShuffleIds,
True,
shuffleType=shuffleType)
#assert(len(existingResults) >= len(requestedShuffleIds)) # The returned array must be at least as large as the requested ids list
assert (len(existingResults) == len(requestedShuffleIds))
logging.info("requestedShuffleIds: %s" % requestedShuffleIds)
logging.info("existingResults.keys(): %s" % existingResults.keys())
assert (frozenset(requestedShuffleIds) == frozenset(
existingResults.keys()))
#existingResults = [None] * (max(requestedShuffleIds)+1)
logging.info("DEBUG: existingResults (%d items): %s\n" %
(len(existingResults), existingResults))
# Check for which of the requested shuffle-ids there are values missing
shuffleIdsToProcess = {}
for shuffleId, r in existingResults.items():
if r is None:
# There are no existing results for shuffled-id n. If it was requested, it should be calculated now (including all windows)
if shuffleId in requestedShuffleIds:
shuffleIdsToProcess[shuffleId] = list(
requestedWindowStarts)
timerForPreFolding.stop()
# ------------------------------------------------------------------------------------
continue # TODO - verify this line; should we abort this sequence by throwing????
# ------------------------------------------------------------------------------------
logging.info("/// shuffleId r = %d %s" % (shuffleId, r))
logging.info("r[MFE-profile] %s" % r["MFE-profile"])
# Check the existing results for this shuffle
alreadyProcessedWindowStarts = frozenset([
i for i, x in enumerate(r["MFE-profile"]) if x is not None
]) # Get the indices (=window starts) of all non-None values
missingWindows = requestedWindowStarts - alreadyProcessedWindowStarts # Are there any requested windows that are not already computed?
if (missingWindows):
shuffleIdsToProcess[shuffleId] = missingWindows
if (not shuffleIdsToProcess):
e = "All requested shuffle-ids in (taxId: %d, protId: %s, seqs: %s) seem to have already been processed. Skipping...\n" % (
taxId, protId, str(list(zip(seqIds, requestedShuffleIds))))
logging.warning(e)
timerForPreFolding.stop()
return
logging.info("DEBUG: shuffleIdsToProcess (%d items): %s\n" %
(len(shuffleIdsToProcess), shuffleIdsToProcess))
logging.info("DEBUG: Before (%d items): %s\n" %
(len(existingResults), existingResults))
# Initialize new results records
for shuffleId in shuffleIdsToProcess.keys():
if existingResults[shuffleId] is None:
logging.info(seqIds)
logging.info(requestedShuffleIds)
logging.info(shuffleId)
thisSeqId = seqIds[requestedShuffleIds.index(shuffleId)]
existingResults[shuffleId] = {
"id":
"%s/%s/%d/%d" % (taxId, protId, thisSeqId, shuffleId),
"seq-crc": None,
"MFE-profile": [],
"MeanMFE": None,
"v": 2,
"shuffle-type": shuffleType
}
logging.info("DEBUG: existingResults (%d items): %s\n" %
(len(existingResults), existingResults))
timerForPreFolding.stop()
# Load the sequences of all shuffle-ids we need to work on
# TODO - combine loading of multiple sequences into one DB operation
for shuffleId, record in existingResults.items():
if record is None:
logging.info(
"DEBUG: skipping empty results record for shuffleId={}".
format(shuffleId))
continue
timerForPreFolding.start()
seq = None
annotatedSeqId = None
# Get the sequence for this entry
if (shuffleId < 0):
seq = cds.sequence()
annotatedSeqId = cds.seqId()
else:
seq = cds.getShuffledSeq(shuffleId, shuffleType)
annotatedSeqId = cds.getShuffledSeqId(shuffleId, shuffleType)
if (seq is None or (not seq is None and len(seq) == 0)):
seq2 = cds.getShuffledSeq2(annotatedSeqId)
seq3 = cds._fetchSequence(annotatedSeqId)
seq4 = cds._cache.get("%d:seq" % annotatedSeqId)
if not seq4 is None:
del cds._cache["%d:seq" % annotatedSeqId]
seq5 = cds.getShuffledSeq2(annotatedSeqId)
e = "Got empty sequence for shuffleId=%d, seqId=%d, taxId=%d, protId=%s, numShuffled=%d, ids[%d:%d]=%s, len(seq2)=%d, len(seq3)=%d, len(seq4)=%d, len(seq5)=%d" % (
shuffleId, annotatedSeqId, taxId, protId,
len(cds.shuffledSeqIds()), shuffleId - 2, shuffleId + 2,
cds.shuffledSeqIds()[shuffleId - 2:shuffleId + 2],
len(seq2) if not seq2 is None else -1, len(seq3)
if not seq3 is None else -1, len(seq4) if not seq4 is None
else -1, len(seq5) if not seq5 is None else -1)
logging.error(e)
timerForPreFolding.stop()
raise Exception(e)
