Demo Data from EDBT 2026 / reviewers in the wild / expert
Mark A. Beaumont
dblp:83/7063
· DBLP profile ↗
5ranked-venue papers
0as first author
0since 2021 · last 2011
0000-0002-8773-2743ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 5
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
4 papers |
Bioinformatics and computational biology · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
population genetics |
0.4 | 4 | 2011 | Mcheza: a workbench to detect selection using dominant markers · Bioinform. 2011 PopABC: a program to infer historical demographic parameters · Bioinform. 2009 A novel parallel approach to the likelihood-based estimation of admixture in population genetics · Bioinform. 2009 |
Bioinformatics and computational biology
approximate bayesian computation |
0.2 | 2 | 2009 | PopABC: a program to infer historical demographic parameters · Bioinform. 2009 Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation · Bioinform. 2008 |
Bioinformatics and computational biology › population genetics › population parameter estimation
demographic inference |
0.2 | 2 | 2009 | PopABC: a program to infer historical demographic parameters · Bioinform. 2009 Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation · Bioinform. 2008 |
Bioinformatics and computational biology › population genetics
selection detection |
0.1 | 1 | 2011 | Mcheza: a workbench to detect selection using dominant markers · Bioinform. 2011 |
Bioinformatics and computational biology › population genetics
admixture analysis |
0.1 | 1 | 2009 | A novel parallel approach to the likelihood-based estimation of admixture in population genetics · Bioinform. 2009 |
Bioinformatics and computational biology › population genetics › population genetics simulation
coalescent simulation |
0.0 | 1 | 2009 | PopABC: a program to infer historical demographic parameters · Bioinform. 2009 |
Methods — techniques the papers use, named apart from their topics
approximate bayesian computation · 0.2contour smoothing · 0.1FST outlier method · 0.1monte carlo simulation · 0.1message passing interface · 0.1coalescent simulation · 0.1bayesian model choice · 0.1parameter estimation · 0.1bias and precision estimation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Mcheza: a workbench to detect selection using dominant markersabstractMOTIVATION: Dominant markers (DArTs and AFLPs) are commonly used for genetic analysis in the fields of evolutionary genetics, ecology and conservation of genetic resources. The recent prominence of these markers has coincided with renewed interest in detecting the effects of local selection and adaptation at the level of the genome. RESULTS: We present Mcheza, an application for detecting loci under selection based on a well-evaluated F(ST)-outlier method. The application allows robust estimates to be made of model parameters (e.g. genome-wide average, neutral F(ST)), provides data import and export functions, iterative contour smoothing and generation of graphics in an easy to use graphical user interface with a computation engine that supports multicore processors for enhanced performance. Mcheza also provides functionality to mitigate common analytical errors when scanning for loci under selection. AVAILABILITY: Mcheza is freely available under GPL version 3 from http://popgen.eu/soft/mcheza. Tiago Rodrigues Antao, Mark A. Beaumont |
Bioinform. | 2 |
| 2009 | A novel parallel approach to the likelihood-based estimation of admixture in population geneticsabstractAbstract Summary: Inferring population admixture from genetic data and quantifying it is a difficult but crucial task in evolutionary and conservation biology. Unfortunately state-of-the-art probabilistic approaches are computationally demanding. Effectively exploiting the computational power of modern multiprocessor systems can thus have a positive impact to Monte Carlo-based simulation of admixture modeling. A novel parallel approach is briefly described and promising results on its message passing interface (MPI)-based C++ implementation are reported. Availability: The software package parLEA is freely available at http://dm.unife.it/parlea. Contact: [email protected] Supplementary information: Additional information, including instructions for installation/use the original sequential LEA code and the data used in this paper, are also available in the web site. Ambra Giovannini, Gaetano Zanghirati, Mark A. Beaumont, Lounès Chikhi, Guido Barbujani |
Bioinform. | 3 |
| 2009 | PopABC: a program to infer historical demographic parametersabstractUNLABELLED: PopABC is a computer package for inferring the pattern of demographic divergence of closely related populations and species. The software performs coalescent simulation in the framework of approximate Bayesian computation (ABC). PopABC can also be used to perform Bayesian model choice to discriminate between different demographic scenarios. The program can be used either for research or for education and teaching purposes. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available at http://www.reading.ac.uk/ approximately sar05sal/software.htm. The program was implemented in C and can run on UNIX, MacOSX and Windows operating systems. Joao S. Lopes, David J. Balding, Mark A. Beaumont |
Bioinform. | 3 |
| 2009 | The Origins of Lactase Persistence in EuropeabstractLactase persistence (LP) is common among people of European ancestry, but with the exception of some African, Middle Eastern and southern Asian groups, is rare or absent elsewhere in the world. Lactase gene haplotype conservation around a polymorphism strongly associated with LP in Europeans (-13,910 C/T) indicates that the derived allele is recent in origin and has been subject to strong positive selection. Furthermore, ancient DNA work has shown that the--13,910*T (derived) allele was very rare or absent in early Neolithic central Europeans. It is unlikely that LP would provide a selective advantage without a supply of fresh milk, and this has lead to a gene-culture coevolutionary model where lactase persistence is only favoured in cultures practicing dairying, and dairying is more favoured in lactase persistent populations. We have developed a flexible demic computer simulation model to explore the spread of lactase persistence, dairying, other subsistence practices and unlinked genetic markers in Europe and western Asia's geographic space. Using data on--13,910*T allele frequency and farming arrival dates across Europe, and approximate Bayesian computation to estimate parameters of interest, we infer that the--13,910*T allele first underwent selection among dairying farmers around 7,500 years ago in a region between the central Balkans and central Europe, possibly in association with the dissemination of the Neolithic Linearbandkeramik culture over Central Europe. Furthermore, our results suggest that natural selection favouring a lactase persistence allele was not higher in northern latitudes through an increased requirement for dietary vitamin D. Our results provide a coherent and spatially explicit picture of the coevolution of lactase persistence and dairying in Europe. Yuval Itan, Adam Powell, Mark A. Beaumont, Joachim Burger, Mark G. Thomas |
PLoS Comput. Biol. | 3 |
| 2008 | Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computationabstractUNLABELLED: Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC. AVAILABILITY: The software DIY ABC is freely available at http://www.montpellier.inra.fr/CBGP/diyabc. Jean-Marie Cornuet, Mark A. Beaumont, Christian P. Robert, Jean-Michel Marin, David J. Balding, Thomas Guillemaud, Arnaud Estoup |
Bioinform. | 3 |