I know we are only suppose to post one question at a time but this all these questions are tied together to be completed. I wil take the code in python or java. Thank you very much

1. (15 points) Write a program that takes as input an alignment, and outputs the PWM and the information content of the PWM. To prevent prob = 0 from happening, add a pseducount 1 for each nucleotide. i.e., p_A = (c_A + 1) / (c_A + c_C + c_G + c_T + 4), where c_A is the number of A's observed in the column. (It is best to implement the main functionality as a function so that you can reuse it in Problem 4 or possibly also Problem 3.) Apply your program on the following alignment and report the results. Also use the web tool enoLogos to compute the sequence logo. Make sure you set %GC to "equiprobable". Click on textout to check the probability matrix and information content there. Do they match your results?

GTGTAGC GTGTTGT GGGTTGC GTGTAGC GTGCTGC GTGTAGC TTGTTGC GTGTAGC ATGTTGC GTGGTGC GTGTTCC GCGTTGC GTGTTAC GCGTTGC GTGCTGC GTTTTGC TTGTTGC GTCTTGC GTGATGC GTGTTGA

2. (25 points) Write a program that takes as input a set of input sequences and a PWM, and outputs the best matches from each sequence. Report both the locations of the matches and the matched subsequences. (Again, it is best to implement the main functionality as a function so that you can reuse it in Problem 4 or Problem 3.)

Apply your program to the input sequences in this file and the PWM you computed above.

(You can verify your results using the program you developed in Problem 1: try to get a new PWM using the matched subsequences - does it look similar to your input PWM?)

THIS FILE:

