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33 results for “approximate bayesian computation”
Cophylogeny reconstruction allowing for multiple associations through approximate Bayesian computation
<p>Phylogenetic tree reconciliation is extensively employed for the examination of coevolution between host and symbiont species. An important concern is the requirement for dependable cost values when selecting event-based parsimonious reconciliation. Although certain approaches deduce event probabilities unique to each pair of host and symbiont trees, which can subsequently be converted into cost values, a significant limitation lies in their inability to model the <em>invasion</em> of diverse host species by the same symbiont species (termed as a spread event), which is believed to occur in symbiotic relationships. Invasions lead to the observation of multiple associations between symbionts and their hosts (indicating that a symbiont is no longer exclusive to a single host), which are incompatible with the existing methods of coevolution. </p> <p>Here, we present a method called AmoCoala (an enhanced version of the tool Coala) that provides a more realistic estimation of cophylogeny event probabilities for a given pair of host and symbiont trees, even in the presence of spread events. We expand the classical 4-event coevolutionary model to include 2 additional spread events (vertical and horizontal spreads) that lead to multiple associations. In the initial step, we estimate the probabilities of spread events using heuristic frequencies. Subsequently, in the second step, we employ an approximate Bayesian computation (ABC) approach to infer the probabilities of the remaining 4 classical events (cospeciation, duplication, host switch, and loss) based on these values.</p> <p>By incorporating spread events, our reconciliation model enables a more accurate consideration of multiple associations. This improvement enhances the precision of estimated cost sets, paving the way to a more reliable reconciliation of host and symbiont trees. To validate our method, we conducted experiments on synthetic datasets and demonstrated its efficacy using real-world examples. Our results showcase that AmoCoala produces biologically plausible reconciliation scenarios, further emphasizing its effectiveness.The software is accessible at <a href="https://github.com/sinaimeri/AmoCoala" rel="noopener">https://github.com/sinaimeri/AmoCoala</a>.</p>
Supplementary Data to *Informative and adaptive distances and summary statistics in approximate Bayesian computation*
<p>Supplementary code and data to <strong>Informative and adaptive distances and summary statistics in approximate Bayesian computation</strong> by <strong>Y. Schaelte et al., 2021</strong>.</p> <p>The archive contains a <strong>README.rst </strong>for information on what is where and how to execute the study and generate the figures. The underlying code without the data can be found at the repository https://github.com/yannikschaelte/study_abc_slad, of which this archive is a snapshot.</p>
Revisiting the historical scenario of a disease dissemination using genetic data and Approximate Bayesian Computation methodology: the case of Pseudocercospora fijiensis invasion in Africa
<p class="MsoNormal"><span>The reconstruction of geographic and demographic scenarios of dissemination for invasive pathogens of crops is a key step towards improving the management of emerging infectious diseases. Nowadays, the reconstruction of biological invasions typically uses the information of both genetic and historical information to test for different hypotheses of colonization. The Approximate Bayesian Computation framework and its recent Random Forest development (ABC-RF) have been successfully used in evolutionary biology to decipher multiple histories of biological invasions. Yet, for some organisms, typically plant pathogens, historical data may not be reliable notably because of the difficulty to identify the organism and the delay between the introduction and the first mention. We investigated the history of the invasion of Africa by the fungal pathogen of banana, <em>Pseudocercospora fijiensis</em>, by testing the historical hypothesis against other plausible hypotheses. We analysed the genetic structure of eight populations from six eastern and western African countries, using 20 microsatellite markers, and tested competing scenarios of population foundation using the ABC-RF methodology. We do find evidence for an invasion front consistent with the historical hypothesis, but also for the existence of another front never mentioned in historical records. We question the historical introduction point of the disease on the continent. Crucially, our results illustrate that even if ABC-RF inferences may sometimes fail to infer a single, well-supported scenario of invasion, they can be helpful in rejecting unlikely scenarios, which can prove much useful to shed light on disease dissemination routes.</span></p>
