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Locating an ori is key for gene therapy (e.g. viral vectors), to introduce a theraupetic gene. Locating an ori is key for gene therapy (e.g. viral vectors), to introduce a theraupetic gene.
**** Exercises: computational approaches to find ori in Vibrio Cholerae **** Computational approaches to find ori in Vibrio Cholerae
***** Exercise: find Pattern ***** Exercise: find Pattern
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We're going to generate the reverse complement of a sequence, which is the complement of a sequence, read in the same direction (5' -> 3'). We're going to generate the reverse complement of a sequence, which is the complement of a sequence, read in the same direction (5' -> 3').
In this case, we're going to use [[./Code/ReverseComplement.py][ReverseComplement]] In this case, we're going to use [[./Code/ReverseComplement.py][ReverseComplement]]
After using our function on the Vibrio Cholerae's genome, we realize that some of the frequent k-mers are reverse complements of other frequent ones. After using our function on the /Vibrio Cholerae's/ genome, we realize that some of the frequent k-mers are reverse complements of other frequent ones.
***** Exercise: Find a subsequence within a sequence ***** Exercise: Find a subsequence within a sequence
We're going to find the ocurrences of a subsquence inside a sequence, and save the index of the first letter in the sequence. We're going to find the ocurrences of a subsquence inside a sequence, and save the index of the first letter in the sequence.
This time, we'll use [[./Code/PatternMatching.py][PatternMatching]] This time, we'll use [[./Code/PatternMatching.py][PatternMatching]]
After using our function on the Vibrio Cholerae's genome, we find out that the /9-mers/ with the highest frequency appear in cluster. After using our function on the /Vibrio Cholerae's/ genome, we find out that the /9-mers/ with the highest frequency appear in cluster.
This is strong statistical evidence that our subsequences are /DnaA boxes/. This is strong statistical evidence that our subsequences are /DnaA boxes/.
**** Computational approaches to find ori in any bacteria
Now that we're pretty confident about the /DnaA boxes/ sequences that we found, we are going to check if they are a common pattern in the rest of bacterias.
We're going to find the ocurrences of the sequences in /Thermotoga petrophila/ using [[./Code/Replication.py][Replication]]
After the execution, we observe that there are *no* ocurrences of the sequences found in /Vibrio Cholerae/.
We can conclude that different bacterias have different /DnaA boxes/.
We have to try another computational approach then, find clusters of /k-mers/ repeated in a small interval.
*** Vocabulary *** Vocabulary
- k-mer: subsquences of length /k/ in a biological sequence - k-mer: subsquences of length /k/ in a biological sequence
- Frequency map: sequence --> frequency of the sequence - Frequency map: sequence --> frequency of the sequence