def bioPython_default_local_aligner(a, b):
aligner = PairwiseAligner()
aligner.mode = 'local'
aligner.match_score = 2
aligner.mismatch_score = -3
aligner.open_gap_score = -7
aligner.extend_gap_score = -2
sequence1 = SeqIO.read('./resource/fasta' + str(a) + '.fasta', 'fasta')
sequence2 = SeqIO.read('./resource/fasta' + str(b) + '.fasta', 'fasta')
alignments = aligner.align(sequence1.seq, sequence2.seq)
type=str,
required=True)
parser.add_argument('-r',
'--reference',
help='Reference to be aligned to',
type=str,
required=True)
parser.add_argument('-n',
'--seq_name',
help='Name of the aligned sequence',
type=str,
required=True)
args = parser.parse_args()
aligner = PairwiseAligner()
aligner.mode = 'global'
aligner.match_score = 1
aligner.mismatch_score = 0
aligner.open_gap_score = -2
aligner.extend_gap_score = -1
ref = SeqIO.read(args.reference, "fasta")
ref.seq = str(ref.seq.upper()).replace('-', 'N')
cons = SeqIO.read(args.infile, "fasta")
aln = aligner.align(ref.seq, cons.seq)
with open(args.outfile, 'w') as out:
print(">", args.seq_name, file=out)
print(str(aln[0]).strip().split('\n')[2], file=out)
if not args.open_gap_score:
args.open_gap_score = -11
if not args.extend_gap_score:
args.extend_gap_score = -1
aligners['global'].open_gap_score = args.open_gap_score
aligners['global'].extend_gap_score = args.extend_gap_score
if args.sim_algo == 'smith-waterman':
aligners['local'] = PairwiseAligner()
aligners['local'].mode = 'local'
if args.seq_type in ('dna', 'rna'):
aligners['local'].match = args.match_score
aligners['local'].mismatch = args.mismatch_score
else:
aligners['local'].substitution_matrix = sub_matrix
aligners['local'].open_gap_score = args.open_gap_score
aligners['local'].extend_gap_score = args.extend_gap_score
# Karlin-Altschul parameter values
if args.seq_type in ('dna', 'rna'):
if ((args.match_score, args.mismatch_score) in KA_PARAMS['na']
and (abs(args.open_gap_score), abs(args.extend_gap_score))
in KA_PARAMS['na'][(args.match_score, args.mismatch_score)]):
args.ka_gapped_l = KA_PARAMS['na'][(args.match_score,
args.mismatch_score)][(
abs(args.open_gap_score),
abs(args.extend_gap_score))][0]
args.ka_gapped_k = KA_PARAMS['na'][(args.match_score,
args.mismatch_score)][(
abs(args.open_gap_score),
abs(args.extend_gap_score))][1]
else: