Faculty Opinions recommendation of Rapid and accurate large-scale coestimation of sequence alignments and phylogenetic trees.

2009 ◽  
Author(s):  
Oliver Pybus
Keyword(s):  
Phylogenetic Trees ◽  
Large Scale ◽  
Sequence Alignments

scholarly journals GeneRax: A tool for species tree-aware maximum likelihood based gene family tree inference under gene duplication, transfer, and loss

2019 ◽  
Author(s):  
Benoit Morel ◽  
Alexey M. Kozlov ◽  
Alexandros Stamatakis ◽  
Gergely J. Szöllősi
Keyword(s):  
Maximum Likelihood ◽  
Phylogenetic Trees ◽  
Large Scale ◽  
Simulated Data ◽  
Gene Families ◽  
Species Tree ◽  
Homologous Gene ◽  
Sequence Alignments ◽  
Full Likelihood ◽  
True Tree

AbstractInferring phylogenetic trees for individual homologous gene families is difficult because alignments are often too short, and thus contain insufficient signal, while substitution models inevitably fail to capture the complexity of the evolutionary processes. To overcome these challenges species tree-aware methods also leverage information from a putative species tree. However, only few methods are available that implement a full likelihood framework or account for horizontal gene transfers. Furthermore, these methods often require expensive data pre-processing (e.g., computing bootstrap trees), and rely on approximations and heuristics that limit the degree of tree space exploration. Here we present GeneRax, the first maximum likelihood species tree-aware phylogenetic inference software. It simultaneously accounts for substitutions at the sequence level as well as gene level events, such as duplication, transfer, and loss relying on established maximum likelihood optimization algorithms. GeneRax can infer rooted phylogenetic trees for multiple gene families, directly from the per-gene sequence alignments and a rooted, yet undated, species tree. We show that compared to competing tools, on simulated data GeneRax infers trees that are the closest to the true tree in 90% of the simulations in terms of relative Robinson-Foulds distance. On empirical datasets, GeneRax is the fastest among all tested methods when starting from aligned sequences, and it infers trees with the highest likelihood score, based on our model. GeneRax completed tree inferences and reconciliations for 1099 Cyanobacteria families in eight minutes on 512 CPU cores. Thus, its parallelization scheme enables large-scale analyses. GeneRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax.

scholarly journals GeneRax: A Tool for Species-Tree-Aware Maximum Likelihood-Based Gene  Family Tree Inference under Gene Duplication, Transfer, and Loss

2020 ◽  
Vol 37 (9) ◽  
pp. 2763-2774 ◽  
Author(s):  
Benoit Morel ◽  
Alexey M Kozlov ◽  
Alexandros Stamatakis ◽  
Gergely J Szöllősi
Keyword(s):  
Maximum Likelihood ◽  
Phylogenetic Trees ◽  
Large Scale ◽  
Simulated Data ◽  
Gene Families ◽  
Species Tree ◽  
Homologous Gene ◽  
Sequence Alignments ◽  
Full Likelihood ◽  
True Tree

Abstract Inferring phylogenetic trees for individual homologous gene families is difficult because alignments are often too short, and thus contain insufficient signal, while substitution models inevitably fail to capture the complexity of the evolutionary processes. To overcome these challenges, species-tree-aware methods also leverage information from a putative species tree. However, only few methods are available that implement a full likelihood framework or account for horizontal gene transfers. Furthermore, these methods often require expensive data preprocessing (e.g., computing bootstrap trees) and rely on approximations and heuristics that limit the degree of tree space exploration. Here, we present GeneRax, the first maximum likelihood species-tree-aware phylogenetic inference software. It simultaneously accounts for substitutions at the sequence level as well as gene level events, such as duplication, transfer, and loss relying on established maximum likelihood optimization algorithms. GeneRax can infer rooted phylogenetic trees for multiple gene families, directly from the per-gene sequence alignments and a rooted, yet undated, species tree. We show that compared with competing tools, on simulated data GeneRax infers trees that are the closest to the true tree in 90% of the simulations in terms of relative Robinson–Foulds distance. On empirical data sets, GeneRax is the fastest among all tested methods when starting from aligned sequences, and it infers trees with the highest likelihood score, based on our model. GeneRax completed tree inferences and reconciliations for 1,099 Cyanobacteria families in 8 min on 512 CPU cores. Thus, its parallelization scheme enables large-scale analyses. GeneRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax (last accessed June 17, 2020).  

