RESUMEN
Maximum likelihood (ML) is a widely used criterion for selecting optimal evolutionary trees. However, the nature of the likelihood surface for trees is still not sufficiently understood, especially with regard to the frequency of multiple optima. Here, we initiate an analytic study for identifying sequences that generate multiple optima. We concentrate on the problem of optimizing edge weights for a given tree or trees (as opposed to searching through the space of all trees). We report a new approach to computing ML directly, which we have used to find large families of sequences that have multiple optima, including sequences with a continuum of optimal points. Such data sets are best supported by different (two or more) phylogenies that vary significantly in their timings of evolutionary events. Some standard biological processes can lead to data with multiple optima, and consequently the field needs further investigation. Our results imply that hill-climbing techniques as currently implemented in various software packages cannot guarantee that one will find the global ML point, even if it is unique.
Asunto(s)
Evolución Molecular , Funciones de Verosimilitud , Modelos Teóricos , Filogenia , Análisis de Secuencia/métodosRESUMEN
Radiation hybrid (RH) mapping is a somatic cell technique that is used for ordering markers along a chromosome and estimating the physical distances between them. With the advent of this mapping technique, analyzing the experimental data is becoming a challenging and demanding computational task. In this paper we present the software package RHO (radiation hybrid ordering). The package implements a number of heuristics that attempt to order genomic markers along a chromosome, given as input the results of an RH experiment. The heuristics are based on reducing an appropriate optimization problem to the traveling salesman problem (TSP). The reduced optimization problem is either the nonparametric obligate chromosome breaks (OCBs) or the parametric maximum likelihood estimation (MLE). We tested our package on both simulated and publicly available RH data. For synthetic RH data, the reconstructed markers' permutation is very close to the original permutation, even with fairly high error rates. For real data we used the framework markers' data from the Whitehead Institute maps. For most of the chromosomes (18 out of 23), there is a perfect agreement or nearly perfect agreement (reversal of chromosome arm or arms) between our maps and the Whitehead framework maps. For the remaining five chromosomes, our maps improve on the Whitehead framework maps with respect to both optimization criteria, having higher likelihood and fewer breakpoints. For three chromosomes, the results differ significantly (lod score >1.75), with chromosome 2 having the largest improvement (lod score 3.776).
Asunto(s)
Mapeo Cromosómico/métodos , Biología Computacional/métodos , Simulación por Computador , Programas Informáticos , Algoritmos , Rotura Cromosómica , Mapeo Cromosómico/instrumentación , Mapeo Cromosómico/estadística & datos numéricos , Marcadores Genéticos , Humanos , Células Híbridas , Funciones de VerosimilitudRESUMEN
In this work we present two new approaches for constructing phylogenetic trees. The input is a list of weighted quartets over n taxa. Each quartet is a subtree on four taxa, and its weight represents a confidence level for the specific topology. The goal is to construct a binary tree with n leaves such that the total weight of the satisfied quartets is maximized (an NP hard problem). The first approach we present is based on geometric ideas. Using semidefinite programming, we embed the n points on the n-dimensional unit sphere, while maximizing an objective function. This function depends on Euclidean distances between the four points and reflects the quartet topology. Given the embedding, we construct a binary tree by performing geometric clustering. This process is similar to the traditional neighbor joining, with the difference that the update phase retains geometric meaning: When two neighbors are joined together, their common ancestor is taken to be the center of mass of the original points. The geometric algorithm runs in poly(n) time, but there are no guarantees on the quality of its output. In contrast, our second algorithm is based on dynamic programming, and it is guaranteed to find the optimal tree (with respect to the given quartets). Its running time is a modest exponential, so it can be implemented for modest values of n. We have implemented both algorithms and ran them on real data for n = 15 taxa (14 mammalian orders and an outgroup taxon). The two resulting trees improve previously published trees and seem to be of biological relevance. On this dataset, the geometric algorithm produced a tree whose score is 98.2% of the optimal value on this input set (72.1% vs. 73.4%). This gives rise to the hope that the geometric approach will prove viable even for larger cases where the exponential, dynamic programming approach is no longer feasible.
Asunto(s)
Algoritmos , Filogenia , Biología ComputacionalRESUMEN
Radiation hybrid (RH) mapping is a somatic cell method for obtaining ordering information of markers on a chromosome, using relatively few experiments. Given the results of a typical RH experiment, finding the true order of the markers is a challenging algorithmic problem. In this work we present several simple algorithms for ordering and mapping the markers, where the input is the genomic data obtained from RH experiments. We provide a rigorous analysis of these algorithms. In particular, we show that under the standard statistical model for RH, our algorithms are "statistically consistent." That is, given enough hybrids, the algorithms do reconstruct the true markers' order (with high probability). We also prove a simple lower bound for the number of hybrids required (by any algorithm) to correctly reconstruct the order. We have implemented these algorithms, and tested them on synthetic and real data. These simulations show that for practical input sizes (number of markers and hybrids) our algorithms produce outputs that are very close to the true ordering. The simulations also indicate that the true ordering of the markers is usually not the one which minimizes the number of obligate chromosome breaks.