Cophylogeny reconstruction allowing for multiple associations through approximate Bayesian computation
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Revisiting the historical scenario of a disease dissemination using genetic data and Approximate Bayesian Computation methodology: the case of Pseudocercospora fijiensis invasion in Africa
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All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
<p>We present two different methods to estimate parameters within a partial differential equation (PDE) model of cancer invasion. The model describes the spatio-temporal evolution of three variables -- tumour cell density, extracellular matrix density and matrix degrading enzyme concentration -- in a one-dimensional tissue domain. The first method is a likelihood-free approach associated with Approximate Bayesian Computation (ABC); the second is a two-stage gradient matching method based on smoothing the data with a Generalized Additive Model (GAM) and matching gradients from the GAM to those from the model. Both methods performed well on simulated data. To increase realism, additionally we tested the gradient matching scheme with simulated measurement error and found that the ability to estimate some model parameters deteriorated rapidly as measurement error increased.</p>
All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
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Measurement of the B-band Galaxy Luminosity Function with Approximate Bayesian Computation
<p>In our recent paper Tortorelli et al. 2020 (arXiv: 2001.07727), we presented the first measurement in literature of galaxy population properties using the Approximate Bayesian Computation (ABC), specifically the B-band Galaxy Luminosity function. The novel methodology we developed that combines forward-modeling and ABC offers excellent prospects to robustly measure galaxy population properties as a function of redshift in large wide-field galaxy surveys, such as LSST and NGRST. The methodology has the advantage of overcoming part of the limitations that arise in wide-field galaxy studies, such as incorrect redshift estimation or separation of galaxies into red and blue. We forward model wide-field broad-band galaxy surveys using the fast image simulator UFig. We use ABC to constrain the galaxy population model parameters of the simulations and match data from the CFHTLS. We define a number of distance metrics between the simulated and the survey data. By exploring the parameter space of the galaxy population model through ABC to find the set of parameters that minimize these distance metrics, we obtain constraints on the LFs of blue and red galaxies as a function of redshift. We compare our results to other measurements, finding good agreement at all redshifts, for both blue and red galaxies. We also compare the redshift distribution we obtain applying the same cuts as the VIPERS survey on our image simulations, finding good agreement with survey data.</p>
Data from: Reconstructing the demographic history of orang-utans using approximate Bayesian computation
Investigating how different evolutionary forces have shaped patterns of DNA variation within and among species requires detailed knowledge of their demographic history. Orang-utans, whose distribution is currently restricted to the Southeast Asian islands of Borneo (Pongo pygmaeus) and Sumatra (Pongo abelii), have likely experienced a complex demographic history, influenced by recurrent changes in climate and sea levels, volcanic activities and anthropogenic pressures. Using the most extensive sample set of wild orang-utans to date, we employed an approximate Bayesian computation (ABC) approach to test the fit of 12 different demographic scenarios to the observed patterns of variation in autosomal, X-chromosomal, mitochondrial and Y-chromosomal markers. In the best-fitting model, Sumatran orang-utans exhibit a deep split of populations north and south of Lake Toba, probably caused by multiple eruptions of the Toba volcano. In addition, we found signals for a strong decline in all Sumatran populations ~24 ka, probably associated with hunting by human colonizers. In contrast, Bornean orang-utans experienced a severe bottleneck ~135 ka, followed by a population expansion and substructuring starting ~82 ka, which we link to an expansion from a glacial refugium. Therefore, we showed that orang-utans went through drastic changes in population size and connectedness, caused by the recurrent contraction and expansion of rainforest habitat during Pleistocene glaciations, and probably also by the impact of hunting by early humans. Our findings also emphasize the fact that important aspects of the evolutionary past of species with complex demographic histories might remain obscured when applying overly simplified models.
