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Please provide a python code that solves this problem. Run your code with the metgenes.fasta file (genes copied below) to get output that look like the one i described. Please, don't give me an explanation of how to do it, I would like code not explanation. I will upvote.
Here is the sequence in the metgenes.fasta file. Test it with your code and if you get an output that looks like this, you're correct.
Output:
SAM2 20.78 TTTTTTT 505
MET30 20.78 TTTTTTT 42
RAD59 20.78 TTTTTTT 15
The word limit is 5000 so I couldn't post all the sequences but you should get an output similar to what's above. Metgenes.fasta Sequence:
>SAM2
TAACACTTACAGAGTCCTATCAGGAAAGAATAAAAGCCGATCAAGCTTCATTCTCAGGTA
ACAACTGGAGCACTGGCGAGCTTTGCTCTATTATATAAGATAAAATATGCACTAAAAGTT
TGCATTTCTTTACATAACTAAAACTAAGACATTATGCATAGCTTACCTGATCAAAAAGTA
TGTAAACTTGTTAACATCTTCACATGTGATTCATCTGGTCGTACTTTCTTGCGGTGCAGT
GTAATATTTCTACCCACGTGACTATAATTGAGCTTGAAAACTGTGGCGTTTTTCCACCGA
TGGGTCCACGCCAGATATTAACCGAAGCCAAAATACCGATGAAATTTCTGAGATAGCTCT
TGTAAACGACGTCAAATCTTCATATGCAAGGAGATCTTGATTTCTTTTTGGTAGTCATCT
GTCGTCTTGAGGCGTATAAGAAGGAGGTTATATCTGTCCTTTCTACAAAGTATTTTCGAG
AATCTTGCTTCTGCCCCTTTTTTCTTTTTTTAAAAGGTTTAAAAAACATAACTGTCTTCA
ATATATCCAGTATTTACGACAATATACAAACATAATC
>MET30
CACGTGATCGGGAAGCCACAGTTTGCGCGGAGATATTTTATTTTTTTTCATCAGCGTAAG
AAGAAAGCAACCTTGCAGTCTGTATCGTAAGAGAAGACTGCAGTTAAAGAAGTTTAGAGA
AGAGGCTTGAGTATCGGTAAAGGGGTGTGTGTTTGGTGATTTATAAAGGAGAAGGGC
>RAD59
TCTTGATAGGAGGGTTGTTATAGATATTAATCACTCGAAGTCGTGAACAAGAATTGTCTT
CTCTCCAGTATTCAGTCTCTGTGATGTCTAAACCCTGTTAATGTGCAAGGTCTATTTTTT
TCGTAAGAACCACGCGCTTTAGGCTTTATTTCTGGTAATAATAGCAAGAAATAAGAACAG
ACAGTTGCAAATCTAAACGACTGTCCAAACGAAAGTATCAAGTTCGCTAAATTTACTTCG
AAGACAGAAGCCAGTAAATTTTGTTTTCTTCATGGAAATAGTTTCCAAAAAGTTCTTAGT
AATTACCATATGTTCTTGTATGTGGCGCTGCGAAAGAAAGGTTAGCCGACCGGCATCACC
CATAATTGTATAATATAGCAATGAAGCAACTTGTTGAAGTTTTCTTTAAAGTACTATAGT
ATTGAATAATATCATGTTCACTTGATAAAATTGGGTATTTTATTGACCATTATATCGCGT
TGGACACTAATGTCTTTCAAGTTGGTGTCACGTCACGTGCTTTTCAATGTACTGGGGCAA
ATTGATTAGAGGAAGCCACAGTTTGGCAAGGGCAGATATGATAGGAAGCAGTAACGGCAA
GGAAGGATAAGAACATCATTGAGGGAGTCTGTGGCAGTTTAGCACATGCTTTGGACCATT
AAAGGGTTACGTAGAGGAGAAGAGCATATTTCAGGATAAACAGACAAAATA
>MET3
AAGAGTACAATTTATAAATTAATGAAAACACAGAAGTATTTAGATCGGCTCAAATGTTTT
TGGACATTAAAAGATCTTGAAACTGAGTAAGATGCTCAGAATACCCGTCAAGATAAGAGT
ATAATGTAGAGTAATATACCAAGTATTCAGCATATTCTCCTCTTCTTTTGTATAAATCAC
GGAAGGGATGATTTATAAGAAAAATGAATACTATTACACTTCATTTACCACCCTCTGATC
TAGATTTTCCAACGATATGTACGTAGTGGTATAAGGTGAGGGGGTCCACAGATATAACAT
CGTTTAATTTAGTACTAACAGAGACTTTTGTCACAACTACATATAAGTGTACAAATATAG
TACAGATATGACACACTTGTAGCGCCAACGCGCATCCTACGGATTGCTGACAGAAAAAAA
GGTCACGTGACCAGAAAAGTCACGTGTAATTTTGTAACTCACCGCATTCTAGCGGTCCCT
GTCGTGCACACTGCACTCAACACCATAAACCTTAGCAACCTCCAAAGGAAATCACCGTAT
AACAAAGCCACAGTTTTACAACTTAGTCTCTTATGAAGTTACTTACCAATGAGAAATAGA
GGCTCTTTCTCGAGAAATATGAATATGGATATATATATATATATATATATATATATATAT
ATATATGTAAACTTGGTTCTTTTTTAGCTTGTGATCTCTAGCTTGGGTCTCTCTCTGTCG
TAACAGTTGTGATATCGTTTCTTAACAATTGAAAAGGAACTAAGAAAGTATAATAATAAC
