A brief history and popularity of methods and tools used to estimate micro‐evolutionary forces

Ecology and Evolution

Martin Taubert¹  Martin Husemann²  Panagiotis Theodorou²  Jonathan Kidner²  Jan O. Engler³ 

[1] Aquatic Geomicrobiology Institute for Biodiversity Friedrich Schiller University Jena Jena Germany;General Zoology Institute for Biology Martin Luther University Halle‐Wittenberg Halle (Saale) Germany;Terrestrial Ecology Unit Department of Biology Ghent University Ghent Belgium;

关键词: drift;    migration;    mutation;    population genetics;    selection;    software;   

DOI  :  10.1002/ece3.8076

来源: DOAJ

【 摘 要 】

Abstract Population genetics is a field of research that predates the current generations of sequencing technology. Those approaches, that were established before massively parallel sequencing methods, have been adapted to these new marker systems (in some cases involving the development of new methods) that allow genome‐wide estimates of the four major micro‐evolutionary forces—mutation, gene flow, genetic drift, and selection. Nevertheless, classic population genetic markers are still commonly used and a plethora of analysis methods and programs is available for these and high‐throughput sequencing (HTS) data. These methods employ various and diverse theoretical and statistical frameworks, to varying degrees of success, to estimate similar evolutionary parameters making it difficult to get a concise overview across the available approaches. Presently, reviews on this topic generally focus on a particular class of methods to estimate one or two evolutionary parameters. Here, we provide a brief history of methods and a comprehensive list of available programs for estimating micro‐evolutionary forces. We furthermore analyzed their usage within the research community based on popularity (citation bias) and discuss the implications of this bias for the software community. We found that a few programs received the majority of citations, with program success being independent of both the parameters estimated and the computing platform. The only deviation from a model of exponential growth in the number of citations was found for the presence of a graphical user interface (GUI). Interestingly, no relationship was found for the impact factor of the journals, when the tools were published, suggesting accessibility might be more important than visibility.

【 授权许可】

Unknown