Data from: Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates
We propose a two-step procedure for estimating multiple migration rates in an approximate Bayesian computation (ABC) framework, accounting for global nuisance parameters. The approach is not limited to migration, but generally of interest for inference problems with multiple parameters and a modular structure (e.g. independent sets of demes or loci). We condition on a known, but complex demographic model of a spatially subdivided population, motivated by the reintroduction of Alpine ibex (Capra ibex) into Switzerland. In the first step, the global parameters ancestral mutation rate and male mating skew have been estimated for the whole population in Aeschbacher et al. (Genetics 2012; 192: 1027). In the second step, we estimate in this study the migration rates independently for clusters of demes putatively connected by migration. For large clusters (many migration rates), ABC faces the problem of too many summary statistics. We therefore assess by simulation if estimation per pair of demes is a valid alternative. We find that the trade-off between reduced dimensionality for the pairwise estimation on the one hand and lower accuracy due to the assumption of pairwise independence on the other depends on the number of migration rates to be inferred: the accuracy of the pairwise approach increases with the number of parameters, relative to the joint estimation approach. To distinguish between low and zero migration, we perform ABC-type model comparison between a model with migration and one without. Applying the approach to microsatellite data from Alpine ibex, we find no evidence for substantial gene flow via migration, except for one pair of demes in one direction.
Data from: Approximate Bayesian computation analysis of EST-associated microsatellites indicates that the broadleaved evergreen tree Castanopsis sieboldii survived the Last Glacial Maximum in multiple refugia in Japan
Climatic changes have played major roles in plants' evolutionary history. Glacial oscillations have been particularly important, but some of their effects on plants' populations are poorly understood, including the numbers and locations of refugia in Asian warm temperate zones. In the present study, we investigated the demographic history of the broadleaved evergreen tree species Castanopsis sieboldii (Fagaceae) during the last glacial period in Japan. We used approximate Bayesian computation (ABC) for model comparison and parameter estimation for the demographic modelling using 27 EST associated microsatellites. We also performed the species distribution modelling (SDM). The results strongly support a demographic scenario that the Ryukyu Islands and the western parts in the main islands (Kyushu and western Shikoku) were derived from separate refugia and the eastern parts in the main islands and the Japan Sea groups were diverged from the western parts prior to the coldest stage of the Last Glacial Maximum (LGM). Our data indicate that multiple refugia survived at least one in the Ryukyu Islands, and the other three regions of the western and eastern parts and around the Japan Sea of the main islands of Japan during the LGM. The SDM analysis also suggests the potential habitats under LGM climate conditions were mainly located along the Pacific Ocean side of coastal region. Our ABC-based study helps efforts resolve the demographic history of a dominant species in warm temperate broadleaved forests during and after the last glacial period, which provides a basic model for future phylogeographical studies using this approach.
Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)
<p><span>State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states (</span><em><span>λ</span></em><sub><span>1</span></sub><span> / <em>λ</em><sub>0 </sub></span><span>= 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s <em>D</em>) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.</span></p>
Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)
<p>State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states (<em>λ</em><sub>1</sub> / <em>λ</em><sub>0 </sub>= 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s <em>D</em>) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.</p>
Data from: Reconstructing the demographic history of orang-utans using approximate Bayesian computation
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Data from: Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates
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Data from: Understanding the recent colonization history of a plant pathogenic fungus using population genetic tools and Approximate Bayesian Computation
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Data from: Approximate Bayesian computation analysis of EST-associated microsatellites indicates that the broadleaved evergreen tree Castanopsis sieboldii survived the Last Glacial Maximum in multiple refugia in Japan
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Data from: Assessing conservation risks to populations of an anadromous Arctic salmonid, the northern Dolly Varden (Salvelinus malma malma), via estimates of effective and census population sizes and approximate Bayesian computation
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Data from: Assessing the dynamics of natural populations by fitting individual based models with approximate Bayesian computation
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Data from: Using a continuum model to decipher the mechanics of embryonic tissue spreading from time-lapse image sequences: an approximate Bayesian computation approach
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