# TODO: Add support for step-size >1
# Establish DB connections
#r = redis.StrictRedis(host=config.host, port=config.port, db=config.db)
#session = db.Session()
skipped = 0
selected = 0
alreadyCompleted = 0
totalMissingResults = 0
queuedDelayedCalls = []
for taxIdForProcessing in species:
print("Processing %d sequences for tax-id %d (%s)..." %
(countSpeciesCDS(taxIdForProcessing), taxIdForProcessing,
getSpeciesName(taxIdForProcessing)))
stats = Counter()
# Iterate over all CDS entries for this species
# TODO - preloading all sequences and results should optimize this
for protId in SpeciesCDSSource(taxIdForProcessing):
stats['all-sequences'] += 1
#protId = codecs.decode(protId)
# Filtering
# Only process 1/N of the sequences, selected randomly (N=randomFraction)
# (if randomFraction==1, all sequences will be processed)
# metadata server (redis)
r = redis.StrictRedis(host=config.host,
port=config.port,
db=config.db,
password=config.password)
# sequences server (mysql)
session = db.Session()
visitedProteinIds = set()
assert (r.exists("species:taxid:%d:name" % taxId))
if (seqSourceTag == db.Sources.External):
# Clear any previously imported CDSs...
#r.delete(speciesCDSList % (taxId,))
count = data_helpers.countSpeciesCDS(taxId)
if (count > 0 and (not args.dry_run)):
print("%d sequences already exist for specied %d. Aborting..." %
(count, taxId))
sys.exit(-1)
elif (sequenceType == "fixCDSkey"):
r.delete(speciesCDSList % (taxId, ))
# Delete and reconstruct the CDS key
else:
assert (data_helpers.countSpeciesCDS(taxId) > 0)
def getCrc(seq):
return crc32(str(seq).lower()) & 0xffffffff
argsParser.add_argument("--taxId", type=int, required=True)
argsParser.add_argument("--keep-first-n-shuffles", type=int, default=None)
args = argsParser.parse_args()
# Configuration
taxId = args.taxId
#statsShuffles = RunningStats()
statsShuffles = OfflineStats()
recordsCount = 0
warningsCount = 0
rl = RateLimit(30)
total = countSpeciesCDS(taxId)
for protId in SpeciesCDSSource(taxId):
cds = CDSHelper(taxId, protId)
statsShuffles.push(
cds.dropShuffledSeqs(lastItemToKeep=args.keep_first_n_shuffles))
recordsCount += 1
if (rl()):
print("processed %d records (%.2g%%)" %
(recordsCount, float(recordsCount) / total * 100))
# DEBUG ONLY # DEBUG ONLY # DEBUG ONLY # DEBUG ONLY # DEBUG ONLY # DEBUG ONLY #
#if( recordsCount > 20 ):
def runDistributed():
import _distributed
import dask
scheduler = _distributed.open()
results = {}
#taxids = []
delayedCalls = []
fractionSize = 20
for taxId in allSpeciesSource():
if randint(0, 20) > 0:
continue
if not getSpeciesProperty(taxId, 'paired-mRNA-fraction')[0] is None:
continue
size = countSpeciesCDS(taxId)
numFractions = size / fractionSize
for i in range(numFractions):
call = dask.delayed(calcNativePairedFraction)(taxId, i,
numFractions)
delayedCalls.append(call)
#taxids.append(taxId)
print("Starting %d calls..." % len(delayedCalls))
futures = scheduler.compute(
delayedCalls
) # submit all delayed calculations; obtain futures immediately
try:
_distributed.progress(futures) # wait for all calculations to complete
except Exception as e:
print(E)
print("\n")
print("Waiting for all tasks to complete...")
