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PRESCOTT
Commit 301e36d5 by Mustafa Tekpinar
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Changes in prescott.py for csv files of gnomAD v4.0.0.

parent 310fd718
Showing with 141 additions and 0 deletions
prescott/prescott.py
......@@ -287,6 +287,136 @@ def getGnomADOverallFrequencyV2(infile, usePopMax="true"):
dfMissense['protein'] = proteinNameList
return (dfMissense)
def getGnomADV4OverallFrequency(infile, usePopMax="true"):
"""
This version is for gnomAD v4.
-Latino/Admixed is renamed as Admixed
-I used Middle Eastern instead of Other field.
"""
df = pd.read_csv(infile)
# print(df.columns)
#print(df1.columns)
dfMissense = df.loc[df['VEP Annotation']=='missense_variant',
['HGVS Consequence', 'Protein Consequence', 'Transcript Consequence',
'VEP Annotation', 'ClinVar Clinical Significance', 'ClinVar Variation ID', 'Flags',
'Allele Count', 'Allele Number', 'Allele Frequency',
'Homozygote Count', 'Hemizygote Count',
'Allele Count African/African American',
'Allele Number African/African American',
'Homozygote Count African/African American',
'Hemizygote Count African/African American',
'Allele Count Admixed American',
'Allele Number Admixed American',
'Homozygote Count Admixed American',
'Hemizygote Count Admixed American',
'Allele Count Ashkenazi Jewish',
'Allele Number Ashkenazi Jewish',
'Homozygote Count Ashkenazi Jewish',
'Hemizygote Count Ashkenazi Jewish',
'Allele Count East Asian',
'Allele Number East Asian',
'Homozygote Count East Asian',
'Hemizygote Count East Asian',
'Allele Count European (Finnish)',
'Allele Number European (Finnish)',
'Homozygote Count European (Finnish)',
'Hemizygote Count European (Finnish)',
'Allele Count European (non-Finnish)',
'Allele Number European (non-Finnish)',
'Homozygote Count European (non-Finnish)',
'Hemizygote Count European (non-Finnish)',
'Allele Count Middle Eastern',
'Allele Number Middle Eastern',
'Homozygote Count Middle Eastern',
'Hemizygote Count Middle Eastern',
'Allele Count South Asian',
'Allele Number South Asian',
'Homozygote Count South Asian',
'Hemizygote Count South Asian']]
dfMissense = dfMissense.reset_index()
dfMissense['Allele Frequency']
dfMissense['Allele Frequency Log'] = ""
if(usePopMax.lower()=="true"):
alleleCountList = ['Allele Count African/African American',
'Allele Count Admixed American',
'Allele Count Ashkenazi Jewish',
'Allele Count East Asian',
'Allele Count European (Finnish)',
'Allele Count European (non-Finnish)',
'Allele Count Middle Eastern',
'Allele Count South Asian']
alleleNumberList = ['Allele Number African/African American',
'Allele Number Admixed American',
'Allele Number Ashkenazi Jewish',
'Allele Number East Asian',
'Allele Number European (Finnish)',
'Allele Number European (non-Finnish)',
'Allele Number Middle Eastern',
'Allele Number South Asian']
for index, row in dfMissense.iterrows():
maxFreq = 0.0
tempIndex = 0
for i in range(len(alleleCountList)):
if(row[alleleNumberList[i]]!=0):
tempValue = (row[alleleCountList[i]]/row[alleleNumberList[i]])
if(tempValue>maxFreq):
maxFreq=tempValue
tempIndex = i
# Avoid zero frequency error by setting it to a very low number such as 10**-10
if(maxFreq==0.0):
maxFreq = 10**-10 # Which means 1 in 10 billion, which is the estimated population in 2050.
# print(maxFreq)
dfMissense.at[index,'Selected Population'] = alleleCountList[tempIndex]
dfMissense.at[index,'Allele Frequency Log'] = np.log10(maxFreq)
#print(np.log10(maxFreq))
else:
# Avoid zero frequency error by setting it to a very low number such as 10**-10
for index, row in dfMissense.iterrows():
if(row['Allele Frequency']==0.0):
dfMissense.at[index,'Allele Frequency Log'] = np.log10(10**-10)
else:
dfMissense.at[index,'Allele Frequency Log'] = np.log10(row['Allele Frequency'])
# sys.exit(-1)
#print(dfMissense['Allele Frequency Log'])
#print(dfMissense[['Allele Frequency', 'ClinVar Clinical Significance']])
# dfMissense.dropna(subset = ['ClinVar Clinical Significance'], inplace=True)
dfMissense = dfMissense.reset_index()
#print(dfMissense[['Allele Frequency', 'ClinVar Clinical Significance']])
# plt.figure()
# plt.hist(dfMissense['Allele Frequency Log'], density=False, color='red', label='pathogenic')
# plt.show()
mutantList = []
proteinNameList = []
for index, row in dfMissense.iterrows():
source = one_letter[row['Protein Consequence'][2:5].upper()]
position = (row['Protein Consequence'][5:-3])
target = one_letter[(row['Protein Consequence'][-3:]).upper()]
mutant = source+position+target
mutantList.append(mutant)
# print(mutant, row['Protein Consequence'])
# print(infile.split('_')[2])
proteinNameList.append(os.path.basename(infile).split('_')[2])
dfMissense['mutant'] = mutantList
dfMissense['protein'] = proteinNameList
return (dfMissense)
alphabeticalAminoAcidsList = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L',
'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']
def writeSinglelineFormat(scanningMatrix, outFile, residueList,
......@@ -479,6 +609,10 @@ def main():
'gemme: a horizontal format of 20 rows and N columns.\n'+\
'singleline: each line contains a mutation and its value separated by a space.\n'+\
'M1A 0.378\n', required=False, default='gemme')
main_parser.add_argument('--gnomadversion', dest='gnomadversion', type=int, \
help='An integer value. Default is version 4 (4.0.0) of GnomAD! \n Other possible versions are 2 and 3.',
required=False, default=4)
# main_parser.add_argument('--colormap', dest='colormap', type=str, \
# help='A colormap as defined in matplotlib',
# required=False, default='coolwarm_r')
......@@ -506,6 +640,7 @@ def main():
print("@> Scaling coefficient (Default=1.0): {}".format(args.coefficient))
print("@> Frequency cutoff (Default=-4.0) : {}".format(args.frequencycutoff))
print("@> Name of the output file : {}".format(args.outputfile))
print("@> GnomAD data version (Default=4) : {}".format(str(args.gnomadversion)))
# End of argument parsing!
protein = os.path.splitext(os.path.basename(args.escottfile))[0]
......@@ -564,7 +699,13 @@ def main():
myBigMergedDF = pd.DataFrame()
myBigMergedDF = pd.concat([myBigMergedDF, dfESCOTT], ignore_index=True)
if (args.gnomadversion==2 or args.gnomadversion==3):
gnomadDF = getGnomADOverallFrequency(args.gnomadfile, usePopMax=usePopMaxOrNot)
elif (args.gnomadversion==4):
gnomadDF = getGnomADV4OverallFrequency(args.gnomadfile, usePopMax=usePopMaxOrNot)
else:
print("ERROR: Unknown GnomAD version!")
sys.exit(-1)
# Assign labels to pathogenic/benign mutations for performance evaluation
gnomadDF['labels'] = ""
......
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