Evolutionary Bioinformatics
Synopsis: An open access, peer reviewed electronic journal that covers computational evolutionary biology and evolutionary bioinformatics.
Indexed by: Pubmed, Pubmed Central, CAS, DOAJ, Embase, Embiology, Science Citation Index Expanded (SciSearch®), Journal Citation Reports/Science Edition, Current Contents®/Agriculture, Biology and Environmental Sciences, Current Contents®/Life Sciences, Google Scholar, EBSCO Academic Search Complete, OAIster.
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About this journal
Aims and scope:
Evolutionary Bioinformatics is an international, peer-reviewed journal focusing on evolutionary bioinformatics. There is growing awareness that to understand organismal form and function, through the use of molecular, genetic, genomic, and proteomic data, due consideration must be given to an organism's evolutionary context - history constrains the path an organism is obliged to take, and leaves an indelible mark on its component parts. Evolutionary Bioinformatics publishes papers on all aspects of computational evolutionary biology and evolutionary bioinformatics.
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All submissions to this journal, with the exception of editorials and dedications (obituaries), are subject to rigorous peer review by a minimum of two peer reviewers who demonstrate current research experience in the paper's subject area. Reviewers are required to provide in-depth, fair and objective reviews. They may not act as reviewers if they are in a conflict of interest. All final publishing decisions are made by the Editor in Chief or Associate Editor.
Official journal of the Bioinformatics Institute:
Evolutionary Bioinformatics was established as the official journal of The Bioinformatics Institute. The Institute is a joint-venture between the University of Auckland, situated in New Zealand’s largest city, and AgResearch, New Zealand’s largest Crown Research Institute. Allen Rodrigo, Professor of Computational Biology and Bioinformatics, is the Institute’s Director, and it was at his initiative that the journal was established.
Working in collaboration with The Institute is Libertas Academica, a publishing firm committed to high editorial standards, open access publishing methodologies and superior user-service standards. There is much work involved ‘behind-the-scenes’ that goes towards the finished result seen by readers of Evolutionary Bioinformatics. Key amongst this work, which also includes attracting the best submissions, supervising effective peer review and typesetting, is gaining acceptance for indexing by outside organizations.
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- Current Contents®/Agriculture, Biology and Environmental Sciences
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- Google Scholar
- EBSCO Academic Search Complete
- OAIster
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Read the Editor in Chief's latest call for papers here.
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Submissions of the following types of manuscripts are accepted:
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Read an interview with editorial board member Dr Olivier Bastien
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2005 backfile now available on Pubmed.
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Dr Dennis Wall appointed as Editor in Chief.
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Peer reviewers are sought. Click here to apply or to update your details.
Evolutionary Bioinformatics is now indexed by Pubmed
Topological Bias in Distance-Based Phylogenetic Methods: Problems with Over- and Underestimated Genetic Distances
Xuhua Xia
Department of Biology, University of Ottawa,Ottawa, Ontario, Canada.
Abstract: I show several types of topological biases in distance-based methods that use the least-squares method to evaluate branch lengths and the minimum evolution (ME) or the Fitch-Margoliash (FM) criterion to choose the best tree. For a 6-species tree, there are two tree shapes, one with three cherries (a cherry is a pair of adjacent leaves descending from the most recent common ancestor), and the other with two. When genetic distances are underestimated, the 3-cherry tree shape is favored with either the ME or FM criterion. When the genetic distances are overestimated, the ME criterion favors the 2-cherry tree, but the direction of bias with the FM criterion depends on whether negative branches are allowed, i.e. allowing negative branches favors the 3-cherry tree shape but disallowing negative branches favors the 2-cherry tree shape. The extent of the bias is explored by computer simulation of sequence evolution.
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