_distributed.wait(futures)
results = {}
errorsCount = 0
for f in futures:
try:
(taxId, fraction, cdsCount, countPairedNucleotides,
countTotalNucleotides) = scheduler.gather(f)
current = None
if taxId in results:
current = results[taxId]
else:
current = (0, 0, 0, set())
current = (current[0] + cdsCount,
current[1] + countPairedNucleotides,
current[2] + countTotalNucleotides,
current[3].union(set((fraction, ))))
results[taxId] = current
except Exception as e:
print(e)
errorsCount += 1
for taxId, result in results.items():
if len(result[3]) != max(result[3]) + 1:
#raise Exception("Found invalid number of items for taxId=%d" % taxId)
print("Found invalid number of items for taxId=%d" % taxId)
continue
fraction = float(result[1]) / result[2]
setSpeciesProperty(taxId,
"paired-mRNA-fraction",
"%.4g" % fraction,
"computed (v3)",
overwrite=False)
print("TaxId: %d\t\tFraction: %.4g" % (taxId, fraction))
print("Finished %d species with %d errors" % (len(results), errorsCount))
return results
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import sys
from time import sleep
from data_helpers import SpeciesCDSSource, CDSHelper, getSpeciesName, countSpeciesCDS, matchCDSKeyNamesSource, r
from rate_limit import RateLimit
taxId = int(sys.argv[1])
rl = RateLimit(10)
if (countSpeciesCDS(taxId) == 0):
print("Species %d (%s) doesn't have any proteins..." %
(taxId, getSpeciesName(taxId)))
print("Nothing left to do...")
sys.exit(0)
print("Species %d (%s) has %d proteins stored." %
(taxId, getSpeciesName(taxId), countSpeciesCDS(taxId)))
print("Will delete it in 10 seconds...")
sleep(10)
count = 0
for protId in SpeciesCDSSource(taxId):
print(protId)
cds = CDSHelper(taxId, protId)
def standalone():
argsParser = argparse.ArgumentParser()
argsParser.add_argument("--taxid", type=int)
argsParser.add_argument("--input")
argsParser.add_argument("--variant",
type=parseOption(
set(("yeastgenome", "NCBI", "Ensembl", "JGI")),
"variant"))
argsParser.add_argument("--type",
type=parseOption(
set(("cds", "shuffle", "fixCDSkey")),
"sequence type"))
argsParser.add_argument("--dry-run", action="store_true", default=False)
argsParser.add_argument("--output-fasta")
argsParser.add_argument("--gene-ids-file")
argsParser.add_argument("--alt-protein-ids",
type=parseOption(set(("locus_tag", )),
"alt-protein-id"))
argsParser.add_argument("--headers-from-another-fasta")
argsParser.add_argument("--ignore-id-check",
action="store_true",
default=False)
args = argsParser.parse_args()
if (args.output_fasta):
if (args.output_fasta == args.input):
raise Exception("Fasta output file cannot match input file!")
#if( len(sys.argv) < 5 ):
# print("Usage: %s <taxid> <fasta-file> <fasta-variant> <cds|shuffle>" % (sys.argv[0],))
# sys.exit(-1)
# command-line arguments
taxId = args.taxid
f = None
if (args.input[-3:] == ".gz"):
f = gzip.open(args.input, "r")
elif (args.input[-4:] == ".bz2"):
# TODO: impl this...
assert (False)
else:
f = open(args.input, 'r')
#sequenceFormat = args.variant
sequenceType = args.type
if (sequenceType == "cds"):
seqSourceTag = db.Sources.External
elif (sequenceType == "shuffle"):
seqSourceTag = db.Sources.ShuffleCDSv2_matlab
elif (sequenceType == "fixCDSkey"):
seqSourceTag = None
else:
raise Exception("Unknown sequence type '%s'" % sequenceType)
# establish connections
# metadata server (redis)
#r = redis.StrictRedis(host=config.host, port=config.port, db=config.db, password=config.password)
# sequences server (mysql)
#session = db.Session()
visitedProteinIds = set()
assert (r.exists("species:taxid:%d:name" % taxId))
if (seqSourceTag == db.Sources.External):