Rapid and Accurate Large-Scale Coestimation of Sequence Alignments and Phylogenetic Trees

Science ◽  
2009 ◽  
Vol 324 (5934) ◽  
pp. 1561-1564 ◽  
Author(s):  
K. Liu ◽  
S. Raghavan ◽  
S. Nelesen ◽  
C. R. Linder ◽  
T. Warnow
Keyword(s):  
Phylogenetic Trees ◽  
Large Scale ◽  
Sequence Alignments

scholarly journals Is cytochrome c oxidase subunit I (COI) the right DNA barcoding marker for the Chaetopteryx villosa group?

2021 ◽  
Vol 4 ◽  
Author(s):  
Dalila Destanović ◽  
Lejla Ušanović ◽  
Lejla Lasić ◽  
Jasna Hanjalić ◽  
Belma Kalamujić Stroil
Keyword(s):  
Phylogenetic Trees ◽  
Pairwise Distance ◽  
Zoological Museum ◽  
Species Determination ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Software Analysis ◽  
Group Data ◽  
Species Specific ◽  
The Right

Chaetopteryx villosa (Fabricius, 1798) is a caddisfly species distributed throughout Europe, except in the Balkan and Apennine Peninsula. However, phylogenetically close species belonging to the C. villosa group are widespread throughout entire Europe. Species of this group (C. villosa, C. gessneri, C. fusca, C. sahlbergi, C. atlantica, C. bosniaca, C. vulture, and C. trinacriae) have distinct distributions with some overlaps. Adult forms of these species are morphologically similar, whereas larval morphology is only known for some species. There are also indications of species hybridization (e.g., C. villosa x fusca). Presumably, the molecular approach for the species determination of this group would be highly beneficial. In the BOLD database, there are 154 specimens with COI-5P barcodes of C. villosa species. Out of the remaining species, C. sahlbergi has 27 specimens with a barcode, C. fusca 20, C. gessneri 5, C. bosniaca 5, and C. atlantica 1, whereas sequences from the species C. vulture and C. trinacriae are missing. Therefore, we tested the power of discrimination of the COI-5P marker in the C. villosa group, as the most common barcoding markers for species identification in animals. Only sequences from public records originating from experienced research groups or taxonomists and containing a specimen photograph were taken as input. A total of 75 sequences from the BOLD database were obtained. Out of these sequences, 11 belonged to C. fusca, 5 to C. gessneri, 52 to C. villosa, 5 to C. bosniaca, and 2 to C. sahlbergi. For the generation of overview trees, COI-5P barcodes of Rhyacophila fasciata and Rh. nubila were used as outgroups. All sequences were trimmed at 5’ and 3’ ends, resulting in a final alignment length of 516 base pairs. Multiple sequence alignments and editing were done in the MEGA-X software. Analysis of nucleotide polymorphism was done in DNASP6 software. MEGA-X was used to calculate the pairwise distance and overall mean p-distance, and to construct the overview trees. Analysis of DNA polymorphism revealed 14 haplotypes of C. villosa, 3 haplotypes of C. fusca, 2 haplotypes of C. gessneri, and one for species C. bosniaca and C. sahlbergi. There were no significant interspecific and intraspecific differences among haplotypes based on pairwise distances. The p-distance between one of the haplotypes of C. fusca and C. villosa was 0.000, whereas the p-distance among haplotypes of C. villosa varied from 0.001 to about 0.055. The mean overall p-distance among haplotypes of all species equaled 0.03. No species-specific clusters were observed when phylogenetic trees were constructed except for C. gessneri, regardless of the method used (i.e., NJ, UPGMA, ML, ME, or MP). To minimize the possibility of species misidentification, we used only records submitted by NTNU-Norwegian University of Science and Technology (Norway), SNSB-Zoologische Staatssammlung Muenchen (Germany), Zoologisches Forschungsmuseum Alexander Koenig (Germany), University of Oulu, Zoological Museum (Finland), prof Hans Malicky and prof Mladen Kučinić. No records identified as hybrids were included in the analyses. With the exception of C. gessneri, COI-5P marker failed to separate the species of the C. villosa group. However, it is highly unlikely that poor species determination was the basis for such a result. To enable the comprehensive and unbiased evaluation of the relationships within this group, data coverage in BOLD database for most of the studied species should be enhanced, encompassing different geographical distribution of samples. Further studies are needed to detect the array of molecular markers suitable for the species delineation in a complex group such as C. villosa.

