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1.9. Predicting the origin of DNA replication?
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Descriptif
We have seen a nice algorithm to draw, let's say, a DNA sequence. We will see that first, we have to correct a little bit this algorithm. And then we will see how such as imple algorithm can provide biological results. Again, this is the aim of bioinformatics: analysing the genomic texts and providing biological results. So, you remember that we had to deal with the problem of the screen size and for that, we decided to change the first version of the algorithm and we introduced a window of fixed length. And we get this algorithm. So, in this algorithm, we repeat the analysis within the window and we make the window progress along the sequence. A problem we didn't deal within this first version is that there is no reason why the lengthof the sequence would be a multiple of the length of the window. It means that it is possible we arrive at this situation. You see here that the window islonger than the last part of the sequence we have to take into account. So, we have to modify a little bit a condition here to take care of the fact that, if we have this case, we have to stop there because here, we don't have any character to take into account in the lastpart of the window.
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1.4. What is an algorithm?
RechenmannFrançoisWe 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.
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1.7. DNA walk
RechenmannFrançoisWe will now design a more graphical algorithm which is called "the DNA walk". We shall see what does it mean "DNA walk". Walk on to DNA. Something like that, yes. But first, just have a look again at
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1.2. At the heart of the cell: the DNA macromolecule
RechenmannFrançoisDuring the last session, we saw how at the heart of the cell there's DNA in the nucleus, sometimes of cells, or directly in the cytoplasm of the bacteria. The DNA is what we call a macromolecule, that
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1.5. Counting nucleotides
RechenmannFrançoisIn this session, don't panic. We will design our first algorithm. This algorithm is forcounting nucleotides. The idea here is that as an input,you have a sequence of nucleotides, of bases, of letters,
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1.8. Compressing the DNA walk
RechenmannFrançoisWe have written the algorithm for the circle DNA walk. Just a precision here: the kind of drawing we get has nothing to do with the physical drawing of the DNA molecule. It is a symbolic
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1.3. DNA codes for genetic information
RechenmannFrançoisRemember at the heart of any cell,there is this very long molecule which is called a macromolecule for this reason, which is the DNA molecule. Now we will see that DNA molecules support what is called
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1.6. GC and AT contents of DNA sequence
RechenmannFrançoisWe have designed our first algorithmfor counting nucleotides. Remember, what we have writtenin pseudo code is first declaration of variables. We have several integer variables that are variables which
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1.1. The cell, atom of the living world
RechenmannFrançoisWelcome 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
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1.10. Overlapping sliding window
RechenmannFrançoisWe have made some drawings along a genomic sequence. And we have seen that although the algorithm is quite simple, even if some points of the algorithmare bit trickier than the others, we were able to
Avec les mêmes intervenants et intervenantes
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1.1. The cell, atom of the living world
RechenmannFrançoisWelcome 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
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1.10. Overlapping sliding window
RechenmannFrançoisWe have made some drawings along a genomic sequence. And we have seen that although the algorithm is quite simple, even if some points of the algorithmare bit trickier than the others, we were able to
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2.3. The genetic code
RechenmannFrançoisGenes 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.
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3.6. Boyer-Moore algorithm
RechenmannFrançoisWe have seen how we can make gene predictions more reliable through searching for all the patterns,all the occurrences of patterns. We have seen, for example, howif we locate the RBS, Ribosome
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4.5. A sequence alignment as a path
RechenmannFrançoisComparing two sequences and thenmeasuring their similarities is an optimization problem. Why? Because we have seen thatwe have to take into account substitution and deletion. During the alignment, the
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5.5. Differences are not always what they look like
RechenmannFrançoisThe algorithm we have presented works on an array of distance between sequences. These distances are evaluated on the basis of differences between the sequences. The problem is that behind the
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1.4. What is an algorithm?
RechenmannFrançoisWe 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.
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2.4. A translation algorithm
RechenmannFrançoisWe 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 the
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3.1. All genes end on a stop codon
RechenmannFrançoisLast 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
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3.9. Benchmarking the prediction methods
RechenmannFrançoisIt 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
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4.2. Why gene/protein sequences may be similar?
RechenmannFrançoisBefore measuring the similaritybetween the sequences, it's interesting to answer the question: why gene or protein sequences may be similar? It is indeed veryinteresting because the answer is related
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5.4. The UPGMA algorithm
RechenmannFrançoisWe know how to fill an array with the values of the distances between sequences, pairs of sequences which are available in the file. This array of distances will be the input of our algorithm for