# Clear any previously imported CDSs...
#r.delete(speciesCDSList % (taxId,))
count = data_helpers.countSpeciesCDS(taxId)
if (count > 0 and (not args.dry_run)):
print("%d sequences already exist for specied %d. Aborting..." %
(count, taxId))
sys.exit(-1)
elif (sequenceType == "fixCDSkey"):
r.delete(speciesCDSList % (taxId, ))
# Delete and reconstruct the CDS key
else:
assert (data_helpers.countSpeciesCDS(taxId) > 0)
reNuclearYeastGene = re.compile("Y[A-P][RL]\d+[CW](-[A-Z])?")
geneIdsToInclude = set()
if (args.gene_ids_file):
with open(args.gene_ids_file, "r") as genesFile:
for geneId in genesFile:
geneIdsToInclude.add(geneId.rstrip())
reNCBIattributes = re.compile("\[(\S+)=([^\]]+)\]")
reNCBIbareheader = re.compile("\w+\|\w+\.\d+_cds_(\w+.\d+)_\d+")
outRecords = []
headersFromAnotherFasta = {}
if args.headers_from_another_fasta:
with open(args.headers_from_another_fasta, "r") as f2:
for record in SeqIO.parse(f2, "fasta", alphabet=generic_dna):
assert (not record.id in headersFromAnotherFasta)
headersFromAnotherFasta[record.id] = record.description
cdsCount = 0
notFoundCount = 0
skippedCount = 0
validNucleotideChars = str.maketrans("ACGTacgt", "%%%%%%%%")
#print("Opening fasta file: {}".format(f))
for record in SeqIO.parse(f, "fasta", alphabet=generic_dna):
#proteinId = regexLocusId.match(record.id).group(1) # Work-around for multiple-transcript identifiers in JGI's Chlamy genome
if args.headers_from_another_fasta:
record.description = headersFromAnotherFasta[record.id]
numNonNucleotideChars = len(record.seq) - str(
record.seq).translate(validNucleotideChars).count("%")
if numNonNucleotideChars:
print(
"Skipping record %s, containing non-nucleotide or ambiguous symbols '%s'"
% (record.id, numNonNucleotideChars))
skippedCount += 1
continue
# yeastgenome.org - skip suspected pseudo-genes
if (args.variant == "yeastgenome"
and record.description.find("Dubious ORF") != -1):
skippedCount += 1
continue
# yeastgenome.org - skip mitochondrial genes
if (args.variant == "yeastgenome"):
geneType = record.id[0]
if geneType == "Q" or geneType == "R":
skippedCount += 1
continue
# yeastgenome.org - verify gene-id conforms to: http://www.yeastgenome.org/help/community/nomenclature-conventions
if (args.variant == "yeastgenome"):
geneId = record.id
assert (reNuclearYeastGene.match(geneId))
# Obtain attributes mapping
attributes = []
if (args.variant == "NCBI"):
attributes = dict(re.findall(reNCBIattributes, record.description))
if (args.variant == "NCBI"):
if ('pseudo' in attributes and attributes['pseudo'] == 'true'):
print("Skipping pseudo-gene entry %s" % (record.id, ))
skippedCount += 1
continue
# Determine gene id
proteinId = None
additionalProteinIds = set()
altProteinId = None
if (args.variant == "yeastgenome"):
proteinId = record.id
elif (args.variant == "NCBI"):
if (sequenceType == "shuffle" and not attributes):
#Workaround for shuffle-seq files missing the header...
