Applications and Algorithms for Inference of Huge Phylogenetic Trees: a Review (original) (raw)

An Integrative Approach for Phylogenetic Inference

2009

Abstract-In the past research efforts on computational phylogenetic analysis were dedicated to the design of heuristics which can quickly find near-optimal trees under a specific optimization criterion. However, all criteria are over-simplified and cannot realistically model the real evolution process. Thus all existing algorithms for phylogenetic analysis have their limitations. It has become a serious issue for many important real-life applications which often demand accurate results from phylogenetic analysis.

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.

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.

Phylogenetic analysis of large sequence data sets

2005

Phylogenetic analysis is an integral part of biological research. As the number of sequenced genomes increases, available data sets are growing in number and size. Several algorithms have been proposed to handle these larger data sets. A family of algorithms known as disc covering methods (DCMs), have been selected by the NSF funded CIPRes project to boost the performance of existing phylogenetic algorithms. Recursive Iterative Disc Covering Method 3 (Rec-I-DCM3), recursively decomposes the guide tree into subtrees, executing a phylogenetic search on the subtree and merging the subtrees, for a set number of iterations. This paper presents a detailed analysis of this algorithm.

Phylogenetic and Phylogenomic Analyses for Large Datasets

Journal of Research and Development on Information and Communication Technology

The phylogenetic tree is a main tool to study the evolutionary relationships among species. Computational methods for building phylogenetic trees from gene/protein sequences have been developed for decades and come of age. Efficient approaches, including distance-based methods, maximum likelihood methods, or classical maximum parsimony methods, are now able to analyze datasets with thousands of sequences. The advanced sequencing technologies have resulted in a huge amount of data including whole genomes. A number of methods have been proposed to analyze the wholegenome datasets, however, numerous challenges need to be addressed and solved to translate phylogenomic inferences into practices. In this paper, we will analyze widely-used methods to construct large phylogenetic trees, and available methods to build phylogenomic trees from whole-genome datasets. We will also give recommendations for best practices when performing phylogenetic and phylogenomic analyses. The paper will enabl...

Coalescent methods for estimating phylogenetic trees

Molecular Phylogenetics and Evolution, 2009

We review recent models to estimate phylogenetic trees under the multispecies coalescent. Although the distinction between gene trees and species trees has come to the fore of phylogenetics, only recently have methods been developed that explicitly estimate species trees. Of the several factors that can cause gene tree heterogeneity and discordance with the species tree, deep coalescence due to random genetic drift in branches of the species tree has been modeled most thoroughly. Bayesian approaches to estimating species trees utilizes two likelihood functions, one of which has been widely used in traditional phylogenetics and involves the model of nucleotide substitution, and the second of which is less familiar to phylogeneticists and involves the probability distribution of gene trees given a species tree. Other recent parametric and nonparametric methods for estimating species trees involve parsimony criteria, summary statistics, supertree and consensus methods. Species tree approaches are an appropriate goal for systematics, appear to work well in some cases where concatenation can be misleading, and suggest that sampling many independent loci will be paramount. Such methods can also be challenging to implement because of the complexity of the models and computational time. In addition, further elaboration of the simplest of coalescent models will be required to incorporate commonly known issues such as deviation from the molecular clock, gene flow and other genetic forces.

Probability distribution of molecular evolutionary trees: A new method of phylogenetic inference

Journal of Molecular Evolution, 1996

A new method is presented for inferring evolutionary trees using nucleotide sequence data. The birth-death process is used as a model of speciation and extinction to specify the prior distribution of phylogenies and branching times. Nucleotide substitution is modeled by a continuous-time Markov process. Parameters of the branching model and the substitution model are estimated by maximum likelihood. The posterior probabilities of different phylogenies are calculated and the phylogeny with the highest posterior probability is chosen as the best estimate of the evolutionary relationship among species. We refer to this as the maximum posterior probability (MAP) tree. The posterior probability provides a natural measure of the reliability of the estimated phylogeny. Two example data sets are analyzed to infer the phylogenetic relationship of human, chimpanzee, gorilla, and orangutan. The best trees estimated by the new method are the same as those from the maximum likelihood analysis of separate topologies, but the posterior probabilities are quite different from the bootstrap proportions. The results of the method are found to be insensitive to changes in the rate parameter of the branching process.

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.