State aggregation for fast likelihood computations in molecular evolution

Davydov, Iakov I.; Robinson‐Rechavi, Marc; Salamin, Nicolas

doi:10.1101/035063

Cited by 2 publications

(2 citation statements)

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“…If lumpability is violated, the phylogenetic inference may be also biased, which was shown for DNA data when four nucleotides were recoded into fewer groups (Vera-Ruiz et al 2014). At the same time, if lumpability is valid, state aggregation can be used in algorithms to reduce the size of rate matrix which improves computational performance (Davydov et al 2017). The simulations for the two-scientist paradox, despite showing a substantially better fit for HMM, detected insignificant differences between HMM and MM rate estimates, which converged on the generating rates (mean squared error was 7×10 -4 and 9×10 -4 respectively).…”

Section: The Two-scientist Paradox: Hidden Markov Modelsmentioning

confidence: 99%

Integration of anatomy ontologies and evo-devo using structured Markov models suggests a new framework for modeling discrete phenotypic traits

Tarasov

2017

Preprint

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Modeling discrete phenotypic traits for either ancestral character state reconstruction or morphologybased phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. These drawbacks occur because trait evolution is driven by two key processes -hierarchical and hidden -which are not accommodated simultaneously by the available phylogenetic methods. The hierarchical process refers to the dependencies between anatomical body parts, while the hidden process refers to the evolution of gene regulatory networks underlying trait development. Herein, I demonstrate that these processes can be efficiently modeled using structured Markov models equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of structured Markov models with anatomy ontologies can adequately incorporate the hierarchical dependencies, while the use of the hidden states accommodates hidden evolution of gene regulatory networks and substitution rate heterogeneity. I assess the new models using simulations and theoretical synthesis. The new approach solves the long-standing tail color problem (that aims at coding tail when it is absent) and presents a previously unknown issue called the "two-scientist paradox". The latter issue refers to the confounding nature of the coding of a trait and the hidden processes driving the trait's evolution; failing to account for the hidden process may result in a bias, which can be avoided by using hidden state models. All this provides a clear guideline for coding traits into characters. This paper gives practical examples of using the new framework for phylogenetic inference and comparative analysis. KEY WORDS:discrete trait, character, morphology, homology, anatomy ontology, structured Markov models, hidden Markov models, lumpability, gene regulatory networks Understanding the processes driving trait evolution is crucial for explaining evolutionary radiations (Price et al. 2010;Van Bocxlaer et al. 2010;Tobias et al. 2014), the origin of complexity and novelty (Moczek 2008;Ramirez and Michalik 2014), and for inferring phylogenies. For many of these analyses, we need to (1) discretize the trait (delimit the trait within a phenotype), (2) assess its primary homology (similarity), and finally (3) encode the trait (observations) into a character string or vector (see the definitions in Box 1, section A). This procedure, called character construction (Wiens 2001), is a basic stage of any analysis, and has a profound influence on all downstream stages. Despite the plethora of inference frameworks -be it parsimony, maximum likelihood or a Bayesian framework [reviewed in O'Meara (2012)] -the lack of repeatable and agreed-upon approaches for character construction generates considerable ambiguity. As a result, different hypotheses of discretization and different ways of coding the same hypothesis into a character may be proposed for the same trait (Hawkins et al. 1997;Strong and Lipscomb 19...

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Section: The Two-scientist Paradox: Hidden Markov Modelsmentioning

confidence: 99%

Integration of anatomy ontologies and evo-devo using structured Markov models suggests a new framework for modeling discrete phenotypic traits

Tarasov

2017

Preprint

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“…First, we used a vertebrate one-toone orthologs dataset (Studer et al 2008, available at http://bioinfo.unil.ch/supdata/ positiveselection/Singleton.html) consisting of 767 genes (singleton dataset). This dataset was already used in previous studies of codon models (Fletcher and Yang, 2010;Gharib and Robinson-Rechavi, 2013;Davydov et al, 2017).…”

Section: Vertebrate and Drosophila Datasetsmentioning

confidence: 99%

Large-Scale Comparative Analysis of Codon Models Accounting for Protein and Nucleotide Selection

Davydov

Salamin

Robinson‐Rechavi

2017

Preprint

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There are numerous sources of variation in the rate of synonymous substitutions inside genes, such as direct selection on the nucleotide sequence, or mutation rate variation. However the majority of the codon models which are developed and widely used today still incorporate an assumption of effectively neutral synonymous substitution rate, constant between sites of each gene. Here we propose a simple yet effective extension to codon models, which incorporates codon substitution rate variation along the gene sequence. We find strong effects of substitution rate variation on positive selection inference. We also demonstrate that the computational load of our approach remains tractable, and therefore we are able to apply it to genome scale positive selection scans. We apply our new method to two datasets: 767 vertebrate orthologs and 8,606 orthologs from twelve Drosophila species. Our new model is strongly favored by the data, and the support of the model increases with the amount of information. Moreover, it is able to capture signatures of nucleotide level selection acting on translation initiation and on splicing sites within the coding region. Finally, we show that rate variation is highest in the highly recombining regions, and we hypothesize that recombination and mutation rate variation, such as high CpG mutation rate, are the two main sources of nucleotide rate variation. Overall, rate variation in substitutions is an important feature to capture, both to detect positive selection and to understand gene evolution, and the approach that we propose allows to do this in genome-wide scans.

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State aggregation for fast likelihood computations in molecular evolution

Cited by 2 publications

References 47 publications

Integration of anatomy ontologies and evo-devo using structured Markov models suggests a new framework for modeling discrete phenotypic traits

Integration of anatomy ontologies and evo-devo using structured Markov models suggests a new framework for modeling discrete phenotypic traits

Large-Scale Comparative Analysis of Codon Models Accounting for Protein and Nucleotide Selection

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