Statistical tests to identify appropriate types of nucleotide sequence recoding in molecular phylogenetics

Vera-Ruiz, Victor A; Lau, Kwok Wai; Robinson, John; Jermiin, Lars S.

doi:10.1186/1471-2105-15-s2-s8

Cited by 12 publications

(9 citation statements)

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“…The PP plot shows the distribution to expect when the data have evolved under homogeneous conditions. This interpretation is consistent with those in Schweder and Spjøtvoll (1982) and Vera-Ruiz et al (2014).…”

Section: Methodssupporting

confidence: 92%

Detecting and visualising the impact of heterogeneous evolutionary processes on phylogenetic estimates

Jermiin

Lovell

Misof

et al. 2019

Preprint

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Most model-based molecular phylogenetic methods assume that the sequences 19 diverged on a tree under homogeneous conditions. If evolution occurred under these 20 conditions, then it is unlikely that the sequences would become compositionally 21 heterogeneous. Conversely, if the sequences are compositionally heterogeneous, then it is 22 unlikely that they have evolved under homogeneous conditions. We present methods to detect 23 and analyse heterogeneous evolution in aligned sequence data and to examine-visually and 24 numerically-its effect on phylogenetic estimates. The methods are implemented in three 25 programs, allowing users to better examine under what conditions their phylogenetic data 26 may have evolved. 27 1Most model-based molecular phylogenetic methods assume that the sequences of 30 nucleotides or amino acids have evolved along the edges of a single bifurcating tree. Often, the 31 methods also assume that the evolutionary processes operating at the variable sites of these 32 data (i.e., the sites that are free to evolve) can be approximated by independent and 33 identically-distributed (iid) Markovian processes. Furthermore, it is often assumed that the 34 evolutionary processes were stationary, reversible and homogeneous (SRH) (for details, see 35 Bryant et al. 2005; Jayaswal et al. 2005; Ababneh et al. 2006a,b; Jermiin et al. 2017), with the 36 term homogeneity implying time-homogeneity (i.e., a constant rate of change between two 37 points in time). 38 In practice, when DNA has evolved under these conditions, commonly-used 39 phylogenetic methods are likely to identify the correct topology (Huelsenbeck and Hillis 1993; 40 Hillis et al. 1994a,b). However, the same methods may not be capable of identifying the 41 correct topology when DNA has evolved under more complex conditions (Huelsenbeck and 42 Hillis 1993; Hillis et al. 1994a,b; Ho and Jermiin 2004; Jermiin et al. 2004). One reason for 43 this failure is that the strength of the historical signal (i.e., the signal in DNA that is due to 44 the order and time of divergence events) decays over time (Ho and Jermiin 2004) whereas the 45 strength of the non-historical signals (Grundy and Naylor 1999) may increase over time (Fig. 46 1). This may lead to situations, where the non-historical signals-individually or jointly-may 47 become stronger than the historical signal (Ho and Jermiin 2004). Unless phylogenetic 48 methods are able to distinguish historical signals from non-historical signals, the latter may be 49 misinterpreted as being part of the historical signal. This is because the non-historical signals 50 are also phylogenetic signals. 51The non-historical signal is a mixed bag of signals that may arise over time due to 52 temporal variations in site-and lineage-specific evolutionary processes. For example, when the 53 homologous sites in a pair of sequences evolve under different conditions, evolutionary 54 processes cannot be homogeneous, and compositional heterogeneity across the sequences may 55 arise. When this ...

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Section: Methodssupporting

confidence: 92%

Detecting and visualising the impact of heterogeneous evolutionary processes on phylogenetic estimates

Jermiin

Lovell

Misof

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Whilst it is widely recognised that maximum likelihood (ML) methods based on nucleotide substitution models most accurately represent the true underlying evolutionary processes [ 30 ] and are therefore superior to amino acid substitution models [ 31 ], they are usually not used for deeply diverged species due to concerns over sequence divergence saturation [ 32 ]. However, amino acid models have been demonstrated to be non-Markovian [ 31 , 33 ], and their congruence with the underlying Markovian process operating on nucleotides is likely to be rare [ 34 , 35 ]. This necessitates use of nucleotide-based models of sequence evolution.…”

Section: Resultsmentioning

confidence: 99%

Comparative genomics reveals the distinct evolutionary trajectories of the robust and complex coral lineages

et al. 2018

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BackgroundDespite the biological and economic significance of scleractinian reef-building corals, the lack of large molecular datasets for a representative range of species limits understanding of many aspects of their biology. Within the Scleractinia, based on molecular evidence, it is generally recognised that there are two major clades, Complexa and Robusta, but the genomic bases of significant differences between them remain unclear.ResultsDraft genome assemblies and annotations were generated for three coral species: Galaxea fascicularis (Complexa), Fungia sp., and Goniastrea aspera (Robusta). Whilst phylogenetic analyses strongly support a deep split between Complexa and Robusta, synteny analyses reveal a high level of gene order conservation between all corals, but not between corals and sea anemones or between sea anemones. HOX-related gene clusters are, however, well preserved across all of these combinations. Differences between species are apparent in the distribution and numbers of protein domains and an apparent correlation between number of HSP20 proteins and stress tolerance. Uniquely amongst animals, a complete histidine biosynthesis pathway is present in robust corals but not in complex corals or sea anemones. This pathway appears to be ancestral, and its retention in the robust coral lineage has important implications for coral nutrition and symbiosis.ConclusionsThe availability of three new coral genomes enabled recognition of a de novo histidine biosynthesis pathway in robust corals which is only the second identified biosynthetic difference between corals. These datasets provide a platform for understanding many aspects of coral biology, particularly the interactions of corals with their endosymbionts.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1552-8) contains supplementary material, which is available to authorized users.

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“…The substantial bias in rate estimation between MM and variable types of HMM were reported for ancestral character state reconstruction with empirical and simulated data sets (Beaulieu et al 2013). 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).…”

Section: Modeling Hidden Processes: Morphology Grns and Smm With Hidmentioning

confidence: 99%

Integration of Anatomy Ontologies and Evo-Devo Using Structured Markov Models Suggests a New Framework for Modeling Discrete Phenotypic Traits

Tarasov

2019

Systematic Biology

127

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Modeling discrete phenotypic traits for either ancestral character state reconstruction or morphology-based 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 (GRNs) underlying trait development. Herein, I demonstrate that these processes can be efficiently modeled using structured Markov models (SMM) equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of SMM with anatomy ontologies can adequately incorporate the hierarchical dependencies, while the use of the hidden states accommodates hidden evolution of GRNs and substitution rate heterogeneity. I assess the new models using simulations and theoretical synthesis. The new approach solves the long-standing “tail color problem,” in which the trait is scored for species with tails of different colors or no tails. It also presents a previously unknown issue called the “two-scientist paradox,” in which the nature of coding the trait and the hidden processes driving the trait’s evolution are confounded; 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 article gives practical examples of using the new framework for phylogenetic inference and comparative analysis.

show abstract

Statistical tests to identify appropriate types of nucleotide sequence recoding in molecular phylogenetics

Cited by 12 publications

References 50 publications

Detecting and visualising the impact of heterogeneous evolutionary processes on phylogenetic estimates

Detecting and visualising the impact of heterogeneous evolutionary processes on phylogenetic estimates

Comparative genomics reveals the distinct evolutionary trajectories of the robust and complex coral lineages

Integration of Anatomy Ontologies and Evo-Devo Using Structured Markov Models Suggests a New Framework for Modeling Discrete Phenotypic Traits

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