Coev-web: a web platform designed to simulate and evaluate coevolving positions along a phylogenetic tree.
- Dib L Department of Ecology and Evolution, University of Lausanne, Lausanne, 1015, Switzerland. linda.dib@unil.ch.
- Meyer X Department of Ecology and Evolution, University of Lausanne, Lausanne, 1015, Switzerland. xavier.meyer@iphc.cnrs.fr.
- Artimo P SIB Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland. Panu.Artimo@isb-sib.ch.
- Ioannidis V SIB Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland. Vassilios.Ioannidis@isb-sib.ch.
- Stockinger H SIB Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland. Heinz.Stockinger@isb-sib.ch.
- Salamin N Department of Ecology and Evolution, University of Lausanne, Lausanne, 1015, Switzerland. nicolas.salamin@unil.ch.
- 2015-11-25
Published in:
- BMC bioinformatics. - 2015
Algorithms
Amino Acids
Bayes Theorem
Computational Biology
Evolution, Molecular
Humans
Internet
Phylogeny
Sequence Analysis, DNA
Software
English
BACKGROUND
Available methods to simulate nucleotide or amino acid data typically use Markov models to simulate each position independently. These approaches are not appropriate to assess the performance of combinatorial and probabilistic methods that look for coevolving positions in nucleotide or amino acid sequences.
RESULTS
We have developed a web-based platform that gives a user-friendly access to two phylogenetic-based methods implementing the Coev model: the evaluation of coevolving scores and the simulation of coevolving positions. We have also extended the capabilities of the Coev model to allow for the generalization of the alphabet used in the Markov model, which can now analyse both nucleotide and amino acid data sets. The simulation of coevolving positions is novel and builds upon the developments of the Coev model. It allows user to simulate pairs of dependent nucleotide or amino acid positions.
CONCLUSIONS
The main focus of our paper is the new simulation method we present for coevolving positions. The implementation of this method is embedded within the web platform Coev-web that is freely accessible at http://coev.vital-it.ch/, and was tested in most modern web browsers.
Available methods to simulate nucleotide or amino acid data typically use Markov models to simulate each position independently. These approaches are not appropriate to assess the performance of combinatorial and probabilistic methods that look for coevolving positions in nucleotide or amino acid sequences.
RESULTS
We have developed a web-based platform that gives a user-friendly access to two phylogenetic-based methods implementing the Coev model: the evaluation of coevolving scores and the simulation of coevolving positions. We have also extended the capabilities of the Coev model to allow for the generalization of the alphabet used in the Markov model, which can now analyse both nucleotide and amino acid data sets. The simulation of coevolving positions is novel and builds upon the developments of the Coev model. It allows user to simulate pairs of dependent nucleotide or amino acid positions.
CONCLUSIONS
The main focus of our paper is the new simulation method we present for coevolving positions. The implementation of this method is embedded within the web platform Coev-web that is freely accessible at http://coev.vital-it.ch/, and was tested in most modern web browsers.
- Language
-
- English
- Open access status
- gold
- Identifiers
-
- DOI 10.1186/s12859-015-0785-8
- PMID 26597459
- Persistent URL
- https://sonar.ch/global/documents/181415
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