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Update createSA.py
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@aysunrhn
aysunrhn authored Sep 18, 2023
1 parent 93ad4a2 commit 0796f59
Showing 1 changed file with 54 additions and 54 deletions.
108 changes: 54 additions & 54 deletions createSA.py
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import networkx as nx
if __name__ == '__main__':

panaroodir = sys.argv[1]
ptolemydir = sys.argv[2]
panaroodir = sys.argv[1]
ptolemydir = sys.argv[2]

# Load and process panaroo outputs
panaroomap = os.path.join(panaroodir, 'gene_data.csv')
panaroograph = os.path.join(panaroodir, 'final_graph.gml')
centroid2Loc = pd.read_csv(panaroomap, index_col=2, header=0, \
names=['strain','location','clusterID','annot','protseq',\
'dnaseq','gene','desc']).drop(columns=['desc'])
centroid2Loc.update(centroid2Loc.strain.apply(lambda x: x.replace('_reformatted','')))
g = nx.read_gml(panaroograph, label='id')
g = nx.relabel_nodes(g,int)
panaroodf = pd.DataFrame([v for k,v in g.nodes.items()], index=g.nodes())
panaroodf.loc[:,'locustag'] = panaroodf.apply(lambda x: \
centroid2Loc.loc[x.seqIDs].annot \
if isinstance(x.seqIDs,str) \
else centroid2Loc.loc[x.seqIDs].annot.values, axis=1)
# Load and process panaroo outputs
panaroomap = os.path.join(panaroodir, 'gene_data.csv')
panaroograph = os.path.join(panaroodir, 'final_graph.gml')
centroid2Loc = pd.read_csv(panaroomap, index_col=2, header=0, \
names=['strain','location','clusterID','annot','protseq',\
'dnaseq','gene','desc']).drop(columns=['desc'])
centroid2Loc.update(centroid2Loc.strain.apply(lambda x: x.replace('_reformatted','')))
g = nx.read_gml(panaroograph, label='id')
g = nx.relabel_nodes(g,int)
panaroodf = pd.DataFrame([v for k,v in g.nodes.items()], index=g.nodes())
panaroodf.loc[:,'locustag'] = panaroodf.apply(lambda x: \
centroid2Loc.loc[x.seqIDs].annot \
if isinstance(x.seqIDs,str) \
else centroid2Loc.loc[x.seqIDs].annot.values, axis=1)

# Load and process ptolemy outputs
ptolemymap = os.path.join(ptolemydir, 'orf2id_mapping.txt')
safile = os.path.join(ptolemydir, 'syntenic_anchors.txt')
# Load and process ptolemy outputs
ptolemymap = os.path.join(ptolemydir, 'orf2id_mapping.txt')
safile = os.path.join(ptolemydir, 'syntenic_anchors.txt')

id2orf = pd.read_table(ptolemymap, delimiter='\t', header=None, index_col=2, \
names=['orf', 'strain', 'id'])
id2orf['locustag'] = id2orf.apply(lambda x: re.sub('^'+x.strain+'_', '', x.orf), axis=1)
id2orf['locustag'].update(id2orf.locustag.apply(lambda x: '_'.join(x.split('_')[1:-2]) \
if len(x.split('_'))<8 else \
'_'.join(x.split('_')[2:-2])))
id2orf = pd.read_table(ptolemymap, delimiter='\t', header=None, index_col=2, \
names=['orf', 'strain', 'id'])
id2orf['locustag'] = id2orf.apply(lambda x: re.sub('^'+x.strain+'_', '', x.orf), axis=1)
id2orf['locustag'].update(id2orf.locustag.apply(lambda x: '_'.join(x.split('_')[1:-2]) \
if len(x.split('_'))<8 else \
'_'.join(x.split('_')[2:-2])))


