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Open Access Highly Accessed Methodology article

Selecting informative subsets of sparse supermatrices increases the chance to find correct trees

Bernhard Misof1*, Benjamin Meyer2, Björn Marcus von Reumont3, Patrick Kück1, Katharina Misof1 and Karen Meusemann14

Author Affiliations

1 , Zoologisches Forschungsmuseum Alexander Koenig, zmb, Adenauerallee 160, 53113 Bonn, Germany

2 Institut für Systematische Neurowissenschaften, Universitätsklinikum Hamburg Eppendorf, Martinistr. 52, 20246 Hamburg, Germany

3 Natural History Museum, London Department of Life Sciences, Cromwell Road, London, SW7 5BD, UK

4 CSIRO Ecosystem Sciences, Australian National Insect Collection, Clunies Ross Street, Acton, ACT, Australia

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BMC Bioinformatics 2013, 14:348  doi:10.1186/1471-2105-14-348

Published: 3 December 2013

Abstract

Background

Character matrices with extensive missing data are frequently used in phylogenomics with potentially detrimental effects on the accuracy and robustness of tree inference. Therefore, many investigators select taxa and genes with high data coverage. Drawbacks of these selections are their exclusive reliance on data coverage without consideration of actual signal in the data which might, thus, not deliver optimal data matrices in terms of potential phylogenetic signal. In order to circumvent this problem, we have developed a heuristics implemented in a software called mare which (1) assesses information content of genes in supermatrices using a measure of potential signal combined with data coverage and (2) reduces supermatrices with a simple hill climbing procedure to submatrices with high total information content. We conducted simulation studies using matrices of 50 taxa × 50 genes with heterogeneous phylogenetic signal among genes and data coverage between 10–30%.

Results

With matrices of 50 taxa × 50 genes with heterogeneous phylogenetic signal among genes and data coverage between 10–30% Maximum Likelihood (ML) tree reconstructions failed to recover correct trees. A selection of a data subset with the herein proposed approach increased the chance to recover correct partial trees more than 10-fold. The selection of data subsets with the herein proposed simple hill climbing procedure performed well either considering the information content or just a simple presence/absence information of genes. We also applied our approach on an empirical data set, addressing questions of vertebrate systematics. With this empirical dataset selecting a data subset with high information content and supporting a tree with high average boostrap support was most successful if information content of genes was considered.

Conclusions

Our analyses of simulated and empirical data demonstrate that sparse supermatrices can be reduced on a formal basis outperforming the usually used simple selections of taxa and genes with high data coverage.