Probabilistic phylogenetic inference with insertions and deletions

A fundamental task in sequence analysis is to calculate the probability of a multiple alignment given a phylogenetic tree relating the sequences and an evolutionary model describing how sequences change over time. However, the most widely used phylogenetic models only account for residue substitution events. We describe a probabilistic model of a multiple sequence alignment that accounts for insertion and deletion events in addition to substitutions, given a phylogenetic tree, using a rate matrix augmented by the gap character. Starting from a continuous Markov process, we construct a non-reversible generative (birth-death) evolutionary model for insertions and deletions. The model assumes that insertion and deletion events occur one residue at a time. We apply this model to phylogenetic tree inference by extending the program dnaml in phylip. Using standard benchmarking methods on simulated data and a new "concordance test" benchmark on real ribosomal RNA alignments, we show that the extended program dnamlepsilon improves accuracy relative to the usual approach of ignoring gaps, while retaining the computational efficiency of the Felsenstein peeling algorithm.     

Character-state space versus rate of evolution in phylogenetic inference

With only four alternative character states, parallelisms and reversals are expected to occur frequently when using nucleotide characters for phylogenetic inference. Greater available character‐state space has been described as one of the advantages of third codon positions relative to first and second codon positions, as well as amino acids relative to nucleotides. We used simulations to quantify how character‐state space and rate of evolution relate to one another, and how this relationship is affected by differences in: tree topology, branch lengths, rate heterogeneity among sites, probability of change among states, and frequency of character states. Specifically, we examined how inferred tree lengths, consistency and retention indices, and accuracy of phylogenetic inference are affected. Our results indicate that the relatively small increases in the character‐state space evident in empirical data matrices can provide enormous benefits for the accuracy of phylogenetic inference. This advantage may become more pronounced with unequal probabilities of change among states. Although increased character‐state space greatly improved the accuracy of topology inference, improvements in the estimation of the correct tree length were less apparent. Accuracy and inferred tree length improved most when character‐state space increased initially; further increases provided more modest improvements. © The Willi Hennig Society 2004.     

Multiple sequence alignment accuracy and phylogenetic inference

Phylogenies are often thought to be more dependent upon the specifics of the sequence alignment rather than on the method of reconstruction. Simulation of sequences containing insertion and deletion events was performed in order to determine the role that alignment accuracy plays during phylogenetic inference. Data sets were simulated for pectinate, balanced, and random tree shapes under different conditions (ultrametric equal branch length, ultrametric random branch length, nonultrametric random branch length). Comparisons between hypothesized alignments and true alignments enabled determination of two measures of alignment accuracy, that of the total data set and that of individual branches. In general, our results indicate that as alignment error increases, topological accuracy decreases. This trend was much more pronounced for data sets derived from more pectinate topologies. In contrast, for balanced, ultrametric, equal branch length tree shapes, alignment inaccuracy had little average effect on tree reconstruction. These conclusions are based on average trends of many analyses under different conditions, and any one specific analysis, independent of the alignment accuracy, may recover very accurate or inaccurate topologies. Maximum likelihood and Bayesian, in general, outperformed neighbor joining and maximum parsimony in terms of tree reconstruction accuracy. Results also indicated that as the length of the branch and of the neighboring branches increase, alignment accuracy decreases, and the length of the neighboring branches is the major factor in topological accuracy. Thus, multiple-sequence alignment can be an important factor in downstream effects on topological reconstruction.     

Quantifying the impact of dependent evolution among sites in phylogenetic inference

Nearly all commonly used methods of phylogenetic inference assume that characters in an alignment evolve independently of one another. This assumption is attractive for simplicity and computational tractability but is not biologically reasonable for RNAs and proteins that have secondary and tertiary structures. Here, we simulate RNA and protein-coding DNA sequence data under a general model of dependence in order to assess the robustness of traditional methods of phylogenetic inference to violation of the assumption of independence among sites. We find that the accuracy of independence-assuming methods is reduced by the dependence among sites; for proteins this reduction is relatively mild, but for RNA this reduction may be substantial. We introduce the concept of effective sequence length and its utility for considering information content in phylogenetics.     

ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference

Highly divergent sites in multiple sequence alignments (MSAs), which can stem from erroneous inference of homology and saturation of substitutions, are thought to negatively impact phylogenetic inference. Thus, several different trimming strategies have been developed for identifying and removing these sites prior to phylogenetic inference. However, a recent study reported that doing so can worsen inference, underscoring the need for alternative alignment trimming strategies. Here, we introduce ClipKIT, an alignment trimming software that, rather than identifying and removing putatively phylogenetically uninformative sites, instead aims to identify and retain parsimony-informative sites, which are known to be phylogenetically informative. To test the efficacy of ClipKIT, we examined the accuracy and support of phylogenies inferred from 14 different alignment trimming strategies, including those implemented in ClipKIT, across nearly 140,000 alignments from a broad sampling of evolutionary histories. Phylogenies inferred from ClipKIT-trimmed alignments are accurate, robust, and time saving. Furthermore, ClipKIT consistently outperformed other trimming methods across diverse datasets, suggesting that strategies based on identifying and retaining parsimony-informative sites provide a robust framework for alignment trimming.

Highly divergent sites in multiple sequence alignments are thought to negatively impact phylogenetic inference; trimming methods aim to remove these sites, but recent analysis suggests that doing so can worsen inference. This study introduces ClipKIT, a trimming method that instead aims to retain parsimony-informative sites; phylogenetic inference using ClipKIT-trimmed alignments is accurate, robust and time-saving.     

ImOSM: intermittent evolution and robustness of phylogenetic methods

Among the criteria to evaluate the performance of a phylogenetic method, robustness to model violation is of particular practical importance as complete a priori knowledge of evolutionary processes is typically unavailable. For studies of robustness in phylogenetic inference, a utility to add well-defined model violations to the simulated data would be helpful. We therefore introduce ImOSM, a tool to imbed intermittent evolution as model violation into an alignment. Intermittent evolution refers to extra substitutions occurring randomly on branches of a tree, thus changing alignment site patterns. This means that the extra substitutions are placed on the tree after the typical process of sequence evolution is completed. We then study the robustness of widely used phylogenetic methods: maximum likelihood (ML), maximum parsimony (MP), and a distance-based method (BIONJ) to various scenarios of model violation. Violation of rates across sites (RaS) heterogeneity and simultaneous violation of RaS and the transition/transversion ratio on two nonadjacent external branches hinder all the methods recovery of the true topology for a four-taxon tree. For an eight-taxon balanced tree, the violations cause each of the three methods to infer a different topology. Both ML and MP fail, whereas BIONJ, which calculates the distances based on the ML estimated parameters, reconstructs the true tree. Finally, we report that a test of model homogeneity and goodness of fit tests have enough power to detect such model violations. The outcome of the tests can help to actually gain confidence in the inferred trees. Therefore, we recommend using these tests in practical phylogenetic analyses.     

Phylogenetic inference from conserved sites alignments.

Molecular sequences provide a rich source of data for inferring the phylogenetic relationships among species. However, recent work indicates that even an accurate multiple alignment of a large sequence set may yield an incorrect phylogeny and that the quality of the phylogenetic tree improves when the input consists only of the highly conserved, motif regions of the alignment. This work introduces two methods of producing multiple alignments that include only the conserved regions of the initial alignment. The first method retains conserved motifs, whereas the second retains individual conserved sites in the initial alignment. Using parsimony analysis on a mitochondrial data set containing 19 species among which the phylogenetic relationships are widely accepted, both conserved alignment methods produce better phylogenetic trees than the complete alignment. Unlike any of the 19 inference methods used before to analyze this data, both methods produce trees that are completely consistent with the known phylogeny. The motif-based method employs far fewer alignment sites for comparable error rates. For a larger data set containing mitochondrial sequences from 39 species, the site-based method produces a phylogenetic tree that is largely consistent with known phylogenetic relationships and suggests several novel placements. J. Exp. Zool. ( Mol. Dev. Evol.) 285:128-139, 1999.     

核糖体RNA二级结构对拓扑结构准确性的影响

探讨了核糖体小亚基二级结构对真菌系统发育分析的影响。对用不同方法构建的系统发育树进行比较,结果表明结合二级结构信息的分析方法较传统方法产生了更为合理的拓扑结构。二级结构信息除用于优化序列比对外,还需整合到核酸替代模型中;恰当的序列比对方法、进化模型和建树运算法则有助于更加准确地揭示类群之间的亲缘关系。     

