An Approximate Bayesian Computation Approach for Modeling Genome Rearrangements

Moshe, Asher; Wygoda, Elya; Ecker, Noa; Loewenthal, Gil; Avram, Oren; Israeli, Omer; Hazkani‐Covo, Einat; Pe’er, Itsik; Pupko, Tal

doi:10.1093/molbev/msac231

Cited by 3 publications

(4 citation statements)

References 38 publications

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Section: Discussionmentioning

confidence: 99%

“…The loss of synteny information with increasing rearrangement rates is hard to overcome within a parsimony-based framework, meaning that a different approach to modelling genome evolution may be required; e.g. estimating rearrangement parameters through simulation (Moshe et al 2022) or performing a Bayesian sampling of rearrangement histories (Miklós and Tannier 2010). Interestingly, Markov models of chromosome number evolution have recently been developed (Yoshida and Kitano 2021;Setter 2023) and could be adapted to estimate model parameters and sample likely rearrangement histories given a set of genomes.…”

Section: Rearrangement Rates and Marker Errormentioning

confidence: 99%

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Inferring inter-chromosomal rearrangements and ancestral linkage groups from synteny

Mackintosh,

de la Rosa,

Martin

et al. 2023

Preprint

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Chromosome rearrangements shape the structure of the genome and influence evolutionary processes. Inferring ancestral chromosomes and rearrangements across a phylogenetic tree is therefore an important analysis within evolutionary genetics. One approach to this inference problem is to focus on synteny information, i.e. the co-occurrence of loci on the same chromosome. Although algorithms for inferring ancestral linkage groups (ALGs) and inter-chromosomal rearrangements from synteny have been previously described, they have seldom been applied to modern genome data. Here we implement these algorithms in a command-line tool, syngraph, and evaluate their performance using simulations that include a mix of different rearrangements and types of error. We show that ALGs and rearrangements can be recovered when the rearrangement frequency per-branch is well below the number of chromosomes. We demonstrate that competing models of rearrangement can be inferred by comparing observed results to simulations. Finally, we reanalyse genome assemblies of rhabditid nematodes and find that independent fusions of the same ALGs pose a challenge that is difficult to overcome without gene-order information. Our simulations and analysis of real data demonstrate both the promise and limitations of using synteny information to infer patterns of genome evolution.

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Section: Discussionmentioning

confidence: 99%

Section: Rearrangement Rates and Marker Errormentioning

confidence: 99%

Inferring inter-chromosomal rearrangements and ancestral linkage groups from synteny

Mackintosh,

de la Rosa,

Martin

et al. 2023

Preprint

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“…Despite ABC does not require likelihood analyses, it can provide estimates with similar (sometimes higher) accuracy compared to those obtained with some likelihood-based methods ( Lopes et al 2014 ). Some previous works demonstrated that ABC can be used to study molecular evolution ( Wilson et al 2009 , Lopes et al 2014 , Arenas et al 2015a , Arenas 2015b , Moshe et al 2022 ). For example, at the protein level, we previously applied ABC to estimate substitution and recombination rates with acceptable accuracy ( Arenas 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Selection among site-dependent structurally constrained substitution models of protein evolution by approximate Bayesian computation

Ferreiro,

Branco,

Arenas

2024

Bioinformatics

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Motivation The selection among substitution models of molecular evolution is fundamental for obtaining accurate phylogenetic inferences. At the protein level, evolutionary analyses are traditionally based on empirical substitution models but these models make unrealistic assumptions and are being surpassed by structurally constrained substitution (SCS) models. The SCS models often consider site-dependent evolution, a process that provides realism but complicates their implementation into likelihood functions that are commonly used for substitution model selection. Results We present a method to perform selection among site-dependent SCS models, also among empirical and site-dependent SCS models, based on the approximate Bayesian computation (ABC) approach and its implementation into the computational framework ProteinModelerABC. The framework implements ABC with and without regression adjustments and includes diverse empirical and site-dependent SCS models of protein evolution. Using extensive simulated data, we found that it provides selection among SCS and empirical models with acceptable accuracy. As illustrative examples, we applied the framework to analyse a variety of protein families observing that SCS models fit them better than the corresponding best-fitting empirical substitution models. Availability ProteinModelerABC is freely available from https://github.com/DavidFerreiro/ProteinModelerABC, can run in parallel and includes a graphical user interface. The framework is distributed with detailed documentation and ready-to-use examples. Supplementary information Supplementary material is available at Bioinformatics online.

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“…ABC is commonly applied when it is challenging to compute the likelihood, as this procedure bypasses the need to explicitly calculate likelihood. The ABC procedure, which was first developed in the field of population genetics ( Beaumont et al 2002 ), has been since utilized successfully in many other studies ( Przeworski 2003 , Tallmon et al 2008 , Kuhlwilm et al 2019 , Moshe et al 2022 ). Our group had previously utilized ABC for inferring the rates and length parameters of indels ( Karin et al 2017 , Loewenthal et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Statistical framework to determine indel-length distribution

Wygoda,

Loewenthal,

Moshe

et al. 2024

Bioinformatics

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Motivation Insertions and deletions (indels) of short DNA segments, along with substitutions, are the most frequent molecular evolutionary events. Indels were shown to affect numerous macro-evolutionary processes. Because indels may span multiple positions, their impact is a product of both their rate and their length distribution. An accurate inference of indel length distribution is important for multiple evolutionary and bioinformatics applications, most notably for alignment software. Previous studies counted the number of continuous gap characters in alignments to determine the best fitting length distribution. However, gap-counting methods are not statistically rigorous, as gap blocks are not synonymous with indels. Furthermore, such methods rely on alignments that regularly contain errors and are biased due to the assumption of alignment methods that indels lengths follow a geometric distribution Results We aimed to determine which indel length distribution best characterizes alignments using statistical rigorous methodologies. To this end, we reduced the alignment bias using a machine-learning algorithm and applied an Approximate Bayesian Computation methodology for model selection. Moreover, we developed a novel method to test if current indel models provide an adequate representation of the evolutionary process. We found that the best fitting model varies among alignments, with a Zipf length distribution fitting the vast majority of them. Supplementary information Supplementary data are available at Bioinformatics online.

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An Approximate Bayesian Computation Approach for Modeling Genome Rearrangements

Cited by 3 publications

References 38 publications

Inferring inter-chromosomal rearrangements and ancestral linkage groups from synteny

Inferring inter-chromosomal rearrangements and ancestral linkage groups from synteny

Selection among site-dependent structurally constrained substitution models of protein evolution by approximate Bayesian computation

Statistical framework to determine indel-length distribution

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