# Match the panaroo outputs to ptolemys ORF indices
keeplocus = set(sum(panaroodf.locustag.apply(lambda x: [x] if isinstance(x,str) \
else list(x)),[]))
keeplocus = set(id2orf.locustag.values).intersection(keeplocus)
df = id2orf[id2orf.apply(lambda x: x.locustag in keeplocus, axis=1)].dropna()
df['newid'] = df.index # df with common ORFs, renumbered (0-based)
df.to_csv(ptolemymap, sep='\t', columns=['orf','strain','newid'], header=False, index=False)
id2orf = df.set_index('locustag')
# Match the panaroo outputs to ptolemys ORF indices
keeplocus = set(sum(panaroodf.locustag.apply(lambda x: [x] if isinstance(x,str) \
else list(x)),[]))
keeplocus = set(id2orf.locustag.values).intersection(keeplocus)
df = id2orf[id2orf.apply(lambda x: x.locustag in keeplocus, axis=1)].dropna()
df['newid'] = df.index # df with common ORFs, renumbered (0-based)
df.to_csv(ptolemymap, sep='\t', columns=['orf','strain','newid'], header=False, index=False)
id2orf = df.set_index('locustag')

# Remove ORFs discarded by panaroo from all ptolemy index files in the database
keepID = df.index
df = pd.read_table(os.path.join(ptolemydir,'id2fasta.txt'), delimiter='\t', header=None, \
index_col=0, names=['id','seq']).loc[keepIDs]
df.to_csv(os.path.join(ptolemydir,'id2fasta.txt'), sep='\t', header=False, index=True)
df = pd.read_table(os.path.join(ptolemydir,'global_z.txt'), delimiter='\t', header=None, \
index_col=0).apply(lambda x: set(map(int,x[1].split(','))), axis=1)
df = df.apply(lambda x: ','.join(map(str,x.intersection(keepID))))
df = df.drop(labels=df[df.apply(lambda x: not(x))].index)
df.to_csv(os.path.join(ptolemydir,'global_z.txt'), sep='\t', header=False, index=True)
df = pd.read_table(os.path.join(ptolemydir,'global_z_prime.txt'), delimiter='\t', header=None, \
index_col=0)
df.loc[keepID].dropna()[1].to_csv(os.path.join(ptolemydir,'global_z_prime.txt'), \
sep='\t', header=False, index=True)
# Remove ORFs discarded by panaroo from all ptolemy index files in the database
keepID = df.index
df = pd.read_table(os.path.join(ptolemydir,'id2fasta.txt'), delimiter='\t', header=None, \
index_col=0, names=['id','seq']).loc[keepIDs]
df.to_csv(os.path.join(ptolemydir,'id2fasta.txt'), sep='\t', header=False, index=True)
df = pd.read_table(os.path.join(ptolemydir,'global_z.txt'), delimiter='\t', header=None, \
index_col=0).apply(lambda x: set(map(int,x[1].split(','))), axis=1)
df = df.apply(lambda x: ','.join(map(str,x.intersection(keepID))))
df = df.drop(labels=df[df.apply(lambda x: not(x))].index)
df.to_csv(os.path.join(ptolemydir,'global_z.txt'), sep='\t', header=False, index=True)
df = pd.read_table(os.path.join(ptolemydir,'global_z_prime.txt'), delimiter='\t', header=None, \
index_col=0)
df.loc[keepID].dropna()[1].to_csv(os.path.join(ptolemydir,'global_z_prime.txt'), \
sep='\t', header=False, index=True)

# Writing out the syntenic anchor file
df = panaroodf.apply(lambda x: id2orf.loc[set(x.locustag).intersection(keeplocus),'newid'].values,axis=1)
anchors = df.apply(lambda x: ''.join(['\t'.join([str(mem), \
','.join(map(str,set(x).difference([mem])))+'\n']) \
for mem in x if len(x)>1]))
anchors[anchors.apply(lambda x: len(x)==0)] = np.nan
anchors.dropna().to_csv(safile, sep='\t', header=False, index=False, na_rep=None)
# Writing out the syntenic anchor file
df = panaroodf.apply(lambda x: id2orf.loc[set(x.locustag).intersection(keeplocus),'newid'].values,axis=1)
anchors = df.apply(lambda x: ''.join(['\t'.join([str(mem), \
','.join(map(str,set(x).difference([mem])))+'\n']) \
for mem in x if len(x)>1]))
anchors[anchors.apply(lambda x: len(x)==0)] = np.nan
anchors.dropna().to_csv(safile, sep='\t', header=False, index=False, na_rep=None)

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