Phylogenetic mixture models can reduce node-density artifacts

We investigate the performance of phylogenetic mixture models in reducing a well-known and pervasive artifact of phylogenetic inference known as the node-density effect, comparing them to partitioned analyses of the same data. The node-density effect refers to the tendency for the amount of evolutionary change in longer branches of phylogenies to be underestimated compared to that in regions of the tree where there are more nodes and thus branches are typically shorter. Mixture models allow more than one model of sequence evolution to describe the sites in an alignment without prior knowledge of the evolutionary processes that characterize the data or how they correspond to different sites. If multiple evolutionary patterns are common in sequence evolution, mixture models may be capable of reducing node-density effects by characterizing the evolutionary processes more accurately. In gene-sequence alignments simulated to have heterogeneous patterns of evolution, we find that mixture models can reduce node-density effects to negligible levels or remove them altogether, performing as well as partitioned analyses based on the known simulated patterns. The mixture models achieve this without knowledge of the patterns that generated the data and even in some cases without specifying the full or true model of sequence evolution known to underlie the data. The latter result is especially important in real applications, as the true model of evolution is seldom known. We find the same patterns of results for two real data sets with evidence of complex patterns of sequence evolution: mixture models substantially reduced node-density effects and returned better likelihoods compared to partitioning models specifically fitted to these data. We suggest that the presence of more than one pattern of evolution in the data is a common source of error in phylogenetic inference and that mixture models can often detect these patterns even without prior knowledge of their presence in the data. Routine use of mixture models alongside other approaches to phylogenetic inference may often reveal hidden or unexpected patterns of sequence evolution and can improve phylogenetic inference.     

More genes or more taxa? The relative contribution of gene number and taxon number to phylogenetic accuracy

The relative contribution of taxon number and gene number to accuracy in phylogenetic inference is a major issue in phylogenetics and of central importance to the choice of experimental strategies for the successful reconstruction of a broad sketch of the tree of life. Maximization of the number of taxa sampled is the strategy favored by most phylogeneticists, although its necessity remains the subject of debate. Vast increases in gene number are now possible due to advances in genomics, but large numbers of genes will be available for only modest numbers of taxa, raising the question of whether such genome-scale phylogenies will be robust to the addition of taxa. To examine the relative benefit of increasing taxon number or gene number to phylogenetic accuracy, we have developed an assay that utilizes the symmetric difference tree distance as a measure of phylogenetic accuracy. We have applied this assay to a genome-scale data matrix containing 106 genes from 14 yeast species. Our results show that increasing taxon number correlates with a slight decrease in phylogenetic accuracy. In contrast, increasing gene number has a significant positive effect on phylogenetic accuracy. Analyses of an additional taxon-rich data matrix from the same yeast clade show that taxon number does not have a significant effect on phylogenetic accuracy. The positive effect of gene number and the lack of effect of taxon number on phylogenetic accuracy are also corroborated by analyses of two data matrices from mammals and angiosperm plants, respectively. We conclude that, for typical data sets, the number of genes utilized may be a more important determinant of phylogenetic accuracy than taxon number.     

Efficient biased estimation of evolutionary distances when substitution rates vary across sites

This paper deals with phylogenetic inference when the variability of substitution rates across sites (VRAS) is modeled by a gamma distribution. We show that underestimating VRAS, which results in underestimates for the evolutionary distances between sequences, usually improves the topological accuracy of phylogenetic tree inference by distance-based methods, especially when the molecular clock holds. We propose a method to estimate the gamma shape parameter value which is most suited for tree topology inference, given the sequences at hand. This method is based on the pairwise evolutionary distances between sequences and allows one to reconstruct the phylogeny of a high number of taxa (>1,000). Simulation results show that the topological accuracy is highly improved when using the gamma shape parameter value given by our method, compared with the true (unknown) value which was used to generate the data. Furthermore, when VRAS is high, the topological accuracy of our distance-based method is better than that of a maximum likelihood approach. Finally, a data set of Maoricicada species sequences is analyzed, which confirms the advantage of our method.     

Phylogenetic inference under varying proportions of indel-induced alignment gaps

Background  

The effect of alignment gaps on phylogenetic accuracy has been the subject of numerous studies. In this study, we investigated the relationship between the total number of gapped sites and phylogenetic accuracy, when the gaps were introduced (by means of computer simulation) to reflect indel (insertion/deletion) events during the evolution of DNA sequences. The resulting (true) alignments were subjected to commonly used gap treatment and phylogenetic inference methods.     

