William Arndt

Gene Order Phylogeny and the Evolution of Methanogens

Methanogens are a phylogenetically diverse group belonging to Euryarchaeota. Previously, phylogenetic approaches using large datasets revealed that methanogens can be grouped into two classes, “Class I” and “Class II”. However, some deep relationships were not resolved. For instance, the monophyly of “Class I” methanogens, which consist of Methanopyrales, Methanobacteriales and Methanococcales, is disputable due to weak statistical support. In this study, we use MSOAR to identify common orthologous genes from eight methanogen species and a Thermococcale species (outgroup), and apply GRAPPA and FastME to compute distance-based gene order phylogeny. The gene order phylogeny supports two classes of methanogens, but it differs from the original classification of methanogens by placing Methanopyrales and Methanobacteriales together with Methanosarcinales in Class II rather than with Methanococcales. This study suggests a new classification scheme for methanogens. In addition, it indicates that gene order phylogeny can complement traditional sequence-based methods in addressing taxonomic questions for deep relationships.

Gene Order Phylogeny of the Genus Prochlorococcus

Background Using gene order as a phylogenetic character has the potential to resolve previously unresolved species relationships. This character was used to resolve the evolutionary history within the genus Prochlorococcus, a group of marine cyanobacteria. Methodology/Principal Findings Orthologous gene sets and their genomic positions were identified from 12 species of Prochlorococcus and 1 outgroup species of Synechococcus. From this data, inversion and breakpoint distance-based phylogenetic trees were computed by GRAPPA and FastME. Statistical support of the resulting topology was obtained by application of a 50% jackknife resampling technique. The result was consistent and congruent with nucleotide sequence-based and gene-content based trees. Also, a previously unresolved clade was resolved, that of MIT9211 and SS120. Conclusions/Significance This is the first study to use gene order data to resolve a bacterial phylogeny at the genus level. It suggests that the technique is useful in resolving the Tree of Life.

Phylogenetic reconstruction from complete gene orders of whole genomes

Reference LCBB-CONF-2007-003 URL: http://sunflower.kuicr.kyoto-u.ac.jp/apbc2008/ Record created on 2007-10-13, modified on 2017-05-12

Phylogenetic analysis of genome rearrangements among five mammalian orders

Evolutionary relationships among placental mammalian orders have been controversial. Whole genome sequencing and new computational methods offer opportunities to resolve the relationships among 10 genomes belonging to the mammalian orders Primates, Rodentia, Carnivora, Perissodactyla and Artiodactyla. By application of the double cut and join distance metric, where gene order is the phylogenetic character, we computed genomic distances among the sampled mammalian genomes. With a marsupial outgroup, the gene order tree supported a topology in which Rodentia fell outside the cluster of Primates, Carnivora, Perissodactyla, and Artiodactyla. Results of breakpoint reuse rate and synteny block length analyses were consistent with the prediction of random breakage model, which provided a diagnostic test to support use of gene order as an appropriate phylogenetic character in this study. We discussed the influence of rate differences among lineages and other factors that may contribute to different resolutions of mammalian ordinal relationships by different methods of phylogenetic reconstruction.

Improving inversion median computation using commuting reversals and cycle information

In the past decade, genome rearrangements have attracted increasing attention fromboth biologists and computer scientists as a newtype of data for phylogenetic analysis.Methods for reconstructing phylogeny fromgenome rearrangements include distance-based methods, MCMC methods and direct optimization methods. The latter, pioneered by Sankoff and extended with the software suite GRAPPA and MGR, is the most accurate approach, but is very limited due to the difficulty of its scoring procedure-it must solvemultiple instances of median problem to compute the score of a given tree. The median problem is known to be NP-hard and all existing solvers are extremely slow when the genomes are distant. In this paper, we present a new inversion median heuristic for unichromisomal genomes. The new method works by applying sets of reversals in a batch where all such reversals both commute and do not break the cycle of any other. Our testing using simulated datasets shows that this method is much faster than the leading solver for difficult datasets with only a slight accuracy penalty, yet retains better accuracy than other heuristics with comparable speed. This new method will dramatically increase the speed of current direct optimization methods and enables us to extend the range of their applicability to organellar and small nuclear genomes with more than 50 inversions along each edge. As a further improvement, this new method can very quickly produce reasonable solutions to problemswith hundreds of genes.

