A Phylogenomic Study of Human, Dog, and Mouse

In recent years the phylogenetic relationship of mammalian orders has been addressed in a number of molecular studies. These analyses have frequently yielded inconsistent results with respect to some basal ordinal relationships. For example, the relative placement of primates, rodents, and carnivores has differed in various studies. Here, we attempt to resolve this phylogenetic problem by using data from completely sequenced nuclear genomes to base the analyses on the largest possible amount of data. To minimize the risk of reconstruction artifacts, the trees were reconstructed under different criteria—distance, parsimony, and likelihood. For the distance trees, distance metrics that measure independent phenomena (amino acid replacement, synonymous substitution, and gene reordering) were used, as it is highly improbable that all of the trees would be affected the same way by any reconstruction artifact. In contradiction to the currently favored classification, our results based on full-genome analysis of the phylogenetic relationship between human, dog, and mouse yielded overwhelming support for a primate–carnivore clade with the exclusion of rodents

Minimum Contradiction Matrices in Whole Genome Phylogenies

Minimum contradiction matrices are a useful complement to distance-based phylogenies. A minimum contradiction matrix represents phylogenetic information under the form of an ordered distance matrix Yi, jn. A matrix element corresponds to the distance from a reference vertex n to the path (i, j). For an X-tree or a split network, the minimum contradiction matrix is a Robinson matrix. It therefore fulfills all the inequalities defining perfect order: Yi, jn ≥ Yi,kn, Yk jn ≥ Yk, In, i ≤ j ≤ k < n. In real phylogenetic data, some taxa may contradict the inequalities for perfect order. Contradictions to perfect order correspond to deviations from a tree or from a split network topology. Efficient algorithms that search for the best order are presented and tested on whole genome phylogenies with 184 taxa including many Bacteria, Archaea and Eukaryota. After optimization, taxa are classified in their correct domain and phyla. Several significant deviations from perfect order correspond to well-documented evolutionary events

A Study Of Vantage Point Neighbourhood Search In The Bees Algorithm For Combinatorial Optimization Problems

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2014Thesis (M.Sc. ) -- İstanbul Technical University, Institute of Science and Technology, 2014Bu tez çalışmasının temel amacı arıların kaynak arama davranışlarını modelleyen arı algoritmasının, kombinatoryal uzaylarda komşuluk arama fazına yeni bir yaklaşım geliştirilmesidir. Geliştirilen yaklaşım Gezgin Satıcı Problemine uygulanarak Gezgin Satıcı Problemi çözümünün en iyilenmesi amaçlanmıştır.This thesis focuses on nature-inspired optimisation algorithms, in particular, the Bees Algorithm that developed for combinatorial domains with new local search procedure and applied to Traveller Salesman Problem (TSP). An efficient and robust local neighborhood search algorithm is proposed for combinatorial domains to increase the efficiency of the Bees Algorithm.Yüksek LisansM.Sc

Using traveling salesman problem algorithms for evolutionary tree construction

Motivation: The construction of evolutionary trees is one of the major problems in computational biology, mainly due to its complexity. Results: We present a new tree construction method that constructs a tree with minimum score for a given set of sequences, where the score is the amount of evolution measured in PAM distances. To do this, the problem of tree construction is reduced to the Traveling Salesman Problem (TSP). The input for the TSP algorithm are the pairwise distances of the sequences and the output is a circular tour through the optimal, unknown tree plus the minimum score of the tree. The circular order and the score can be used to construct the topology of the optimal tree. Our method can be used for any scoring function that correlates to the amount of changes along the branches of an evolutionary tree, for instance it could also be used for parsimony scores, but it cannot be used for least squares fit of distances. A TSP solution reduces the space of all possible trees to \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} $2^{n}$ \end{document}. Using this order, we can guarantee that we reconstruct a correct evolutionary tree if the absolute value of the error for each distance measurement is smaller than \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} $\frac{x}{2}$ \end{document}, where \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} $x$ \end{document}is the length of the shortest edge in the tree. For data sets with large errors, a dynamic programming approach is used to reconstruct the tree. Finally simulations and experiments with real data are shown. Availability: The software may be used via our cbrg server at http://cbrg.inf.ethz.ch/MultAlign. Contact: [email protected] Supplementary information: An html and postscript version of this paper is available at http://chantal.nobilitas.com/(Papers section). * To whom correspondence should be addresse