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Infering population history with DIY ABC: a user-friendly approach to Approximate Bayesian Computation
- Source :
- Bioinformatics, Bioinformatics, Oxford University Press (OUP), 2008, 24 (23), pp.2713-2719. ⟨10.1093/bioinformatics/btn514⟩, Bioinformatics, 2008, 24 (23), pp.2713-2719. ⟨10.1093/bioinformatics/btn514⟩
- Publication Year :
- 2008
- Publisher :
- HAL CCSD, 2008.
-
Abstract
- Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract this information (at least partially) 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 (DIYABC) 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 stepwise population size changes. DIYABC 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 data set, both with complex evolutionary scenarios, illustrates the main possibilities of DIYABC<br />submitted
- Subjects :
- 0106 biological sciences
Computer science
Inference
computer.software_genre
01 natural sciences
Biochemistry
Bayes' theorem
HISTORY
Statistics
0303 health sciences
education.field_of_study
BIOLOGIE DES POPULATIONS
Population size
Genetics and Population Analysis
HISTOIRE
Original Papers
STATISTICS
Computer Science Applications
Computational Mathematics
Computational Theory and Mathematics
Approximate Bayesian computation
[STAT.ME]Statistics [stat]/Methodology [stat.ME]
Algorithms
Statistics and Probability
Population
[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]
Machine learning
010603 evolutionary biology
Evolution, Molecular
03 medical and health sciences
Population Groups
POPULATION GENETICS
Humans
Quantitative Biology - Populations and Evolution
education
Molecular Biology
030304 developmental biology
MATHEMATICAL PROGRAMMING
[SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE]
business.industry
Populations and Evolution (q-bio.PE)
Bayes Theorem
GENETIQUE
POPULATION DYNAMICS
Genetics, Population
FOS: Biological sciences
Key (cryptography)
Artificial intelligence
business
computer
Software
Subjects
Details
- Language :
- English
- ISSN :
- 13674803, 14602059, and 13674811
- Database :
- OpenAIRE
- Journal :
- Bioinformatics, Bioinformatics, Oxford University Press (OUP), 2008, 24 (23), pp.2713-2719. ⟨10.1093/bioinformatics/btn514⟩, Bioinformatics, 2008, 24 (23), pp.2713-2719. ⟨10.1093/bioinformatics/btn514⟩
- Accession number :
- edsair.doi.dedup.....40d5318f194a1cba712277c389c1ebb6