Computing evolutionary distinctiveness indices in large scale analysis

Martyn, Iain, Kuhn, Tyler S., Mooers, Arne O., Moulton, Vincent ORCID: https://orcid.org/0000-0001-9371-6435 and Spillner, Andreas (2012) Computing evolutionary distinctiveness indices in large scale analysis. Algorithms for Molecular Biology, 7 (1). ISSN 1748-7188

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Abstract

We present optimal linear time algorithms for computing the Shapley values and 'heightened evolutionary distinctiveness' (HED) scores for the set of taxa in a phylogenetic tree. We demonstrate the efficiency of these new algorithms by applying them to a set of 10,000 reasonable 5139-species mammal trees. This is the first time these indices have been computed on such a large taxon and we contrast our finding with an ad-hoc index for mammals, fair proportion (FP), used by the Zoological Society of London's EDGE programme. Our empirical results follow expectations. In particular, the Shapley values are very strongly correlated with the FP scores, but provide a higher weight to the few monotremes that comprise the sister to all other mammals. We also find that the HED score, which measures a species' unique contribution to future subsets as function of the probability that close relatives will go extinct, is very sensitive to the estimated probabilities. When they are low, HED scores are less than FP scores, and approach the simple measure of a species' age. Deviations (like the Solendon genus of the West Indies) occur when sister species are both at high risk of extinction and their clade roots deep in the tree. Conversely, when endangered species have higher probabilities of being lost, HED scores can be greater than FP scores and species like the African elephant Loxondonta africana, the two solendons and the thumbless bat Furipterus horrens can move up the rankings. We suggest that conservation attention be applied to such species that carry genetic responsibility for imperiled close relatives. We also briefly discuss extensions of Shapley values and HED scores that are possible with the algorithms presented here.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Computational Biology
Faculty of Science > Research Groups > Computational Biology > Computational biology of RNA (former - to 2018)
Faculty of Science > Research Groups > Computational Biology > Phylogenetics (former - to 2018)
Faculty of Science > Research Groups > Norwich Epidemiology Centre
Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Depositing User: Users 2731 not found.
Date Deposited: 12 Nov 2012 16:49
Last Modified: 15 Jun 2023 07:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/40094
DOI: 10.1186/1748-7188-7-6

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