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Proceedings ArticleDOI

Reconstructing phylogenetic network with ReTF algorithm (rearranging transcriptional factor)

01 Nov 2013-pp 1-4
TL;DR: The idea behind ReTF is rearranging the input sequences in a way that the new arrangement gives a better tree, since the order of input sequences affects the outcomes of phylogenetic network.
Abstract: The term Phylogentics is the study of evolutionary relationship between different species, organisms or genes. These relationships are depicted as branched, tree like diagrams that provide insight into the events that occurred during the evolution process. These trees may also have a root which is known as the common ancestor. Building the “Tree of Life” has been the prime objective of many researchers, until it was proved that the tree of life cannot be represented by a single? tree. Many evolutionary events cannot be represented with the help of a simple tree, hence phylogenetic networks came into picture. Phylogenetic networks can be classified into different categories. In this paper, an algorithm (ReTF) has been proposed which would improve the results of the current phylogenetic network reconstruction algorithms. The idea behind ReTF is rearranging the input sequences in a way that the new arrangement gives a better tree, since the order of input sequences affects the outcomes of phylogenetic network.
Citations
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Journal ArticleDOI
TL;DR: The newly designed D-Phylo is a more advanced algorithm for phylogenetic analysis using maximum likelihood approach and has the capacity to accomplish a near direct speed if there should arise an occurrence of huge number of processors.
Abstract: This study explains a newly developed parallel algorithm for phylogenetic analysis of DNA sequences. The newly designed D-Phylo is a more advanced algorithm for phylogenetic analysis using maximum likelihood approach. The D-Phylo while misusing the seeking capacity of k-means keeps away from its real constraint of getting stuck at privately conserved motifs. The authors have tested the behaviour of D-Phylo on Amazon Linux Amazon Machine Image(Hardware Virtual Machine)i2.4xlarge, six central processing unit, 122 GiB memory, 8 × 800 Solid-state drive Elastic Block Store volume, high network performance up to 15 processors for several real-life datasets. Distributing the clusters evenly on all the processors provides us the capacity to accomplish a near direct speed if there should arise an occurrence of huge number of processors.

4 citations

Proceedings ArticleDOI
27 Oct 2014
TL;DR: A new algorithmic rule first rudiment to spot sample set, provides a promising new model for the strong reasoning of substructure and ancestry of wildlife trade and establishes a clear evolutionary connection among many different problem sets.
Abstract: There has been continuous development in the wildlife DNA forensics research that relied on the collection and analysis of the biological samples over the past many years. But there is not enough progress to develop computational algorithms which could make the process of finding the origin of species easier and faster. Computational algorithms based on phylogenetic networks are capable of providing evidence to assist in wildlife law enforcement and species conversation. Our new algorithmic rule first rudiment to spot sample set, provides a promising new model for the strong reasoning of substructure and ancestry of wildlife trade. Our findings establish a clear evolutionary connection among many different problem sets.

3 citations

Journal ArticleDOI
TL;DR: The most conserved protein motifs exist at the roots of the system, whereas newer motifs with mutations have a tendency to dwell on the branches, which demonstrates several important aspects for future studies focusing to enhance phylogenetic profiling systems.
Abstract: In this study, a novel substitution method for finding potential protein-protein interactions (PPIs) has been discussed. This newly designed method for analyzing PPI also aids in the comparison of evolutionary distances. The method deals with various data sets, and additionally performs measurable assessment to determine PPIs is introduced. PPIs are biologically relevant and aid in better conceptual framework of phylogenetic profiling. The newly designed framework gives vision to relate the topological properties of the system with evolutionary behavior of datasets. Firstly, this study found that the most conserved protein motifs exist at the roots of the system, whereas newer motifs with mutations have a tendency to dwell on the branches. In-depth functional analysis revealed that the most conserved motifs have high specificity for improved structural procedures and pathway engagements, which may help identify their formative parts in cells. In conclusion, this study demonstrates several important aspects for future studies focusing to enhance phylogenetic profiling systems. This study can also be used effectively to utilize such strategies to develop new biological insights which will further lead to understanding of disease mechanisms.