AAGAATAAAGTATAATTAAC
>MET28
GACTGTGATAATATGCTAGTTACACTGTTTATGTTGTGTGAACTTGTTGTAATATGGTTA
ACTTCACTTTCAGTGATTGATATGATAGCGACATCACTGCCGTGCAAAAAGACCATTCCA
TTACTGCACCTTTTTGTCCTTTTCCGTGGAATAAAAGTTCACTCGTCAGTTCCATGCATT
CTGGAAAAAAATGATCTGAAAGATGCCACAGTTGTGGGGCCCGCCCGGCCCAATAGGTAA
ACTAAAATACAATAGAAGGGGTACTGAGTGCACGTGACTTATTTTTTTTTTTTGGTTTTA
GGTTTCGCTTTTTTCACCTTTTTCTACTTTCTAACACCACAGTTTTGGGCGGGAAGCGGA
AACGCCATAGTTGTAGGTCACTGGCGTGAGTCAAGGCCGGGCAGCCAATGACTAAGAACA
CGAGGTAACTTGAATTTAACTATTTATAACCAGTGGTAGTTACGAAGACAAATTGTTTTG
TTCGTCAAT
>BNA3
TTTTTATCCAGTGAGTTTCCTTGTTCACTTTCGAGAATGGAAGGCTGATTTCGAATTTTT
ATATTCAACAACTGTATTTGTATTAGGCTTTGCAATAGTAGATGAAAAGTGCAGAAGACG
AATCTGCTACCCTCACAATACAAAGAAAAACAAAATCGTCATTTTAATATAGGAAGACAC
CGCTCAGGTAATTCAATATTGAATCTGGTTGCACTTTTCTTACGATGTAAATACCCACAA
ACTTCTTGTACGGTGAGCGTCTGACAACGAAATCAATTTACGGAGCAAGGATTTTGACAG
TAGAGCAGATTGTTTTGAGTAGGATTTAGGAATCAAGACCTCCATCTTTGTCGCATTATT
CCTAAATGTAACGTAACTCGTTTGATAAGAGAATGTCTAATCGAAGAGAGTTAATAACTT
AATAAGCTCTTTAAAAGAACGATGGCATTTATCGTCTCCTATGCCAAGATAATTACTGGC
TCAAAATTGTTCAGCGTTCATAAAACTTTGATATCACTTTCTGGCGCACAAGCTAACCTT
TATGTAGTTCTTACGTAGATTCTTTTTAGCAAGTGCCTGGTAGTGGTTATTACATAAATG
TATCTTTTCATTTGATAACAATTTCTTCAGTAGCATGTCGTGTCTAGCACGTGACGTAGA
ACTGTGGCTTTTTTGTTGTCATTATGACAATCAAGATACCAAAATTCAGTCATGTTTAAA
AGGGGAAGGTACGATAGAGATATATATAAAGTGTTCAATTTACTATAATTGCGTATAGAA
TCCATTGTTACTTGCTCTCA
>GSH1
CAAGGGATTTTATCGGTCAAAGGGGAAATCAATGCGAAAGACAGTAATGATGAGAGAAAA
ACTCTCCGTAACCACCAAGTTTGGTTCAGCGCGACGAGATTTTTATCGATTATCGAGAAA
AATACCTGTATATCTACATTTCTATGTCAGTGATATATACTTCTTAGATAAGTTATGCCA
CCAGTGCATACGCTTACGCACACACACGTATTCTTGTGCACACGCCTGTTACTTCTTGCA
GACATCAGACATACTATTGTAATTCAAAAAAAAAAAGCGAATCTTCCCATGCCTGTTGCT
GCTCTTGAATGGCGACAGCCTATTGCCCCAGTGTTCCCTCAACAACCTTGGTAGTTGGAG
CGCAATTAGCGTATCCTGTACCATACTAATTCTCTTCTGCCCAACGACGGCTGCCATTAG
TCAGCATGGCGCGCACGTGACTACAACTGTGGCTGGAAACCTTTTCGTCCTCCCCGGTTT
TTCAGTGAGCCGACTCTACTACAATGCTTTTTCATTTTTCACTCAGAAAAACCTGCAATT
TGCCAAATTGGCCATGCTCTGTGCCTCCCTTGACAAAGGACATCTTCCCTGTTTATAAAC
GGCGGCTTACCAAAAGTTGAAGCTTGTTCTTGCCTCTTATGAGTGGAGCAATCGATTATA
TTGAATCGTTGTGCTGGAGTAGTTGGATCTTTCCACGTGGTCTCGAGTCACTTGTAGAAG
![Question 5
For each of the 18 sequences in metgenes.fasta, find the best match to the following PWM. Print the gene name, PWM score, sequence and position. For
example, the first two genes should look like this:
SAM2 20.78 TTTTTTT 505
MET30 20.78 TTTTTTT 42
In [13]: # PWM for Question 5
PFM = np.matrix([
[
[
[
30, 10, 10, 10, 10, 10, 30],
30, 10, 10, 10, 10, 10, 30],
30, 10, 10, 10, 10, 10, 30],
[ 30, 90, 90, 90, 90, 90, 30]])
PPM = PFM / np.sum(PFM,axis=0)
PWM = np.log2 (PPM) np.log2 (bj.reshape(4,1))
In [14]: # Answer
#Please note, the output should Look Like this:
SAM2 20.78 TTTTTTT 505
MET30 20.78 TTTTTTT 42
RAD59 20.78 TTTTTTT 15
and it should print about 18 of them.](https://content.bartleby.com/qna-images/question/22bc06e6-76c3-4a59-ac51-4727217bc90e/a65173f6-59c4-4b8c-8538-b2c8a51dc493/nexp8cf_processed.png)
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