#Extract the protein-id from sequence-id like this:
#>lcl|NC_002516.2_cds_NP_064721.1_1
if not args.alt_protein_ids:
proteinId = reNCBIbareheader.match(record.id).group(1)
elif args.alt_protein_ids == "locus_tag":
if ('locus_tag' not in attributes):
print("Skipping entry %s missing locus_tag - %s" %
(record.id, attributes))
skippedCount += 1
continue
proteinId = attributes['locus_tag']
print(proteinId)
else:
assert False
else:
# Note - not currently used
#if 'db_xref' in attributes:
# _db_xrefs = attributes['db_xref'].split(",")
# db_xrefs = dict(map( lambda x: tuple(x.split(":")), _db_xrefs))
if not args.alt_protein_ids:
if ('protein_id' not in attributes):
print("Skipping entry %s missing protein_id - %s" %
(record.id, attributes))
skippedCount += 1
continue
proteinId = attributes['protein_id']
elif args.alt_protein_ids == "locus_tag":
if ('locus_tag' not in attributes):
print("Skipping entry %s missing locus_tag - %s" %
(record.id, attributes))
skippedCount += 1
continue
proteinId = attributes['locus_tag']
if ('protein_id' in attributes):
altProteinId = attributes['protein_id']
else:
assert (False)
elif (args.variant == "Ensembl"):
# Sample id: ABD29211.1
dotPos = record.id.rfind('.')
if (dotPos > 3):
proteinId = record.id[:dotPos]
additionalProteinIds.add(
record.id
) # also allow matching the full format (including the transcript-id) - some CDS files include it...
else:
proteinId = record.id
elif (args.variant == "JGI"):
# Variant 1 (Phytozome, Mpus)
# (gff3): 60050
# (fasta): 60050
# Variant 2 (Phytozome, Dsal)
# (gff3): Dusal.1637s00001.1
# (fasta): Dusal.1637s00001.1
# Variant 3:
# (gff3): jgi.p|Ostta1115_2|10314
# (fasta): jgi|Ostta1115_2|10314|CE10313_131
proteinId = record.id
if record.id.startswith("jgi|"):
parts = record.id.split('|')
parts[0] = 'jgi.p' # add the '.p'
additionalProteinIds.add('|'.join(
parts[:3])) # drop the suffix (parts[4])
else:
assert (False)
if not args.ignore_id_check:
assert (len(proteinId) > 2)
# Skip sequences that have non-standard translations
if (args.variant == "NCBI"):
if "transl_except" in attributes:
print("Skipping %s (because of transl_except)" % (proteinId, ))
skippedCount += 1
continue
# If an inclusion list (white list) is defined, skip sequences missing from it
if args.gene_ids_file:
if (proteinId not in geneIdsToInclude):
# Also try the additional ids
if (not geneIdsToInclude.intersection(additionalProteinIds)):
print("Skipping %s (sequence %s, alternate ids=%s)" %
(proteinId, record.id, list(additionalProteinIds)))
skippedCount += 1
continue
print("Inserting %s (sequence %s)..." % (proteinId, record.id))
# Verify there are no duplicates entries
if (proteinId in visitedProteinIds):
print("MULTIPLE Entry: %s", proteinId)
skippedCount += 1
continue
#assert(proteinId not in visitedProteinIds)
visitedProteinIds.add(proteinId)
# Write the filtered sequences into an output file (if needed)
# Note - this also works in dry-run...
if (args.output_fasta):
outRecords.append(record)
if (args.dry_run):
continue
if (sequenceType == "fixCDSkey"):
cds = data_helpers.CDSHelper(taxId, proteinId)
seqId = cds.seqId()
if (not seqId is None):
r.sadd(speciesCDSList % (taxId, ), proteinId)
else:
print("Couldn't find entry for proteinId=%s" % proteinId)
continue # Skip the rest of the processing...
