The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Lindell Bromham - One of the best experts on this subject based on the ideXlab platform.
-
Substitution Rate Analysis and Molecular Evolution
2020Co-Authors: Lindell BromhamAbstract:The study of the tempo and mode of Molecular Evolution has played a key role in Evolutionary biology, both as a stimulant for theoretical enrichment and as the foundation of useful analytical tools. When protein and DNA sequences were first produced, the surprising constancy of rates of change brought Molecular Evolution into conflict with mainstream Evolutionary biology, but also stimulated the formation of new theoretical understanding of the processes of genetic change, including the recognition of the role of neutral mutations and genetic drift in genomic Evolution. As more data were collected, it became clear that there were systematic differences in the substitution rate between species, which prompted further elaboration of ideas such as the generation time effect and the nearly neutral theory. Comparing substitution rates between species continues to provide a window on fundamental Evolutionary processes. However, investigating patterns of substitution rates requires attention to potential complicating factors such as the phylogenetic non-independence of rates estimates and the time-dependence of measurement error. This chapter compares different analytical approaches to study the tempo and mode of Molecular Evolution, and considers the way a richer biological understanding of the causes of variation in substitution rate might inform our attempts to use Molecular data to uncover Evolutionary history.
-
eLS - Molecular Evolution: Rates
eLS, 2013Co-Authors: Lindell BromhamAbstract:The rate of Molecular Evolution varies dramatically between taxa, for example, some viruses have a rate of genome Evolution a million times faster than mammals. Although some rate variation may be due to random fluctuations or locus-specific effects, studies have revealed strong and predictable patterns in the differences in the rate of Molecular Evolution between species. In particular, large, long-lived organisms with low reproductive output tend to have slower rates of Molecular Evolution than related species with shorter lives, faster generations or higher fecundity. Studies of the variation in the rate of Molecular Evolution between species may reveal the mechansims underlying these differences, and can inform analyses that seek to derive information on Evolutionary history and processes from Molecular data. Key Concepts: The number of genetic differences between lineages increases with the time since their separation, but differences do not accrue in the same rate in all lineages. Variation in the rate of Molecular Evolution can be compared between species by comparing absolute rates, derived from laboratory experiments or estimated by comparing sequences where the age of the divergence is known. A more common approach is to compare the relative rate differences between species by comparing the number of sequence changes that have accumulated since they last shared a common ancestor. Mutation rate varies between species, at least in part due to the action of selection finding a balance between the competing costs of DNA repair and mutation. Many mutations arise from DNA replication errors, so the more times DNA is copied per unit time, the higher the mutation rate will be. Rates of Molecular Evolution in many taxa scale with body size, possibly because smaller-bodied taxa go through more genome replications per unit time, a hypothesis referred to as the generation time effect. Metabolic rate has been suggested to play a role in species differences in mutation rate, on the assumption that species with higher mass-specific metabolic rate will suffer more DNA damage per unit time, though there is little direct evidence for this hypothesis. Natural selection might play a role in fine-tuning mutation rates to fit different life history strategies, for example, reducing mutation rates in large, long-lived organisms. Keywords: Molecular clock; substitution; mutation; relative rates; generation time; metabolic rate; longevity; population size
-
Watching the clock: Studying variation in rates of Molecular Evolution between species
Trends in ecology & evolution, 2010Co-Authors: Robert Lanfear, John J. Welch, Lindell BromhamAbstract:Evidence is accumulating that rates of Molecular Evolution vary substantially between species, and that this rate variation is partly determined by species characteristics. A better understanding of how and why rates of Molecular Evolution vary provides a window on Evolutionary processes, and might facilitate improvements in DNA sequence analysis. Measuring rates of Molecular Evolution and identifying the correlates of rate variation present a unique set of challenges. We describe and compare recent methodological advances that have been proposed to deal with these challenges. We provide a guide to the theoretical basis and practical application of the methods, outline the types of data on which they can be used, and indicate the types of questions they can be used to ask.
-
A Generation Time Effect on the Rate of Molecular Evolution in Invertebrates
Molecular biology and evolution, 2010Co-Authors: Jessica A. Thomas, John J. Welch, Robert Lanfear, Lindell BromhamAbstract:The rate of genome Evolution varies significantly between species. Evidence is growing that at least some of this variation is associated with species characteristics, such as body size, diversification rate, or population size. One of the strongest correlates of the rate of Molecular Evolution in vertebrates is generation time (GT): Species with faster generation turnover tend to have higher rates of Molecular Evolution, presumably because their genomes are copied more frequently and therefore collect more DNA replication errors per unit time. But the GT effect has never been tested for nonvertebrate animals. Here, we present the first general test of the GT effect in invertebrates, using 15 genes from 143 species spread across the major eumetazoan superphyla (including arthropods, nematodes, molluscs, annelids, platyhelminthes, cnidarians, echinoderms, and urochordates). We find significant evidence that rates of Molecular Evolution are correlated with GT in invertebrates and that this effect applies consistently across genes and taxonomic groups. Furthermore, the GT effect is evident in nonsynonymous substitutions, whereas theory predicts (and most previous evidence has supported) a relationship only in synonymous changes. We discuss both the practical and theoretical implications of these findings.