scholarly journals Optimal Network Alignment with Graphlet Degree Vectors

2010 ◽  
Vol 9 ◽  
pp. CIN.S4744 ◽  
Author(s):  
Tijana Milenković ◽  
Weng Leong Ng ◽  
Wayne Hayes ◽  
NatašA PržUlj
Keyword(s):  
Cost Function ◽  
Network Topology ◽  
Biological Networks ◽  
Phylogenetic Trees ◽  
Biological Function ◽  
Network Alignment ◽  
New Method ◽  
Biological Information ◽  
Global Alignment ◽  
Sequence Alignments

Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology.

scholarly journals Large scale genotype phenotype correlation analysis based on phylogenetic trees

2007 ◽  
Vol 23 (7) ◽  
pp. 785-788 ◽  
Author(s):  
F. Habib ◽  
A. D. Johnson ◽  
R. Bundschuh ◽  
D. Janies
Keyword(s):  
Correlation Analysis ◽  
Phylogenetic Trees ◽  
Large Scale ◽  
Phenotype Correlation ◽  
Genotype Phenotype Correlation

scholarly journals In SilicoIdentification of Functional Protein Interfaces

2003 ◽  
Vol 4 (4) ◽  
pp. 420-423 ◽  
Author(s):  
Rachel E. Bell ◽  
Nir Ben-Tal
Keyword(s):  
Phylogenetic Trees ◽  
Large Scale ◽  
Hypothetical Protein ◽  
Evolutionary Information ◽  
Structural Constraints ◽  
Functional Protein ◽  
Homologous Proteins ◽  
Geometrical Properties ◽  
Protein Interfaces ◽  
Branch Lengths

Proteins perform many of their biological roles through protein–protein, protein–DNA or protein–ligand interfaces. The identification of the amino acids comprising these interfaces often enhances our understanding of the biological function of the proteins. Many methods for the detection of functional interfaces have been developed, and large-scale analyses have provided assessments of their accuracy. Among them are those that consider the size of the protein interface, its amino acid composition and its physicochemical and geometrical properties. Other methods to this effect use statistical potential functions of pairwise interactions, and evolutionary information. The rationale of the evolutionary approach is that functional and structural constraints impose selective pressure; hence, biologically important interfaces often evolve at a slower pace than do other external regions of the protein. Recently, an algorithm, Rate4Site, and a web-server, ConSurf (http://consurf.tau.ac.il/), for the identification of functional interfaces based on the evolutionary relations among homologous proteins as reflected in phylogenetic trees, were developed in our laboratory. The explicit use of the tree topology and branch lengths makes the method remarkably accurate and sensitive. Here we demonstrate its potency in the identification of the functional interfaces of a hypothetical protein, the structure of which was determined as part of the international structural genomics effort. Finally, we propose to combine complementary procedures, in order to enhance the overall performance of methods for the identification of functional interfaces in proteins.

Creoles are typologically distinct from non-creoles

2011 ◽  
Vol 26 (1) ◽  
pp. 5-42 ◽  
Author(s):  
Peter Bakker ◽  
Aymeric Daval-Markussen ◽  
Mikael Parkvall ◽  
Ingo Plag
Keyword(s):  
Empirical Study ◽  
Multiple Regression ◽  
Special Class ◽  
Phylogenetic Trees ◽  
Large Scale ◽  
Computational Tools ◽  
The Status ◽  
Creole Languages ◽  
First Time ◽  
Robust Evidence

In creolist circles, there has been a a long-standing debate whether creoles differ structurally from non-creole languages and thus would form a special class of languages with specific typological properties. This debate about the typological status of creole languages has severely suffered from a lack of systematic empirical study. This paper presents for the first time a number of large-scale empirical investigations of the status of creole languages as a typological class on the basis of different and well-balanced samples of creole and non-creole languages. Using statistical modeling (multiple regression) and recently developed computational tools of quantitative typology (phylogenetic trees and networks), this paper provides robust evidence that creoles indeed form a structurally distinguishable subgroup within the world’s languages. The findings thus seriously challenge approaches that hold that creole languages are structurally indistinguishable from non-creole languages.

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