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1.1. The cell, atom of the living world
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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 first week, we will speak of genomic texts and we will see how algorithms can deal with these texts. First, we will see what most often is called "the atom of the living world" that is the cell. What is a cell? The first scientist who spoke of the cells was Robert Hooke, in 1667. Robert Hooke saw the walls of the cells, not the cell itself because he studied with a microscope of his own design, a very thin slice of cork. And within this slice, what he did see is this very tiny space.Like this or this. And he decided that this tiny space looked like monk cells and then the term cell remained.
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1.7. DNA walk
RECHENMANN François
We 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
RECHENMANN François
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
RECHENMANN François
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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1.5. Counting nucleotides
RECHENMANN François
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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1.8. Compressing the DNA walk
RECHENMANN François
We 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
RECHENMANN François
Remember 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
RECHENMANN François
We 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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1.9. Predicting the origin of DNA replication?
RECHENMANN François
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
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1.4. What is an algorithm?
RECHENMANN François
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.
Avec les mêmes intervenants et intervenantes
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1.4. What is an algorithm?
RECHENMANN François
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.
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2.2. Genes: from Mendel to molecular biology
RECHENMANN François
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
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2.10. How to find genes?
RECHENMANN François
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
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3.8. Probabilistic methods
RECHENMANN François
Up to now, to predict our gene,we only rely on the process of searching certain strings or patterns. In order to further improve our gene predictor, the idea is to use, to rely onprobabilistic methods
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4.3. Measuring sequence similarity
RECHENMANN François
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
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5.3. Building an array of distances
RECHENMANN François
So using the sequences of homologous gene between several species, our aim is to reconstruct phylogenetic tree of the corresponding species. For this, we have to comparesequences and compute distances
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1.7. DNA walk
RECHENMANN François
We 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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2.6. Algorithms + data structures = programs
RECHENMANN François
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,
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3.3. Searching for start and stop codons
RECHENMANN François
We have written an algorithm for finding genes. But you remember that we arestill to write the two functions for finding the next stop codonand the next start codon. Let's see how we can do that. We
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4.1. How to predict gene/protein functions?
RECHENMANN François
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
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4.10. How efficient is this algorithm?
RECHENMANN François
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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5.7. The application domains in microbiology
RECHENMANN François
Bioinformatics relies on many domains of mathematics and computer science. Of course, algorithms themselves on character strings are important in bioinformatics, we have seen them. Algorithms and