>1 CGAGTTCCTTTCATCCTTAGGCCGGCTTGAGATATCCGTTGCAGGTGTGCATTGCCCGCATCTCCCGCCGTAGTTATGTCTGGTCTCACAATGGCTTCTGAGTCTTATTTGCCCCTTCTGTTATGGTTTATAATCACCGACACTTGTGTAGCAAGACTGTCAAGGGGTCAAGAGGTATTTGACATTTCGCAAGGCGGGGT >2 ACAATGGTTGCATTATCCTGGCTGGGAACTGTATCGTACGCGATAGTCACGTTTGTTCAAGAACGCAGGTCCACAAAAATGCTACGTGTTGTATACAGGTGCTTACCACGTCGCTGCACGGCTTGTACTTGCTGCATCCGTGTGACAGGAGGCTAGAAGCAGTGAGGCCAAGGTTGCCTTGACTTCCAAACTTGCGACCC >3 ATGCATGCTTGCCCTGTGGATGATTTGGCTACGCATCCGCTCTTAGCCTTGAAGACACGACACAGGCGTGGAAAGGGTAACCCTGGCGGCCCTGATTGACATGCGCTCTGAACGGACTTCAAGGGAGGACTACAAGTGGGGTTGCCCGTCATACATACACGTGATGCCTCTTACGGACCATCAACGCGTAACGCGTGAGT >4 CGAGCCTAGACTCGTAATTTCGGTCACACATGCTGCTATTGACCTACCACCAGGACTATGAGGTCTGTCGCATACATCATCAAAGCCTGGAAGGCCGATCTATGTAGGGCCTTCTAGTCCAGGAAGCACCTGATTCAAGTCACATGTTCCTTGCCCAAACGTCGACATGGTGTAGCGGGACCTGTGTTATACAAACCATA >5 GCCTGTTAAGGTTCCTCGTCGAAAGCCTGGGGGGGGGTTGTGCTGCCTAAAAACACAATGTTAAGGACTGAATTCTGCCAAGTCACTTTCGCAAAACACTTTGTGTGATGGGACACTTAGTAAAAGGGTAGGCTTCTTGATTCAGAATTACGTCGCTCAATGAGGATTTGCTGGGAGCGAGGAGTGCTGTCTAACCTATT >6 GCCCTCGCTACGTGGCATACCCAGGTTCCTAATACGGTACTTGCGTATTGAAGTCTCAGAATTATCTCCTCTGCTGGCAGGGTCGATCTAGTTCAAGATTAGATAATGGGCAATAGGACGTGTGTGCGGTCCTGACACCTGGCGGTTTGTACAGATAAGACCCTCGCGGGGCGCAGGTGTAGCCTCTCAGCATGTGATAC >7 TTGGAACGCGCTGGTCACGAGAGGGACTATGACTCCCAGCTAATACATGGTCATGAGAGGCGTGTTCCGATAGAGCCGGTGGGGGGTTCCCACGGGTTAAATCGGATGTAAACTGTTGTTGTGATCGTGTCGAGCTATACTAACGTATACAACTTAATAAGGATAATTCCTGTGCCAGATACGGTTACTTGTTGCAGTTG >8 TTTGAATCACCATCCTCTATTACCTACTAAGCTCTTGGCCTGACAAAGCTCAGACCCCGCCGGGCAAAGAATTTCGAGCACTTACGGTACAAATGACCGCATGGAAGGAATCGAAATGAACACACCGGTACATCGACCCGTGACACGTTCTCTCTAGATGTGTAGCTAAGAGCCGGATCGCAATGGCGTCGGGCTCCATC >9 TCCGCCAAGACTACACTTTTATTGCAACGCATCTGTTCGATATAGCCAAGTCCTATGGGACAATCTTCCATCTTATGTTGCGGTTTGTAGCCGTGTCAGGGCATGGGCTGGCTCCCATGTTGAAGATTGCCGGCTTGATCAGTGCGCGTCAACATGAAACGAATTGACCAAATTGTACGCAACAGGCCGTTGATCAACGC >10 GTCTGAGATTTCAATCCGATCTCCACCACAAAATACTACGCCCCCAGGGCCATTTCTCCCTCGATGAAGTGAGCTCTTGTTGTGGTGCTCCATAATGCGTCTTCTTGTAGCAATACTAGCATTCTGTACCCCGGAGAACTACGCTCAGGCTGTATTGTTGCCGGAACGTTAGCAATCTCATACAGGACCAATGTGGAGGC >11 AAATTCATGAGTCATGAAAGCCGGCGCTCCTGAACATGGACGGCTAGTTCCTTCAGTGTTCCGTTCGGCCAACCGCAGTCCAGACCGTCCATGATCTGCTAACAGGTACCTGGTAGGCGTGAGAGTGCTAATCAACATAACTCTCTCCATACCATTCGCCCGGTAACCAGTCTGTACGACTTGTTGAGTACTTCTTGGTG >12 TCCCGACTCCCCGCAGGCAGTGCACGGTGTTAAGCTACGTTCCGCAGACAGTATAATATCAGCCTTAGAAGCCGTGCTTGGTTTCAACTGCCCCAAGTAATCAAGTAGTAACTCGCTGACACGAAGAGGTGCCTGACAATACCCATGTAAGACCTACACCACCTACAGCGTTGCCAAGGTGGTTTGCGAGTGCGTTTCTT >13 TTCCGGGAACCCAAGAACAAAGCTGGAGACACGCTCGTGAGGTCTCTATGAAACAAGTTCTCTCTCGTCAGTAGGTAGGGTTGCCTAAAACCGTACTCTTCCATTATATCCAGGTTTGGTAGACCTACCGGTGGAAAATCATACTAACGACTAATAACTTGGTAGAGTAACCCAATGTGTTACGCGCTTCTTTCGGGGGC >14 TTCAACAGGGGTGGGTGCGCCACATAGCCGCCGCTGCGTTTACGAACTCAACCATGAGATTGACCGAGGCACCGGTTGCTTGGTAAGTTCTCAAGAGAGCCACTATATAGATCTCTTATCGGCGGCGGTCGCGGGGGGGGAACTTTCCCTAACAGGCCTCCTTCCATGATCGTGTACAACGAATCTTGCGTTGCGAGGGG >15 AGAGAGCGGGGGTGTCCGTCGGCCCTTTACCACAGTGCTGCTACTGTATATGTGCCCCTACACTTTGCTGGGGGCTTGTCGCCGGGTAAAAAGAAGAGGCGTTCTACAGTATCACATCGTCGAAGCACTAACAATCCCTTTGGCAGGGTTAGGAGGGCGTTCCGTATGTAAACAGAGATCCGAGTTCGAGAGGTGGACCA >16 TGGGAGGTTTTGCGCCTGTCCGCGTTGGCGTGTAAGATGATTAACGGCGACTTGACAATCTAGCGCTGAGGGGCTGATCTTGGTATCTCGTACGAATGGGCGACGAAAGGCGCCCACATCTCGTAAATCTCGGCGGGTCCCGTTCGGTTAGTACAGAGAAGGACGAATTTGGGTCACTACACGTACTCGTCTCGACGACT >17 CAATTCTTGTTGCGCAACCCGAGTTCGCCCAGTATCCGCATAGGTCTGATTAGAGTAGTAGTGTCTCAGTATCGATCCGTGTAGATCTTTATAGATAGGCAACTGAGAGGACCTAGGCGCAACCAATTGCTACGCTCTCCCTGACACAGACAGAAATTACTCGAACTTCCACGTCGGATGCTACAGAGCTGACAACTAAT >18 GACCTTGAAGCGCAGTCTCCACCATGTGAGGATGTCGAGGCTTGTCTTTTTCTGAGCACCACCGCCGAACGCCGACACGTCGTGTCTGCCATTGCGTTACACATCGGCCCAACAGAGAACTTACATCAGGGTGTTTCCTCAGATACTGCAGGAACTGTCTTGCGCCAACGATAGACGCAAAGAAATCTTGAAACACCGGA >19 CTTGGAACGGCTCACAACAGCTTTTGCGCAACAAGCCTATATAGAATCAATGCCGCCAGGGAGTAAACCGATCCCCGCTTGGCGCATCGCTTACGAGGTCCTGGTCGCGAGGGTCCTGCATTCTAGCGTAAGGAATCCAGCAACTGGTTTGCTTAAGATATACCGTGGACGCGTGATGCAGCAGGACACACTGACAGGGG >20 AAACAGACACTGTTAGCAACCTTTCACGGCGTATGATGACCAGTTCAGCTCCTCCCGGGTTCCGATAGTGTTGAGTAGCCGCGTAGCGGTTGTAGTGGGCTAGTCGTTTACTGATTTGTTTCCCCGAGTGGCTGAGAGAAGAAAAGCTTAGACAGCAGCTGTCGCGGGCTCAGGCTTGCAGGGGGCCCCGATTCACCCAA 

4. (40 points) Probabilistic motif finding.

Utilize the code you wrote for 1 and 2 to implement a Viterbi-like probabilistic motif finding algorithm (i.e., always pick the locations with the best matches to the current PWM as your next set of starting points).