1 citations

Book ChapterDOI
01 Jan 2018
TL;DR: The objective of this paper is to test and analyse the transmission of commonly occurring diseases to fit into more realistic models and to know how they came into existence and how they migrated, helpful for the treatment of such diseases and drug discovery.
Abstract: It is generally believed that the existence of all organisms present on this earth has their point of convergence in a common gene pool. The current species passed through an evolutionary process which is still underway. The theoretical assumptions relating to the common descent of all organisms are based on four simple facts: first, they had wide geographical dispersal; second, the different life forms were not remarkably unique and did not possess mutually exclusive characteristics; third, some of their attributes which apparently served no purpose had an uncanny similarity with some of their lost functional traits; and last, based on their common attributes these organisms can be put together into a well-defined, hierarchical and coherent group, like a family tree. Phylogenetic networks are the main tools that can be used to represent biological relationship between different species. Biologists, mathematicians, statisticians, computer scientists and others have designed various models for the reconstruction of evolutionary networks and developed numerous algorithms for efficient predictions and analysis. Even though these problems have been studied for a very long time, but the computational model built to solve the biological problems fail to give accurate results while working on real biological data, which could be due to the premises on which the model is based. The objective of this paper is to test and analyse the transmission of commonly occurring diseases to fit into more realistic models. The problems are not only important because we need to know how they came into existence and how they migrated, but also helpful for the treatment of such diseases and drug discovery.
References
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Journal ArticleDOI
TL;DR: The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models, inferring ancestral states and sequences, and estimating evolutionary rates site-by-site.
Abstract: Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.

39,110 citations


"Reconstructing phylogenetic network..." refers methods in this paper

  • ...In this paper, we have used MEGA (Molecular Evolutionary Genetics Analysis) software for network construction [18]....

    [...]

Journal ArticleDOI
TL;DR: This article reviews the terminology used for phylogenetic networks and covers both split networks and reticulate networks, how they are defined, and how they can be interpreted and outlines the beginnings of a comprehensive statistical framework for applying split network methods.
Abstract: The evolutionary history of a set of taxa is usually represented by a phylogenetic tree, and this model has greatly facilitated the discussion and testing of hypotheses. However, it is well known that more complex evolutionary scenarios are poorly described by such models. Further, even when evolution proceeds in a tree-like manner, analysis of the data may not be best served by using methods that enforce a tree structure but rather by a richer visualization of the data to evaluate its properties, at least as an essential first step. Thus, phylogenetic networks should be employed when reticulate events such as hybridization, horizontal gene transfer, recombination, or gene duplication and loss are believed to be involved, and, even in the absence of such events, phylogenetic networks have a useful role to play. This article reviews the terminology used for phylogenetic networks and covers both split networks and reticulate networks, how they are defined, and how they can be interpreted. Additionally, the article outlines the beginnings of a comprehensive statistical framework for applying split network methods. We show how split networks can represent confidence sets of trees and introduce a conservative statistical test for whether the conflicting signal in a network is treelike. Finally, this article describes a new program, SplitsTree4, an interactive and comprehensive tool for inferring different types of phylogenetic networks from sequences, distances, and trees.

7,273 citations

Journal ArticleDOI
25 Jun 1999-Science
TL;DR: Molecular phylogeneticists will have failed to find the “true tree,” not because their methods are inadequate or because they have chosen the wrong genes, but because the history of life cannot properly be represented as a tree.
Abstract: From comparative analyses of the nucleotide sequences of genes encoding ribosomal RNAs and several proteins, molecular phylogeneticists have constructed a "universal tree of life," taking it as the basis for a "natural" hierarchical classification of all living things. Although confidence in some of the tree's early branches has recently been shaken, new approaches could still resolve many methodological uncertainties. More challenging is evidence that most archaeal and bacterial genomes (and the inferred ancestral eukaryotic nuclear genome) contain genes from multiple sources. If "chimerism" or "lateral gene transfer" cannot be dismissed as trivial in extent or limited to special categories of genes, then no hierarchical universal classification can be taken as natural. Molecular phylogeneticists will have failed to find the "true tree," not because their methods are inadequate or because they have chosen the wrong genes, but because the history of life cannot properly be represented as a tree. However, taxonomies based on molecular sequences will remain indispensable, and understanding of the evolutionary process will ultimately be enriched, not impoverished.

1,585 citations

Journal ArticleDOI
TL;DR: A method is described for demonstrating the statistical significance of such mosaic structure and for locating the crossover points separating different regions in prokaryotes.
Abstract: Some genes in prokaryotes consist of a mosaic of regions derived from different ancestors by horizontal gene transfer. A method is described for demonstrating the statistical significance of such mosaic structure and for locating the crossover points separating different regions.

1,527 citations


"Reconstructing phylogenetic network..." refers background in this paper

  • ...Cells must keep on adapting to changing conditions by altering their gene expression pattern [4]....

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