storeSeqInDB(nucSeq=record.seq,
taxId=taxId,
proteinId=proteinId,
seqSourceTag=seqSourceTag)
cdsCount += 1
if (notFoundCount + skippedCount > 0):
print("Warning: %d entries skipped and %d entries not found" %
(skippedCount, notFoundCount))
print("Processed %d CDS entries" % (cdsCount, ))
print("(out of %d CDS entries for this species)" %
(r.scard("species:taxid:%d:CDS" % (taxId, ))))
if (args.output_fasta):
with open(args.output_fasta, "w") as outfile:
out = SeqIO.write(outRecords, outfile, "fasta")
def readSeriesResultsForSpecies(seriesSourceNumber,
species,
minShuffledGroups=20,
maxShuffledGroups=20,
shuffleType=db.Sources.ShuffleCDSv2_python,
cdsFilter=None,
returnCDS=True):
if isinstance(
species, Iterable
): # usually, species will be a sequence of numeric taxid values
if isinstance(species, basestring):
raise Exception("species cannot be string")
# all set - proceed...
else:
species = (species, ) # assume we got a single (numeric) taxid value
assert (minShuffledGroups <= maxShuffledGroups)
for taxIdForProcessing in species:
print("Procesing %d sequences for tax-id %d (%s)..." %
(countSpeciesCDS(taxIdForProcessing), taxIdForProcessing,
getSpeciesName(taxIdForProcessing)))
computed = getAllComputedSeqsForSpecies(seriesSourceNumber,
taxIdForProcessing,
maxShuffledGroups,
shuffleType=shuffleType)
computedIds = frozenset(computed.keys())
print("Collecting data from %d computation results..." % len(computed))
skipped = 0
selected = 0
alreadyCompleted = 0
# Iterate over all CDS entries for this species
for protId in SpeciesCDSSource(taxIdForProcessing):
cds = CDSHelper(taxIdForProcessing, protId)
if (not cdsFilter is None) and (not cdsFilter(cds)):
continue
cdsSeqId = cds.seqId()
shuffledIds = cds.shuffledSeqIds(shuffleType=shuffleType)
# How many shuffles (for this cds) exist in the data we found?
computedShufflesCount = len(
computedIds.intersection(frozenset(shuffledIds)))
if (computedShufflesCount < minShuffledGroups
or (not cdsSeqId in computedIds)):
#print("%s - found only %d groups, skipping" % (protId, computedShufflesCount))
skipped += 1
continue
# Get the computed results for this CDS
seqIds = [cds.seqId()]
seqIds.extend(cds.shuffledSeqIds(shuffleType=shuffleType))
if (len(seqIds) > maxShuffledGroups + 1):
seqIds = seqIds[:maxShuffledGroups + 1]
results = [computed.get(x) for x in seqIds]
if (results is None or len([() for x in results if not x is None])
< minShuffledGroups):
print("Not enough results found for %s" % protId)
skipped += 1
continue
# Decode the results
results = list(
map(
lambda x: decodeJsonSeriesRecord(decompressSeriesRecord(x))
if not x is None else None, results))
if (returnCDS):
yield {
"taxid": taxIdForProcessing,
"content": results,
"cds": cds
}
else:
yield {"taxid": taxIdForProcessing, "content": results}
del results
del cds
selected += 1
if (rl()):
print("# %s - %d records included, %d records skipped" %
(datetime.now().isoformat(), selected, skipped))
def speciesStatisticsAndValidityReport(args):
import _distributed
speciesDf = pd.DataFrame({
'TaxId': pd.Series([], dtype='int'), # Species TaxId
'Species': pd.Series([], dtype='str'), # Species binomial name
'Nickname': pd.Series([], dtype='str'),
'Domain': pd.Categorical([]), # Bacteria, Eukaryota, Archaea
'Phylum': pd.Categorical([]), # Phylum name (string)
'NumCDSs': pd.Series([], dtype='int'), # CDS count for this species
'NumCDSsInProfile':
pd.Series([], dtype='int'
), # Num seqs with 20 shuffled profiles for this species
'AnnotatedNumCDSs': pd.Series([], dtype='int'), #
'CDSDifference': pd.Series([], dtype='float'), #
'NumNativeSeqs': pd.Series([], dtype='int'), #
'GCContentInCDS': pd.Series([], dtype='float'), #
'AnnotatedGCContent': pd.Series([], dtype='float'), #
'RowType': pd.Categorical([]), # Species count or total
'Warnings': pd.Series([], dtype='str'), #
'CDSWarnings': pd.Series([], dtype='int'), #
