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4.1. How to predict gene/protein functions?
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Last week we have seen that annotating a genome means first locating the genes on the DNA sequences that is the genes, the region coding for proteins. But this is indeed the first step,the next very important step is to be able to predict thefunctions of the genes. That is more correctly, the function of the protein coded by the genes. How can we predict thisgene or function protein? It is essentially based on thefact that we will retrieve genes or protein for which the sequenceis similar and for which we know the function. So we will seehow we can measure and compute the similarity between DNA or protein sequences. But first let's come back onthis idea of data basis. We have seen that people from labs can deposit DNA sequences they obtained through sequencing on data banks which can be used by other people. Such databanks are GenBank or EMBL in Europe for DNA sequencing, it's alsoUniprot for protein sequences. The interest, of course, is notonly to deposit a sequence but information we have on the sequence,especially if it is a gene or if it is a protein, what are thefunctions of the genes of protein. These functions can be describedas free text, as commands in free text, by keywords or by morespecific descriptive means like enzymatic classification entries. What does it mean? You remember this organisation of genes to proteins via the RNA. OK. Some of the proteins may be enzymes. What are enzymes? Enzymes are ableto catalyse biochemical reactions so as to accelerate them.
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4.6. A path is optimal if all its sub-paths are optimal
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A sequence alignment between two sequences is a path in a grid. So that, an optimal sequence alignmentis an optimal path in the same grid. We'll see now that a property of this optimal path provides
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4.9. Recursion can be avoided: an iterative version
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We have written a recursive function to compute the optimal path that is an optimal alignment between two sequences. Here all the examples I gave were onDNA sequences, four letter alphabet. OK. The
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4.4. Aligning sequences is an optimization problem
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We have seen a nice and a quitesimple solution for measuring the similarity between two sequences. It relied on the so-called hammingdistance that is counting the number of differencesbetween two
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4.7. Alignment costs
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We have seen how we can compute the cost of the path ending on the last node of our grid if we know the cost of the sub-path ending on the three adjacent nodes. It is time now to see more deeply why
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4.2. Why gene/protein sequences may be similar?
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Before 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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4.10. How efficient is this algorithm?
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We have seen the principle of an iterative algorithm in two paths for aligning and comparing two sequences of characters, here DNA sequences. And we understoodwhy the iterative version is much more
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4.5. A sequence alignment as a path
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Comparing 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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4.8. A recursive algorithm
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We have seen how we can computethe optimal cost, the ending node of our grid if we know the optimal cost of the three adjacent nodes. This is this computation scheme we can see here using the notation
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4.3. Measuring sequence similarity
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So we understand why gene orprotein sequences may be similar. It's because they evolve togetherwith the species and they evolve in time, there aremodifications in the sequence and that the sequence
Avec les mêmes intervenants et intervenantes
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1.2. At the heart of the cell: the DNA macromolecule
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During 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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1.10. Overlapping sliding window
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We 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
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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.
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3.6. Boyer-Moore algorithm
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We 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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4.4. Aligning sequences is an optimization problem
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We have seen a nice and a quitesimple solution for measuring the similarity between two sequences. It relied on the so-called hammingdistance that is counting the number of differencesbetween two
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5.2. The tree, an abstract object
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When we speak of trees, of species,of phylogenetic trees, of course, it's a metaphoric view of a real tree. Our trees are abstract objects. Here is a tree and the different components of this tree.
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1.5. Counting nucleotides
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In 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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2.4. A translation algorithm
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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 the
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3.1. All genes end on a stop codon
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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
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3.9. Benchmarking the prediction methods
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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
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4.8. A recursive algorithm
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We have seen how we can computethe optimal cost, the ending node of our grid if we know the optimal cost of the three adjacent nodes. This is this computation scheme we can see here using the notation
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5.6. The diversity of bioinformatics algorithms
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In this course, we have seen a very little set of bioinformatic algorithms. There exist numerous various algorithms in bioinformatics which deal with a large span of classes of problems. For example,
