Abstract
BackgroundEvolutionary methods are increasingly challenged by the wealth of fast growing resources of genomic sequence information. Evolutionary events, like gene duplication, loss, and deep coalescence, account more then ever for incongruence between gene trees and the actual species tree. Gene tree reconciliation is addressing this fundamental problem by invoking the minimum number of gene duplication and losses that reconcile a rooted gene tree with a rooted species tree. However, the reconciliation process is highly sensitive to topological error or wrong rooting of the gene tree, a condition that is not met by most gene trees in practice. Thus, despite the promises of gene tree reconciliation, its applicability in practice is severely limited.ResultsWe introduce the problem of reconciling unrooted and erroneous gene trees by simultaneously rooting and error-correcting them, and describe an efficient algorithm for this problem. Moreover, we introduce an error-corrected version of the gene duplication problem, a standard application of gene tree reconciliation. We introduce an effective heuristic for our error-corrected version of the gene duplication problem, given that the original version of this problem is NP-hard. Our experimental results suggest that our error-correcting approaches for unrooted input trees can significantly improve on the accuracy of gene tree reconciliation, and the species tree inference under the gene duplication problem. Furthermore, the efficiency of our algorithm for error-correcting reconciliation is capable of handling truly large-scale phylogenetic studies.ConclusionsOur presented error-correction approach is a crucial step towards making gene tree reconciliation more robust, and thus to improve on the accuracy of applications that fundamentally rely on gene tree reconciliation, like the inference of gene-duplication supertrees.
Highlights
Introduction to unrooted reconciliationHere we highlight some results from [18] that are used for the design of our algorithm
Most inference methods used in practice return only unrooted gene trees that have to be rooted for the gene tree reconciliation process
First we describe our algorithm for computing the optimal cost and the set of optimal edges after one nearest neighbor interchange (NNI) operation performed on an unrooted gene tree, and extend it to a general case with k NNI operations
Summary
Introduction to unrooted reconciliationHere we highlight some results from [18] that are used for the design of our algorithm. Gene tree reconciliation is addressing this fundamental problem by invoking the minimum number of gene duplication and losses that reconcile a rooted gene tree with a rooted species tree. Topological error results in an incorrect topology of the gene tree that can be caused by the inference process (e.g. noise in the underlying sequence data) or the inference method itself (e.g. heuristic results). This problem has been addressed for rooted gene trees by ‘correcting the error’; that is, editing the given tree such that the number of invoked gene-duplications and losses is minimized [16,17]. Rooting problems can be bypassed by identifying roots that minimize the invoked number of gene duplications and losses [7,16,17,18,19]
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