Write a Python program that computes the global and local alignment
score with the BLOSUM_62 subsitution matrix of a given DNA sequence in 
query.txt to several other ones in a file called database.txt. 
In both files the sequences are in non-interleaved FASTA format. You
are free to choose your own design but I recommend the design below.

First make a copy of main_database_search.py and alignment_function2.py
and call them main_database_search2.py and alignment_functions2.py. 
Modify these with the steps below.

1. Read in the BLOSUM_62 matrix into a dictionary from the file called
BLOSUM_62.txt posted on the course website. In your dictionary the keys
would be pairs of amino acids and the value would be their substitution 
cost as given by the BLOSUM_62 matrix. For example let blosum62 be the name
of the dictionary. Then blosum62['AA'] would have the value 4 and 
blosum62['AR'] would have the value -1. 

2. Modify the function computeVandT to use the dictionary instead of match 
and mismatch costs. To do this you would have to change 

def computeVandT(seq1, seq2, V, T, m, mm, gap):

to 

def computeVandT(seq1, seq2, V, T, blosum62, gap):

where blosum62 is the dictionary created in step 1 above.

Then modify the code

if(seq2[i-1] == seq1[j-1]):
    D = V[i-1][j-1] + m
else:
    D = V[i-1][j-1] + mm

to use the dictionary instead of m and mm which are the match and
mismatch costs respectively.

3. Do the same for computelocalVandT. 

4. Modify optimal_alignment_score and optimal_local_alignment_score functions
to use the dictionary instead of match and mismatch costs. 

In your main_database_search2.py program you would call these functions
as

score = alignment_functions.optimal_alignment_score(query, sequences[i], blosum62, g)

instead of

score = alignment_functions.optimal_alignment_score(query, sequences[i], m, mm, g)

5. Test your program on the two datasets provided on the course website.
The answer with the BLOSUM_62 matrix for the first dataset (haemoglobin)
is provided on the website. Your numbers should match them or be very close.

HOW TO SUBMIT YOUR ASSIGNMENT:

Submit your assignment main_database_search2.py and alignment_functions2.py files
by copying them to the directory /afs/cad/courses/ccs/s12/bnfo/136/002/<UCID>
where <UCID> stands for your NJIT UCID. We will test your program by running
it on a different dataset.

DUE DATE:

Submit your assignment before 11:30am Monday March 18th. Any file with a timestamp 
greater than 11:30am will not be considered for grading.

EXTRA CREDIT:

You have the opportunity to add 5 points to your grade of the first
mid-term. So if your program is fully correct, which means it runs and
produces correct output, then your score in the first exam will be increased
by 5.

Good luck!