-
Why do species vary in their rate of Molecular Evolution
Biology letters, 2009Co-Authors: Lindell BromhamAbstract:Despite hopes that the processes of Molecular Evolution would be simple, clock-like and essentially universal, variation in the rate of Molecular Evolution is manifest at all levels of biological organization. Furthermore, it has become clear that rate variation has a systematic component: rate of Molecular Evolution can vary consistently with species body size, population dynamics, lifestyle and location. This suggests that the rate of Molecular Evolution should be considered part of life-history variation between species, which must be taken into account when interpreting DNA sequence differences between lineages. Uncovering the causes and correlates of rate variation may allow the development of new biologically motivated models of Molecular Evolution that may improve bioinformatic and phylogenetic analyses.
Eduardo Ruiz-pesini - One of the best experts on this subject based on the ideXlab platform.
-
MEvoLib v1.0: The first Molecular Evolution library for Python
BMC bioinformatics, 2016Co-Authors: Jorge Alvarez-jarreta, Eduardo Ruiz-pesiniAbstract:Background Molecular Evolution studies involve many different hard computational problems solved, in most cases, with heuristic algorithms that provide a nearly optimal solution. Hence, diverse software tools exist for the different stages involved in a Molecular Evolution workflow.
-
MEvoLib v1.0: the first Molecular Evolution library for Python
BMC Bioinformatics, 2016Co-Authors: Jorge Alvarez-jarreta, Eduardo Ruiz-pesiniAbstract:Background Molecular Evolution studies involve many different hard computational problems solved, in most cases, with heuristic algorithms that provide a nearly optimal solution. Hence, diverse software tools exist for the different stages involved in a Molecular Evolution workflow. Results We present MEvoLib, the first Molecular Evolution library for Python, providing a framework to work with different tools and methods involved in the common tasks of Molecular Evolution workflows. In contrast with already existing bioinformatics libraries, MEvoLib is focused on the stages involved in Molecular Evolution studies, enclosing the set of tools with a common purpose in a single high-level interface with fast access to their frequent parameterizations. The gene clustering from partial or complete sequences has been improved with a new method that integrates accessible external information (e.g. GenBank’s features data). Moreover, MEvoLib adjusts the fetching process from NCBI databases to optimize the download bandwidth usage. In addition, it has been implemented using parallelization techniques to cope with even large-case scenarios. Conclusions MEvoLib is the first library for Python designed to facilitate Molecular Evolution researches both for expert and novel users. Its unique interface for each common task comprises several tools with their most used parameterizations. It has also included a method to take advantage of biological knowledge to improve the gene partition of sequence datasets. Additionally, its implementation incorporates parallelization techniques to enhance computational costs when handling very large input datasets.
Jorge Alvarez-jarreta - One of the best experts on this subject based on the ideXlab platform.
-
MEvoLib v1.0: The first Molecular Evolution library for Python
BMC bioinformatics, 2016Co-Authors: Jorge Alvarez-jarreta, Eduardo Ruiz-pesiniAbstract:Background Molecular Evolution studies involve many different hard computational problems solved, in most cases, with heuristic algorithms that provide a nearly optimal solution. Hence, diverse software tools exist for the different stages involved in a Molecular Evolution workflow.
-
MEvoLib v1.0: the first Molecular Evolution library for Python
BMC Bioinformatics, 2016Co-Authors: Jorge Alvarez-jarreta, Eduardo Ruiz-pesiniAbstract:Background Molecular Evolution studies involve many different hard computational problems solved, in most cases, with heuristic algorithms that provide a nearly optimal solution. Hence, diverse software tools exist for the different stages involved in a Molecular Evolution workflow. Results We present MEvoLib, the first Molecular Evolution library for Python, providing a framework to work with different tools and methods involved in the common tasks of Molecular Evolution workflows. In contrast with already existing bioinformatics libraries, MEvoLib is focused on the stages involved in Molecular Evolution studies, enclosing the set of tools with a common purpose in a single high-level interface with fast access to their frequent parameterizations. The gene clustering from partial or complete sequences has been improved with a new method that integrates accessible external information (e.g. GenBank’s features data). Moreover, MEvoLib adjusts the fetching process from NCBI databases to optimize the download bandwidth usage. In addition, it has been implemented using parallelization techniques to cope with even large-case scenarios. Conclusions MEvoLib is the first library for Python designed to facilitate Molecular Evolution researches both for expert and novel users. Its unique interface for each common task comprises several tools with their most used parameterizations. It has also included a method to take advantage of biological knowledge to improve the gene partition of sequence datasets. Additionally, its implementation incorporates parallelization techniques to enhance computational costs when handling very large input datasets.
