# Coev-web: a web platform designed to simulate and evaluate coevolving positions along a phylogenetic tree

- Linda Dib
^{1, 2, 4}, - Xavier Meyer
^{1, 2, 3}, - Panu Artimo
^{2}, - Vassilios Ioannidis
^{2}, - Heinz Stockinger
^{2}and - Nicolas Salamin
^{1, 2}Email author

**Received: **4 May 2015

**Accepted: **20 October 2015

**Published: **23 November 2015

## Abstract

### 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.

### Keywords

Nucleotide Amino acid Coevolution Phylogeny Simulator Probabilistic Web-platform## Background

This process of simultaneous evolution has been described in various biological systems and can be an essential process behind changes occurring at the molecular level [1]. Several studies have demonstrated that coevolving sites are critical positions in proteins since they play a role in the folding intermediates [2] and allosteric movements [3–5]. The relevance of these sites has also been shown in disease related protein such as Amyloid beta protein [2]. Moreover coevolving sites play a role in RNA sequences [6, 7] and coevolution is often located on helices that are subject to Watson-Crick constraint (i.e. guanine-cytosine and adenine-thymine complementarity). Several methods have been developed to predict coevolving positions in molecular data [2, 3, 7–10]. However, the full evaluation of the performance of these methods requires large scale simulations and their use is currently impaired by the lack of an appropriate framework to reproduce the process leading to the profiles of coevolution [11]. Indeed, available tools to create in silico nucleotide or amino acid data typically use Markov models to simulate each position independently, which is not appropriate in the case of coevolution [12–15].

We previously developed the Markov model Coev that evaluates the score of coevolution of nucleotide positions using either Maximum Likelihood (ML) or Bayesian inference based on a substitution matrix of size 16×16 [7]. The model describes the transitions between the positions along the branches of a phylogenetic tree and the corresponding profile of coevolution, which represents the set of nucleotides that changed in a coordinated way during sequence evolution.

*Q*where each state represents a combination of sites. The matrix

*Q*contains 4 continuous parameters and a discrete parameter representing the profile

*ϕ*. The ratio

*d*/

*s*indicates the strength of coevolution between a pair of sites. No coevolution is defined when

*d*/

*s*=1, while larger

*d*/

*s*ratios represent stronger coevolution. The parameters

*r*

_{1}and

*r*

_{2}represent the rate of single substitutions for position 1 and position 2, respectively, and they can take arbitrary values when the pair is highly coevolving but will be more accurately estimated when the pair is not coevolving. To assess the coevolution between two sites, we can also calculate a

*Δ*AIC score to compare the likelihood of the the Coev model with the likelihood of an independent model of evolution [16].

The likelihood of the Coev model also depends on the profile of coevolution for the pair of sites. The total number of profiles depends on the alphabet and it equals to 192 in the case of a nucleotide alphabet (size 4). The Coev model estimates the probability of a pair of positions *X* coevolving along a phylogenetic tree with topology *τ* and branch lengths *ν* as described by *P*
*r*
*o*
*b*(*X*|*ϕ*,*s, d,*
*r*
_{1},*r*
_{2},*τ*,*ν*).

For simplicity, we assume that *τ* and *ν* are known and are not estimated [7]. We use Felsenstein’s pruning algorithm [17] to evaluate the likelihood of the model. This is done by calculating, for each branch of a phylogenetic tree, the transition probability matrix *P*(*t*)=*e*
^{
Q
t
}, where the branch length *t* is a finite time interval. Since the matrices size, *n*
^{4}, grows exponentially with the size of the alphabet, the matrix exponentiation requires high performance computing. We therefore implemented the software in C/C++ and used several external tools for matrix exponentiation (Linear Algebra PACKage) and optimisation (nlopt, library for nonlinear optimisation; [7, 18]). These dependencies might be difficult to install for non-expert users. For this reason, we designed a user friendly and publicly available web server to analyse and simulate coevolution in nucleotide sequence data.

In this Software paper, we present two novel extensions of Coev model, that enables the simulation of coevolving pairs of nucleotide or amino acid along a phylogenetic tree. The software is accessible through a web platform, hosted on a high performance computing infrastructure (http://www.vital-it.ch). The user friendly Coev-web platform also allows the user to evaluate the probability of coevolving nucleotide and their respective evolutionary profile based on the aligned sequences and a phylogenetic tree.

## Implementation

### Coev-web platform workflow

*s*,

*d*,

*r*

_{1}and

*r*

_{2}parameters (2) estimate the coevolving score and

*s*,

*d*,

*r*

_{1}and

*r*

_{2}parameters using maximum likelihood or Bayesian framework within DNA sequences.

