The objective is get the Coding sequence of the mRNA given the complete mRNA sequence and the Amino acid sequence. Then put all of this in codon format. I feel that I have finding the possible list codons figured out. I am just not sure how to systematically match with the given mRNA sequence. So here is what I have so far.
xAA = 'MDFFASGLPLVTEETPSGEAGSEEDDEVVAMIKELLDTRIRPTVQEDGGDVIYKGFEDGIVQLKLQGSCTSCPSSIITLKNGIQNMLQFYIPEVEGVEQVMDDESDEKEANSP'
xmRNA = 'GUUCCCCGGCCUCUCUUGGUCAGGGUGACGCAGUAGCCUGCAAACCUCGGCGCGUAGGCCACCGCACUUAUCCGCAGCAGGACCGCCCGCAGCCGGUAGGGUGGGCUCUUCCCAGUGCCCGCCCAGCUACCGGCCAGCCUGCGGCUGCGCAGAUCUUUCGUGGUUCUGUCAGGGAGACCCUUAGGCACUCCGGACUAAGAUGGCGGCGACGGCCAGGCGGGGCUGGGGAGCUGCGGCUGUUGCCGCCGGGCUGCGCAGGCGGUUCUGUCAUAUGUUGAAGAAUCCAUACACCAUUAAGAAACAGCCUCUGCAUCAGUUUGUACAAAGACCACUUUUCCCACUACCUGCAGCCUUUUAUCACCCAGGCAGUUAUUUAGGAUUGAAGGAGUAAAAAGUGUCUUCUUUGGACCAGAUUUCAUCACUGUCACAAAGGAAAAUGAAGAAUUAGACUGGAAUUUACUGAAACCAGAUAUUUAUGCAACAAUCAUGGACUUCUUUGCAUCUGGCUUACCCCUGGUUACUGAGGAAACACCUUCAGGAGAAGCAGGAUCUGAAGAAGAUGAUGAAGUUGUGGCAAUGAUUAAGGAAUUGUUAGAUACUAGAAUACGGCCAACUGUGCAGGAAGAUGGAGGGGAUGUAAUCUACAAAGGCUUUGAAGAUGGCAUUGUACAGCUGAAACUCCAGGGUUCUUGUACCAGCUGCCCUAGUUCAAUCAUUACUCUGAAAAAUGGAAUUCAGAACAUGCUGCAGUUUUAUAUUCCGGAGGUAGAAGGCGUAGAACAGGUUAUGGAUGAUGAAUCAGAUGAAAAAGAAGCAAACUCACCUUAAAAUAAUCUGGAUUUUCUUUGGGCAUAACAGUCAGACUUGUUGAUAAUAUAUAUCAAGUUUUUAUUAUUAAUAUGCUGAGGAACUUGAAGAUUAAUAAAAUAUGCUCUUCAGAGAAUGAUAUAUAAAA'
d = {'mRNA': ['UUU','UUC','UUA','UUG','UCU','UCC','UCA','UCG','UAU','UAC','UAA','UAG','UGU','UGC','UGA','UGG','CUU','CUC','CUA','CUG','CCU','CCC','CCA','CCG','CAU','CAC','CAA','CAG','CGU','CGC','CGA','CGG','AUU','AUC','AUA','AUG','ACU','ACC','ACA','ACG','AAU','AAC','AAA','AAG','AGU','AGC','AGA','AGG','GUU','GUC','GUA','GUG','GCU','GCC','GCA','GCG','GAU','GAC','GAA','GAG','GGU','GGC','GGA','GGG'], 'AA': ['F','F','L','L','S','S','S','S','Y','Y','_','_','C','C','_','W','L','L','L','L','P','P','P','P','H','H','Q','Q','R','R','R','R','I','I','M','M','T','T','T','T','N','N','K','K','S','S','R','R','V','V','V','V','A','A','A','A','D','D','E','E','G','G','G','G']}
AA= pandas.DataFrame(data=d)
for i in xAA:
codons = list(AA.mRNA.loc[AA['AA'] == i])
print codons
This is the output:
['AUA', 'AUG']
['GAU', 'GAC']
['UUU', 'UUC']
['UUU', 'UUC']
['GCU', 'GCC', 'GCA', 'GCG']
['UCU', 'UCC', 'UCA', 'UCG', 'AGU', 'AGC']
['GGU', 'GGC', 'GGA', 'GGG']
['UUA', 'UUG', 'CUU', 'CUC', 'CUA', 'CUG']
['CCU', 'CCC', 'CCA', 'CCG']
['UUA', 'UUG', 'CUU', 'CUC', 'CUA', 'CUG']
['GUU', 'GUC', 'GUA', 'GUG']
['ACU', 'ACC', 'ACA', 'ACG']
['GAA', 'GAG']
['GAA', 'GAG']
['ACU', 'ACC', 'ACA', 'ACG']
