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1. Measuring Phylogenetic Information of Incomplete Sequence Data

   https://doi.org/10.1093/sysbio/syab073  

   Seo, Tae-Kun ; Gascuel, Olivier ; Thorne, Jeffrey L ( September 2021 , Systematic Biology)

   Ho, Simon (Ed.)

   Abstract Widely used approaches for extracting phylogenetic information from aligned sets of molecular sequences rely upon probabilistic models of nucleotide substitution or amino-acid replacement. The phylogenetic information that can be extracted depends on the number of columns in the sequence alignment and will be decreased when the alignment contains gaps due to insertion or deletion events. Motivated by the measurement of information loss, we suggest assessment of the effective sequence length (ESL) of an aligned data set. The ESL can differ from the actual number of columns in a sequence alignment because of the presence of alignment gaps. Furthermore, the estimation of phylogenetic information is affected by model misspecification. Inevitably, the actual process of molecular evolution differs from the probabilistic models employed to describe this process. This disparity means the amount of phylogenetic information in an actual sequence alignment will differ from the amount in a simulated data set of equal size, which motivated us to develop a new test for model adequacy. Via theory and empirical data analysis, we show how to disentangle the effects of gaps and model misspecification. By comparing the Fisher information of actual and simulated sequences, we identify which alignment sites and tree branches are most affected by gaps and model misspecification. [Fisher information; gaps; insertion; deletion; indel; model adequacy; goodness-of-fit test; sequence alignment.] 

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2. MAST: Phylogenetic Inference with Mixtures Across Sites and Trees

   https://doi.org/10.1093/sysbio/syae008  

   Wong, Thomas K. F. ; Cherryh, Caitlin ; Rodrigo, Allen G. ; Hahn, Matthew W. ; Minh, Bui Quang ; Lanfear, Robert ; Matschiner, ed., Michael ( February 2024 , Systematic Biology)

   Abstract.—Hundreds or thousands of loci are now routinely used in modern phylogenomic studies. Concatenation approaches to tree inference assume that there is a single topology for the entire dataset, but different loci may have different evolutionary histories due to incomplete lineage sorting (ILS), introgression, and/or horizontal gene transfer; even single loci may not be treelike due to recombination. To overcome this shortcoming, we introduce an implementation of a multi-tree mixture model that we call mixtures across sites and trees (MAST). This model extends a prior implementation by Boussau et al. (2009) by allowing users to estimate the weight of each of a set of pre-specified bifurcating trees in a single alignment. The MAST model allows each tree to have its own weight, topology, branch lengths, substitution model, nucleotide or amino acid frequencies, and model of rate heterogeneity across sites. We implemented the MAST model in a maximum-likelihood framework in the popular phylogenetic software, IQ-TREE. Simulations show that we can accurately recover the true model parameters, including branch lengths and tree weights for a given set of tree topologies, under a wide range of biologically realistic scenarios. We also show that we can use standard statistical inference approaches to reject a single-tree model when data are simulated under multiple trees (and vice versa). We applied the MAST model to multiple primate datasets and found that it can recover the signal of ILS in the Great Apes, as well as the asymmetry in minor trees caused by introgression among several macaque species. When applied to a dataset of 4 Platyrrhine species for which standard concatenated maximum likelihood (ML) and gene tree approaches disagree, we observe that MAST gives the highest weight (i.e., the largest proportion of sites) to the tree also supported by gene tree approaches. These results suggest that the MAST model is able to analyze a concatenated alignment using ML while avoiding some of the biases that come with assuming there is only a single tree. We discuss how the MAST model can be extended in the future.