Different requirements are necessary for each type of experiment as detailed in the Usage paragraph. When the user submits the form, several controls are performed to verify if the form is complete and correctly filled. If this is not the case, an error message is displayed to inform the user about the issue.

When the process is completed, the user receives an e-mail containing the results. For the simulation step, it will be composed of the alignment file with the simulated sequences in FASTA format. For the evaluation step under ML, it will contain the values of the rate parameters that were optimised and the best profile. A *Δ*AIC associated value is also provided to the users as a testing criterion that reflects how coevolving a pair is. The bigger the value is the more reliable the results are [16]. Whereas for the Bayesian evaluation, it will contain a log file readable by the graphical tool for visualisation and diagnostics of MCMC output Tracer [19].

The time to complete the evaluation or simulation runs depends on the size of the phylogenetic tree and other parameters such as the number of iterations, the sampling frequency, etc.

### Usage

- 1.
Upload the rooted binary phylogenetic tree in Newick format

- 2.
Specify the values of the 4 continuous evolutionary rates (

*s*,*d*,*r*_{1}and*r*_{2}) - 3.
Set the number of pairs to simulate under the same coevolving profile

- 4.
Provide an e-mail address

- 1.
Upload the aligned sequences in FASTA format

- 2.
Upload the rooted binary phylogenetic tree in Newick format

- 3.
Specify the inference method: ML or Bayesian

- 4.
Set the positions that will be tested using two input fields

- 5.
Provide an e-mail address

For the Bayesian inference, there are some extra parameters to fill: the number of iterations, the burn-in and the sample frequency. To make things simple, we could consider the Bayesian algorithms as iterative algorithms that repeat themselves several times by changing the model parameters values. The number of times they iterate is defined by the “iterations” value, when the “burn-in” is a term that describes the practice of throwing away the initial iterations before the chain reached the equilibrium representing the posterior distribution. The sampling frequency is the frequency of the algorithm reporting. For example, when the sampling is set to 1,000, the software reports its state every 1,000 iterations. By default we advise the Coev-web platform users to consider 1,000,000 iterations and a burn-in of 1,000 and a sampling frequency of 1,000 for the Bayesian implementation.

### Data curation

During the analysis that evaluates the score of coevolution, we took particular care to check the input file containing the alignment. Since the model cannot consider gaps sites and fully conserved sites, we therefore filter the alignment by removing conserved sites and sites containing gaps. We also remove all sites containing letter that do not belong to the nucleic alphabet {A, C, G, T}. Once processed, the alignment file can be downloaded by the user to validate the filtering.

## Results and discussion

We developed a new and user-friendly web platform, called Coev-web, that provides an easy access to the model described in [7]. We discuss below new extensions to the existing Coev model that enable more generality in the type of data being analysed and propose the first tool to simulate coevolving positions.

Extension1: model generalisation

The original Coev model was created to analyse nucleotide sequences and involved the search through the 192 profiles existing for a nucleotide alphabet [7]. The Coev-web platform provides a user friendly interface to evaluate the score of coevolution using either ML or Bayesian frameworks. We extended the capabilities of the Coev model by increasing the alphabet size of the substitution matrix from *n*=4 to *n*=20 to analyse amino acid sequences. This resulted in a drastic increase of the computational complexity of the analyses. Although the 4 continuous parameters *s*, *d*, *r*
_{1} and *r*
_{2} apply to both data types, the size of the instantaneous rate matrix increases from 16×16 to 400×400, which makes the matrix exponentiation steps required to calculate the likelihood much more computationally demanding. The number of possible profiles also increases drastically since for an alphabet of size *n*, it amounts to \(\sum _{k=2}^{n} \left (\frac {n!}{(n-k)!} \times \frac {1}{k!} \right)^{2}\). For amino acids, the number of profiles to search through is increasing to an order of 10^{21} possible profiles.

The increased complexity of the computations to generalize the Coev model to amino acids requires a high performance computing approach. We therefore implemented the software in C/C++ and used several external tools to speed up the costly matrix exponentiation [7, 20]. The dependencies might be difficult to install for non-expert users. For this reason, we designed the publicly available Coev-web platform that analyses coevolving pairs of positions for nucleotide and amino acid sequences.

*Δ*AIC shows that the Coev model is preferred to the independent model for amino acid especially the number of double substitution is big (Fig. 2). This experiment validates Dib et al. ([7]) assumption using amino acid alphabet and suggests that Coev model can distinguish coevolving from co-inherited pairs.

Extension2: Simulating coevolving pairs

*Q*. The software therefore simulates pairs of positions along each branch of a tree by assigning a state composed of two letters from the given alphabet to the leaves.