['CCU', 'CCC', 'CCA', 'CCG']
['UCU', 'UCC', 'UCA', 'UCG', 'AGU', 'AGC']
['GGU', 'GGC', 'GGA', 'GGG']
['GAA', 'GAG']
['GCU', 'GCC', 'GCA', 'GCG']
['GGU', 'GGC', 'GGA', 'GGG']
['UCU', 'UCC', 'UCA', 'UCG', 'AGU', 'AGC']
['GAA', 'GAG']
['GAA', 'GAG']
['GAU', 'GAC']
['GAU', 'GAC']
['GAA', 'GAG']
['GUU', 'GUC', 'GUA', 'GUG']
['GUU', 'GUC', 'GUA', 'GUG']
['GCU', 'GCC', 'GCA', 'GCG']
['AUA', 'AUG']
['AUU', 'AUC']
['AAA', 'AAG']
['GAA', 'GAG']
['UUA', 'UUG', 'CUU', 'CUC', 'CUA', 'CUG']
['UUA', 'UUG', 'CUU', 'CUC', 'CUA', 'CUG']
['GAU', 'GAC']
['ACU', 'ACC', 'ACA', 'ACG']
['CGU', 'CGC', 'CGA', 'CGG', 'AGA', 'AGG']
['AUU', 'AUC']
['CGU', 'CGC', 'CGA', 'CGG', 'AGA', 'AGG']
['CCU', 'CCC', 'CCA', 'CCG']
['ACU', 'ACC', 'ACA', 'ACG']
['GUU', 'GUC', 'GUA', 'GUG']
['CAA', 'CAG']
['GAA', 'GAG']
['GAU', 'GAC']
['GGU', 'GGC', 'GGA', 'GGG']
['GGU', 'GGC', 'GGA', 'GGG']
['GAU', 'GAC']
['GUU', 'GUC', 'GUA', 'GUG']
['AUU', 'AUC']
['UAU', 'UAC']
['AAA', 'AAG']
['GGU', 'GGC', 'GGA', 'GGG']
['UUU', 'UUC']
['GAA', 'GAG']
['GAU', 'GAC']
['GGU', 'GGC', 'GGA', 'GGG']
['AUU', 'AUC']
['GUU', 'GUC', 'GUA', 'GUG']
['CAA', 'CAG']
['UUA', 'UUG', 'CUU', 'CUC', 'CUA', 'CUG']
['AAA', 'AAG']
['UUA', 'UUG', 'CUU', 'CUC', 'CUA', 'CUG']
['CAA', 'CAG']
['GGU', 'GGC', 'GGA', 'GGG']
['UCU', 'UCC', 'UCA', 'UCG', 'AGU', 'AGC']
['UGU', 'UGC']
['ACU', 'ACC', 'ACA', 'ACG']
['UCU', 'UCC', 'UCA', 'UCG', 'AGU', 'AGC']
['UGU', 'UGC']
['CCU', 'CCC', 'CCA', 'CCG']
['UCU', 'UCC', 'UCA', 'UCG', 'AGU', 'AGC']
['UCU', 'UCC', 'UCA', 'UCG', 'AGU', 'AGC']
['AUU', 'AUC']
['AUU', 'AUC']
['ACU', 'ACC', 'ACA', 'ACG']
['UUA', 'UUG', 'CUU', 'CUC', 'CUA', 'CUG']
['AAA', 'AAG']
['AAU', 'AAC']
['GGU', 'GGC', 'GGA', 'GGG']
['AUU', 'AUC']
['CAA', 'CAG']
['AAU', 'AAC']
['AUA', 'AUG']
['UUA', 'UUG', 'CUU', 'CUC', 'CUA', 'CUG']
['CAA', 'CAG']
['UUU', 'UUC']
['UAU', 'UAC']
['AUU', 'AUC']
['CCU', 'CCC', 'CCA', 'CCG']
['GAA', 'GAG']
['GUU', 'GUC', 'GUA', 'GUG']
['GAA', 'GAG']
['GGU', 'GGC', 'GGA', 'GGG']
['GUU', 'GUC', 'GUA', 'GUG']
['GAA', 'GAG']
['CAA', 'CAG']
['GUU', 'GUC', 'GUA', 'GUG']
['AUA', 'AUG']
['GAU', 'GAC']
['GAU', 'GAC']
['GAA', 'GAG']
['UCU', 'UCC', 'UCA', 'UCG', 'AGU', 'AGC']
['GAU', 'GAC']
['GAA', 'GAG']
['AAA', 'AAG']
['GAA', 'GAG']
['GCU', 'GCC', 'GCA', 'GCG']
['AAU', 'AAC']
['UCU', 'UCC', 'UCA', 'UCG', 'AGU', 'AGC']
['CCU', 'CCC', 'CCA', 'CCG']
If I add the for c loop shown here I get
codingseq = ""
for i in xAA:
codons = list(AA.mRNA.loc[AA['AA'] == i])
for c in codons:
xmRNA.find(c)
codingseq+= c
This gives every combination is there a way to a compartive analysis to find which of these is most like full mRNA seq?
