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Evolutionary Bioinformatics

Synopsis: An open access, peer reviewed electronic journal that covers computational evolutionary biology and evolutionary bioinformatics.


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About this journal

ISSN: 1176-9343



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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As of April 7 2008, the US NIH Public Access Policy requires that all peer reviewed articles resulting from research carried out with NIH funding be deposited in the Pubmed Central archive.

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Using minimum bootstrap support for splits to construct confidence regions for trees

Authors: Edward Susko
Publication Date: 03 Feb 2007
Evolutionary Bioinformatics 2006:2 129-143

Edward Susko

Genome Atlantic, Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, Canada.

Abstract: Many of the estimated topologies in phylogenetic studies are presented with the bootstrap support for each of the splits in the topology indicated. If phylogenetic estimation is unbiased, high bootstrap support for a split suggests that there is a good deal of certainty that the split actually is present in the tree and low bootstrap support suggests that one or more of the taxa on one side of the estimated split might in reality be located with taxa on the other side. In the latter case the follow-up questions about how many and which of the taxa could reasonably be incorrectly placed as well as where they might alternatively be placed are not addressed through the presented bootstrap support. We present here an algorithm that finds the set of all trees with minimum bootstrap support for their splits greater than some given value. The output is a ranked list of trees, ranked according to the minimum bootstrap supports for splits in the trees. The number of such trees and their topologies provides useful supplementary information in bootstrap analyses about the reasons for low bootstrap support for splits. We also present ways of quantifying low bootstrap support by considering the set of all topologies with minimum bootstrap greater than some quantity as providing a confidence region of topologies. Using a double bootstrap we are able to choose a cutoff so that the set of topologies with minimum bootstrap support for a split greater than that cutoff gives an approximate 95% confidence region. As with bootstrap support one advantage of the methods is that they are generally applicable to the wide variety of phylogenetic estimation methods.

Abbreviations: BP, bootstrap support; EF-1α, elongation factor 1α; minBP, minimum bootstrap support for a split



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