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Python 8 - Data Structures -Problem Set

Don't forget to use a small test data set when you are testing your code. Make sure you know what the correct answer should be

  1. Take a multi-FASTA Python_08.fasta file from user input and calculate the nucleotide composition for each sequence. Use a datastructure to keep count. Print out each sequence name and its compostion in this format seqName\tA_count\tT_count\tG_count\C_count

Here is a structure of a handy datastructure to store this information


seqs['geneA']['A'] = 2
seqs['geneA']['T'] = 3
seqs['geneA']['G'] = 3
seqs['geneA']['C'] = 1

seqs['geneB']['A'] = 1
seqs['geneB']['T'] = 5
seqs['geneB']['G'] = 2
seqs['geneB']['C'] = 2
  1. Write a script that takes a multi-FASTA file Python_08.fasta from user input and breaks each sequence into codons (every three nucleotides is a codon) in just the first reading frame. Your output should look like this

Write the output to a file called 'Python_08.codons-frame-1.nt'.

  1. Add in exception handling. Throw and handle (try/except) the exception

    • if no input is provided
    • if the file cannot be opened
    • if the file does not end in '.fasta' or '.fa' or '.nt'
    • if a non ATGCN charcter is found in the sequence
  2. Now produce codons in the first three reading frames for each sequence and print out ids and sequence records for each frame and print to a file called 'Python_08.codons-3frames.nt'

For example

  1. Now reverse complement each sequence and print out all six reading frames to a file called 'Python_08.codons-6frames.nt'

  2. Translate each of the six reading frames into amino acids. Create one file for which you print the six reading frames (Python_08.codons-6frames.nt) and one file for which you print the translation of the six reading frames (Python_08.translated.aa). Use the following translation table:

translation_table = {
    'GCT':'A', 'GCC':'A', 'GCA':'A', 'GCG':'A',
    'CGT':'R', 'CGC':'R', 'CGA':'R', 'CGG':'R', 'AGA':'R', 'AGG':'R',
    'AAT':'N', 'AAC':'N',
    'GAT':'D', 'GAC':'D',
    'TGT':'C', 'TGC':'C',
    'CAA':'Q', 'CAG':'Q',
    'GAA':'E', 'GAG':'E',
    'GGT':'G', 'GGC':'G', 'GGA':'G', 'GGG':'G',
    'CAT':'H', 'CAC':'H',
    'ATT':'I', 'ATC':'I', 'ATA':'I',
    'TTA':'L', 'TTG':'L', 'CTT':'L', 'CTC':'L', 'CTA':'L', 'CTG':'L',
    'AAA':'K', 'AAG':'K',
    'TTT':'F', 'TTC':'F',
    'CCT':'P', 'CCC':'P', 'CCA':'P', 'CCG':'P',
    'TCT':'S', 'TCC':'S', 'TCA':'S', 'TCG':'S', 'AGT':'S', 'AGC':'S',
    'ACT':'T', 'ACC':'T', 'ACA':'T', 'ACG':'T',
    'TAT':'Y', 'TAC':'Y',
    'GTT':'V', 'GTC':'V', 'GTA':'V', 'GTG':'V',
    'TAA':'*', 'TGA':'*', 'TAG':'*'
  1. Find the longest peptide sequence (M => Stop) of all the six translated reading frames for a single sequence. Do this for all the sequence records. For each sequence, print out in FASTA format the six frames of codons to one file (Python_08.codons-6frames.nt), the translations to a second file (Python_08.translated.aa), and the single longest translated peptide to a third file (Python_08.translated-longest.aa).

  2. Finally determine which subset of codons produced the longest peptide for each sequence record. Print this to a fourth file in FASTA format (Python_08.orf-longest.nt).