Rasmus Nielsen - One of the best experts on this subject based on the ideXlab platform.
-
Statistical Methods in Molecular Evolution - Statistical Methods in Molecular Evolution
Systematic Biology, 2006Co-Authors: Simon Whelan, Rasmus NielsenAbstract:Markov models in Molecular Evolution.- Introduction to Applications of the Likelihood Function in Molecular Evolution.- Introduction to Markov Chain Monte Carlo Methods in Molecular Evolution.- Population Genetics of Molecular Evolution.- Maximum Likelihood Methods for Detecting Adaptive Protein Evolution.- HyPhy: Hypothesis Testing Using Phylogenies.- Bayesian Analysis of Molecular Evolution using MrBayes.- Estimation of divergence times from Molecular sequence data.- Markov Models of Protein Sequence Evolution.- Models of Microsatellite Evolution.- Genome Rearrangement.- Phylogenetic Hidden Markov Models.- The Evolutionary Causes and Consequences of Base Composition Variation.- Statistical Alignment: Recent Progress, New Applications, and Challenges.- Estimating Substitution Matrices.- Posterior Mapping and Posterior Predictive Distributions.- Assessing the Uncertainty in Phylogenetic Inference.
-
statistical methods in Molecular Evolution
Systematic Biology, 2005Co-Authors: Simon Whelan, Rasmus NielsenAbstract:Markov models in Molecular Evolution.- Introduction to Applications of the Likelihood Function in Molecular Evolution.- Introduction to Markov Chain Monte Carlo Methods in Molecular Evolution.- Population Genetics of Molecular Evolution.- Maximum Likelihood Methods for Detecting Adaptive Protein Evolution.- HyPhy: Hypothesis Testing Using Phylogenies.- Bayesian Analysis of Molecular Evolution using MrBayes.- Estimation of divergence times from Molecular sequence data.- Markov Models of Protein Sequence Evolution.- Models of Microsatellite Evolution.- Genome Rearrangement.- Phylogenetic Hidden Markov Models.- The Evolutionary Causes and Consequences of Base Composition Variation.- Statistical Alignment: Recent Progress, New Applications, and Challenges.- Estimating Substitution Matrices.- Posterior Mapping and Posterior Predictive Distributions.- Assessing the Uncertainty in Phylogenetic Inference.
Michael R. Dietrich - One of the best experts on this subject based on the ideXlab platform.
-
Molecular Evolution, History of
Encyclopedia of Evolutionary Biology, 2016Co-Authors: Michael R. Dietrich, E. Suarez-diazAbstract:Molecular biology had a tremendous impact on Evolutionary biology beginning in the 1960s. New experimental techniques from biochemistry and Molecular biology brought new experimental techniques and Molecular data to Evolutionary biology. As biologists sought to understand the implications of Molecular phenomena, they articulated new concepts and theories such as the Molecular clock and the neutral theory of Molecular Evolution. These distinguished Evolution at the Molecular level from the organismic level in the 1970s. The rise of DNA sequencing in the 1980s and 1990s reinforced the distinction between organismal and Molecular Evolution with new statistical tests to differentiate selection from neutrality at the sequence level.
-
Three Perspectives on Neutrality and Drift in Molecular Evolution
Philosophy of Science, 2006Co-Authors: Michael R. DietrichAbstract:This article offers three contrasting cases of the use of neutrality and drift in Molecular Evolution. In the first, neutrality is assumed as a simplest case for modeling. In the second and third, concepts of drift and neutrality are developed within the context of population genetics testing and the development and application of the Molecular clock.
-
The origins of the neutral theory of Molecular Evolution
Journal of the history of biology, 1994Co-Authors: Michael R. DietrichAbstract:Molecular biology has had a profound impact on the nature of the biological sciences in the twentieth century. Molecular techniques are found now in virtually every area of biology. To reduce the impact of Molecular biology to the dissemination of its technologies, however, is to sell it short: it has also dramatically altered researchers' attitudes toward the question which problems or problem areas are of fundamental importance. This shift in attitude is nowhere more evident than in hybrid fields such as Molecular Evolution. When the first major conferences on Molecular Evolution were held in 1964,1 the vast majority of Evolutionary biologists saw the world through the lens of panselectionism natural selection was accepted as the dominant and most important mechanism of biological Evolution.2 This panselectionism was a product of the Evolutionary synthesis in the 1930s and 1940s and the related effort to demonstrate the central role of Evolution within the biological sciences. Architects of the Evolutionary synthesis, such as George G. Simpson and Ernst Mayr, attended these early conferences on Molecular Evolution and actively promoted the power of natural selection.3 For Mayr and Simpson, Molecular biology was threat-