AUAAUG
AUAAUGGAUGAC
AUAAUGGAUGACUUUUUC
AUAAUGGAUGACUUUUUCUUUUUC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGAC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGAC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGAC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCGACUACCACAACG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCGACUACCACAACGGUUGUCGUAGUG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCGACUACCACAACGGUUGUCGUAGUGCAACAG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCGACUACCACAACGGUUGUCGUAGUGCAACAGGAAGAG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCGACUACCACAACGGUUGUCGUAGUGCAACAGGAAGAGGAUGAC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCGACUACCACAACGGUUGUCGUAGUGCAACAGGAAGAGGAUGACGGUGGCGGAGGG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCGACUACCACAACGGUUGUCGUAGUGCAACAGGAAGAGGAUGACGGUGGCGGAGGGGGUGGCGGAGGG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCGACUACCACAACGGUUGUCGUAGUGCAACAGGAAGAGGAUGACGGUGGCGGAGGGGGUGGCGGAGGGGAUGAC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCGACUACCACAACGGUUGUCGUAGUGCAACAGGAAGAGGAUGACGGUGGCGGAGGGGGUGGCGGAGGGGAUGACGUUGUCGUAGUG
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCGACUACCACAACGGUUGUCGUAGUGCAACAGGAAGAGGAUGACGGUGGCGGAGGGGGUGGCGGAGGGGAUGACGUUGUCGUAGUGAUUAUC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCGACUACCACAACGGUUGUCGUAGUGCAACAGGAAGAGGAUGACGGUGGCGGAGGGGGUGGCGGAGGGGAUGACGUUGUCGUAGUGAUUAUCUAUUAC
AUAAUGGAUGACUUUUUCUUUUUCGCUGCCGCAGCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGUUAUUGCUUCUCCUACUGCCUCCCCCACCGUUAUUGCUUCUCCUACUGGUUGUCGUAGUGACUACCACAACGGAAGAGGAAGAGACUACCACAACGCCUCCCCCACCGUCUUCCUCAUCGAGUAGCGGUGGCGGAGGGGAAGAGGCUGCCGCAGCGGGUGGCGGAGGGUCUUCCUCAUCGAGUAGCGAAGAGGAAGAGGAUGACGAUGACGAAGAGGUUGUCGUAGUGGUUGUCGUAGUGGCUGCCGCAGCGAUAAUGAUUAUCAAAAAGGAAGAGUUAUUGCUUCUCCUACUGUUAUUGCUUCUCCUACUGGAUGACACUACCACAACGCGUCGCCGACGGAGAAGGAUUAUCCGUCGCCGACGGAGAAGGCCUCCCCCACCGACUACCACAACGGUUGUCGUAGUGCAACAGGAAGAGGAUGACGGUGGCGGAGGGGGUGGCGGAGGGGAUGACGUUGUCGUAGUGAUUAUCUAUUACAAAAAG
Please note not all results shown as it exceeds the character limit. Any help on this would be awesome!
