A Probabilistic Model for Indel Evolution: Differentiating Insertions from Deletions

Loewenthal, Gil; Rapoport, Dana; Avram, Oren; Moshe, Asher; Wygoda, Elya; Itzkovitch, Alon; Israeli, Omer; Azouri, Dana; Cartwright, Reed A.; Mayrose, Itay; Pupko, Tal

doi:10.1093/molbev/msab266

Cited by 25 publications

(30 citation statements)

References 70 publications

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“…Therefore, given an empirical dataset (or datasets) one could use the presented method to extract estimations for the posterior distribution of the genome rearrangement parameters. Similar tools can be used to extract other evolutionary parameters, for example SPARTA-ABC ( Loewenthal et al 2021 ) can be used to extract posterior distributions of parameters relevant for small insertions and deletions. These posterior distributions can be used to simulate an entire genome data, which will better reflect empirical datasets compared to data for which the parameters were drawn randomly.…”

Section: Discussionmentioning

confidence: 99%

An Approximate Bayesian Computation Approach for Modeling Genome Rearrangements

Moshe

Wygoda

Ecker

et al. 2022

Molecular Biology and Evolution

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The inference of genome rearrangement events has been extensively studied, as they play a major role in molecular evolution. However, probabilistic evolutionary models that explicitly imitate the evolutionary dynamics of such events, as well as methods to infer model parameters, are yet to be fully utilized. Here, we developed a probabilistic approach to infer genome rearrangement rate parameters using an Approximate Bayesian Computation (ABC) framework. We developed two genome rearrangement models, a basic model, which accounts for genomic changes in gene order, and a more sophisticated one which also accounts for changes in chromosome number. We characterized the ABC inference accuracy using simulations and applied our methodology to both prokaryotic and eukaryotic empirical datasets. Knowledge of genome-rearrangement rates can help elucidate their role in evolution as well as help simulate genomes with evolutionary dynamics that reflect empirical genomes.

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Section: Discussionmentioning

confidence: 99%

An Approximate Bayesian Computation Approach for Modeling Genome Rearrangements

Moshe

Wygoda

Ecker

et al. 2022

Molecular Biology and Evolution

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“…In the last 10 years, several models and computational tools to predict different effects of residue coevolution, such as the nonlinearity of effects caused by substitutions and their different combinations, have been created. , An overview of the main computational concepts of these models and an explanation of the data they use to infer information about residue coevolution will be provided later. Furthermore, computational methods have been reported to model the functional effects and evolution of indels and a protein design tool to predict amino acid indels (and substitutions) for protein engineering.…”

Section: Enzyme Engineering Strategiesmentioning

confidence: 99%

“…53,54 An overview of the main computational concepts of these models and an explanation of the data they use to infer information about residue coevolution will be provided later. Furthermore, computational methods have been reported to model the functional effects 55 and evolution 56 of indels and a protein design tool to predict amino acid indels (and substitutions) 57 for protein engineering.…”

Section: Directed Evolutionmentioning

confidence: 99%

Learning Epistasis and Residue Coevolution Patterns: Current Trends and Future Perspectives for Advancing Enzyme Engineering

2022

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Engineering proteins and enzymes with the desired functionality has broad applications in molecular biology, biotechnology, biomedical sciences, health, and medicine. The vastness of protein sequence space and all the possible proteins it represents can pose a considerable barrier for enzyme engineering campaigns through directed evolution and rational design. The nonlinear effects of coevolution between amino acids in protein sequences complicate this further. Data-driven models increasingly provide scientists with the computational tools to navigate through the largely undiscovered forest of protein variants and catch a glimpse of the rules and effects underlying the topology of sequence space. In this review, we outline a complete theoretical journey through the processes of protein engineering methods such as directed evolution and rational design and reflect on these strategies and data-driven hybrid strategies in the context of sequence space. We discuss crucial phenomena of residue coevolution, such as epistasis, and review the history of models created over the past decade, aiming to infer rules of protein evolution from data and use this knowledge to improve the prediction of the structure− function relationship of proteins. Data-driven models based on deep learning algorithms are among the most promising methods that can account for the nonlinear phenomena of sequence space to some degree. We also critically discuss the available models to predict evolutionary coupling and epistatic effects (classical and deep learning) in terms of their capabilities and limitations. Finally, we present our perspective on possible future directions for developing data-driven approaches and provide key orientation points and necessities for the future of the fast-evolving field of enzyme engineering.

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“…Thus, we expect that ANNs, which can implicitly construct indel models from training MSA by ANNs, may have the potential to improve branch length estimation relative to traditional methods that ignore indel information. Indel events provide useful information for evolutionary inference for highly diverged sequences (Rokas and Holland 2000;Loewenthal et al 2021) or in scenarios where the number of substitutions is insufficient for reliable estimation (Redelings and Suchard 2007), so indels could have a positive effect on branch length estimation for trees with long or short branches. Acknowledgements…”

Section: Concluding Remarks and Path Forwardmentioning

confidence: 99%

Reliable estimation of tree branch lengths using deep neural networks

Suvorov

Schrider

2022

Preprint

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A phylogenetic tree represents hypothesized evolutionary history for a set of taxa. Besides the branching patterns (i.e., tree topology), phylogenies contain information about the evolutionary distances (i.e. branch lengths) between all taxa in the tree, which include extant taxa (external nodes) and their last common ancestors (internal nodes). During phylogenetic tree inference, the branch lengths are typically co-estimated along with other phylogenetic parameters during tree topology space exploration. There are well-known regions of the branch length parameter space where accurate estimation of phylogenetic trees is especially difficult. Several novel studies have recently demonstrated that machine learning approaches have the potential to help solve phylogenetic problems with greater accuracy and computational efficiency. In this study, as a proof of concept, we sought to explore the possibility of machine learning models to predict branch lengths. To that end, we designed several deep learning frameworks to estimate branch lengths on fixed tree topologies from multiple sequence alignments or its representations. Our results show that deep learning methods can exhibit superior performance in some difficult regions of branch length parameter space. For example, in contrast to maximum likelihood inference, which is typically used for estimating branch lengths, deep learning methods are more efficient and accurate when inferring long branches that are associated with distantly related taxa and perform well in the aforementioned challenging regions of the parameter space. Together, our findings represent a next step toward accurate, fast, and reliable phylogenetic inference with machine learning approaches.

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A Probabilistic Model for Indel Evolution: Differentiating Insertions from Deletions

Cited by 25 publications

References 70 publications

An Approximate Bayesian Computation Approach for Modeling Genome Rearrangements

An Approximate Bayesian Computation Approach for Modeling Genome Rearrangements

Learning Epistasis and Residue Coevolution Patterns: Current Trends and Future Perspectives for Advancing Enzyme Engineering

Reliable estimation of tree branch lengths using deep neural networks

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