74,638 research outputs found
Fast neighbor joining
AbstractReconstructing the evolutionary history of a set of species is a fundamental problem in biology and methods for solving this problem are gaged based on two characteristics: accuracy and efficiency. Neighbor Joining (NJ) is a so-called distance-based method that, thanks to its good accuracy and speed, has been embraced by the phylogeny community. It takes the distances between n taxa and produces in Θ(n3) time a phylogenetic tree, i.e., a tree which aims to describe the evolutionary history of the taxa. In addition to performing well in practice, the NJ algorithm has optimal reconstruction radius.The contribution of this paper is twofold: (1) we present an algorithm called Fast Neighbor Joining (FNJ) with optimal reconstruction radius and optimal run time complexity O(n2) and (2) we present a greatly simplified proof for the correctness of NJ. Initial experiments show that FNJ in practice has almost the same accuracy as NJ, indicating that the property of optimal reconstruction radius has great importance to their good performance. Moreover, we show how improved running time can be achieved for computing the so-called correction formulas
Why neighbor-joining works
We show that the neighbor-joining algorithm is a robust quartet method for constructing trees from distances. This leads to a new performance guarantee that contains Atteson's optimal radius bound as a special case and explains many cases where neighbor-joining is successful even when Atteson's criterion is not satisfied. We also provide a proof for Atteson's conjecture on the optimal edge radius of the neighbor-joining algorithm. The strong performance guarantees we provide also hold for the quadratic time fast neighbor-joining algorithm, thus providing a theoretical basis for inferring very large phylogenies with neighbor-joining
Circular Networks from Distorted Metrics
Trees have long been used as a graphical representation of species
relationships. However complex evolutionary events, such as genetic
reassortments or hybrid speciations which occur commonly in viruses, bacteria
and plants, do not fit into this elementary framework. Alternatively, various
network representations have been developed. Circular networks are a natural
generalization of leaf-labeled trees interpreted as split systems, that is,
collections of bipartitions over leaf labels corresponding to current species.
Although such networks do not explicitly model specific evolutionary events of
interest, their straightforward visualization and fast reconstruction have made
them a popular exploratory tool to detect network-like evolution in genetic
datasets.
Standard reconstruction methods for circular networks, such as Neighbor-Net,
rely on an associated metric on the species set. Such a metric is first
estimated from DNA sequences, which leads to a key difficulty: distantly
related sequences produce statistically unreliable estimates. This is
problematic for Neighbor-Net as it is based on the popular tree reconstruction
method Neighbor-Joining, whose sensitivity to distance estimation errors is
well established theoretically. In the tree case, more robust reconstruction
methods have been developed using the notion of a distorted metric, which
captures the dependence of the error in the distance through a radius of
accuracy. Here we design the first circular network reconstruction method based
on distorted metrics. Our method is computationally efficient. Moreover, the
analysis of its radius of accuracy highlights the important role played by the
maximum incompatibility, a measure of the extent to which the network differs
from a tree.Comment: Submitte
Using Avida to test the effects of natural selection on phylogenetic reconstruction methods
Phylogenetic trees group organisms by their ancestral relationships. There are a number of distinct algorithms used to reconstruct these trees from molecular sequence data, but different methods sometimes give conflicting results. Since there are few precisely known phylogenies, simulations are typically used to test the quality of reconstruction algorithms. These simulations randomly evolve strings of symbols to produce a tree, and then the algorithms are run with the tree leaves as inputs. Here we use Avida to test two widely used reconstruction methods, which gives us the chance to observe the effect of natural selection on tree reconstruction. We find that if the organisms undergo natural selection between branch points, the methods will be successful even on very large time scales. However, these algorithms often falter when selection is absent
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