'CDSWarnings_': pd.Series([], dtype='str'), #
'FirstAA': pd.Series([], dtype='str'), #
'LastAA': pd.Series([], dtype='str') #
})
scheduler = _distributed.open()
results = {}
delayedCalls_native = []
shuffledCounts = {}
delayedCalls_shuffledProfiles = []
for taxId in allSpeciesSource():
if taxId in speciesToExclude:
continue # always exclude species from the blacklist
if args.taxid and taxId not in args.taxid:
continue # if a whitelist is specified, skip other species
warnings = []
## DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ##
#if randint(0, 20) > 0:
# continue
## DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ##
cdsCountInRedis = countSpeciesCDS(taxId)
#cdsCountProfiles = countx(taxId, (310, 10, "begin", 0), 102, 11)
annotatedProteinCount = getSpeciesProperty(taxId, 'protein-count')[0]
annotatedGCContent = getSpeciesProperty(taxId, 'gc-content')[0]
proteinDifference = None
if not annotatedProteinCount is None:
proteinDifference = (1.0 - float(cdsCountInRedis) /
float(annotatedProteinCount)) * 100.0
if abs(proteinDifference) > 9.9:
warnings.append("CDS_count")
else:
warnings.append("No_CDS_count")
# Determine phylum
lineage = ncbiTaxa.get_lineage(taxId)
names = ncbiTaxa.get_taxid_translator(lineage)
ranks = ncbiTaxa.get_rank(lineage)
# Determine kingdom/domain
domain = ""
kingdomTaxId = [
t for t, rank in ranks.items() if rank == 'superkingdom'
]
if not kingdomTaxId:
kingdomTaxId = [
t for t, rank in ranks.items() if rank == 'kingdom'
]
domain = names[kingdomTaxId[0]]
phylumName = ""
# Determine phylum
phylumTaxId = [t for t, rank in ranks.items() if rank == 'phylum']
if phylumTaxId:
phylumName = names[phylumTaxId[0]]
speciesDf = speciesDf.append(
pd.DataFrame({
'TaxId':
pd.Series([taxId], dtype='int'), # Species TaxId
'Species':
pd.Series([getSpeciesName(taxId)], dtype='str'),
'Nickname':
pd.Series([shortNames[taxId]], dtype='str'),
'Domain':
pd.Categorical([domain]), # Bacteria, Eukaryota, Archaea
'Phylum':
pd.Categorical([phylumName]), # Phylum name (string)
'NumCDSs':
pd.Series([cdsCountInRedis],
dtype='int'), # CDS count for this species
'NumCDSsInProfile':
pd.Series([0],
dtype='int'), # Num seqs with 20 shuffled profiles
'AnnotatedNumCDSs':
pd.Series([
0
if annotatedProteinCount is None else annotatedProteinCount
],
dtype='int'), #
'CDSDifference':
pd.Series([proteinDifference], dtype='float'), #
'NumNativeSeqs':
pd.Series([0], dtype='int'), #
'GCContentInCDS':
pd.Series([0.0], dtype='float'), #
'AnnotatedGCContent':
pd.Series([annotatedGCContent], dtype='float'), #
'RowType':
pd.Categorical(["species"]), # Species count or total
'Warnings':
pd.Series([", ".join(warnings)], dtype='str'), #
'CDSWarnings':
pd.Series([0], dtype='int'),
'CDSWarnings_':
pd.Series([""], dtype='str'),
'FirstAA':
pd.Series([""], dtype='str'),
'LastAA':
pd.Series([""], dtype='str'),
'Source':
pd.Series([""], dtype='str')
}))
fractionSize = 1000 # How many sequences (roughly) to process in each task
numFractions = cdsCountInRedis / fractionSize
if numFractions == 0: numFractions = 1
for i in range(numFractions):
# DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #
#if i%100!=5: continue
# DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #
call = dask.delayed(calcNativeSequencesStatistics)(taxId, i,
numFractions)
delayedCalls_native.append(call)
call = dask.delayed(countShuffledProfiles)(taxId,
(310, 10, "begin", 0), 102,
11)
delayedCalls_shuffledProfiles.append(call)
speciesDf.set_index('TaxId', inplace=True)
print("Starting {} calls...".format(
len(delayedCalls_native) + len(delayedCalls_shuffledProfiles)))
futures = scheduler.compute(
delayedCalls_native + delayedCalls_shuffledProfiles
) # submit all delayed calculations; obtain futures immediately
try:
_distributed.progress(futures) # wait for all calculations to complete
except Exception as e:
print(E)
print("\n")
print("Waiting for all tasks to complete...")