#
# Disabled - calculation needn't include the native sequence...
#
#if( annotatedSeqId not in seqIds ):
# e = "Error: SeqId specified in queue item %s does not match annotated seq-id %d\n" % (itemToProcess, annotatedSeqId)
# f.write(e)
# f.write("Current shuffle-id: %d\n" % shuffleId)
# f.write("Ids in existing results:\n")
# for shuffleId, record in enumerate(existingResults):
# f.write(" %d) %s\n" % (shuffleId, record['id']))
# f.write("Debug info:\n")
# f.write("\n".join(cds.getDebugInfo()))
# f.write("\n")
# f.write("Skipping...\n")
# print("Skipping...")
# raise Exception(e)
expectedSeqLength = cds.length()
if (not expectedSeqLength is None):
if (expectedSeqLength != len(seq)):
e = "Warning: taxid=%d, protid=%s, seqid=%d - unexpected length %d (expected: %d)\n" % (
taxId, protId, annotatedSeqId, len(seq),
expectedSeqLength)
f.write(e)
logging.error(e)
timerForPreFolding.stop()
raise Exception(e)
if (len(seq) < self._windowWidth):
# Sequence is shorter than required window; skip
e = "Warning: skipping sequence because it is shorter than the requested window...\n"
f.write(e)
logging.error(e)
timerForPreFolding.stop()
raise Exception(e)
logging.info(
"DEBUG: Processing item taxId=%d, protId=%s, shuffle=%d (length=%d, %d windows)...\n"
% (taxId, protId, shuffleId, len(seq),
len(requestedWindowStarts)))
# TODO - Remove any old value stored in this key?
# Skip this for now
# This will be made redundant by completing the "updating" implementation
#
#if( cds.isCalculationDone( seriesSourceNumber, shuffleId )):
# # Sufficient data seems to exist. Skip...
# f.write("Item %s appears to be already completed, skipping..." % itemToProcess)
# continue
logging.info(seq[:50])
#f.write("\n")
MFEprofile = record["MFE-profile"]
#f.write("Profile: %s\n" % MFEprofile)
# Make sure the profile array contains enough entries for all new windows (and possibly, if windows are non-contiguous, entries between them that we are not going to compute right now)
if (len(MFEprofile) < max(requestedWindowStarts)):
entriesToAdd = max(requestedWindowStarts) - len(MFEprofile) + 1
MFEprofile.extend([None] * entriesToAdd)
assert (len(MFEprofile) >= max(requestedWindowStarts))
stats = RunningStats()
stats.extend([x for x in MFEprofile if x is not None])
timerForPreFolding.stop()
timerForFolding.start()
for start in requestedWindowStarts:
fragment = seq[start:(start + self._windowWidth)]
assert (len(fragment) == self._windowWidth)
if self._seriesSourceNumber == db.Sources.RNAfoldEnergy_SlidingWindow40_v2:
# Calculate the RNA folding energy. This is the computation-heavy part.
#strct, energy = RNA.fold(fragment)
energy = RNAfold_direct(fragment)
assert (energy <= 0.0)
elif self._seriesSourceNumber == db.Sources.RNAfoldEnergy_SlidingWindow40_v2_native_temp:
# Calculate the RNA folding energy. This is the computation-heavy part.
#strct, energy = RNA.fold(fragment)
energy = RNAfold_direct(fragment,
explicitCalculationTemperature=
optimalSpeciesGrowthTemperature)
assert (energy <= 0.0)
elif self._seriesSourceNumber == db.Sources.TEST_StepFunction_BeginReferenced:
if shuffleId < 0:
energy = 0
else:
energy = start % 50 - 20
elif self._seriesSourceNumber == db.Sources.TEST_StepFunction_EndReferenced:
if shuffleId < 0:
energy = 0
else:
energy = (expectedSeqLength - self._windowWidth -
start) % 50 - 20
else:
logging.error(
"Received unknown seriesSourceNumber {}".format(
self._seriesSourceNumber))
assert (False)
# Store the calculation result
#print("%d:%s --> %f" % (taxId, protId, energy))
stats.push(energy)
MFEprofile[start] = energy
print(
"/////////////////// shuffleId={} (len={}) //////////////////////////"
.format(shuffleId, expectedSeqLength))
prettyPrintProfile(MFEprofile)
timerForFolding.stop()
timerForPostFolding.start()
# Format
crc = calcCrc(seq)
#result = """{"id":"%s","seq-crc":%d,"MFE-profile":[%s],"MeanMFE":%.6g,v:2}""" % (itemToProcess, crc, ",".join(map(lambda x: "%.3g" % x, MFEprofile)), stats.mean())
record["seq-crc"] = crc
record["MFE-profile"] = [
round4(x) for x in MFEprofile
] # Round items down to save space (these are not exact numbers anyway)
record["MeanMFE"] = stats.mean()
result = json.dumps(record)
f.write(result)
f.write("\n")
if (not self._debugDoneWriteResults):
cds.saveCalculationResult2(self._seriesSourceNumber, result,
annotatedSeqId, False)
timerForPostFolding.stop()
timerForPostFolding.start()
if (not self._debugDoneWriteResults):
cds.commitChanges()
timerForPostFolding.stop()
statsShuffles = RunningStats()
recordsCount = 0
warningsCount = 0
rl = RateLimit(30)
total = countSpeciesCDS(taxId)
for protId in SpeciesCDSSource(taxId):
cds = CDSHelper(taxId, protId)
recordsCount += 1
statsLength.push(cds.length())
if (len(cds.sequence()) != cds.length()):
print(
"WARNING: incorrect sequence length detected for record (taxid=%d, protId=%s); real-length=%d, recorded-length=%d."
