Abstract
BackgroundThe estimation of the difference between two evolutionary distances within a triplet of homologs is a common operation that is used for example to determine which of two sequences is closer to a third one. The most accurate method is currently maximum likelihood over the entire triplet. However, this approach is relatively time consuming.ResultsWe show that an alternative estimator, based on pairwise estimates and therefore much faster to compute, has almost the same statistical power as the maximum likelihood estimator. We also provide a numerical approximation for its variance, which could otherwise only be estimated through an expensive re-sampling approach such as bootstrapping. An extensive simulation demonstrates that the approximation delivers precise confidence intervals. To illustrate the possible applications of these results, we show how they improve the detection of asymmetric evolution, and the identification of the closest relative to a given sequence in a group of homologs.ConclusionThe results presented in this paper constitute a basis for large-scale protein cross-comparisons of pairwise evolutionary distances.
Highlights
The estimation of the difference between two evolutionary distances within a triplet of homologs is a common operation that is used for example to determine which of two sequences is closer to a third one
In the present section, we compare the estimators Δtriplet and Δpairwise, and introduce a numerical approximation to estimate the variance of Δpairwise, and show that it gives accurate confidence intervals
Computing the difference of two evolutionary distances that are not independent is a common operation in an increasing number of bioinformatics analyses
Summary
The estimation of the difference between two evolutionary distances within a triplet of homologs is a common operation that is used for example to determine which of two sequences is closer to a third one. The most accurate method is currently maximum likelihood over the entire triplet The most accurate way of estimating evolutionary distances is currently maximum likelihood, but the procedure is so time-consuming that is hardly practical when dealing with large datasets. In such cases, complexity is often tackled by working on the basis of individual pairs, such as in distance tree methods or in the "all-against-all" at the beginning of many comparative genomics analyses. As two examples of (page number not for citation purposes)
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