_distributed.wait(futures)
results = {}
errorsCount = 0
for f in futures:
try:
ret = scheduler.gather(f)
if (len(ret) == 9):
(taxId, fraction, cdsCount, gcCounts, totalCounts, cdsWarnings,
warnings, firstAA, lastAA) = ret
current = None
if taxId in results:
current = results[taxId]
else:
current = (0, 0, 0, 0, Counter(), Counter(), Counter())
current = (current[0] + cdsCount, current[1] + gcCounts,
current[2] + totalCounts, current[3] + cdsWarnings,
current[4] + warnings, current[5] + firstAA,
current[6] + lastAA)
results[taxId] = current
elif (len(ret) == 2):
(taxId, numShuffledSeqs) = ret
shuffledCounts[taxId] = numShuffledSeqs
else:
assert (False)
except Exception as e:
print(e)
errorsCount += 1
for taxId, result in results.items():
(numNativeSeqs, gcCounts, totalCounts, cdsWarnings, warnings, firstAA,
lastAA) = result
speciesDf.at[taxId, 'NumNativeSeqs'] = numNativeSeqs
speciesDf.at[taxId, 'GCContentInCDS'] = round(
float(gcCounts) / float(totalCounts) * 100.0, 1)
speciesDf.at[taxId, 'CDSWarnings'] = cdsWarnings
speciesDf.at[taxId, 'CDSWarnings_'] = summarizeCounter(warnings)
speciesDf.at[taxId, 'FirstAA'] = summarizeCounter(firstAA)
speciesDf.at[taxId, 'LastAA'] = summarizeCounter(lastAA)
#if numNativeSeqs < species.at[taxId, 'NumCDSs']:
# pass
for taxId, result in shuffledCounts.items():
speciesDf.at[taxId, 'NumCDSsInProfile'] = result
speciesDf = speciesDf.sort_values(by=['Domain', 'Species']) # sort rows
speciesDf.to_html('species_report.html',
float_format='{0:.1f}'.format,
columns=[
'Species', 'Nickname', 'NumCDSs', 'NumCDSsInProfile',
'AnnotatedNumCDSs', 'CDSDifference', 'NumNativeSeqs',
'GCContentInCDS', 'AnnotatedGCContent', 'Phylum',
'Domain', 'Warnings', 'CDSWarnings', 'CDSWarnings_',
'FirstAA', 'LastAA'
])
with open("species_report_simple.rst", "w") as f:
f.write(
speciesDf.drop([
'RowType', 'Warnings', 'CDSWarnings', 'CDSWarnings_',
'FirstAA', 'LastAA', 'CDSDifference'
],
axis=1).pipe(tabulate,
headers='keys',
tablefmt='rst'))
speciesDf.to_html('species_report_simple.html',
float_format='{0:.1f}'.format,
columns=[
'Species', 'Nickname', 'NumCDSs', 'NumCDSsInProfile',
'AnnotatedNumCDSs', 'CDSDifference', 'NumNativeSeqs',
'GCContentInCDS', 'AnnotatedGCContent', 'Phylum',
'Domain'
])
speciesDf.to_excel('species_report.xlsx', sheet_name='Species summary')