Improving reversal median computation using commuting reversals and cycle information.

In the past decade, genome rearrangements have attracted increasing attention from both biologists and computer scientists as a new type of data for phylogenetic analysis. Methods for reconstructing phylogeny from genome rearrangements include distance-based methods, MCMC methods, and direct optimization methods. The latter, pioneered by Sankoff and extended with the software suites GRAPPA and MGR, is the most accurate approach, but is very limited due to the difficulty of its scoring procedure--it must solve multiple instances of the reversal median problem to compute the score of a given tree. The reversal median problem is known to be NP-hard and all existing solvers are extremely slow when the genomes are distant. In this paper, we present a new reversal median heuristic for unichromosomal genomes. The new method works by applying sets of reversals in a batch where all such reversals both commute and do not break the cycle of any other. Our testing using simulated datasets shows that this method is much faster than the leading solver for difficult datasets with only a slight accuracy penalty, yet retains better accuracy than other heuristics with comparable speed, and provides the additional option of searching for multiple medians. This method dramatically increases the speed of current direct optimization methods and enables us to extend the range of their applicability to organellar and small nuclear genomes with more than 50 reversals along each edge.

Genome3D: A viewer-model framework for integrating and visualizing multi-scale epigenomic information within a three-dimensional genome

New technologies permit the measurement of many types of genomic and epigenomic information at scales ranging from the atomic to nuclear. Much of this new data is structural in nature and challenging to coordinate with existing data formats. There is an increasing need to integrate and visualize disparate data sets in order to reveal structural relationships not apparent when viewing these data formats separately. We have developed the Genome3D software package in order to integrate and display epigenomic data within the context of a three-dimensional physical model of the human genome. To our knowledge, this is the first such tool developed to visualize human genome in three dimensions. Here we describe the major features of Genome3D and discuss our multi-scale data framework using a representative basic physical model. Additionally, utilities to generate demonstration models of common chromosome interaction scenarios derived from High-C or CHIA-PET analysis are under development. These models are compatible with existing annotation data formats and can demonstrate common situations where one or more chromosomes are arranged such that two or more interaction sites appear in close proximity. These models can be used to directly create publication images without resorting to intermediate use of image editing software. Availability: http://genomebioinfo.musc.edu/Genome3D/Index.html

Emulating Insertion and Deletion Events in Genome Rearrangement Analysis

Great advancements have been achieved in phylogenetic reconstruction from genome rearrangement events but difficult problems still remain. One challenge is to deal with more complex events such as gene insertions and deletions such that we can analyze both gene order and gene content changes in tandem. We propose the concept of prosthetic chromosomes to incorporate these events into the standard double-cut-and-join (DCJ) distance metric widely used for genome scale rearrangement analysis. In this paper, we also introduce our new software package Egchel (Extended Gene Content HEuristic Layer) which implements this prosthetic chromosome model. Egchel can modify unequal content gene order data sets such that they are able to be analyzed by existing phylogenetic tree builders specifically designed to work with equal content gene order data sets. When compared to existing pair wise analysis of unequal content data sets, Egcheluses a global approach, produces substantially more accurate phylogenetic trees, and is significantly more likely to generate the best available tree.

Isolating - a new resampling method for gene order data

The purpose of using resampling methods on phylogenetic data is to estimate the confidence value of branches. In recent years, bootstrapping and jackknifing are the two most popular resampling schemes which are widely used in biological reserach. However, for gene order data, traditional bootstrap procedures can not be applied because gene order data is viewed as one character with various states. Experience in the biological community has shown that jackknifing is a useful means of determining the confidence value of a gene order phylogeny. When genomes are distant, however, applying jackknifing tends to give low confidence values to many valid branches, causing them to be mistakenly removed. In this paper, we propose a new method that overcomes this disadvantage of jackknifing and achieves better accuracy and confidence values for gene order data. Compared to jackknifing, our experimental results show that the proposed method can produce phylogenies with lower error rates and much stronger support for good branches. We also establish a theoretic lower bound regarding how many genes should be isolated, which is confirmed empirically.