Open Access Highly Accessed Editorial

State-of the art methodologies dictate new standards for phylogenetic analysis

Maria Anisimova12*, David A Liberles3, Hervé Philippe4, Jim Provan5, Tal Pupko6 and Arndt von Haeseler7

Author affiliations

1 Department of Computer Science, Swiss Federal Institute of Technology (ETH), Zürich 8092, Switzerland

2 Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland

3 Department of Molecular Biology, University of Wyoming, Laramie, WY 82071, USA

4 Departement de Biochimie, Université de Montréal, Montréal, Qc H3C 1J7, Canada

5 School of Biological Sciences, Queen’s University Belfast, Belfast BT9 7BL, UK

6 Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978, Tel Aviv, Israel

7 Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Dr.-Bohr-Gasse 9, A-1030, Vienna, Austria

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Citation and License

BMC Evolutionary Biology 2013, 13:161  doi:10.1186/1471-2148-13-161

Published: 1 August 2013


The intention of this editorial is to steer researchers through methodological choices in molecular evolution, drawing on the combined expertise of the authors. Our aim is not to review the most advanced methods for a specific task. Rather, we define several general guidelines to help with methodology choices at different stages of a typical phylogenetic ‘pipeline’. We are not able to provide exhaustive citation of a literature that is vast and plentiful, but we point the reader to a set of classical textbooks that reflect the state-of-the-art. We do not wish to appear overly critical of outdated methodology but rather provide some practical guidance on the sort of issues which should be considered. We stress that a reported study should be well-motivated and evaluate a specific hypothesis or scientific question. However, a publishable study should not be merely a compilation of available sequences for a protein family of interest followed by some standard analyses, unless it specifically addresses a scientific hypothesis or question. The rapid pace at which sequence data accumulate quickly outdates such publications. Although clearly, discoveries stemming from data mining, reports of new tools and databases and review papers are also desirable.