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le (5m42s)

2.1. The sequence as a model of DNA

Welcome back to our course on genomes and algorithms that is a computer analysis ofgenetic information.Last week we introduced the very basic concept in biology that is cell, DNA, genome, genes,proteins and this week we will concentrate on these last two concepts, that is genes and proteins and we will see how proteins are coded by genes and what is the process of translating genes into proteins. Then we will design some algorithms for this translation. But first let's come back on the idea that the sequence, that is the string of characters returning these four letters of the alphabet A, C, G and T is a model ...
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le (4m59s)

2.2. Genes: from Mendel to molecular biology

The notion of gene emerged withthe works of Gregor Mendel. Mendel studied the inheritance on some traits like the shape of pea plant seeds,through generations. He stated the famous laws of inheritance which, by the way, were rediscovered 50 years later. The important thing here tounderline is that these concepts of inheritance of genes and so on were very abstract. No physical supports ofthese genes were clarified. So, it's something which appearedlater through molecular biology. We now know that genes are thoseregions of DNA which code the information used by the cell to produce proteins. And, this is what Francis Crick stated as the central dogma of molecular biology. One gene ...
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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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le (5m54s)

2.3. The genetic code

Genes code for proteins. What is the correspondence betweenthe genes, DNA sequences, and the structure of proteins? The correspondence isthe genetic code. Proteins have indeedsequences of amino acids. There are 20 amino acidsin the living world. They can be named by a single letter,3 letters or their full name. It means that a protein can berepresented by a sequence of letters in a 20 letter alphabet. Let's come back again on thiscorrespondence between gene and protein. Genes are regions of DNA. These regions are first transcribedinto RNA and then RNA into proteins. And proteins’ sequences of aminoacids fold into 3D structures. Like here, some helixes. Translation is the process whichgoes from RNA to ...
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le (4m55s)

2.9. Whole genome sequencing

Sequencing is anexponential technology. The progresses in this technologyallow now to a sequence whole genome, complete genome. What does it mean? Well let'stake two examples: some twenty years ago, to sequence the bacillus subtilis bacteria genome took something like ten years,thirty five laboratories and several millions of euros. It was partly a European project,now some hundreds of dollars and it can be done within a day. The human genome project, famoushuman genome project, more than ten years, three billiondollars, 19-91 dollars OK.Tomorrow certainly less than 1000 dollars per genome, it means that we can now sequence humangenomes, not one but many many human genomes for the sake ofcomparison, diagnosis and so ...
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le (5m38s)

2.10. How to find genes?

Getting the sequence of the genome is only the beginning, as I explained, once you have the sequence what you want to do is to locate the gene, to predict the function of the gene and maybe study the interaction between genes and proteins. Let's concentrate on the prediction of genes on a genome. How can we find genes using,of course, algorithms? That's what we call genome annotation, the prediction of gene location and the prediction ofthe function of the genes, of the protein coded by the genes. A typical bacterial genome like the E. coli genome is four by five megabases and is the support of 4,500 genes. A ...
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