This finds a codon sequence in xmRNA tha matches xAA. NOTE the d["AA"] data was corrected at index 34 ("M" replaced with "I"), to match the translation used on the http://web.expasy.org/ site. I haven't used pandas, just plain Python. I just did this one as a simple test to try and find where xAA is found in xmRNA (and what codons are used). It should be fast enough even for very long sequences (even 100000 RNAs should be near-instant).
xAA = 'MDFFASGLPLVTEETPSGEAGSEEDDEVVAMIKELLDTRIRPTVQEDGGDVIYKGFEDGIVQLKLQGSCTSCPSSIITLKNGIQNMLQFYIPEVEGVEQVMDDESDEKEANSP'
xmRNA = 'GUUCCCCGGCCUCUCUUGGUCAGGGUGACGCAGUAGCCUGCAAACCUCGGCGCGUAGGCCACCGCACUUAUCCGCAGCAGGACCGCCCGCAGCCGGUAGGGUGGGCUCUUCCCAGUGCCCGCCCAGCUACCGGCCAGCCUGCGGCUGCGCAGAUCUUUCGUGGUUCUGUCAGGGAGACCCUUAGGCACUCCGGACUAAGAUGGCGGCGACGGCCAGGCGGGGCUGGGGAGCUGCGGCUGUUGCCGCCGGGCUGCGCAGGCGGUUCUGUCAUAUGUUGAAGAAUCCAUACACCAUUAAGAAACAGCCUCUGCAUCAGUUUGUACAAAGACCACUUUUCCCACUACCUGCAGCCUUUUAUCACCCAGGCAGUUAUUUAGGAUUGAAGGAGUAAAAAGUGUCUUCUUUGGACCAGAUUUCAUCACUGUCACAAAGGAAAAUGAAGAAUUAGACUGGAAUUUACUGAAACCAGAUAUUUAUGCAACAAUCAUGGACUUCUUUGCAUCUGGCUUACCCCUGGUUACUGAGGAAACACCUUCAGGAGAAGCAGGAUCUGAAGAAGAUGAUGAAGUUGUGGCAAUGAUUAAGGAAUUGUUAGAUACUAGAAUACGGCCAACUGUGCAGGAAGAUGGAGGGGAUGUAAUCUACAAAGGCUUUGAAGAUGGCAUUGUACAGCUGAAACUCCAGGGUUCUUGUACCAGCUGCCCUAGUUCAAUCAUUACUCUGAAAAAUGGAAUUCAGAACAUGCUGCAGUUUUAUAUUCCGGAGGUAGAAGGCGUAGAACAGGUUAUGGAUGAUGAAUCAGAUGAAAAAGAAGCAAACUCACCUUAAAAUAAUCUGGAUUUUCUUUGGGCAUAACAGUCAGACUUGUUGAUAAUAUAUAUCAAGUUUUUAUUAUUAAUAUGCUGAGGAACUUGAAGAUUAAUAAAAUAUGCUCUUCAGAGAAUGAUAUAUAAAA'
d = {'mRNA': ['UUU','UUC','UUA','UUG','UCU','UCC','UCA','UCG','UAU','UAC','UAA','UAG','UGU','UGC','UGA','UGG','CUU','CUC','CUA','CUG','CCU','CCC','CCA','CCG','CAU','CAC','CAA','CAG','CGU','CGC','CGA','CGG','AUU','AUC','AUA','AUG','ACU','ACC','ACA','ACG','AAU','AAC','AAA','AAG','AGU','AGC','AGA','AGG','GUU','GUC','GUA','GUG','GCU','GCC','GCA','GCG','GAU','GAC','GAA','GAG','GGU','GGC','GGA','GGG'],
'AA': ['F', 'F', 'L', 'L', 'S', 'S', 'S', 'S', 'Y', 'Y', '_', '_', 'C', 'C', '_','W','L','L','L','L','P','P','P','P','H','H','Q','Q','R','R','R','R','I','I', 'I' ,'M','T','T','T','T','N','N','K','K','S','S','R','R','V','V','V','V','A','A','A','A','D','D','E','E','G','G','G','G']}
r2a = { d['mRNA'][i] : d['AA'][i] for i in range(len(d['AA'])) }
s=0
found = False
for s in (0,1,2):
# t3 = codon sequence starting from s
t = list(xmRNA[s:])
t3 = [ t[i]+t[i+1]+t[i+2] for i in range(0,len(t)-2,3) ]
# transcribe to AAs
aa = [ r2a[i] for i in t3 ]
aa = ''.join(aa)
print (aa)
try:
idx = aa.index(xAA)
except ValueError:
continue
# found it
pos = idx*3 + s
t3 = t3[idx:idx+len(xAA)]
found = True
break
if found:
print ("found in frame {} at {} (pos={})".format(s+1, idx, pos))
print ("codons: " + repr(t3))
else:
print ("Not found")