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le (6m10s)

2.4. A translation algorithm

We have seen that the genetic codeis a correspondence between the DNA or RNA sequences and aminoacid sequences that is proteins. Our aim here is to design atranslation algorithm, we make thehypothesis that the genetic codehas been implemented as an array as presented in the lastslide of the previous session. We have seen transcriptions and translationsfrom DNA to RNA and proteins. An important thing to notice hereis that most of the time computer scientists and bioinformaticiansjust forget about RNA. When they speak about translating,they say translating from DNA to proteins directly becausethe differences between the DNA and RNA is only T and U sowhat they do is this ...
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le (5m51s)

2.5. Implementing the genetic code

Remember we were designing our translation algorithm and since we are a bit lazy, we decided to make the hypothesis that there was the adequate function forimplementing the genetic code. It's now time to see this lookupfunction but just before that come back on this condition herewhich is a bit more complex than the first attempt in writing the algorithm. Here you see the keyword OR, itmeans that this condition is true if this one is true or thisone is true or this one is true. Why do we need this morecomplex condition? Imagine our sequence and there washere the last triplet we translated. Now we increase our index ...
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le (5m48s)

2.6. Algorithms + data structures = programs

By writing the Lookup GeneticCode Function, we completed our translation algorithm. So we may ask the question about the algorithm, does it terminate? Andthe answer is yes, obviously. Is it pertinent, that is, doesit return the expected answer? The answer is yes, if you giveas an input a sequence of DNA, you will get as an output asequence of amino acids unless, of course, one of the tripletsis not one of the 64 expected triplets and then you will get, ofcourse, a nonsense protein sequence. Is it efficient? Well, for measuring the efficiency of an algorithm, you can ask the question, how manybasic operations you have to execute. In ...
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le (6m58s)

2.7. The algorithm design trade-off

We saw how to increase the efficiencyof our algorithm through the introduction of a data structure. Now let's see if we can do even better. We had a table of index and weexplain how the use of these small arrays allowed us to increase the efficiency that is to reduce the number of comparison to be executed when looking up a triplet in the genetic code. Now what I propose is an alternative to this data structure, it's to compute the indexes. OK. So we have this algorithm which uses here a function. You are now familiar with thisnotion of function, the idea is to fragment the complexity ofan algorithm ...
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le (8m22s)

2.8. DNA sequencing

During the last session, I explained several times how it was important to increase the efficiency of sequences processing algorithm because sequences arevery long and there are large volumes of sequences, so it's now the time to ask: but where these sequences come from? This is the process of sequencing. DNA sequencing is a physical operation through which a DNA molecule is read, that is every nucleotide along the strand of the molecule is read and then a text is producedas a succession of the nucleotides as letters. So from the DNA molecule tothe text through what is a sequencer. Sequencers are smaller and smaller and smaller and they ...
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