A reversible jump method for Bayesian phylogenetic inference with a nonhomogeneous substitution model

Nonhomogeneous substitution models have been introduced for phylogenetic inference when the substitution process is nonstationary, for example, when sequence composition differs between lineages. Existing models can have many parameters, and it is then difficult and computationally expensive to learn the parameters and to select the optimal model complexity. We extend an existing nonhomogeneous substitution model by introducing a reversible jump Markov chain Monte Carlo method for efficient Bayesian inference of the model order along with other phylogenetic parameters of interest. We also introduce a new hierarchical prior which leads to more reasonable results when only a small number of lineages share a particular substitution process. The method is implemented in the PHASE software, which includes specialized substitution models for RNA genes with conserved secondary structure. We apply an RNA-specific nonhomogeneous model to a structure-based alignment of rRNA sequences spanning the entire tree of life. A previous study of the same genes from a similar set of species found robust evidence for a mesophilic last universal common ancestor (LUCA) by inference of the G+C composition at the root of the tree. In the present study, we find that the helical GC composition at the root is strongly dependent on the root position. With a bacterial rooting, we find that there is no longer strong support for either a mesophile or a thermophile LUCA, although a hyperthermophile LUCA remains unlikely. We discuss reasons why results using only RNA helices may differ from results using all aligned sites when applying nonhomogeneous models to RNA genes.     

The deterministic effects of alignment bias in phylogenetic inference

Alignment of nucleotide and/or amino acid sequences is a fundamental component of sequence‐based molecular phylogenetic studies. Here we examined how different alignment methods affect the phylogenetic trees that are inferred from the alignments. We used simulations to determine how alignment errors can lead to systematic biases that affect phylogenetic inference from those sequences. We compared four approaches to sequence alignment: progressive pairwise alignment, simultaneous multiple alignment of sequence fragments, local pairwise alignment and direct optimization. When taking into account branch support, implied alignments produced by direct optimization were found to show the most extreme behaviour (based on the alignment programs for which nearly equivalent alignment parameters could be set) in that they provided the strongest support for the correct tree in the simulations in which it was easy to resolve the correct tree and the strongest support for the incorrect tree in our long‐branch‐attraction simulations. When applied to alignment‐sensitive process partitions with different histories, direct optimization showed the strongest mutual influence between the process partitions when they were aligned and phylogenetically analysed together, which makes detecting recombination more difficult. Simultaneous alignment performed well relative to direct optimization and progressive pairwise alignment across all simulations. Rather than relying upon methods that integrate alignment and tree search into a single step without accounting for alignment uncertainty, as with implied alignments, we suggest that simultaneous alignment using the similarity criterion, within the context of information available on biological processes and function, be applied whenever possible for sequence‐based phylogenetic analyses.     

Evolutionary Triplet Models of Structured RNA

The reconstruction and synthesis of ancestral RNAs is a feasible goal for paleogenetics. This will require new bioinformatics methods, including a robust statistical framework for reconstructing histories of substitutions, indels and structural changes. We describe a “transducer composition” algorithm for extending pairwise probabilistic models of RNA structural evolution to models of multiple sequences related by a phylogenetic tree. This algorithm draws on formal models of computational linguistics as well as the 1985 protosequence algorithm of David Sankoff. The output of the composition algorithm is a multiple-sequence stochastic context-free grammar. We describe dynamic programming algorithms, which are robust to null cycles and empty bifurcations, for parsing this grammar. Example applications include structural alignment of non-coding RNAs, propagation of structural information from an experimentally-characterized sequence to its homologs, and inference of the ancestral structure of a set of diverged RNAs. We implemented the above algorithms for a simple model of pairwise RNA structural evolution; in particular, the algorithms for maximum likelihood (ML) alignment of three known RNA structures and a known phylogeny and inference of the common ancestral structure. We compared this ML algorithm to a variety of related, but simpler, techniques, including ML alignment algorithms for simpler models that omitted various aspects of the full model and also a posterior-decoding alignment algorithm for one of the simpler models. In our tests, incorporation of basepair structure was the most important factor for accurate alignment inference; appropriate use of posterior-decoding was next; and fine details of the model were least important. Posterior-decoding heuristics can be substantially faster than exact phylogenetic inference, so this motivates the use of sum-over-pairs heuristics where possible (and approximate sum-over-pairs). For more exact probabilistic inference, we discuss the use of transducer composition for ML (or MCMC) inference on phylogenies, including possible ways to make the core operations tractable.     

