AliSim: A Fast and Versatile Phylogenetic Sequence Simulator for the Genomic Era

Ly-Trong, Nhan; Naser-Khdour, Suha; Lanfear, Robert; Minh, Bùi Quang

doi:10.1093/molbev/msac092

Cited by 53 publications

(67 citation statements)

References 39 publications

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“…Different R, H, and F were simulated over the trees in the first simulation experiment, while the same R, H, and F were shared among the trees in the second simulation experiment. The alignments were then simulated according to the tree, the GTR model, and the gamma rate using AliSim (38). Each simulated dataset contained 100k bases, regardless of the number of trees m. The proportions of the lengths of each of the m alignments simulated from each of the m trees were the ratios of m random integers drawn from a uniform distribution between 1 and 10.…”

Section: Model Description the Mast Model Consists Of M Classesmentioning

confidence: 99%

MAST: Phylogenetic Inference with Mixtures Across Sites and Trees

Wong

Cherryh

Rodrigo

et al. 2022

Preprint

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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, introgression, and/or horizontal gene transfer; even single loci may not be treelike due to recombination. To overcome this shortcoming, we introduce the mixture across sites and trees (MAST) model, which uses a mixture of bifurcating trees to represent multiple histories in a single concatenated alignment. The MAST model allows each tree to have its own 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 (i.e. frequencies) for a given set of tree topologies. 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 incomplete lineage sorting 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 four Platyrrhine species for which standard concatenated maximum likelihood and gene tree approaches disagree, we find that MAST gives the highest weight to the tree favored by gene tree approaches. These results suggest that the MAST model is able to analyse a concatenated alignment using maximum likelihood, while avoiding some of the biases that come with assuming there is only a single tree. The MAST model can therefore offer unique biological insights when applied to datasets with multiple evolutionary histories. We discuss how it can be extended in the future.

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Section: Model Description the Mast Model Consists Of M Classesmentioning

confidence: 99%

MAST: Phylogenetic Inference with Mixtures Across Sites and Trees

Wong

Cherryh

Rodrigo

et al. 2022

Preprint

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“…To investigate if certain regions in the M. bovis genome have significantly higher values of either SNP density or selective sweep sites, a probabilistic hypothesis test was used to find highly significant genomic regions. For that, we produced a simulated alignment from the original alignment with the tool Alisim using the same alignment length, nucleotide substitution model, and phylogenetic tree topology (created from the previously computed Maximum Likelihood tree; Ly-Trong et al, 2022 ). Alisim next created a random sequence based on the previous specifications and then simulated nucleotide substitutions that were independently added while also conforming to the phylogenetic topology, resulting in a simulated alignment output.…”

Section: Methodsmentioning

confidence: 99%

Selective sweep sites and SNP dense regions differentiate Mycobacterium bovis isolates across scales

Legall

Salvador

2022

Front. Microbiol.

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Mycobacterium bovis, a bacterial zoonotic pathogen responsible for the economically and agriculturally important livestock disease bovine tuberculosis (bTB), infects a broad mammalian host range worldwide. This characteristic has led to bidirectional transmission events between livestock and wildlife species as well as the formation of wildlife reservoirs, impacting the success of bTB control measures. Next Generation Sequencing (NGS) has transformed our ability to understand disease transmission events by tracking variant sites, however the genomic signatures related to host adaptation following spillover, alongside the role of other genomic factors in the M. bovis transmission process are understudied problems. We analyzed publicly available M. bovis datasets collected from 700 hosts across three countries with bTB endemic regions (United Kingdom, United States, and New Zealand) to investigate if genomic regions with high SNP density and/or selective sweep sites play a role in Mycobacterium bovis adaptation to new environments (e.g., at the host-species, geographical, and/or sub-population levels). A simulated M. bovis alignment was created to generate null distributions for defining genomic regions with high SNP counts and regions with selective sweeps evidence. Random Forest (RF) models were used to investigate evolutionary metrics within the genomic regions of interest to determine which genomic processes were the best for classifying M. bovis across ecological scales. We identified in the M. bovis genomes 14 and 132 high SNP density and selective sweep regions, respectively. Selective sweep regions were ranked as the most important in classifying M. bovis across the different scales in all RF models. SNP dense regions were found to have high importance in the badger and cattle specific RF models in classifying badger derived isolates from livestock derived ones. Additionally, the genes detected within these genomic regions harbor various pathogenic functions such as virulence and immunogenicity, membrane structure, host survival, and mycobactin production. The results of this study demonstrate how comparative genomics alongside machine learning approaches are useful to investigate further the nature of M. bovis host-pathogen interactions.

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“…These methods take distance matrices, which are usually derived from sequence alignments, as input and produce a phylogenetic tree as output. Our simulations are implemented mostly in R, using the packages phangorn (Schliep, 2011) and ape (Paradis and Schliep, 2019), and for simulating alignments we use the tool AliSim from the software package IQ-TREE (Ly-Trong et al ., 2021). All implementations can be found in (Collienne, 2022).…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…For simulating sequence alignments we use the tool AliSim from the software package IQ-TREE (Ly-Trong et al, 2021). Sequences of length 1 , 000 are computed for a random Yule-Harding tree on n leaves, assuming an HKY model (Hasegawa et al, 1985) with transition/transversion ratio of 2 and base frequencies 0.1, 0.2, 0.3, 0.4 for A, C, G, and T, respectively.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Online Bayesian Analysis with BEAST 2

Bouckaert

Collienne

Gavryushkin

2022

Preprint

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There are a growing number of areas, e.g. epidemiology and within-organism cancer evolution, where re-analysing all available data from scratch every time new data becomes available or old data is refined is no longer feasible. All these and related areas can benefit from online phylogenetic inference that can booster previous data analyses. Here, we make the case that adding/removing taxa from an analysis can have substantial non-local impact on the tree that is inferred, both in a model based setting, as well as for distance based methods. Consequently, online phylogenetic algorithms may require global updates of the trees and other parameters, a task that in general is highly non-trivial. Motivated by this observation, we designed an online algorithm that benefits from a parallelism in a Bayesian setting that is substantially more efficient than re-running the analysis from scratch. Furthermore, our algorithm is not sensitive to the number of sequences added, allowing the sequence data to grow/be refined iteratively. We show how this approach can be used in a maximum likelihood setting, and -- apart from adding/removing new sequences -- demonstrate a number of practical alternative use cases of our algorithm, including how to break up a single (offline) large analysis to get results faster. An open source implementation is available under GPL3 license as the 'online' package for BEAST 2 at https://github.com/rbouckaert/online and a tutorial at https://github.com/rbouckaert/online-tutorial.

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AliSim: A Fast and Versatile Phylogenetic Sequence Simulator for the Genomic Era

Cited by 53 publications

References 39 publications

MAST: Phylogenetic Inference with Mixtures Across Sites and Trees

MAST: Phylogenetic Inference with Mixtures Across Sites and Trees

Selective sweep sites and SNP dense regions differentiate Mycobacterium bovis isolates across scales

Online Bayesian Analysis with BEAST 2

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