    

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3. A new parameter‐rich structure‐aware mechanistic model for amino acid substitution during evolution

   https://doi.org/10.1002/prot.25429  

   Chi, Peter B. ; Kim, Dohyup ; Lai, Jason K. ; Bykova, Nadia ; Weber, Claudia C. ; Kubelka, Jan ; Liberles, David A. ( December 2017 , Proteins: Structure, Function, and Bioinformatics)

   Improvements in the description of amino acid substitution are required to develop better pseudo‐energy‐based protein structure‐aware models for use in phylogenetic studies. These models are used to characterize the probabilities of amino acid substitution and enable better simulation of protein sequences over a phylogeny. A better characterization of amino acid substitution probabilities in turn enables numerous downstream applications, like detecting positive selection, ancestral sequence reconstruction, and evolutionarily‐motivated protein engineering. Many existing Markov models for amino acid substitution in molecular evolution disregard molecular structure and describe the amino acid substitution process over longer evolutionary periods poorly. Here, we present a new model upgraded with a site‐specific parameterization of pseudo‐energy terms in a coarse‐grained force field, which describes local heterogeneity in physical constraints on amino acid substitution better than a previous pseudo‐energy‐based model with minimum cost in runtime. The importance of each weight term parameterization in characterizing underlying features of the site, including contact number, solvent accessibility, and secondary structural elements was evaluated, returning both expected and biologically reasonable relationships between model parameters. This results in the acceptance of proposed amino acid substitutions that more closely resemble those observed site‐specific frequencies in gene family alignments. The modular site‐specific pseudo‐energy function is made available for download through the following website:https://liberles.cst.temple.edu/Software/CASS/index.html.

    

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4. Numerical Encodings of Amino Acids in Multivariate Gaussian Modeling of Protein Multiple Sequence Alignments

   https://doi.org/10.3390/molecules24010104  

   Koehl, Patrice ; Orland, Henri ; Delarue, Marc ( January 2019 , Molecules)

   Residues in proteins that are in close spatial proximity are more prone to covariate as their interactions are likely to be preserved due to structural and evolutionary constraints. If we can detect and quantify such covariation, physical contacts may then be predicted in the structure of a protein solely from the sequences that decorate it. To carry out such predictions, and following the work of others, we have implemented a multivariate Gaussian model to analyze correlation in multiple sequence alignments. We have explored and tested several numerical encodings of amino acids within this model. We have shown that 1D encodings based on amino acid biochemical and biophysical properties, as well as higher dimensional encodings computed from the principal components of experimentally derived mutation/substitution matrices, do not perform as well as a simple twenty dimensional encoding with each amino acid represented with a vector of one along its own dimension and zero elsewhere. The optimum obtained from representations based on substitution matrices is reached by using 10 to 12 principal components; the corresponding performance is less than the performance obtained with the 20-dimensional binary encoding. We highlight also the importance of the prior when constructing the multivariate Gaussian model of a multiple sequence alignment. 

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5. TAPER: Pinpointing errors in multiple sequence alignments despite varying rates of evolution

   https://doi.org/10.1111/2041-210X.13696  

   Zhang, Chao ; Zhao, Yiming ; Braun, Edward L. ; Mirarab, Siavash ( August 2021 , Methods in Ecology and Evolution)

   Erroneous data can creep into sequence datasets for reasons ranging from contamination to annotation and alignment mistakes and reduce the accuracy of downstream analyses. As datasets keep getting larger, it has become difficult to check multiple sequence alignments visually for errors, and thus, automatic error detection methods are needed more than ever before. Alignment masking methods, which are widely used, remove entire aligned sites and may reduce signal as much as or more than they reduce the noise.

   The alternative we propose here is a surprisingly under‐explored approach: looking for errors in small species‐specific stretches of the multiple sequence alignments. We introduce a method called TAPER that uses a novel two‐dimensional outlier detection algorithm. Importantly, TAPER adjusts its null expectations per site and species, and in doing so, it attempts to distinguish the real heterogeneity (signal) from errors (noise).

   Our results show that TAPER removes very little data yet finds much of the error. The effectiveness of TAPER depends on several properties of the alignment (e.g. evolutionary divergence levels) and the errors (e.g. their length).

   By enabling data clean up with minimal loss of signal, TAPER can improve downstream analyses such as phylogenetic reconstruction and selection detection. Data errors, small or large, can reduce confidence in the downstream results, and thus, eliminating them can be beneficial even when downstream analyses are not impacted.

    

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