def run(self, seq1_fasta_file, seq2_fasta_file, subst_matrix_fn,
cost_gap_open, complete_traceback):
"""This is the main function which parses fasta files,
calls functions to create Needleman Wunsch and traceback
matrices, and calls another function to print the final result"""
fr = FriendClass()
# Parse the fasta files. Get sequences out of them,
# record1 and record2 are lists containing the sequences and ids in
# fasta file 1 and 2 respectively.
record1, record2 = fr.parseFastaFiles(seq1_fasta_file, seq2_fasta_file)
# if there is a problem with the fasta files, list(SeqIO.parse) returns an empty list
if len(record1) == 0 or len(record2) == 0:
print(
"You have a problem with one of your FASTA files. Hint: check if the first character is '>'"
)
sys.exit(1) # error code 1
id1 = record1[0].id
s1 = str(record1[0].seq) # convert from Bio.Seq.Seq to str
# Make sure s1 doesn't contain non-amino acid characters
fr = FriendClass()
if fr.validateAminoSequence(s1) == 0:
print("You have invalid character(s) in your 1st file")
sys.exit(11) # error code 11
id2 = record2[0].id
s2 = str(record2[0].seq) # convert from Bio.Seq.Seq to str
#Make sure s2 doesn't contain any non-amino acid characters
if fr.validateAminoSequence(s2) == 0:
print("You have invalid character(s) in your 2nd file")
sys.exit(12) # error code 12
# If gap cost is positive, take the additive inverse, return the
# negative version of the same value.
if cost_gap_open > 0:
print(
"Your gap cost is positive. I assume you want it to be negative, I have added a minus"
)
cost_gap_open = -cost_gap_open
(traceback, optimalScore) = self.buildMatrices(s1, s2, subst_matrix_fn,
cost_gap_open)
alignment_strings = self.getAlignmentsFromTracebacks(s1, s2, traceback)
num_alignments = len(alignment_strings)
if complete_traceback == False:
randomNum = random.randint(0, num_alignments - 1)
alignment_strings = [alignment_strings[randomNum]]
# Call a function which prints the 3 strings on the console
self.printer(alignment_strings, num_alignments, optimalScore,
complete_traceback, id1, id2, s1, s2, subst_matrix_fn)
return (id1, s1, id2, s2, optimalScore, alignment_strings,
num_alignments)
def run(self, seq_fasta_file, subst_matrix_fn, cost_gap_open, clustering):
"""This is the main function which parses the fasta file,
calls functions to create the UPGMA and WPGMA trees, and
calls another function to print the final result"""
fr = FriendClass()
# Parse the fasta file. Get 2 sequences out of them
# record is a list containing the sequences and ids in
# the fasta file.
record = fr.parseMultSequenceFastaFile(seq_fasta_file)
# if there is a problem with the fasta file, list(SeqIO.parse) returns an empty list
if len(record) == 0:
print(
"You have a problem with your FASTA file. Hint: check if the first character is '>'"
)
sys.exit(1) # error code 1
# If gap cost is positive, take the additive inverse, return the
# negative version of the same value.
if cost_gap_open > 0:
print(
"Your gap cost is positive. I assume you want it to be negative, I have added a minus"
)
cost_gap_open = -cost_gap_open
# The number of sequences is obtained from the length of the list 'record'
num_sequences = len(record)
# Get the pairwise similarities using Needleman-Wunsch.
ids = []
s = []
for i in range(0, num_sequences):
ids.append(record[i].id)
s.append(str(record[i].seq)) # convert from Bio.Seq.Seq to str
# Make sure s doesn't contain non-amino acid characters
if fr.validateAminoSequence(s[i]) == 0:
print("You have invalid character(s) in your FASTA file")
sys.exit(11) # error code 11
# Call the function which will call other functions to create a UPGMA/WPGMA
# tree based on the 'clustering' argument that is sent.
newick, newickNoDistance, distanceMatrix, newickIds = self.UandWpgma(
ids, s, num_sequences, subst_matrix_fn, cost_gap_open, clustering)
# Call a function which prints the 3 strings on the console
self.printTree(newickIds, clustering)
# Return both the Newick output with distances and the output with just
# the amino acid names.
return newick, newickNoDistance, newickIds
def run(self, seq_fasta_file, subst_matrix_fn, cost_gap_open, clustering):
"""This is the main function which parses the fasta file,
calls functions to create the UPGMA and WPGMA trees, and
calls another function to print the final result"""
self.subsMat = subst_matrix_fn
self.gapOpenCost = cost_gap_open
fr = FriendClass()
# Parse the fasta file. Get 2 sequences out of them
# record is a list containing the sequences and ids in
# the fasta file.
record = fr.parseMultSequenceFastaFile(seq_fasta_file)
# if there is a problem with the fasta file, list(SeqIO.parse) returns an empty list
if len(record) == 0:
print(
"You have a problem with your FASTA file. Hint: check if the first character is '>'"
)
sys.exit(1) # error code 1
# If gap cost is positive, take the additive inverse, return the
# negative version of the same value.
if cost_gap_open > 0:
print(
"Your gap cost is positive. I assume you want it to be negative, I have added a minus"
)
cost_gap_open = -cost_gap_open
# The number of sequences is obtained from the length of the list 'record'
num_sequences = len(record)
# Get the pairwise similarities using Needleman-Wunsch.
ids = []
s = []
for i in range(0, num_sequences):
ids.append(record[i].id)
s.append(str(record[i].seq)) # convert from Bio.Seq.Seq to str
# Make sure s doesn't contain non-amino acid characters
if fr.validateAminoSequence(s[i]) == 0:
print("You have invalid character(s) in your FASTA file")
sys.exit(11) # error code 11
# Call the function in Xpgma which will call other functions to create
# a UPGMA/WPGMA tree based on the 'clustering' argument that is sent.
# 2 Newick format outputs string are returned: with and without distances.
# Only the one without distances will be used. The 3rd Newick format,
# contains the original IDs in the fasta file, and is only needed for display
gma = Xpgma()
newick, newickNoDistance, distanceMatrix, newickIds = gma.UandWpgma(
ids, s, num_sequences, subst_matrix_fn, cost_gap_open, clustering)
# seqClusterMap is a dict with cluster names as keys and the
# corresponding sequences as values.
seqClusterMap = {}
cl = 0
for seq in s:
seqClusterMap['C' + str(cl)] = seq
cl += 1
# Call a function which will read and parse the Newick string, and
# will internally call other functions to create groups and get
# the final multiple sequence alignment.
self.processNewickString(newickNoDistance, seqClusterMap)
SOP = self.sumOfPairs()
self.printer(newickIds, SOP)
return SOP, newick, newickNoDistance