MC-Net: a method for the construction of phylogenetic networks based on the Monte-Carlo method

Abstract

BackgroundA phylogenetic network is a generalization of phylogenetic trees that allows the representation of conflicting signals or alternative evolutionary histories in a single diagram. There are several methods for constructing these networks. Some of these methods are based on distances among taxa. In practice, the methods which are based on distance perform faster in comparison with other methods. The Neighbor-Net (N-Net) is a distance-based method. The N-Net produces a circular ordering from a distance matrix, then constructs a collection of weighted splits using circular ordering. The SplitsTree which is a program using these weighted splits makes a phylogenetic network. In general, finding an optimal circular ordering is an NP-hard problem. The N-Net is a heuristic algorithm to find the optimal circular ordering which is based on neighbor-joining algorithm.ResultsIn this paper, we present a heuristic algorithm to find an optimal circular ordering based on the Monte-Carlo method, called MC-Net algorithm. In order to show that MC-Net performs better than N-Net, we apply both algorithms on different data sets. Then we draw phylogenetic networks corresponding to outputs of these algorithms using SplitsTree and compare the results.ConclusionsWe find that the circular ordering produced by the MC-Net is closer to optimal circular ordering than the N-Net. Furthermore, the networks corresponding to outputs of MC-Net made by SplitsTree are simpler than N-Net.

NeighborNet: improved algorithms and implementation

“…Hence, we can infer a circular split network consistently by solving the travelling salesman problem (TSP). This approach was explored by Eslahchi et al (2010), who proposed a simple insertion scheme followed by randomized local search to find an ordering with small total length. One problem in using the TSP to infer the ordering is that it appears highly vulnerable to noise in the distance estimates.…”

Affine and Projective Tree Metric Theorems

“…We do not provide a definition in this paper, but show an example in Figure 2 (drawn using the software SplitsTree4). The neighbor-net [3] and MC-net [13] algorithms provide a way to construct circular split systems from dissimilarity maps, but despite having a number of useful properties [4,22], have not been widely adopted in the phylogenetics community. This is likely because split networks (such as in Figure 2) fail to reveal the "treeness" of the data.…”

QS-Net: Reconstructing Phylogenetic Networks Based on Quartet and Sextet

“…Methods in this category include local maximum likelihood using triplets (Ranwez and Gascuel, 2002), Quartet-Net (Yang et al, 2013), tree with strong combinatorial evidence (Berry and Gascuel, 2000), QNet (Grünewald et al, 2007), SuperQ (Grunewald et al, 2013), DistiQue (Sayyari and Mirarab, 2016), level 1 network from a dense quartet (Keijsper and Pendavingh, 2014), and weighted QMC (Avni et al, 2015). In addition, there are other methods using statistical models such as stochastic local search method (Tria et al, 2010), clusters (Van Iersel et al, 2010), Bayesian inference (Zhang et al, 2017), statistical model (Pickrell and Pritchard, 2012), and Monte Carlo method (Eslahchi et al, 2010).…”