print(line)
# Filter self-matches, add to the database
if line[0] != line[1]:
resultsDB.addBLASTresult(line[0], line[1], line[2], line[3])
"""
# Create the LSH tables in the results database
resultsDB.createLSHtable("lshresults3")
resultsDB.deleteTable("lshresults3")
resultsDB.createLSHtable("lshresults3")
for query in minhash3.minhashes.keys():
print(query)
matches = minhash3.queryProtein(query)
for match in matches:
# Filter self-matches
if query != match:
jaccard = minhash3.estimateJaccard(query, match)
resultsDB.addLSHresult(query, match, jaccard, "lshresults3")
# Create the LSH tables in the results database
resultsDB.createLSHtable("lshresults4")
resultsDB.deleteTable("lshresults4")
resultsDB.createLSHtable("lshresults4")
for query in minhash4.minhashes.keys():
matches = minhash4.queryProtein(query)
for match in matches:
# Filter self-matches
if query != match:
jaccard = minhash4.estimateJaccard(query, match)
resultsDB.addLSHresult(query, match, jaccard, "lshresults4")
# Create the LSH tables in the results database
def run(self):
print(\
"""Local Sensitivity Hashing-based protein similarity search.
Options: E[X]it, [L]oad Database, [D]elete Database,
[C]alculate LSH, [RC] Recalculate LSH, [LL] Load LSH, [S]ave LSH
[Q]uery LSH, Query [A]ll LSH, Read [B]LAST, Compare [R]esults,
""")
mode = input('Choose option:')
uniDB = UniprotDB("Uniprot_DB.sqlite")
minhash = LSH(0.5, 96)
while (mode != 'Exit' and mode != 'X'):
if (mode == 'Delete Database' or mode == 'D'):
uniDB.deleteProteins()
if (mode == 'Load Database' or mode == 'L'):
protManager = ProteinsManager()
uniDB.createTables()
filename = input(
'XML filename (e.g. Ecolx.xml or PseA7.xml or Human.xml): '
)
protManager.loadProteins(filename, uniDB)
if (mode == 'Calculate LSH' or mode == 'C'):
uniDB = UniprotDB("Uniprot_DB.sqlite")
proteins = uniDB.extractProteins()
minhashes, lsh = minhash.calculateLSH(proteins, 3)
print("Calculated")
if (mode == 'Recalculate LSH' or mode == 'RC'):
jaccardThreshold = float(
input(
"Specify a Jaccard similarity threshold (default: 0.5): "
))
permutations = int(
input(
"Specify the number of permutations(default: 96) : "))
shinglesize = int(
input("Specify the shingle size (default: 3): "))
minhash = LSH(jaccardThreshold, permutations)
proteins = uniDB.extractProteins()
minhashes, lsh = minhash.calculateLSH(proteins, shinglesize)
print("Recalculated")
if (mode == 'Query LSH' or mode == 'Q'):
protein = input('Protein accession: ')
start_time = time.time()
result = minhash.queryProtein(protein)
if result is not None:
jaccResultsDict = minhash.checkJaccardResultsOfProtein(
protein, result)
# Return the results in sorted order, big to small Jaccard score
sorted_jaccResultsDict = OrderedDict(
sorted(jaccResultsDict.items(), key=lambda x: -x[1]))
for jaccRes in sorted_jaccResultsDict.items():
print("\nMatch with Jaccard:", jaccRes[1])
information = uniDB.extractProteinInformation(
jaccRes[0])
proteininfo = uniProtein(*information)
proteininfo.printUniProtein(printSeq=False)
print("Runtime of query search: %s seconds " %
(time.time() - start_time))
if (mode == 'Calculate All' or mode == 'CA'):
start_time = time.time()
uniDB = UniprotDB("Uniprot_DB.sqlite")
#uni_DB.close()
proteins = uniDB.extractProteins()
#minhash.calculateLSH([protein[1] for protein in proteins])
minhashes, lsh = minhash.calculateLSH(proteins, 3)
for protein in proteins:
print("Protein ", protein[0])
result = minhash.queryProtein(protein[0])
if result is not None:
jaccResultsDict = minhash.checkJaccardResultsOfProtein(
protein[0], result)
sorted_jaccResultsDict = OrderedDict(
sorted(jaccResultsDict.items(),
key=lambda x: -x[1]))
for jaccRes in sorted_jaccResultsDict.items():
print(jaccRes[0], " - Jaccard: ", jaccRes[1])
print("Runtime of query all: %s seconds " %
(time.time() - start_time))
if (mode == 'Query All LSH' or mode == 'A'):
resultsDB = ResultsDB("Results_DB.sqlite")
resultsDB.createLSHtable("lshresults")
resultsDB.deleteTable("lshresults")
resultsDB.createLSHtable("lshresults")
for query in minhash.minhashes.keys():
matches = minhash.queryProtein(query)
for match in matches:
# Filter self-matches
if query != match:
jaccard = minhash.estimateJaccard(query, match)
resultsDB.addLSHresult(query, match, jaccard,
"lshresults")
print(resultsDB.extractLSHresults("lshresults"))
if (mode == 'Read BLAST Results' or mode == 'B'):
filename = input('Filename: ')
handle = open(filename, 'r')
resultsDB = ResultsDB("Results_DB.sqlite")
resultsDB.createBLASTtable()
resultsDB.deleteBLASTresults()
resultsDB.createBLASTtable()
for line in handle:
line = line[:-1].split('\t')
# Extract accessions from 'sp|A0A0R6L508|MCR1_ECOLX'-like string
line[0] = line[0].split('|')[1]
line[1] = line[1].split('|')[1]
print(line)
# Filter self-matches, add to the database
if line[0] != line[1]:
resultsDB.addBLASTresult(line[0], line[1], line[2],
line[3])
print(resultsDB.extractBLASTresults())
if (mode == 'Compare Results' or mode == 'R'):
# Database with all LSH and BLASTp results
resultsDB = ResultsDB("Results_DB.sqlite")
identity_th, alignment_th, jaccard_th = 80.0, 100, 0.5
precisions = []
recalls = []
# Load in all protein ids to loop over
uniDB = UniprotDB("Uniprot_DB.sqlite")
proteins = uniDB.extractProteins()
# Store all precisions and recalls per query, to calculate the average
for query in proteins:
intersect = resultsDB.extractIntersectCountPerProtein(
query[0], 'lshresults', identity_th, alignment_th,
jaccard_th)
lshresults = resultsDB.extractLSHcountPerProtein(
query[0], 'lshresults', jaccard_th)
blastresults = resultsDB.extractBLASTcountPerProtein(
query[0], identity_th, alignment_th)
tp = intersect
fp = lshresults - intersect
fn = blastresults - intersect
precision = tp / (tp + fp) if (tp + fp) != 0 else -1
recall = tp / (tp + fn) if (tp + fn) != 0 else -1
# Exclude results without any similar proteins / division by zero
if precision != -1:
precisions.append(precision)
if recall != -1:
recalls.append(recall)
print("Comparison of BLAST and LSH results:\n Number of proteins queried: %i \n Average precision: %0.3f Average recall: %0.3f\n" \
% (len(proteins), sum(precisions)/len(precisions), sum(recalls)/len(recalls)))
if (mode == 'Save LSH' or mode == 'S'):
number = int(input('Suffix number: '))
minhash.saveLSH(number)
if (mode == 'Load LSH' or mode == 'LL'):
number = int(input('Suffix number: '))
minhash.loadLSH(number)
mode = input('Choose option: ')
