An Integrative Approach for Phylogenetic Inference (original) (raw)

Applications and Algorithms for Inference of Huge Phylogenetic Trees: a Review

American Journal of Bioinformatics Research, 2012

Phylogenetics enables us to use various techniques to extract evolutionary relationships from sequence analysis. Most of the phylogenetic analysis techniques produce phylogenetic trees that represent relationship between any set of species or their evolutionary history. This article presents a comprehensive survey of the applications and the algorithms for inference of huge phylogenetic trees and also gives the reader an overview of the methods currently employed for the inference of phylogenetic trees. A comprehensive comparison of the methods and algorithms is presented in this paper.

A Review on Phylogenetic Analysis: A Journey through Modern Era

Phylogenetic analysis may be considered to be a highly reliable and important bioinformatics tool. The importance of phylogenetic analysis lies in its simple manifestation and easy handling of data. The simple tree representation of the evolution makes the phylogenetic analysis easier to comprehend and represent as well. The varied applications of phylogenetics in different fields of biology make this analysis an absolute necessity. The different aspects of phylogenetic analysis have been described in a comprehensive manner. This review may be useful to those who would like to have a firsthand knowledge of phylogenetics.

Phylogenetic inference using molecular data

2009

We review phylogenetic inference methods with a special emphasis on inference from molecular data. We begin with a general comment on phylogenetic inference using DNA sequences, followed by a clear statement of the relevance of a good alignment of sequences. Then we provide a general description of models of sequence evolution, including evolutionary models that account for rate heterogeneity along the DNA sequences or complex secondary structure (i.e., ribosomal genes). We then present an overall description of the most relevant inference methods, focusing on key concepts of general interest. We point out the most relevant traits of methods such as maximum parsimony (MP), distance methods, maximum likelihood (ML) and Bayesian inference (BI). Finally, we discuss different measures of support for the estimated phylogeny and discuss how this relates to confidence in particular nodes of a phylogeny reconstruction.

A Detailed Survey on Approaches of Phylogenetic Analysis

All organisms have evolved from a common ancestor. The distance between these species is measured using phylogenetic analysis. It enables us to extract evolutionary relationship from sequence analysis. These relationships are depicted on phylogenetic trees. This article provides a detailed survey on different sequential approaches of sequential alignment, clustering and complete details of how a mapreduce technology improves the performance of phylogenetic analysis. A comprehensive comparison of these methods is presented in this paper.

On the maximum likelihood method in molecular phylogenetics

Journal of Molecular Evolution, 1991

The efficiency of obtaining the correct tree by the maximum likelihood method (Felsenstein 1981) for inferring trees from DNA sequence data was compared with trees obtained by distance methods. It was shown that the maximum likelihood method is superior to distance methods in the efficiency particularly when the evolutionary rate differs among lineages.

A novel quartet-based method for phylogenetic inference

2005

In this paper we introduce a new quartet-based method. This method makes use of the Bayes (or quartet) weights of quartets as those used in the quartet puzzling. However, all the weights from the related quartets are accumulated to form a global quartet weight matrix. This matrix provides integrated information and can lead us to recursively merge small sub-trees to larger ones until the final single tree is obtained. The experimental results show that the probability for the correct tree to be among a very small number of trees constructed using our method is very high. These significant results open a new research direction to further investigate more efficient algorithms for phylogenetic inference. 1.

SAWSA-LPR: Astochastic search strategy for estimation of maximum likelihood DNA phylogenetic trees

Applied Soft Computing, 2014

In the spirit of the "grand challenge", this paper covers the development of novel concepts for inference of large phylogenies based on the maximum likelihood method, which has proved to be the most accurate model for inference of huge and complex phylogenetic trees. Here, a novel method called Leaf Pruning and Re-grafting (LPR) has being presented, which is a variant of standard Sub-tree Pruning and Re-grafting (SPR) technique. LPR is a systematic approach where only unique topologies are generated at each step. Various stochastic search strategies for estimation of the maximum likelihood (ML) tree have also being proposed. Here, simulated annealing has been combined with steepest accent method to improve the quality of the final tree obtained. All the current simulated annealing approaches are used with simple hill climbing method to avoid the large number of repeated topologies that are normally generated by SPR. This easily leads to local maxima. However in the present study steepest accent with simulated annealing by way of LPR (SAWSA-LPR) has being used; the chances of returning local maxima has being significantly reduced. A straightforward and efficient parallel version of simulated annealing with steepest accent to accelerate the process of DNA phylogenetic tree inference has also being presented. It was observed that the implementation of the algorithm based on random DNA sequences gave better results as compared to other tree construction methods.