Bioinformatics: Different methods used to build phylogenetic trees

Bioinformatics: Different methods used to build phylogenetic trees

In Bioinformatics there are three major methods used in building phylogenetic trees, every one of these methods have its own weaknesses and strengths as the case with every bioinformatics program or method.

These methods are:

1- Distance methods: In this method the algorithm takes the data (sequences) and construct a distance matrix between each 2 sequences, after that the sequences are regrouped depending on their relative distance, the last step is to construct a tree that matches this data.

2- Parcimony methods: This method searches in all possible phylogenetic trees that needs the minimum number of substitutions of nucleic acids or amino acids (mutations), so the best tree is the one that have the minimum number of mutations.

3- Likelihood methods: This method means that the best estimate of a parameter is that giving the highest probability that the observed set of measurements will be obtained.

Bioinformaticians say that Likelihood methods are the most accurate and the best, because most researchers use them, but the problem is that they run very slow because of their long algorithms.

Parcimony methods have great results but they have probably the same negative side of Likelihood methods.

Distance methods or distance based trees are easy to set up, and you can apply them in most situations, but they aren't necessarily the most accurate.



How to prepare your sequences for a phylogenetic tree

What Phylogenetic Trees can do for you?



Any question comment.

Bioinfrmatics:video tutorial:using Genomescan to parse genomes (find exons)

Bioinfrmatics:Video Tutorial:using Genomescan to parse genomes (find exons)

In this video tutorial we are going to see how to use Genomescan to parse large DNA sequences and find coding regions or Exons.

As you know higher organisms genes like vertebrates are more complex then others, because they contain coding regions called Exons and between these Exons we find non coding regions called Introns.

To predict these genes which contain several Exons, you have to use a very sophisticated algorithms, that can locate Exons and Introns and by that locating genes.

You can read this post (Open Reading Frame (ORF)) to understand what are ORFs.

You  can read this post (Using ORF Finder to locate open reading frames) for a basic software that can find ORFs.

You can read this post (Sophisticated ORF prediction with GenMark) for a more sophisticated ORF prediction software.