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le (5m25s)

1.1. The cell, atom of the living world

Welcome to this introduction to bioinformatics. We will speak of genomes and algorithms. More specifically, we will see how genetic information can be analysed by algorithms. In these five weeks to come, we will see first, what are these genomic texts, we will try to analyse using algorithms and programs. We will then speak of genes and proteins. Proteins being coded by genes. We will study and design algorithms to predict genes on the DNA sequences or genomic texts. We will study, more deeply, an algorithm to compare genomic sequences. And we will use this algorithm to reconstruct phylogenetic trees that is to say the evolution of species over time. During this ...
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le (5m36s)

3.9. Benchmarking the prediction methods

It is necessary to underline that gene predictors produce predictions. Predictions mean that you have no guarantees that the coding sequences, the coding regions,the genes you get when applying your algorithm, are true genes, thatis genes which have a biological existence. Only experimental analysiscan confirm or infirm your predictions. Nevertheless it is interesting and also important to be able to evaluate your algorithm, thisis the role of benchmarking. Benchmarking means measuring the capacity of your algorithm to produce good predictions. How can we make thiskind of measurement? We need a reference, an idealreference would be a genome which is well annotated and for whichall of the annotations have been confirmed through ...
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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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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 (5m42s)

3.1. All genes end on a stop codon

Last week we studied genes and proteins and so how genes, portions of DNA, are translated into proteins.We also saw the very fast evolutionof the sequencing technology which allows for producing large genomic texts, it is now possible to sequence a whole genome. But it is just thebeginning of the story. The challenge to come is to analysethe texts of these genomes and find genes, so this week wewill see how we can design avery first algorithm for predicting genes on a bacterial genome. We will first remember the conditionfor finding genes, we will design and propose an algorithmfor that and we will see that a part of ...
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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 (5m49s)

1.4. What is an algorithm?

We have seen that a genomic textcan be indeed a very long sequence of characters. And to interpret this sequence of characters, we will need to use computers. Using computers means writing program. Writing program means designing first algorithm. So, let's see what an algorithm is. An algorithm is a series of operationsto be executed by a computer, but maybe also executed by ahuman, for solving a problem.  In the first algorithm we will study in this session and next one, the problem will be to count the number of different of the four different nucleotides which appeared in the sequence. It's a sequence of operations. You may say that in ...
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