% (taxId, protId, len(cds.sequence()), cds.length()))
warningsCount += 1
recomputedCrc = calcCrc(cds.sequence())
annotatedCrc = cds.crc()
assert (recomputedCrc == annotatedCrc)
print(cds.sequence()[:15])
seq1trans = Seq(cds.sequence(), generic_dna).translate()
crc1 = calcCrc(seq1trans)
shuffles = cds.shuffledSeqIds()
unique = len(frozenset(shuffles))
def testCDSand3UTRRandomizationIncludingNextCDS(
taxId: int = 511145,
geneticCode: int = 11,
constantOverlaps: bool = False) -> int:
from data_helpers import SpeciesCDSSource
from genome_model import getGenomeModelFromCache
rand = CDSand3UTRRandomizationIncludingNextCDS(
SynonymousCodonPermutingRandomization(geneticCode=geneticCode),
NucleotidePermutationRandomization(),
taxId,
constantOverlaps=constantOverlaps)
#for protId in SpeciesCDSSource(taxId):
countOK = 0
countNotOK = 0
countNotOK2 = 0
countSkipped = 0
for protId in getGenomeModelFromCache(taxId).allCDSSource():
try:
cds = CDSHelper(taxId, protId)
seq = cds.sequence()
#if str(seq).find("n") != -1:
# countSkipped += 1
# continue
except Exception as e:
countNotOK += 1
continue
for i in range(20):
try:
ret = rand.randomize(seq, protId)
except Exception as e:
print(
"Caught exception during call to randomize(), protId={}!".
format(protId))
print(e)
countNotOK += 1
countNotOK2 += 1
continue
if ret[0] < 1e5:
print(protId)
if not (len(ret[2]) == len(seq)):
print(ret)
rand.randomize(seq, protId)
assert (len(ret[2]) == len(seq))
countOK += 1
#print("{} -> {}".format( protId, ret ))
print("OK: {}, NotOK: {}, Skipped: {}, Total: {}".format(
countOK, countNotOK, countSkipped,
countOK + countNotOK + countSkipped))
print("randomize exception: {}".format(countNotOK2))
return 0
#fnormpval.write("%s,%s\n" % (protId, ','.join(map(str, normpval))))
#fshapiro.write("%s,%s\n" % (protId, ','.join(map(str, shapiro))))
#fshapiropval.write("%s,%s\n" % (protId, ','.join(map(str, shapiropval))))
#fkurtosis.write("%s,%s\n" % (protId, ','.join(map(str, kurtosis))))
#frefgamma.write("%s,%s\n" % (protId, ','.join(map(str, refgamma))))
# Perform Wilcoxon signed-rank test
#print("DF: ", df.shape) # (20,151)
#print("s: ", s.shape) # (20,)
#print("u: ", df.iloc[1,:].shape) # (151,)
#print("mean(u): ", df.mean(axis=0).shape)
#print(np.array(profile).shape) #(151,)
# Update the GC profile
cdsSequence = cds.sequence()
gcContent = calcGCcontent(cdsSequence)
#print(gcContent)
if( len(gcContent) ):
for i in range(profileSpec.numProfileWindows()): # Limit to 150; TODO - treat this generically?
GCProfile[i].push( gcContent[i] )
medianGCContent.append( np.median( gcContent ) )
del cds
# Method 1 -- Wilcoxon, native vs. mean(shuffled), window step=1nt, per gene
meanOfShufflesProfileForW = df.mean(axis=0)
nativeProfileForW = np.array(profile)
assert(meanOfShufflesProfileForW.shape == nativeProfileForW.shape)
for taxId in species:
proteinsDone = 0
#nativeColumns = [[] for x in range(maxCodons)]
#shuffledColumns = [[[] for x in range(maxCodons)] for y in range(maxShuffles)]
allNativeSeqs = {}
allShuffledSeqs = {}
for protId in SpeciesCDSSource(taxId):
cds = CDSHelper(taxId, protId)
warnings.update(("total-cds", ))
allIds = cds.shuffledSeqIds(shuffleType=shuffleType)[:maxShuffles]
nativeSeq = cds.sequence()
if (len(nativeSeq) % 3 != 0):
warnings.update(("has-broken-codons", ))
continue
nativeCodons = Counter(splitCodons(nativeSeq))
hasMismatchedCodons = False
allNativeSeqs[protId] = nativeSeq
hashesForShuffles = set()
#for i, c in enumerate(splitCodons(nativeSeq)[:maxCodons]):
# nativeColumns[i].append(c)
shuffledSeqs = []