Using Confidence Set Heuristics During Topology Search Improves the Robustness of Phylogenetic Inference

We examine the impact of likelihood surface characteristics on phylogenetic inference. Amino acid data sets simulated from topologies with branch length features chosen to represent varying degrees of difficulty for likelihood maximization are analyzed. We present situations where the tree found to achieve the global maximum in likelihood is often not equal to the true tree. We use the program covSEARCH to demonstrate how the use of adaptively sized pools of candidate trees that are updated using confidence tests results in solution sets that are highly likely to contain the true tree. This approach requires more computation than traditional maximum likelihood methods, hence covSEARCH is best suited to small to medium-sized alignments or large alignments with some constrained nodes. The majority rule consensus tree computed from the confidence sets also proves to be different from the generating topology. Although low phylogenetic signal in the input alignment can result in large confidence sets of trees, some biological information can still be obtained based on nodes that exhibit high support within the confidence set. Two real data examples are analyzed: mammal mitochondrial proteins and a small tubulin alignment. We conclude that the technique of confidence set optimization can significantly improve the robustness of phylogenetic inference at a reasonable computational cost. Additionally, when either very short internal branches or very long terminal branches are present, confident resolution of specific bipartitions or subtrees, rather than whole-tree phylogenies, may be the most realistic goal for phylogenetic methods. [Reviewing Editor: Dr. Nicolas Galtier]     

Sensitivity of Ribosomal RNA Character Sampling in the Phylogeny of Rhabditida

Near-full-length 18S and 28S rRNA gene sequences were obtained for 33 nematode species. Datasets were constructed based on secondary structure and progressive multiple alignments, and clades were compared for phylogenies inferred by Bayesian and maximum likelihood methods. Clade comparisons were also made following removal of ambiguously aligned sites as determined using the program ProAlign. Different alignments of these data produced tree topologies that differed, sometimes markedly, when analyzed by the same inference method. With one exception, the same alignment produced an identical tree topology when analyzed by different methods. Removal of ambiguously aligned sites altered the tree topology and also reduced resolution. Nematode clades were sensitive to differences in multiple alignments, and more than doubling the amount of sequence data by addition of 28S rRNA did not fully mitigate this result. Although some individual clades showed substantially higher support when 28S data were combined with 18S data, the combined analysis yielded no statistically significant increases in the number of clades receiving higher support when compared to the 18S data alone. Secondary structure alignment increased accuracy in positional homology assignment and, when used in combination with paired-site substitution models, these structural hypotheses of characters and improved models of character state change yielded high levels of phylogenetic resolution. Phylogenetic results included strong support for inclusion of Daubaylia potomaca within Cephalobidae, whereas the position of Fescia grossa within Tylenchina varied depending on the alignment, and the relationships among Rhabditidae, Diplogastridae, and Bunonematidae were not resolved.     

Modeling the evolution of regulatory elements by simultaneous detection and alignment with phylogenetic pair HMMs

The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation.     

Parametric Analysis of Alignment and Phylogenetic Uncertainty

To infer a phylogenetic tree from a set of DNA sequences, typically a multiple alignment is first used to obtain homologous bases. The inferred phylogeny can be very sensitive to how the alignment was created. We develop tools for analyzing the robustness of phylogeny to perturbations in alignment parameters in the NW algorithm. Our main tool is parametric alignment, with novel improvements that are of general interest in parametric inference. Using parametric alignment and a Gaussian distribution on alignment parameters, we derive probabilities of optimal alignment summaries and inferred phylogenies. We apply our method to analyze intronic sequences from Drosophila flies. We show that phylogeny estimates can be sensitive to the choice of alignment parameters, and that parametric alignment elucidates the relationship between alignment parameters and reconstructed trees.     

Accurate Detection of Recombinant Breakpoints in Whole-Genome Alignments

We propose a novel method for detecting sites of molecular recombination in multiple alignments. Our approach is a compromise between previous extremes of computationally prohibitive but mathematically rigorous methods and imprecise heuristic methods. Using a combined algorithm for estimating tree structure and hidden Markov model parameters, our program detects changes in phylogenetic tree topology over a multiple sequence alignment. We evaluate our method on benchmark datasets from previous studies on two recombinant pathogens, Neisseria and HIV-1, as well as simulated data. We show that we are not only able to detect recombinant regions of vastly different sizes but also the location of breakpoints with great accuracy. We show that our method does well inferring recombination breakpoints while at the same time maintaining practicality for larger datasets. In all cases, we confirm the breakpoint predictions of previous studies, and in many cases we offer novel predictions.