*s*,

*d*,

*r*

_{1},

*r*

_{2}) and observed that the proportion of coevolving combinations becomes higher when

*d*is larger than

*s*(Fig. 4). This observation is true regardless of the branch lengths tested. We are therefore able to simulate coevolving and non-coevolving sites by simply changing the values of the

*s*and

*d*parameters.

## Conclusions

Coev-web is the first web platform that gives access to a phylogenetic-based simulator of nucleotide or amino acid coevolving positions. It also provides a way to evaluate the score of coevolution between pairs of positions in a nucleotide or amino acid sequence that can predict coevolving positions and their evolutionary profile based on the aligned sequences and a phylogenetic tree.

## Declarations

### Acknowledgements

The computations are performed at the Vital-IT (http://www.vital-it.ch) Center for high-performance computing of the SIB Swiss Institute of Bioinformatics.

**Open Access** This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

## Authors’ Affiliations

## References

- Gobel U, Sander C, Schneider R, Valencia A. Correlated mutations and residue contacts in proteins. Proteins. 2004; 18:309–17.View ArticleGoogle Scholar
- Dib L, Carbone A. Protein fragments: functional and structural roles of their coevolution networks. PLoS ONE. 2012; 7:e48124.View ArticlePubMedPubMed CentralGoogle Scholar
- Lockless SW, Ranganathan R. Evolutionarily Conserved Pathways of Energetic Connectivity in Protein Families. Science. 1999; 286:295–9.View ArticlePubMedGoogle Scholar
- Baussand J, Carbone A. A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence. Plos Comput Biol. 2009; 5:e1000488.View ArticlePubMedPubMed CentralGoogle Scholar
- Hopf T, Schärfe CI, Rodrigues J, Green A, Kohlbacher O, Sander C, et al. Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife. 2014;3. DOI:10.7554/eLife.03430.
- Dutheil JY, Jossinet F, Westhof E. Base pairing constraints drive structural epistasis in ribosomal RNA sequences. Mol Phylogenet Evol. 2010; 27:1868–76.View ArticleGoogle Scholar
- Dib L, Silvestro D, Salamin N. Evolutionary footprint of coevolving positions in genes. Bioinformatics. 2014; 30(9):1241–9.View ArticlePubMedGoogle Scholar
- Gloor GB, Martin LC, Wahl LM, Dunn SD. Mutual information in protein multiple sequence alignments reveals two classes of coevolving positions. Biochemistry. 2005; 44:7156–65.View ArticlePubMedGoogle Scholar
- Dutheil J, Pupko T, Jean-Marie A, Galtier N. A model-based approach for detecting coevolving positions in a molecule. Mol Phylogenet Evol. 2005; 22:1919–28.View ArticleGoogle Scholar
- Yeang CH, Darot JFJ, Noller HF, Haussler D. Detecting the coevolution of biosequences-an example of RNA interaction prediction. Mol Biol Evol. 2007; 24:2119–31.View ArticlePubMedGoogle Scholar
- Carbone A, Dib L. Coevolution and information signals in biological sequences. Theor Comput Sci. 2011; 412:2486–2495.View ArticleGoogle Scholar
- Arenas M. Simulation of Molecular Data under Diverse Evolutionary Scenarios. PLoS Comput Biol. 2012; 8(5):e1002495.View ArticlePubMedPubMed CentralGoogle Scholar
- Strope CL, Scott SD, Moriyama EN. indel-Seq-Gen: a new protein family simulator incorporating domains, motifs, and indels. Mol Biol Evol. 2007; 24:640–9.View ArticlePubMedGoogle Scholar
- Rambaut A, Grassly NC. Seq-Gen: an application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees. Comput Appl Biosci. 1997; 13:235–8.PubMedGoogle Scholar
- Yang Z. PAML 4: Phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007; 24:1586–91.View ArticlePubMedGoogle Scholar
- Burnham P, Anderson R. Model Selection and Multimodel Inference: a Practical Information-Theoretic Approach. New York: Springer; 2002.Google Scholar
- Felsenstein J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol. 1981; 17:368–76.View ArticlePubMedGoogle Scholar
- Johnson S. The NLopt nonlinear-optimization package. http://ab-initio.mit.edu/nlopt.
- Drummond AJ, Rambaut A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007; 7:214.View ArticlePubMedPubMed CentralGoogle Scholar
- Valle M, Schabauer H, Pacher C, Stockinger H, Stamatakis A, Robinson-Rechavi M, et al.Optimization strategies for fast detection of positive selection on phylogenetic trees. Bioinformatics. 2014; 30:1129–37.View ArticlePubMedPubMed CentralGoogle Scholar